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Contents of https://dev.to/events:
We're a place where coders share, stay up-to-date and grow their careers.

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Contents of https://www.google.com/search?q=site%3Adev.to+Hacktoberfest+%22Touch+Grass%22+Week+1:
Google Search

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Sep 28, 2026 · If you remember Hacktoberfest as "open four pull requests, get a T-shirt," that version is over. Hacktoberfest 2026 is …

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TOUCH definition: 1. to put your hand or another part of your body lightly onto and off something or someone: 2. (of…. Learn more.

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Contents of https://www.bing.com/search?q=site%3Adev.to%2Fchallenges+touch+grass+hacktoberfest:
TOUCH definition: 1. to put your hand or another part of your body lightly onto and off something or someone: 2. (of…. Learn more.

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Sep 28, 2026 · If you remember Hacktoberfest as "open four pull requests, get a T-shirt," that version is over. Hacktoberfest 2026 is …

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Contents of https://dev.to/challenges/hacktoberfest-week1-2026-10-05:
### The Five Challenges

#### Do pull requests count this year?

No. This year's Hacktoberfest is not about open source pull requests. There's no PR count to hit and no repos to hunt for. Instead, you build a brand-new project with open-source AI at its core, and write about it on DEV. [Here's everything you need to know about Hacktoberfest 2026.](https://dev.to/mlh/everything-you-need-to-know-about-hacktoberfest-2026-ai-belongs-to-everyone-mdk)

#### How do the Hacktoberfest challenges work?

We're running five DEV Challenges back to back through October: a Weekend Challenge to start (October 2 to 5), then a new challenge every Monday (October 5, 12, 19 and 26). Every challenge uses the same prompt, and each one gets its own theme. We reveal each theme the day its challenge launches. See all five on the [HF26 DEV Challenge Hub](https://dev.to/challenges/hf26).

#### What's the prompt?

The same for all five challenges: build something with open-source AI at its core (an open-weight model, an open-source agent harness or framework, local inference, or any mix), and tell us why open innovation matters for what you built.

#### Can I enter more than one challenge?

Yes, enter as many as you like. You can publish **one submission per challenge**, so that's up to five in October. Each submission has to be a new project built during that challenge's window.

#### Can one submission win more than once?

No. Each submission is automatically in the running for the overall prize and every partner category it qualifies for, but you can win once per challenge, and we'll aim to celebrate as many different builders as possible across the month.

#### I'm joining late. Is it worth it?

Absolutely. Every challenge is a fresh start, so someone who finds us in week four competes on the same footing as someone who's been here since day one.

### Prizes and Partner Categories

#### What can I win?

Every challenge has its own winners, and we'll share each challenge's prizes when it launches. Here's what's up for grabs in the [Weekend Challenge](https://dev.to/challenges/hacktoberfest-weekend-2026-10-01):

*   **Overall winner:** $250, a [DEV++](https://dev.to/++) membership, and an exclusive DEV badge.

*   **Featured partner categories:** $200 for each winner.

*   **Partner categories:** $100 for each winner.

Every winner also gets an exclusive DEV badge, and **everyone** with a valid submission gets a completion badge.

#### How do partner categories work?

You opt in by using the partner's technology in your project and listing each category you're entering in the Prize Categories section of your post. One project can enter as many categories as it genuinely uses. You don't need to use any partner technology to win the overall prize.

#### Which partner categories are open?

Featured categories ($200):

Partner categories ($100):

Partner categories may change from challenge to challenge as new partners join. Each challenge page lists the categories open for that challenge.

#### Who can enter Best Use of Arduino?

Anyone building with an Arduino UNO Q, whether you got one at an in-person Fest or have your own.

#### How do I claim partner credits?

Several partners are giving participants credits and promo codes, including Tinker, Render, Backboard, and ElevenLabs. Claim yours at [hacktoberfest.com/my/promos](https://hacktoberfest.com/my/promos/?utm_source=dev.to&utm_medium=challenge-page&utm_campaign=hacktoberfest-2026&utm_content=faq-claim-credits).

### Submission

#### Can I submit a pull request to an existing project?

No. **New projects, not pull requests:** each submission is a new project built during that challenge's window. Pull requests to existing projects don't count this year.

#### Can I work on an old project?

No, all submissions and their respective repositories must be started and completed within the challenge window. Commits made after the submission deadline must be noted in the project's readme. Failure to do so may result in disqualification.

#### Can I work on a team?

Yes, you can work on teams of up to four people.

*   If you collaborate with anyone, you'll need to list their DEV handles in your submission post so we can award a badge to your entire team! **Please only publish one submission per team.**

*   DEV does not handle prize-splitting, so in the event that your submission wins, you will need to split the prize amongst yourselves. Thank you for understanding!

#### Should I include my agent session?

**Show your work.** We'd love to see how you built it. Save your agent session with [DevRelay](https://devrelay.com) and embed it in your post, or link to it. It's optional, but it helps the judges understand your process.

#### Can my submission include open source code?

Riffing on open source code and borrowing and improving on previous work/ideas is encouraged but it's important your changes are significant enough to ensure your submission is valid.

#### What happens if my submission is considered plagiarized or invalid?

Anything deemed to be plagiarism will not be eligible for prizes. Incidental plagiarism may simply result in your disqualification from the challenge (regardless of the number of other valid submissions you have published). Egregious plagiarism will result in your suspension from DEV entirely. Any non-generic, non-trivial usage of prior work, including open source code must be credited in your submission.

#### Do submissions have to be in English?

Non-english submissions are eligible for a completion badge but not eligible for prizes due to the current limitations of our judges. We will not be judging on mastery of the English language, so please don't let this deter you from submitting if you are not a native English speaker! We hope to evolve this in the future to be more accommodating.

#### Can I use AI?

Use of AI is allowed as long as all other rules are followed. We want to give you a chance to show off your skills in realistic scenarios. If you use AI tools to help you achieve your submission, all the power to you.

### Eligibility

#### How old do I have to be to participate?

Participants need to be 18+ in order to participate.

#### If I live in X, am I eligible to participate?

For eligibility rules, see our [official challenge rules](https://dev.to/page/official-hackathon-rules).

### Judging and Prizing

#### How are submissions judged?

This is DEV, so **your write-up matters most**. A great project with a thin write-up won't win. We judge on Writing Quality (weighted most heavily), Relevance to the Prompt and Theme, Creativity, Technical Execution, and, if you're entering a partner category, Use of Partner Technology.

#### When are winners announced?

Winners are announced the week after each challenge closes. Each challenge's page lists the week under Key Dates.

#### Can there be ties?

In the event of a tie in scoring between judges, the judges will select the entry that received the highest number of positive reactions on their DEV post to determine the winner.

#### When will I receive my DEV badge?

Both participation and winner badges will be awarded, in most cases, the same day as the winner announcement.

#### When will I receive my prizes?

The DEV Team will contact you via the email associated with your DEV profile within, at most, 10 business days of the announcement date to share the details of claiming your prizes.

#### What steps do I need to take to receive my cash prize?

The winner (including each member of a team) may be required to sign and return an affidavit of eligibility and publicity/liability release, and provide any additional tax filing information (such as a W-9, social security number or Federal tax ID number) within seven (7) business days following the date of your first email notification.

### Questions

#### Where can I ask questions?

Drop them in the comments on the challenge's announcement post, and follow the [#hf26challenge](https://dev.to/t/hf26challenge) tag so you don't miss a theme reveal.

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{"command":"$urls=@('https://dev.to/api/articles?tag=hacktoberfest&per_page=100','https://dev.to/api/articles?tag=hacktoberfest&top=7&per_page=100'); foreach($u in $urls){$r=Invoke-RestMethod -Uri $u;...

Output too large to read at once (19.7 KB). Saved to: C:\Users\Lenovo\AppData\Local\Temp\1791598660238-copilot-tool-output-11456-7ecbfed0-dd19-49db-91cf-77c8d243fd84.txt
Consider using tools like rg (for searching), head/tail (for viewing start/end), view with view_range (for specific sections), or jq (for JSON) to examine portions of the output.

Preview (first 500 chars):

     id title                                                                                                          
     -- -----                                                                                                          
4826102 I Taught Local AI to Help Me Notice the World.                                                                 
4826090 Thirsty Leaf: A Tiny Plant Reminder App That Gets You Off Your Screen 🪴                                       
4825380 EcoID: An O
<shellId: 12 completed with exit code 0>

{"command":"$r=Invoke-RestMethod -Uri 'https://dev.to/api/articles?tag=hacktoberfest&per_page=100'; $r | Where-Object {$_.published_at -ge '2026-10-05' -and $_.published_at -lt '2026-10-11'} | Select-...

Output too large to read at once (19.4 KB). Saved to: C:\Users\Lenovo\AppData\Local\Temp\1791598667873-copilot-tool-output-11456-e2d00850-7246-416e-b8c2-220d57ac8170.txt
Consider using tools like rg (for searching), head/tail (for viewing start/end), view with view_range (for specific sections), or jq (for JSON) to examine portions of the output.

Preview (first 500 chars):
"id","title","path","published_at","positive_reactions_count","comments_count","tags"
"4826102","I Taught Local AI to Help Me Notice the World.","/ishagautam504/i-taught-local-ai-to-help-me-notice-the-world-5a45","2026-10-10T01:54:46Z","0","0","ai,devchallenge,hacktoberfest,opensource"
"4826090","Thirsty Leaf: A Tiny Plant Reminder App That Gets You Off Your Screen ??","/ayushjhawar/thirsty-leaf-a-tiny-plant-reminder-app-that-gets-you-off-your-screen-787","2026-10-10T01:50:17Z","0","0","devchall
<shellId: 13 completed with exit code 0>

{"command":"Import-Csv 'C:\\Users\\Lenovo\\.copilot\\session-state\\36a364a0-4d59-4fe3-8349-b59433759a92\\files\\submissions.csv' | Select-Object -First 40 id,title,path,published_at,positive_reaction...

id      title                                                                                                          
--      -----                                                                                                          
4826102 I Taught Local AI to Help Me Notice the World.                                                                 
4826090 Thirsty Leaf: A Tiny Plant Reminder App That Gets You Off Your Screen ??                                       
4825380 EcoID: An Offline Plant Identifier That Gets You Outside                                                       
4824064 Plain Sight Walk is a walking game that lives in your pocket.                                                  
4825711 ?? Ruta Viva AI: Turning Everyday Walks into AI-Powered Adventures                                             
4825776 Nikal Pado: An offline-first outdoor adventure planner for the Touch Grass challenge                           
4825097 Comparing AI Models in Cursor: Opus 5.5 vs. Grok 4.7                                                           
4825056 Outbound ?? An AI Quest App That Wants You to Close It                                                         
4824813 your first open-source contribution in 2026: what to hand the agent, what to keep for yourself                 
4822262 Trailside: an open-weight bird call identifier that talks back                                                 
4823882 I Built an Offline AI That Tells You What to Plant in One of the Hottest Climates on Earth ??                  
4824446 How to build a Biomedical Literature Search Agent                                                              
4822843 Shabdasakhi (???????): A Gemma-Powered Marathi Expression Coach                                                
4821556 RaahSaathi AI: An Offline Companion for Touching Grass                                                         
4821642 RaahSaathi AI: An Offline Companion for Touching Grass                                                         
4822447 Building an AI Code Visualizer for Hacktoberfest 2026                                                          
4825033 My First Hacktoberfest: Starting My Open Source Journey ??                                                     
4820459 Touch Grass: Verified Field Notes From Your Hike (Hacktoberfest Week 1)                                        
4823898 First Dev Post                                                                                                 
4819133 Local Micro-Garden & Frost Date Advisor | Touch Grass Challenge                                                
4819120 NQT Saathi: An Offline Hinglish Prep Buddy I Built for My Friend                                               
4817565 Building SortSense: A Local AI File Organizer Built in a Single Hack Day                                       
4817238 Prompt Heist: a game where you talk your way past Gemma-powered guards (and learn how to defend AI apps)       
4817413 ClaimPilot: Building an AI Evidence Intelligence System with Gemma 4                                           
4817441 ArchGuard: The Visual Architecture Linter for Open-Source Repositories                                         
4817160 DebugBuddy: an AI mentor that teaches beginners to fix their own bugs                                          
4817191 Campus Lost & Found                                                                                            
4817062 GitHygiene: Does This Vulnerability Actually Matter in My Code?                                                
4817067 Our Experience at the Hacktober Fest                                                                           
4817054 PCSense - an AI technician that proves it fixed your PC before it tells you so                                 
4816912 Chronicles of Hacktoberfest Amrita                                                                             
4817073 We built a second brain that fact-checks itself, running Gemma 4 locally, in one hackathon day Connectore      
        -Hacktoberfest hackathon - Team WInDaChat                                                                      
4817327 HACKTOBER FEST                                                                                                 
4817088 Red-Team AI: Stress-Test Your Ideas Before They Fail                                                           
4816079 ?? Starting My Hacktoberfest 2026 Journey                                                                      
4814396 I Built DevDrop to Help My Friend Turn an Idea Into a Website                                                  
4813234 NaturePulse: An Offline AI Companion to Get You Outside                                                        
4816636 ?? TouchGrass AI: Turning Screen Time Into Outdoor Time with Local Gemma                                       
4810215 TouchGrass AI � An Open-Source AI Outdoor Challenge Generator ??                                               
4813037 [Boost]
<shellId: 14 completed with exit code 0>

{"command":"$h=(Invoke-WebRequest -Uri 'https://dev.to/challenges/hacktoberfest-week1-2026-10-05').Content; $h | Select-String -Pattern 'Featured|Partner|Prize|Touch Grass|Week 1|categories' -AllMatch...

Invoke-WebRequest : Windows PowerShell is in NonInteractive mode. Read and Prompt functionality is not available.
At line:2 char:5
+ $h=(Invoke-WebRequest -Uri 'https://dev.to/challenges/hacktoberfest-w ...
+     ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
    + CategoryInfo          : InvalidOperation: (:) [Invoke-WebRequest], PSInvalidOperationException
    + FullyQualifiedErrorId : InvalidOperation,Microsoft.PowerShell.Commands.InvokeWebRequestCommand
<shellId: 15 completed with exit code 1>

{"command":"curl.exe -L 'https://dev.to/challenges/hacktoberfest-week1-2026-10-05' -o 'C:\\Users\\Lenovo\\.copilot\\session-state\\36a364a0-4d59-4fe3-8349-b59433759a92\\files\\challenge.html'; Select-...

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C:\Users\Lenovo\.copilot\session-state\36a364a0-4d59-4fe3-8349-b59433759a92\files\challenge.html:909:  /* Hub prompt 
panel: no prize box, so the judging criteria sit in a compact card
C:\Users\Lenovo\.copilot\session-state\36a364a0-4d59-4fe3-8349-b59433759a92\files\challenge.html:1156:  /* A heading 
that groups prize categories, with a one-line prize summary. */
C:\Users\Lenovo\.copilot\session-state\36a364a0-4d59-4fe3-8349-b59433759a92\files\challenge.html:1157:  #custom 
.challenge-prize-group {
C:\Users\Lenovo\.copilot\session-state\36a364a0-4d59-4fe3-8349-b59433759a92\files\challenge.html:1165:  #custom 
.challenge-panel + .challenge-prize-group {
C:\Users\Lenovo\.copilot\session-state\36a364a0-4d59-4fe3-8349-b59433759a92\files\challenge.html:1169:  #custom 
.challenge-prize-group h3 {
C:\Users\Lenovo\.copilot\session-state\36a364a0-4d59-4fe3-8349-b59433759a92\files\challenge.html:1175:  #custom 
.challenge-prize-group p {
C:\Users\Lenovo\.copilot\session-state\36a364a0-4d59-4fe3-8349-b59433759a92\files\challenge.html:1180:  #custom 
.challenge-prize-categories-note {
C:\Users\Lenovo\.copilot\session-state\36a364a0-4d59-4fe3-8349-b59433759a92\files\challenge.html:1186:  /* Prize 
category panels with a partner logo tile on the left (Hacktoberfest 2026),
C:\Users\Lenovo\.copilot\session-state\36a364a0-4d59-4fe3-8349-b59433759a92\files\challenge.html:1188:  #custom 
.challenge-prize-category {
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C:\Users\Lenovo\.copilot\session-state\36a364a0-4d59-4fe3-8349-b59433759a92\files\challenge.html:1198:  #custom 
.challenge-prize-category__main {
C:\Users\Lenovo\.copilot\session-state\36a364a0-4d59-4fe3-8349-b59433759a92\files\challenge.html:1205:  #custom 
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.challenge-prize-category__logos img {
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.challenge-prize-category p {
C:\Users\Lenovo\.copilot\session-state\36a364a0-4d59-4fe3-8349-b59433759a92\files\challenge.html:1234:  #custom 
.challenge-prize-category .challenge-panel__aside {
C:\Users\Lenovo\.copilot\session-state\36a364a0-4d59-4fe3-8349-b59433759a92\files\challenge.html:1241:  #custom 
.challenge-prize-category .challenge-panel__aside h4 {
C:\Users\Lenovo\.copilot\session-state\36a364a0-4d59-4fe3-8349-b59433759a92\files\challenge.html:1413:    #custom 
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C:\Users\Lenovo\.copilot\session-state\36a364a0-4d59-4fe3-8349-b59433759a92\files\challenge.html:1428:    #custom 
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<shellId: 16 completed with exit code 0>

{"command":"Select-String -Path 'C:\\Users\\Lenovo\\.copilot\\session-state\\36a364a0-4d59-4fe3-8349-b59433759a92\\files\\challenge.html' -Pattern 'Prize Categories|Featured partner|Partner categories...

Output too large to read at once (45.4 KB). Saved to: C:\Users\Lenovo\AppData\Local\Temp\1791598689361-copilot-tool-output-11456-4f8b19e2-0b19-45d6-98da-85b41dfc2e8c.txt
Consider using tools like rg (for searching), head/tail (for viewing start/end), view with view_range (for specific sections), or jq (for JSON) to examine portions of the output.

Preview (first 500 chars):
<h3>Best Use of Render</h3>
<p class="tw-text-black dark:tw-text-zinc-50">Use Render as your project's AI runtime, to host an agent's front end, or to run Hermes or OpenClaw.</p>
<div class="challenge-panel challenge-prompt challenge-prize-category" id="best-use-of-tinker">
<h3>Best Use of Tinker</h3>
<p class="tw-text-black dark:tw-text-zinc-50">Use Thinking Machines' Tinker to fine-tune a model for a specific task, and show a clear improvement in performance, latency, or cost over a baseline.<
<shellId: 17 completed with exit code 0>

{"command":"$lines=Get-Content 'C:\\Users\\Lenovo\\.copilot\\session-state\\36a364a0-4d59-4fe3-8349-b59433759a92\\files\\challenge.html'; for($i=0;$i -lt $lines.Count;$i++){if($lines[$i] -match '<h3>(...

Featured categories --         $200 for each winner
          Best Use of Render --           Use Render as your project's AI runtime, to host an agent's front end, or to run Hermes or OpenClaw.
          Best Use of TabPFN --           Use TabPFN, Prior Labs' tabular foundation model, to forecast, predict, classify, or spot anomalies from historical data like a CSV.  Use it inside an agent tool (with or without the MCP server) or on its own.
          Best Use of Tinker --           Use Thinking Machines' Tinker to fine-tune a model for a specific task, and show a clear improvement in performance, latency, or cost over a baseline.
          Best Use of Arduino --           Build with an Arduino UNO Q: run a model on the board, build a physical agent that senses and acts, or optimize a model for it with Qualcomm AI Hub.  Open to anyone building with an UNO Q, whether you got one at an in-person Fest or have your own.
          Best Use of DigitalOcean --           Build or deploy your project on DigitalOcean: host your app or agent, run an open-weight model on a GPU Droplet (1-Click Models make this quick), or build an agent on the Gradient AI Platform.
          Best Use of Gemma --           Use Gemma, Google's open-weight model, in building your project: run it locally, fine-tune it, or serve it through Google Cloud or another provider.
        Partner categories --         $100 for each winner
          Best Use of Backboard --           Build with R-CLI, Backboard's open-source terminal coding agent, compare open-weight models through a single Backboard API key, or give an open-source project's assistant memory and RAG.
          Best Use of ElevenLabs --           Give an open-source agent a voice, transcribe audio for a local model, or generate narration for your demo.
          Best Use of Entire --           Share the agent sessions behind your project in the write-up, or search past agent work to explain why code exists.
          Best Use of GitHub Copilot --           Build with Copilot's coding agent, the Copilot CLI, or the new Copilot app, automate your project with GitHub Actions, or review contributors' PRs with Copilot.
          Best Use of Mastra --           Orchestrate an agent over open models, add memory and tools to a chat agent, or build a workflow that triages GitHub issues.
          Best Use of MongoDB Atlas --           Use Atlas as your data layer: Atlas Vector Search for retrieval, long-term memory for an agent, or the database behind an app built on an open-weight model.
          Best Use of Sentry Agent Tracing --           Show your agent's work: how it's set up, how it performs (latency, tokens, cost), what you found, and how you debugged it.  Include traces or screenshots in your write-up.
          Best Use of SerpApi --           Give an agent live web search, ground a RAG app in fresh results, or track news for an open-source project.
          Best Use of Temporal --           Make your agent durable.  Wrap it in a Temporal workflow so it survives failures, retries flaky tool calls, and picks up where it left off.
          Best Use of Tiger Data --           Store embeddings with pgvector, run hybrid keyword and vector search for an agent, or let an agent manage a database through Tiger MCP.
<shellId: 18 completed with exit code 0>

{"command":"$lines=Get-Content 'C:\\Users\\Lenovo\\.copilot\\session-state\\36a364a0-4d59-4fe3-8349-b59433759a92\\files\\challenge.html'; for($i=0;$i -lt $lines.Count;$i++){if($lines[$i] -match 'Key D...

1619 <h2>Key Dates</h2>
1631 <span class="tw-italic">Week of October 12</span>
2349 <p>We're running five DEV Challenges back to back through October: a Weekend Challenge to start (October 2 to 5), then a new challenge every Monday (October 5, 12, 19 and 26).  Every challenge uses the same prompt, and each one gets its own theme.  We reveal each theme the day its challenge launches.  See all five on the <a href="https://dev.to/challenges/hf26">HF26 DEV Challenge Hub</a>.</p>
2439 <p>No, all submissions and their respective repositories must be started and completed within the challenge window.  Commits made after the submission deadline must be noted in the project's readme.  Failure to do so may result in disqualification.</p>
2469 <p>Winners are announced the week after each challenge closes.  Each challenge's page lists the week under Key Dates.</p>
2491 <p>NO PURCHASE NECESSARY.  Open only to 18+.  Contest entry period ends October 11, 2026 at 11:59 PM PDT.  Contest is void where prohibited or restricted by law or regulation.  All entries must be submitted during the contest period.  For Official Rules, see <a href="https://dev.to/page/hacktoberfest-week1-2026-10-05-contest-rules">Hacktoberfest Open-Source AI Challenge: Week 1 Contest Rules</a> and <a href="https://dev.to/page/official-hackathon-rules">General Contest Official Rules</a>.</p>
<shellId: 19 completed with exit code 0>

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Contents of https://dev.to/api/articles/4826102:
{"type_of":"article","id":4826102,"title":"I Taught Local AI to Help Me Notice the World.","description":"Your World Is More Interesting Than Your Feed.   This is a submission for the Hacktoberfest...","readable_publish_date":"Oct 10","slug":"i-taught-local-ai-to-help-me-notice-the-world-5a45","path":"/ishagautam504/i-taught-local-ai-to-help-me-notice-the-world-5a45","url":"https://dev.to/ishagautam504/i-taught-local-ai-to-help-me-notice-the-world-5a45","comments_count":0,"public_reactions_count":0,"collection_id":null,"published_timestamp":"2026-10-10T01:54:46Z","language":"en","subforem_id":1,"ai_disclosure_level":"not_disclosed","ai_disclosure_label":"Not Disclosed","positive_reactions_count":0,"cover_image":null,"social_image":"https://media2.dev.to/dynamic/image/width=1200,height=627,fit=cover,gravity=auto,format=auto/https%3A%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Farticles%2Fzlo656u088u66liiygb2.png","canonical_url":"https://dev.to/ishagautam504/i-taught-local-ai-to-help-me-notice-the-world-5a45","created_at":"2026-10-10T01:54:46Z","edited_at":null,"crossposted_at":null,"published_at":"2026-10-10T01:54:46Z","last_comment_at":"2026-10-10T01:54:46Z","reading_time_minutes":4,"tag_list":"ai, devchallenge, hacktoberfest, opensource","tags":["ai","devchallenge","hacktoberfest","opensource"],"body_html":"\u003ch1\u003e\n  \u003ca name=\"your-world-is-more-interesting-than-your-feed\" href=\"#your-world-is-more-interesting-than-your-feed\"\u003e\n  \u003c/a\u003e\n  Your World Is More Interesting Than Your Feed.\n\u003c/h1\u003e\n\n\u003cp\u003e\u003cem\u003eThis is a submission for the \u003ca href=\"https://dev.to/challenges/hacktoberfest-week1-2026-10-05\"\u003eHacktoberfest Open-Source AI Challenge Week 1: Touch Grass\u003c/a\u003e.\u003c/em\u003e\u003c/p\u003e\n\n\u003cp\u003eWhat if the things you noticed on a walk mattered more than the posts you scrolled past?\u003c/p\u003e\n\n\u003cp\u003eA strange-looking plant. An interesting rock. The sound of birds from a place you've never stopped to notice. Tiny details we usually walk past without a second thought.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eThat's the idea behind TRACE.\u003c/strong\u003e\u003c/p\u003e\n\n\u003cp\u003eTRACE is a map-based exploration app that turns real-world observations into a personal, AI-assisted record of the world around you. It encourages you to step outside, pay attention, capture what you find, and slowly build a map of your own discoveries.\u003c/p\u003e\n\n\u003cp\u003eThe goal isn't to spend more time in an app. It's to give you a reason to spend more time noticing the world outside it.\u003c/p\u003e\n\n\u003ch2\u003e\n  \u003ca name=\"what-i-built\" href=\"#what-i-built\"\u003e\n  \u003c/a\u003e\n  🌍 What I Built\n\u003c/h2\u003e\n\n\u003cp\u003eTRACE combines real-world exploration, multimedia capture, local AI, and a little gamification to make curiosity a habit.\u003c/p\u003e\n\n\u003cp\u003eHere's what you can do with it:\u003c/p\u003e\n\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003e🗺️ Trace Map:\u003c/strong\u003e Explore observations on an interactive map and see the world through the things people notice.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e📍 Trace Capture:\u003c/strong\u003e Create observations with text, photos, or audio. Attach a location using GPS or place it manually on the map.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e🧠 Local AI Processing:\u003c/strong\u003e Turn captured observations into more structured records using a locally running AI model.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e🌱 Sticker Garden:\u003c/strong\u003e Earn daily stickers for qualifying observations, build exploration streaks, and collect sticker artwork as you keep exploring.\u003c/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e✨ A Personal Exploration Record:\u003c/strong\u003e Revisit your saved traces and build a growing record of the places, sights, and sounds you've encountered.\u003c/li\u003e\n\u003c/ul\u003e\n\n\u003cp\u003eThe small details matter. A photo captures what you saw. Audio captures what you heard. A location gives that moment a place on the map. Together, they help turn an ordinary walk into something worth remembering.\u003c/p\u003e\n\n\u003ch2\u003e\n  \u003ca name=\"demo\" href=\"#demo\"\u003e\n  \u003c/a\u003e\n  🎥 Demo\n\u003c/h2\u003e\n\n\u003cp\u003e\u003cstrong\u003eWatch TRACE in action:\u003c/strong\u003e\u003c/p\u003e\n\n\n\u003cdiv class=\"crayons-card c-embed text-styles text-styles--secondary\"\u003e\n    \u003cdiv class=\"c-embed__content\"\u003e\n      \u003cdiv class=\"c-embed__body\"\u003e\n        \u003ch2 class=\"fs-xl lh-tight\"\u003e\n          \u003ca href=\"https://drive.google.com/file/d/1zdN7hmWnpcH1dYPVikhVnQPVfflRq4Xy/view?usp=drive_link\" target=\"_blank\" rel=\"noop

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{"type_of":"article","id":4826090,"title":"Thirsty Leaf: A Tiny Plant Reminder App That Gets You Off Your Screen 🪴","description":"This is a submission for the Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass          ...","readable_publish_date":"Oct 10","slug":"thirsty-leaf-a-tiny-plant-reminder-app-that-gets-you-off-your-screen-787","path":"/ayushjhawar/thirsty-leaf-a-tiny-plant-reminder-app-that-gets-you-off-your-screen-787","url":"https://dev.to/ayushjhawar/thirsty-leaf-a-tiny-plant-reminder-app-that-gets-you-off-your-screen-787","comments_count":0,"public_reactions_count":0,"collection_id":null,"published_timestamp":"2026-10-10T01:50:17Z","language":"en","subforem_id":1,"ai_disclosure_level":"not_disclosed","ai_disclosure_label":"Not Disclosed","positive_reactions_count":0,"cover_image":null,"social_image":"https://media2.dev.to/dynamic/image/width=1200,height=627,fit=cover,gravity=auto,format=auto/https%3A%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Farticles%2Frb5b3eqz4ile0fhpuw0a.png","canonical_url":"https://dev.to/ayushjhawar/thirsty-leaf-a-tiny-plant-reminder-app-that-gets-you-off-your-screen-787","created_at":"2026-10-10T01:50:17Z","edited_at":"2026-10-10T01:51:12Z","crossposted_at":null,"published_at":"2026-10-10T01:50:17Z","last_comment_at":"2026-10-10T01:50:17Z","reading_time_minutes":2,"tag_list":"devchallenge, hf26challenge, hacktoberfest, webdev","tags":["devchallenge","hf26challenge","hacktoberfest","webdev"],"body_html":"\u003cp\u003e\u003cem\u003eThis is a submission for the \u003ca href=\"https://dev.to/challenges/hacktoberfest-week1-2026-10-05\"\u003eHacktoberfest Open-Source AI Challenge Week 1: Touch Grass\u003c/a\u003e\u003c/em\u003e\u003c/p\u003e\n\n\u003ch2\u003e\n  \u003ca name=\"what-i-built\" href=\"#what-i-built\"\u003e\n  \u003c/a\u003e\n  What I Built\n\u003c/h2\u003e\n\n\u003cp\u003eThirstyLeaf is a tiny plant-watering reminder app. Add a plant, pick a date and time, and it tells you when it’s thirsty. It’s a nudge to look up from the screen, walk over to your plants, and actually touch some (potted) grass.\u003c/p\u003e\n\n\u003cp\u003eIt’s for anyone who keeps killing houseplants because they forget, which includes me.\u003c/p\u003e\n\n\u003cp\u003eQuick dates: Today / Tomorrow / In 3 days / Next week chips set the date in one tap.\u003cbr\u003e\nRepeats: daily through monthly. Marking a plant watered reschedules from now, so a late watering still gets a full interval.\u003cbr\u003e\nNeeds water: everything that’s due is collected in one place, and the count shows in the browser tab title so you can spot it from another tab.\u003cbr\u003e\nWatered / +1 hour / +1 day: mark a plant done or snooze it. One-time reminders move to Done, where Undo brings them back.\u003cbr\u003e\nNo account, no backend: everything lives in your browser’s localStorage.\u003c/p\u003e\n\u003ch2\u003e\n  \u003ca name=\"demo\" href=\"#demo\"\u003e\n  \u003c/a\u003e\n  Demo\n\u003c/h2\u003e\n\n\u003cp\u003e\u003ca href=\"https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Farticles%2F798f4bp3u7u3vr6id7ei.png\" class=\"article-body-image-wrapper\"\u003e\u003cimg src=\"https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Farticles%2F798f4bp3u7u3vr6id7ei.png\" alt=\" \" loading=\"lazy\" width=\"800\" height=\"375\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003ch2\u003e\n  \u003ca name=\"code\" href=\"#code\"\u003e\n  \u003c/a\u003e\n  Code\n\u003c/h2\u003e\n\n\n\u003cdiv class=\"ltag-github-readme-tag\"\u003e\n  \u003cdiv class=\"readme-overview\"\u003e\n    \u003ch2\u003e\n      \u003cimg src=\"https://assets.dev.to/assets/github-logo-5a155e1f9a670af7944dd5e12375bc76ed542ea80224905ecaf878b9157cdefc.svg\" alt=\"GitHub logo\"\u003e\n      \u003ca href=\"https://github.com/Ayushjhawar8\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\n        Ayushjhawar8\n      \u003c/a\u003e / \u003ca style=\"font-weight: 600;\" href=\"https://github.com/Ayushjhawar8/ThirstyLeaf\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\n        ThirstyLeaf\n      \u003c/a\u003e\n    \u003c/h2\u003e\n    \u003ch3\u003e\n      Set a reminder to water any plant on any day and time. No account, no backend — reminders are saved in your browser's `localStorage`.\n    \u003c/h3\u003e\n  \u003c/div\u003e\n  \u003cdiv class=\"ltag-github-body\"\u003e\n    \n\u003cdiv id=\"readme\" class=\"md\" data-path=\"README.md\"\u003e\u003carticle class=\"markdown-body entry-content container-lg\" itemprop=\"text\"\u003e\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\n\u003ch1 class=\"heading-element\" dir=\"auto\"\u003e🪴 Plant Re

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{"type_of":"article","id":4825380,"title":"EcoID: An Offline Plant Identifier That Gets You Outside","description":"This is a submission for the Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass.          ...","readable_publish_date":"Oct 9","slug":"ecoid-an-offline-plant-identifier-that-gets-you-outside-4b6e","path":"/bangkah/ecoid-an-offline-plant-identifier-that-gets-you-outside-4b6e","url":"https://dev.to/bangkah/ecoid-an-offline-plant-identifier-that-gets-you-outside-4b6e","comments_count":0,"public_reactions_count":2,"collection_id":null,"published_timestamp":"2026-10-09T20:55:09Z","language":"en","subforem_id":1,"ai_disclosure_level":"not_disclosed","ai_disclosure_label":"Not Disclosed","positive_reactions_count":2,"cover_image":null,"social_image":"https://media2.dev.to/dynamic/image/width=1200,height=627,fit=cover,gravity=auto,format=auto/https%3A%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Farticles%2Fhalcp7to4rknldjvi6ze.png","canonical_url":"https://dev.to/bangkah/ecoid-an-offline-plant-identifier-that-gets-you-outside-4b6e","created_at":"2026-10-09T20:55:09Z","edited_at":"2026-10-09T21:02:14Z","crossposted_at":null,"published_at":"2026-10-09T20:55:09Z","last_comment_at":"2026-10-09T20:55:09Z","reading_time_minutes":6,"tag_list":"hacktoberfest, opensource, devchallenge, hf26challenge","tags":["hacktoberfest","opensource","devchallenge","hf26challenge"],"body_html":"\u003cp\u003eThis is a submission for the \u003ca href=\"https://dev.to/challenges/hacktoberfest-week1-2026-10-05\"\u003eHacktoberfest Open-Source AI Challenge Week 1: Touch Grass\u003c/a\u003e.\u003c/p\u003e\n\n\u003ch2\u003e\n  \u003ca name=\"what-i-built\" href=\"#what-i-built\"\u003e\n  \u003c/a\u003e\n  What I Built\n\u003c/h2\u003e\n\n\u003cp\u003eEcoID is an offline-first plant identification tool for field observations.\u003c/p\u003e\n\n\u003cp\u003eThe idea is simple: take a laptop or phone outside, photograph a real plant, and use a local AI model to get identification candidates. Then look at the plant yourself and verify the result.\u003c/p\u003e\n\n\u003cp\u003eEcoID is designed for:\u003c/p\u003e\n\n\u003cul\u003e\n\u003cli\u003estudents learning about local plants\u003c/li\u003e\n\u003cli\u003efield researchers and citizen scientists\u003c/li\u003e\n\u003cli\u003egardeners and small-scale farmers\u003c/li\u003e\n\u003cli\u003epeople who want to explore nature without sending their photos to a cloud service\u003c/li\u003e\n\u003c/ul\u003e\n\n\u003cp\u003eThe current model supports five plant classes:\u003c/p\u003e\n\n\u003cul\u003e\n\u003cli\u003eMango — \u003ccode\u003eMangifera indica\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eCoconut — \u003ccode\u003eCocos nucifera\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eBanana — \u003ccode\u003eMusa acuminata\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003ePapaya — \u003ccode\u003eCarica papaya\u003c/code\u003e\n\u003c/li\u003e\n\u003cli\u003eCassava — \u003ccode\u003eManihot esculenta\u003c/code\u003e\n\u003c/li\u003e\n\u003c/ul\u003e\n\n\u003cp\u003eThe AI does not present its output as a fact. It returns ranked identification candidates with confidence scores. The user then chooses whether the result is:\u003c/p\u003e\n\n\u003cul\u003e\n\u003cli\u003eVerified\u003c/li\u003e\n\u003cli\u003eRejected\u003c/li\u003e\n\u003cli\u003eUncertain\u003c/li\u003e\n\u003c/ul\u003e\n\n\u003cp\u003eThis human-in-the-loop flow matters because a model prediction is only a suggestion until somebody looks at the actual plant.\u003c/p\u003e\n\n\u003cp\u003eEcoID stores field observations locally, including the original photo, model prediction, alternative predictions, confidence score, verification status, notes, optional GPS coordinates, capture time from EXIF metadata, and user corrections.\u003c/p\u003e\n\n\u003cp\u003eThe project is designed to make the computer useful for a short moment, then get the person back to observing the real world.\u003c/p\u003e\n\n\u003ch2\u003e\n  \u003ca name=\"demo\" href=\"#demo\"\u003e\n  \u003c/a\u003e\n  Demo\n\u003c/h2\u003e\n\n\u003cp\u003eEcoID runs locally on a laptop or a phone-accessible local network. There is no public hosted demo because local execution is part of the project's privacy and offline design.\u003c/p\u003e\n\n\u003cp\u003eThe trained model and downloaded dataset are not committed to the repository. Model weights and image data are kept outside Git because of their size and individual dataset licensing terms.\u003c/p\u003e\n\n\u003cp\u003eAfter training or obtaining the ONNX model locally:\u003cbr\u003e\n\u003c/p\u003e\n\n\u003cdiv class=\"highlight js-code-highlight\"\u003e\n\u003cpre class=\"highlight shell\"\u003e\u003ccode\u003epython \u003cspan class=\"nt\"\u003e-m\u003c/span\u003e app \u003cspan class=\"nt\"\u003e--model\u003c/span\u003e models/e

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{"type_of":"article","id":4824064,"title":"Plain Sight Walk is a walking game that lives in your pocket.","description":"This is a submission for the Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass          ...","readable_publish_date":"Oct 9","slug":"plain-sight-walk-is-a-walking-game-that-lives-in-your-pocket-gjo","path":"/maitrivv/plain-sight-walk-is-a-walking-game-that-lives-in-your-pocket-gjo","url":"https://dev.to/maitrivv/plain-sight-walk-is-a-walking-game-that-lives-in-your-pocket-gjo","comments_count":0,"public_reactions_count":5,"collection_id":null,"published_timestamp":"2026-10-09T15:18:26Z","language":"en","subforem_id":1,"ai_disclosure_level":"not_disclosed","ai_disclosure_label":"Not Disclosed","positive_reactions_count":5,"cover_image":null,"social_image":"https://media2.dev.to/dynamic/image/width=1200,height=627,fit=cover,gravity=auto,format=auto/https%3A%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Farticles%2F8gthn1l61indujppiyh6.png","canonical_url":"https://dev.to/maitrivv/plain-sight-walk-is-a-walking-game-that-lives-in-your-pocket-gjo","created_at":"2026-10-09T15:18:27Z","edited_at":"2026-10-09T15:35:12Z","crossposted_at":null,"published_at":"2026-10-09T15:18:26Z","last_comment_at":"2026-10-09T15:18:26Z","reading_time_minutes":5,"tag_list":"devchallenge, hf26challenge, hacktoberfest","tags":["devchallenge","hf26challenge","hacktoberfest"],"body_html":"\u003cp\u003e\u003cem\u003eThis is a submission for the \u003ca href=\"https://dev.to/challenges/hacktoberfest-week1-2026-10-05\"\u003eHacktoberfest Open-Source AI Challenge Week 1: Touch Grass\u003c/a\u003e\u003c/em\u003e\u003c/p\u003e\n\n\u003ch2\u003e\n  \u003ca name=\"what-i-built\" href=\"#what-i-built\"\u003e\n  \u003c/a\u003e\n  What I Built\n\u003c/h2\u003e\n\n\u003cp\u003ePlain Sight Walk is a walking game that lives in your pocket.\u003c/p\u003e\n\n\u003cp\u003e\u003ca href=\"https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Farticles%2Faj58p5tskg98baczo7wy.png\" class=\"article-body-image-wrapper\"\u003e\u003cimg src=\"https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Farticles%2Faj58p5tskg98baczo7wy.png\" alt=\"plain-sight-walk powered by Gemma\" loading=\"lazy\" width=\"800\" height=\"400\"\u003e\u003c/a\u003e\u003c/p\u003e\n\n\u003cp\u003eEvery walk has a secret word, say WANDER. You spell it one letter at a time with things you actually find outside: W is for wall, A is for acorn, N is for nest. Gemma writes you a riddle for each letter and can read it aloud, so the phone stays away. When you find the thing, you photograph it, and an open vision model checks the photo. When the word is complete, the app turns your walk into a photo carousel: each slide is one of your photos, stamped with its letter, the object, and how many kilometres into the walk you took it. The secret word picks the look of the whole set. WANDER becomes \"Roadside Almanac\", a warm sepia. SHADOW becomes \"Chiaroscuro\", black and white.\u003c/p\u003e\n\n\u003cp\u003eThe screen is only for the start and the end. The middle of the experience is looking at things.\u003c/p\u003e\n\n\u003ch2\u003e\n  \u003ca name=\"demo\" href=\"#demo\"\u003e\n  \u003c/a\u003e\n  Demo\n\u003c/h2\u003e\n\n\u003cp\u003eLive app: \u003ca href=\"\"\u003eplain-sight-walk.vercel.app\u003c/a\u003e (best in recent Chrome or Edge; it needs WebGPU for Gemma)\u003c/p\u003e\n\n\u003ch2\u003e\n  \u003ca name=\"code\" href=\"#code\"\u003e\n  \u003c/a\u003e\n  Code\n\u003c/h2\u003e\n\n\u003cp\u003eCode (MIT):\u003c/p\u003e\n\n\n\u003cdiv class=\"ltag-github-readme-tag\"\u003e\n  \u003cdiv class=\"readme-overview\"\u003e\n    \u003ch2\u003e\n      \u003cimg src=\"https://assets.dev.to/assets/github-logo-5a155e1f9a670af7944dd5e12375bc76ed542ea80224905ecaf878b9157cdefc.svg\" alt=\"GitHub logo\"\u003e\n      \u003ca href=\"https://github.com/maitri-vv\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\n        maitri-vv\n      \u003c/a\u003e / \u003ca style=\"font-weight: 600;\" href=\"https://github.com/maitri-vv/plain-sight-walk\" target=\"_blank\" rel=\"noopener noreferrer\"\u003e\n        plain-sight-walk\n      \u003c/a\u003e\n    \u003c/h2\u003e\n    \u003ch3\u003e\n      \n    \u003c/h3\u003e\n  \u003c/div\u003e\n  \u003cdiv class=\"ltag-github-body\"\u003e\n    \n\u003cdiv id=\"readme\" class=\"md\" data-path=\"README.md\"\u003e\u003carticle class=\"markdown-body entry-content container-lg\" itemprop=\"text\"\u003e\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\n\u003ch1 class=\"heading-element\" dir=\"auto\"\u003ePlain Sight Walk\u003c/h1\u003e\n\u003c/div\u003e\n\u

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Contents of https://dev.to/api/articles/4825711:
{"type_of":"article","id":4825711,"title":"🌿 Ruta Viva AI: Turning Everyday Walks into AI-Powered Adventures","description":"What if AI could help us spend less time staring at screens and more time exploring the...","readable_publish_date":"Oct 9","slug":"ruta-viva-ai-turning-everyday-walks-into-ai-powered-adventures-1apa","path":"/ezequie1sc/ruta-viva-ai-turning-everyday-walks-into-ai-powered-adventures-1apa","url":"https://dev.to/ezequie1sc/ruta-viva-ai-turning-everyday-walks-into-ai-powered-adventures-1apa","comments_count":0,"public_reactions_count":0,"collection_id":null,"published_timestamp":"2026-10-09T22:43:26Z","language":"en","subforem_id":1,"ai_disclosure_level":"not_disclosed","ai_disclosure_label":"Not Disclosed","positive_reactions_count":0,"cover_image":null,"social_image":"https://media2.dev.to/dynamic/image/width=1200,height=627,fit=cover,gravity=auto,format=auto/https%3A%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Farticles%2Fgq4xouzkh8cbb8cjzzmk.png","canonical_url":"https://dev.to/ezequie1sc/ruta-viva-ai-turning-everyday-walks-into-ai-powered-adventures-1apa","created_at":"2026-10-09T22:43:26Z","edited_at":"2026-10-09T22:47:10Z","crossposted_at":null,"published_at":"2026-10-09T22:43:26Z","last_comment_at":"2026-10-09T22:43:26Z","reading_time_minutes":5,"tag_list":"devchallenge, hf26challenge, hacktoberfest, programming","tags":["devchallenge","hf26challenge","hacktoberfest","programming"],"body_html":"\u003cp\u003eWhat if AI could help us spend less time staring at screens and more time exploring the world?\u003c/p\u003e\n\n\u003cp\u003eThat's the idea behind Ruta Viva AI, an AI-powered outdoor adventure platform that transforms ordinary walks into interactive missions. Instead of spending more time scrolling, users can discover their surroundings, complete real-world challenges, and earn XP while exploring nature and local culture.\u003c/p\u003e\n\n\u003cp\u003eThe goal is simple: make the screen the shortest part of the adventure.\u003c/p\u003e\n\n\u003cp\u003e\u003ca href=\"https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Farticles%2F75tovh37q4cmparxflor.png\" class=\"article-body-image-wrapper\"\u003e\u003cimg src=\"https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Farticles%2F75tovh37q4cmparxflor.png\" alt=\" \" loading=\"lazy\" width=\"800\" height=\"376\"\u003e\u003c/a\u003e\u003c/p\u003e\n\n\u003cp\u003eWhat I Built\u003c/p\u003e\n\n\u003cp\u003eRuta Viva AI turns going outside into a game.\u003c/p\u003e\n\n\u003cp\u003eUsers choose their preferred adventure type, difficulty, and duration. The application then selects an adventure containing missions designed to encourage real-world exploration.\u003c/p\u003e\n\n\u003cp\u003eAdventure categories include:\u003c/p\u003e\n\n\u003cp\u003e🌳 Nature: Observe plants, discover patterns, identify colors, and listen to the sounds around you.\u003c/p\u003e\n\n\u003cp\u003e🏛️ Culture: Explore architecture, public art, and interesting details in your community.\u003c/p\u003e\n\n\u003cp\u003e🚶 Walking: Turn an ordinary walk into a sequence of small challenges.\u003c/p\u003e\n\n\u003cp\u003e🎲 Surprise: Let the adventure system choose an unexpected exploration experience.\u003c/p\u003e\n\n\u003cp\u003eEach adventure includes missions, descriptions, and experience points (XP). The idea is to give people a reason to step outside, pay attention to their surroundings, and turn everyday activities into memorable experiences.\u003c/p\u003e\n\n\u003cp\u003eThe project is designed for curious people who want to explore their communities, enjoy the outdoors, and make their walks more engaging.\u003c/p\u003e\n\n\u003cp\u003e\u003ca href=\"https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Farticles%2Fjynmqtb5ey8d08q1hlua.png\" class=\"article-body-image-wrapper\"\u003e\u003cimg src=\"https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Farticles%2Fjynmqtb5ey8d08q1hlua.png\" alt=\" \" loading=\"lazy\" width=\"800\" height=\"391\"\u003e\u003c/a\u003e\u003c/p\u003e\n\n\u003cp\u003e\u003ca href=\"https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.us-east-2.amazonaws.com%2Fuploads%2Farticles%2F2e3i119ktbc2op5o6d2p.png\" class=\"article-body-image-wrapper\"\u003e\u003cimg src=\"https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-do

[Truncated — full output (12.0 KB) temporarily saved to C:\Users\Lenovo\AppData\Local\Temp\1791598710823-copilot-tool-output-f94209cbf6984ba6965feaecc8088a90.txt]

{"items":[{"type":"website","label":"Hacktoberfest Week 1 challenge","isArtifact":false,"link":"https://dev.to/challenges/hacktoberfest-week1-2026-10-05"},{"type":"file","label":"Current submission sn...

Added reference: 345b6238-bf33-4c86-a077-4bd3d0a5e7e2
Added artifact: 8671077e-fd5f-4e24-a63e-e8454fe5100d

{
  "command": "Get-ChildItem -Force | Select-Object Mode,Length,LastWriteTime,Name",
  "description": "Inspect workspace structure"
}

<shellId: 20 completed with exit code 0>

{
  "command": "Get-ChildItem -Recurse -File -ErrorAction SilentlyContinue | Select-Object -First 80 FullName",
  "description": "Identify project files and existing app"
}

<shellId: 21 completed with exit code 0>

{
  "command": "Get-ChildItem -Force -Name README*,package.json,pyproject.toml,requirements*.txt,*.sln,*.csproj,Cargo.toml 2>$null",
  "description": "Detect project stack and entrypoints"
}

<shellId: 22 completed with exit code 1>

{"command":"Write-Output \"PWD: $(Get-Location)\"; Get-ChildItem -Force | Format-List Name,FullName,Mode,Length; Get-ChildItem -Recurse -Force -File -ErrorAction SilentlyContinue | Select-Object -Firs...

PWD: E:\HACKTOBERFEST WEEK 1 CHALLENGE
<shellId: 23 completed with exit code 0>

{"command":"Get-ChildItem -Force | Select-Object Mode,Length,Name; Get-ChildItem -Recurse -File -ErrorAction SilentlyContinue | Select-Object -First 120 FullName","description":"Inspect current reposi...

Mode   Length Name              
----   ------ ----              
d--h--        .git              
d-----        api               
d-----        node_modules      
d-----        public            
d-----        src               
d-----        venv              
-a---- 285    .env              
-a---- 95     .env.example      
-a---- 531    .gitignore        
-a---- 245    .oxlintrc.json    
-a---- 400    index.html        
-a---- 1089   LICENSE           
-a---- 77014  package-lock.json 
-a---- 790    package.json      
-a---- 3544   README.md         
-a---- 655    tsconfig.app.json 
-a---- 119    tsconfig.json     
-a---- 558    tsconfig.node.json
-a---- 220    vite.config.ts
<shellId: 0 completed with exit code 0>

{
  "command": "git status --short; git log --oneline -5",
  "description": "Check current git state and recent work"
}

f51f1a9 chore: rename FieldProof to WildProof across entire stack
98ae635 docs: update README with correct working directory for backend
ae889c1 feat: Phase 8 UI Polish and README
fab4ab1 chore: ensure venv is ignored
52dcef8 fix: load .env using python-dotenv for Sentry
<shellId: 1 completed with exit code 0>

{
  "pattern": "**/*",
  "paths": "E:\\HACKTOBERFEST WEEK 1 CHALLENGE"
}

{
  "command": "Get-ChildItem src -Recurse -File | Select-Object FullName; Get-ChildItem api -Recurse -File | Select-Object FullName",
  "description": "List implementation files"
}

FullName                                                                           
--------                                                                           
E:\HACKTOBERFEST WEEK 1 CHALLENGE\src\App.css                                      
E:\HACKTOBERFEST WEEK 1 CHALLENGE\src\App.tsx                                      
E:\HACKTOBERFEST WEEK 1 CHALLENGE\src\index.css                                    
E:\HACKTOBERFEST WEEK 1 CHALLENGE\src\main.tsx                                     
E:\HACKTOBERFEST WEEK 1 CHALLENGE\src\assets\hero.png                              
E:\HACKTOBERFEST WEEK 1 CHALLENGE\src\assets\react.svg                             
E:\HACKTOBERFEST WEEK 1 CHALLENGE\src\assets\vite.svg                              
E:\HACKTOBERFEST WEEK 1 CHALLENGE\src\lib\db.ts                                    
E:\HACKTOBERFEST WEEK 1 CHALLENGE\api\main.py                                      
E:\HACKTOBERFEST WEEK 1 CHALLENGE\api\requirements.txt                             
E:\HACKTOBERFEST WEEK 1 CHALLENGE\api\tabpfn_experiment.py                         
E:\HACKTOBERFEST WEEK 1 CHALLENGE\api\temporal_worker.py                           
E:\HACKTOBERFEST WEEK 1 CHALLENGE\api\temporal_workflow.py                         
E:\HACKTOBERFEST WEEK 1 CHALLENGE\api\__pycache__\main.cpython-311.pyc             
E:\HACKTOBERFEST WEEK 1 CHALLENGE\api\__pycache__\temporal_workflow.cpython-311.pyc
<shellId: 2 completed with exit code 0>

{
  "command": "Get-Content package.json; Get-Content README.md",
  "description": "Read project configuration and documentation"
}

{
  "name": "hacktoberfest-week-1-challenge",
  "private": true,
  "version": "0.0.0",
  "type": "module",
  "scripts": {
    "dev": "vite",
    "build": "tsc -b && vite build",
    "lint": "oxlint",
    "preview": "vite preview"
  },
  "dependencies": {
    "@sentry/react": "^11.6.0",
    "idb": "^8.0.4",
    "lucide-react": "^1.54.0",
    "react": "^19.2.8",
    "react-dom": "^19.2.8",
    "react-router-dom": "^7.18.4"
  },
  "devDependencies": {
    "@tailwindcss/vite": "^4.3.3",
    "@types/node": "^24.13.3",
    "@types/react": "^19.2.18",
    "@types/react-dom": "^19.2.7",
    "@vitejs/plugin-react": "^6.1.1",
    "autoprefixer": "^10.6.1",
    "oxlint": "^1.81.0",
    "postcss": "^8.5.29",
    "tailwindcss": "^4.3.3",
    "typescript": "~6.0.2",
    "vite": "^8.3.0"
  }
}
# WildProof 🌿

**WildProof** is an offline-first, uncertainty-aware field observation app designed to help researchers, hikers, and citizen scientists confidently log outdoor evidence. Built for the **Hacktoberfest DEV Challenge (Week 1: Touch Grass)**.

Unlike typical AI identification apps that confidently guess incorrect species, WildProof embraces uncertainty. It acts as an offline journal for your expedition (capturing photos, notes, and locations via IndexedDB). When you return to civilization, a local AI (Gemma 2B) analyzes your evidence, generating a structured report that explicitly separates *observed facts* from *inferred deductions*.

## How it works

1. **Mission Selection**: Choose a mission type (e.g., Habitat Comparison, Pattern Hunt).
2. **Offline Evidence Capture**: Go outside! Log field notes and snap photos. Everything is saved locally via IndexedDB, meaning it works deep in the woods without cell service.
3. **Durable Analysis pipeline**: Back on Wi-Fi, the app submits your evidence to a local FastAPI backend. A Temporal workflow coordinates the AI analysis, ensuring no data is lost even if the AI model crashes or times out.
4. **Structured Report**: The local Gemma model structures the raw evidence into a rigid JSON schema, highlighting what was directly observed, what is missing, and suggesting your next actionable step in the field.

## Challenge Categories Targeted

We built WildProof to specifically target three prize categories:

### 1. Best Use of TabPFN
We integrated TabPFN to predict "Expedition Quality" based on tabular features (number of photos, note length, mission duration). Because TabPFN is optimized for small tabular datasets, it perfectly captures the non-linear relationships in small field-data sets. 
*See `api/tabpfn_experiment.py` for the reproducible baseline-vs-TabPFN experiment script!*

### 2. Best Use of Temporal
The AI analysis pipeline is notoriously flaky (GPU OOMs, local model timeouts). We wrapped the entire backend analysis in a **Temporal Workflow** (`api/temporal_workflow.py`). If the Gemma analysis step fails or times out, Temporal durably retries the specific activity without losing the user's hard-earned field data or restarting the entire expedition process.

### 3. Best Use of Sentry
We instrumented the entire stack for deep performance tracing and error tracking:
- **React Frontend**: `@sentry/react` captures browser interactions and session replays.
- **FastAPI Backend & Temporal Worker**: `sentry-sdk[fastapi]` tracks request latency and catches model timeouts. 
*(You can simulate a crash and generate a trace link using the `GET /sentry-debug` endpoint!)*

## Local Setup

### Prerequisites
- Node.js (v18+)
- Python 3.10+
- Local [Ollama](https://ollama.com/) with `gemma:2b` pulled (`ollama run gemma:2b`)
- (Optional) Temporal CLI for running the durable workflow locally.

### 1. Frontend Setup
```bash
npm install
npm run dev
```
The React app will be available at `http://localhost:5173`.

### 2. Backend Setup
Create a `.env` file from `.env.example` and add your Sentry DSN if you wish to track errors.
```bash
python -m venv venv
# Windows:
.\venv\Scripts\activate
# Mac/Linux:
# source venv/bin/activate

pip install -r api/requirements.txt
```

### 3. Running the Backend
To start the standard FastAPI server:
```bash
cd api
uvicorn main:app --reload
```

*(Optional)* To run the Temporal worker (requires Temporal running locally `temporal server start-dev`):
```bash
cd api
python temporal_worker.py
```

## License
MIT
<shellId: 3 completed with exit code 0>

{
  "command": "Get-Content src\\App.tsx; Get-Content src\\main.tsx",
  "description": "Read main frontend entrypoints"
}

Output too large to read at once (15.5 KB). Saved to: C:\Users\Lenovo\AppData\Local\Temp\1791603007322-copilot-tool-output-7892-d2f6babe-1fcc-4e42-9d49-9b280f369dc6.txt
Consider using tools like rg (for searching), head/tail (for viewing start/end), view with view_range (for specific sections), or jq (for JSON) to examine portions of the output.

Preview (first 500 chars):
import { useState, useEffect } from 'react';
import { BrowserRouter as Router, Routes, Route, Link, useParams, useNavigate } from 'react-router-dom';
import { Leaf, Map, ClipboardList, Info, Camera, Trash, Plus, Check } from 'lucide-react';
import { getExpedition, saveExpedition } from './lib/db';
import type { Expedition, EvidenceItem, MissionType, FieldReport } from './lib/db';

function generateId() {
  return Math.random().toString(36).substr(2, 9);
}

function Home() {
  return (
    <div c
<shellId: 5 completed with exit code 0>

{
  "command": "Get-Content api\\main.py; Get-Content api\\requirements.txt",
  "description": "Read backend entrypoint and dependencies"
}

from fastapi import FastAPI, HTTPException, Request
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel, ValidationError, Field
from typing import List, Optional, Literal
import httpx
import json
import logging
from temporalio.client import Client
from temporal_workflow import EvidenceData
import sentry_sdk
import os
from dotenv import load_dotenv

load_dotenv()

sentry_dsn = os.environ.get("SENTRY_DSN")
if sentry_dsn:
    sentry_sdk.init(
        dsn=sentry_dsn,
        traces_sample_rate=1.0,
        profiles_sample_rate=1.0,
    )

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

app = FastAPI(title="WildProof API")

# Allow frontend to access the API
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"], # In production, restrict to frontend URL
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

OLLAMA_API_URL = "http://localhost:11434/api/generate"
MODEL_NAME = "gemma:2b" # Or "gemma", "llama3.2-vision", depending on what is installed locally

class EvidencePayload(BaseModel):
    id: str
    kind: str
    text: Optional[str] = None
    capturedAt: str
    localUrl: Optional[str] = None # Base64 representation if sent

class AnalyzeRequest(BaseModel):
    missionType: str
    evidence: List[EvidencePayload]

class Observation(BaseModel):
    claim: str
    confidence: Literal["observed", "inferred", "unknown"]
    supportingEvidenceIds: List[str]

class FieldReport(BaseModel):
    summary: str
    observations: List[Observation]
    missingEvidence: List[str] = Field(alias="missing_evidence")
    suggestedNextStep: str = Field(alias="suggested_next_step")

    class Config:
        populate_by_name = True

@app.get("/health")
def health_check():
    return {"status": "ok"}

@app.get("/sentry-debug")
def trigger_error():
    logger.error("Simulated performance issue / timeout for Sentry Tracing.")
    raise Exception("Simulated timeout for Sentry!")

async def generate_from_ollama(prompt: str) -> str:
    async with httpx.AsyncClient(timeout=60.0) as client:
        try:
            response = await client.post(
                OLLAMA_API_URL,
                json={
                    "model": MODEL_NAME,
                    "prompt": prompt,
                    "stream": False,
                    "format": "json"
                }
            )
            response.raise_for_status()
            return response.json().get("response", "")
        except Exception as e:
            logger.error(f"Failed to communicate with Ollama: {e}")
            raise HTTPException(status_code=503, detail="Local AI model is unavailable.")

@app.post("/analyze", response_model=FieldReport)
async def analyze_expedition(request: AnalyzeRequest):
    logger.info(f"Analyzing expedition: {request.missionType} with {len(request.evidence)} items.")
    
    # Formulate prompt from evidence
    text_evidence = []
    for ev in request.evidence:
        if ev.kind == "note" and ev.text:
            text_evidence.append(f"Note ID {ev.id}: {ev.text}")
        elif ev.kind == "photo":
            text_evidence.append(f"Photo Evidence ID {ev.id} captured.")

    evidence_str = "\n".join(text_evidence)
    
    prompt = f"""
You are an expert field researcher analyzing data from an outdoor expedition.
Mission Type: {request.missionType}

Here is the collected evidence:
{evidence_str}

Analyze this evidence and provide a structured JSON response. Do NOT output markdown, ONLY valid JSON.
Your JSON must match this structure exactly:
{{
  "summary": "A 1-2 sentence overall summary of the expedition.",
  "observations": [
    {{
      "claim": "Specific observation here.",
      "confidence": "observed" | "inferred" | "unknown",
      "supportingEvidenceIds": ["id1", "id2"]
    }}
  ],
  "missing_evidence": ["What evidence is missing to make a stronger conclusion?"],
  "suggested_next_step": "A single actionable next step."
}}

Rule for confidence:
- "observed": Only if directly stated in a note or directly visible in a photo (if provided).
- "inferred": A logical deduction based on the evidence.
- "unknown": Something guessed or speculative.

Ensure the "supportingEvidenceIds" match the Note IDs or Photo IDs provided above.
"""
    
    raw_response = await generate_from_ollama(prompt)
    
    try:
        data = json.loads(raw_response)
        report = FieldReport(**data)
        return report
    except (json.JSONDecodeError, ValidationError) as e:
        logger.warning(f"First parse attempt failed: {e}. Attempting repair.")
        
        repair_prompt = f"""
You previously returned invalid JSON. Fix the following JSON string so it matches the expected schema perfectly.
Schema:
{{
  "summary": "...",
  "observations": [
    {{
      "claim": "...",
      "confidence": "observed" | "inferred" | "unknown",
      "supportingEvidenceIds": ["id1"]
    }}
  ],
  "missing_evidence": ["..."],
  "suggested_next_step": "..."
}}

Invalid JSON:
{raw_response}
"""
        repair_response = await generate_from_ollama(repair_prompt)
        
        try:
            data = json.loads(repair_response)
            report = FieldReport(**data)
            return report
        except Exception as e:
            logger.error(f"Repair attempt failed: {e}")
            raise HTTPException(status_code=500, detail="AI returned malformed data. Please try again.")

@app.post("/analyze-durable")
async def analyze_expedition_durable(request: AnalyzeRequest):
    logger.info("Triggering durable analysis via Temporal...")
    try:
        client = await Client.connect("localhost:7233")
        
        # Convert Pydantic request to workflow dataclass
        evidence_data = EvidenceData(
            mission_type=request.missionType,
            evidence_items=[ev.model_dump() for ev in request.evidence]
        )
        
        # Execute workflow
        result = await client.execute_workflow(
            "ExpeditionAnalysisWorkflow",
            evidence_data,
            id=f"expedition-analysis-{request.missionType}-{len(request.evidence)}",
            task_queue="wildproof-analysis-queue",
        )
        return {"status": "Workflow completed", "result": result}
    except Exception as e:
        logger.error(f"Temporal execution failed: {e}")
        raise HTTPException(status_code=500, detail="Temporal workflow failed or server is not running.")

class QualityPredictionRequest(BaseModel):
    num_photos: int
    num_notes: int
    total_note_length: int
    duration_seconds: int

@app.post("/predict-quality")
def predict_quality(features: QualityPredictionRequest):
    # This uses the baseline heuristic. 
    # For the TabPFN experiment (which can be memory-heavy), see tabpfn_experiment.py
    evidence_count = features.num_photos + features.num_notes
    is_high_quality = (evidence_count >= 3) and (features.total_note_length >= 40)
    
    return {
        "is_high_quality": is_high_quality,
        "score": evidence_count + (features.total_note_length / 10),
        "model": "baseline_heuristic"
    }
fastapi==0.111.0
uvicorn==0.30.1
pydantic==2.8.2
httpx==0.27.0
tabpfn==0.1.10
scikit-learn==1.5.0
pandas==2.2.2
numpy==1.26.4
temporalio==1.6.0
sentry-sdk[fastapi]==2.13.0
python-dotenv==1.0.1
<shellId: 4 completed with exit code 0>

{
  "command": "Get-Content src\\lib\\db.ts; Get-Content src\\main.tsx; Get-Content src\\App.css",
  "description": "Inspect persistence and UI styling"
}

import { openDB } from 'idb';
import type { DBSchema, IDBPDatabase } from 'idb';

export type MissionType = 'habitat-comparison' | 'pattern-hunt' | 'sound-and-scene';
export type EvidenceKind = 'photo' | 'audio' | 'note';
export type Confidence = 'observed' | 'inferred' | 'unknown';
export type ExpeditionStatus = 'draft' | 'queued' | 'processing' | 'complete' | 'failed';

export interface EvidenceItem {
  id: string;
  kind: EvidenceKind;
  localUrl?: string; // object URL or base64
  text?: string;
  capturedAt: string;
  latitude?: number;
  longitude?: number;
}

export interface Observation {
  claim: string;
  confidence: Confidence;
  supportingEvidenceIds: string[];
}

export interface FieldReport {
  summary: string;
  observations: Observation[];
  missingEvidence: string[];
  suggestedNextStep: string;
  qualityScore?: number;
}

export interface Expedition {
  id: string;
  missionType: MissionType;
  startedAt: string;
  completedAt?: string;
  evidence: EvidenceItem[];
  status: ExpeditionStatus;
  report?: FieldReport;
}

interface WildProofDB extends DBSchema {
  expeditions: {
    key: string;
    value: Expedition;
    indexes: { 'by-status': string };
  };
}

let dbPromise: Promise<IDBPDatabase<WildProofDB>> | null = null;

export async function getDb() {
  if (!dbPromise) {
    dbPromise = openDB<WildProofDB>('wildproof-db', 1, {
      upgrade(db) {
        const store = db.createObjectStore('expeditions', { keyPath: 'id' });
        store.createIndex('by-status', 'status');
      },
    });
  }
  return dbPromise;
}

export async function saveExpedition(exp: Expedition) {
  const db = await getDb();
  await db.put('expeditions', exp);
}

export async function getExpedition(id: string) {
  const db = await getDb();
  return db.get('expeditions', id);
}

export async function getAllExpeditions() {
  const db = await getDb();
  return db.getAll('expeditions');
}

export async function deleteExpedition(id: string) {
  const db = await getDb();
  await db.delete('expeditions', id);
}
import { StrictMode } from 'react'
import { createRoot } from 'react-dom/client'
import './index.css'
import App from './App.tsx'
import * as Sentry from '@sentry/react'

if (import.meta.env.VITE_SENTRY_DSN) {
  Sentry.init({
    dsn: import.meta.env.VITE_SENTRY_DSN,
    integrations: [
      Sentry.browserTracingIntegration(),
      Sentry.replayIntegration(),
    ],
    tracesSampleRate: 1.0,
    tracePropagationTargets: ["localhost"],
    replaysSessionSampleRate: 0.1,
    replaysOnErrorSampleRate: 1.0,
  });
}

createRoot(document.getElementById('root')!).render(
  <StrictMode>
    <App />
  </StrictMode>,
)
.counter {
  font-size: 16px;
  padding: 5px 10px;
  border-radius: 5px;
  color: var(--accent);
  background: var(--accent-bg);
  border: 2px solid transparent;
  transition: border-color 0.3s;
  margin-bottom: 24px;

  &:hover {
    border-color: var(--accent-border);
  }
  &:focus-visible {
    outline: 2px solid var(--accent);
    outline-offset: 2px;
  }
}

.hero {
  position: relative;

  .base,
  .framework,
  .vite {
    inset-inline: 0;
    margin: 0 auto;
  }

  .base {
    width: 170px;
    position: relative;
    z-index: 0;
  }

  .framework,
  .vite {
    position: absolute;
  }

  .framework {
    z-index: 1;
    top: 34px;
    height: 28px;
    transform: perspective(2000px) rotateZ(300deg) rotateX(44deg) rotateY(39deg)
      scale(1.4);
  }

  .vite {
    z-index: 0;
    top: 107px;
    height: 26px;
    width: auto;
    transform: perspective(2000px) rotateZ(300deg) rotateX(40deg) rotateY(39deg)
      scale(0.8);
  }
}

#center {
  display: flex;
  flex-direction: column;
  gap: 25px;
  place-content: center;
  place-items: center;
  flex-grow: 1;

  @media (max-width: 1024px) {
    padding: 32px 20px 24px;
    gap: 18px;
  }
}

#next-steps {
  display: flex;
  border-top: 1px solid var(--border);
  text-align: left;

  & > div {
    flex: 1 1 0;
    padding: 32px;
    @media (max-width: 1024px) {
      padding: 24px 20px;
    }
  }

  .icon {
    margin-bottom: 16px;
    width: 22px;
    height: 22px;
  }

  @media (max-width: 1024px) {
    flex-direction: column;
    text-align: center;
  }
}

#docs {
  border-right: 1px solid var(--border);

  @media (max-width: 1024px) {
    border-right: none;
    border-bottom: 1px solid var(--border);
  }
}

#next-steps ul {
  list-style: none;
  padding: 0;
  display: flex;
  gap: 8px;
  margin: 32px 0 0;

  .logo {
    height: 18px;
  }

  a {
    color: var(--text-h);
    font-size: 16px;
    border-radius: 6px;
    background: var(--social-bg);
    display: flex;
    padding: 6px 12px;
    align-items: center;
    gap: 8px;
    text-decoration: none;
    transition: box-shadow 0.3s;

    &:hover {
      box-shadow: var(--shadow);
    }
    .button-icon {
      height: 18px;
      width: 18px;
    }
  }

  @media (max-width: 1024px) {
    margin-top: 20px;
    flex-wrap: wrap;
    justify-content: center;

    li {
      flex: 1 1 calc(50% - 8px);
    }

    a {
      width: 100%;
      justify-content: center;
      box-sizing: border-box;
    }
  }
}

#spacer {
  height: 88px;
  border-top: 1px solid var(--border);
  @media (max-width: 1024px) {
    height: 48px;
  }
}

.ticks {
  position: relative;
  width: 100%;

  &::before,
  &::after {
    content: '';
    position: absolute;
    top: -4.5px;
    border: 5px solid transparent;
  }

  &::before {
    left: 0;
    border-left-color: var(--border);
  }
  &::after {
    right: 0;
    border-right-color: var(--border);
  }
}
<shellId: 6 completed with exit code 0>

{
  "command": "Get-Content api\\temporal_workflow.py; Get-Content api\\temporal_worker.py; Get-Content api\\tabpfn_experiment.py",
  "description": "Inspect Temporal and TabPFN implementation"
}

from datetime import timedelta
import asyncio
from temporalio import activity, workflow
from typing import List, Dict, Any

# Define data classes used in the workflow
from dataclasses import dataclass

@dataclass
class EvidenceData:
    mission_type: str
    evidence_items: List[Dict[str, Any]]

@dataclass
class ReportData:
    summary: str
    observations: List[Dict[str, Any]]
    missing_evidence: List[str]
    suggested_next_step: str

# ---------------------------------------------------------
# ACTIVITIES
# ---------------------------------------------------------

@activity.defn
async def normalize_evidence(evidence: EvidenceData) -> EvidenceData:
    # Simulates preparing/cleaning local evidence payload for AI
    activity.logger.info("Normalizing evidence payload...")
    # Add dummy location normalization logic or size reduction
    return evidence

# A mock counter to simulate a transient failure for demonstration purposes
gemma_failures = 0

@activity.defn
async def run_gemma_analysis(evidence: EvidenceData) -> ReportData:
    global gemma_failures
    activity.logger.info(f"Running Gemma Analysis for {evidence.mission_type}...")
    
    # DEMONSTRATION OF TEMPORAL'S DURABLE RETRY
    # We force the activity to fail the first time to show how Temporal resumes it
    # without restarting the entire expedition process.
    if gemma_failures < 1:
        gemma_failures += 1
        activity.logger.error("Simulated GPU OOM or API Timeout error in Gemma.")
        raise RuntimeError("Gemma model failed to respond (Simulated Timeout)")
    
    # If we get here, it succeeded (on the retry)
    return ReportData(
        summary="A durable field report generated after a simulated failure recovery.",
        observations=[
            {"claim": "Evidence recovered seamlessly.", "confidence": "observed", "supportingEvidenceIds": []}
        ],
        missing_evidence=[],
        suggested_next_step="Review the Sentry tracing logs."
    )

@activity.defn
async def validate_report(report: ReportData) -> ReportData:
    activity.logger.info("Validating AI JSON Schema...")
    # Simulates Pydantic validation
    return report

@activity.defn
async def run_tabpfn_quality_prediction(report: ReportData) -> int:
    activity.logger.info("Running TabPFN tabular prediction on expedition quality...")
    return 95 # Dummy high quality score

@activity.defn
async def persist_report(report: ReportData) -> str:
    activity.logger.info("Persisting durable report to Database...")
    return "Report fully processed and saved!"

# ---------------------------------------------------------
# WORKFLOW
# ---------------------------------------------------------

@workflow.defn
class ExpeditionAnalysisWorkflow:
    @workflow.run
    async def analyze_expedition(self, evidence: EvidenceData) -> str:
        # Step 1: Normalize
        normalized = await workflow.execute_activity(
            normalize_evidence,
            evidence,
            start_to_close_timeout=timedelta(seconds=10),
        )

        # Step 2: Run Gemma Analysis
        # This activity is prone to "failure", but Temporal will automatically retry it
        # based on the default retry policy, ensuring the walk data isn't lost.
        raw_report = await workflow.execute_activity(
            run_gemma_analysis,
            normalized,
            start_to_close_timeout=timedelta(seconds=60),
        )

        # Step 3: Validate
        validated_report = await workflow.execute_activity(
            validate_report,
            raw_report,
            start_to_close_timeout=timedelta(seconds=10),
        )

        # Step 4: Quality Prediction
        quality_score = await workflow.execute_activity(
            run_tabpfn_quality_prediction,
            validated_report,
            start_to_close_timeout=timedelta(seconds=30),
        )

        # Step 5: Persist
        result = await workflow.execute_activity(
            persist_report,
            validated_report,
            start_to_close_timeout=timedelta(seconds=10),
        )

        return f"{result} (Quality Score: {quality_score})"
import asyncio
import logging
from temporalio.client import Client
from temporalio.worker import Worker

# Import our workflow and activities
from temporal_workflow import (
    ExpeditionAnalysisWorkflow,
    normalize_evidence,
    run_gemma_analysis,
    validate_report,
    run_tabpfn_quality_prediction,
    persist_report,
)
import sentry_sdk
import os
from dotenv import load_dotenv

load_dotenv()

sentry_dsn = os.environ.get("SENTRY_DSN")
if sentry_dsn:
    sentry_sdk.init(
        dsn=sentry_dsn,
        traces_sample_rate=1.0,
        profiles_sample_rate=1.0,
    )

async def main():
    logging.basicConfig(level=logging.INFO)
    
    # Connect to local Temporal server (requires Temporal CLI or Docker running locally)
    # E.g., `temporal server start-dev`
    try:
        client = await Client.connect("localhost:7233")
    except Exception as e:
        logging.error("Failed to connect to Temporal Server. Make sure it is running on localhost:7233.")
        return

    # Run the worker
    worker = Worker(
        client,
        task_queue="wildproof-analysis-queue",
        workflows=[ExpeditionAnalysisWorkflow],
        activities=[
            normalize_evidence,
            run_gemma_analysis,
            validate_report,
            run_tabpfn_quality_prediction,
            persist_report,
        ],
    )
    
    logging.info("Starting Temporal Worker for WildProof Analysis...")
    await worker.run()

if __name__ == "__main__":
    asyncio.run(main())
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from tabpfn import TabPFNClassifier

# 1. Generate Synthetic Dataset
# Features: num_photos, num_notes, total_note_length, duration_seconds
np.random.seed(42)
n_samples = 150

num_photos = np.random.randint(0, 5, n_samples)
num_notes = np.random.randint(0, 5, n_samples)
total_note_length = np.random.randint(0, 200, n_samples)
duration_seconds = np.random.randint(60, 3600, n_samples)

# A bit of noise
X = pd.DataFrame({
    'num_photos': num_photos,
    'num_notes': num_notes,
    'total_note_length': total_note_length,
    'duration_seconds': duration_seconds
})

# True labels (Is it a high-quality report?)
# Real world: determined by human review. Here: synthetically generated based on complex underlying rules.
y = ((num_photos >= 1) & (total_note_length >= 20) & (duration_seconds > 300)).astype(int)
# Add some random noise to the labels to make it non-deterministic
flip_mask = np.random.rand(n_samples) < 0.1
y = np.where(flip_mask, 1 - y, y)

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 2. Baseline Model
# Rule: evidence_count >= 3 && note_length >= 40
def baseline_predict(df):
    evidence_count = df['num_photos'] + df['num_notes']
    return ((evidence_count >= 3) & (df['total_note_length'] >= 40)).astype(int)

baseline_preds = baseline_predict(X_test)
baseline_acc = accuracy_score(y_test, baseline_preds)

# 3. TabPFN Model
# TabPFN is a pre-trained transformer for tabular data that needs no hyperparameter tuning for small datasets
classifier = TabPFNClassifier(device='cpu', N_ensemble_configurations=32)
classifier.fit(X_train, y_train)
tabpfn_preds = classifier.predict(X_test)
tabpfn_acc = accuracy_score(y_test, tabpfn_preds)

print("=== WildProof TabPFN Quality Prediction Experiment ===")
print(f"Dataset Size: {n_samples} samples")
print(f"Features: {list(X.columns)}")
print(f"Baseline Accuracy: {baseline_acc * 100:.2f}%")
print(f"TabPFN Accuracy:   {tabpfn_acc * 100:.2f}%")
print("\nLimitations: TabPFN is optimized for small tabular datasets (<10k samples) and can be memory intensive for inference in a lightweight API compared to a simple heuristic. However, it perfectly captures non-linear relationships in the field data.")
<shellId: 7 completed with exit code 0>

{
  "command": "npm run build; npm run lint",
  "description": "Validate frontend build and lint",
  "initial_wait": 120,
  "mode": "sync"
}

> [email protected] build
> tsc -b && vite build

vite v8.3.4 building client environment for production...
transforming...
✓ 2189 modules transformed.
rendering chunks...
computing gzip size...
dist/index.html                   0.49 kB │ gzip:   0.33 kB
dist/assets/index-BVzDAGDx.css   23.10 kB │ gzip:   5.13 kB
dist/assets/index-CPZGUSMf.js   564.68 kB │ gzip: 182.73 kB

[plugin builtin:vite-reporter] 
(!) Some chunks are larger than 500 kB after minification. Consider:
- Using dynamic import() to code-split the application
- Use build.rolldownOptions.output.codeSplitting to improve chunking: https://rolldown.rs/reference/OutputOptions.codeSplitting
- Adjust chunk size limit for this warning via build.chunkSizeWarningLimit.
✓ built in 8.35s
[PLUGIN_TIMINGS] JavaScript callbacks ran for 4.6s of this 8.2s build (56%).
The slowest callbacks, timed inside each callback (the wait before a callback starts is excluded, the time it awaits is included):
  - plugin @tailwindcss/vite:generate:build transform (40%, 3.3s, 1 call)
Additional callback time came from callbacks under 1s.
See https://rolldown.rs/reference/InputOptions.checks#bundlertimings for more details.


> [email protected] lint
> oxlint
<shellId: 8 completed with exit code 0>

{
  "filePaths": [
    "E:\\HACKTOBERFEST WEEK 1 CHALLENGE"
  ]
}

No errors found.

{"command":"$i=0; Get-Content src\\App.tsx | ForEach-Object { $i++; if($_ -match 'function |fetch\\(|analyze|saveExpedition|MediaRecorder|input|mission|report|predict|durable|quality|offline|navigator...

4: import { getExpedition, saveExpedition } from './lib/db';
5: import type { Expedition, EvidenceItem, MissionType, FieldReport } from './lib/db';
7: function generateId() {
11: function Home() {
19:         An offline-first open-AI companion for turning ordinary walks into trustworthy field notes.
22:         to="/missions" 
31: function Missions() {
34:   const startMission = async (id: MissionType) => {
40:       missionType: id,
45:     await saveExpedition(newExp);
62:       <h2 className="text-2xl font-bold mb-4 text-neutral-800">Select Mission</h2>
71:             onClick={() => startMission(m.id as MissionType)} 
84: function ActiveExpedition() {
100:   const addPhoto = (e: React.ChangeEvent<HTMLInputElement>) => {
112:         await saveExpedition(updated);
128:     await saveExpedition(updated);
135:     await saveExpedition(updated);
141:     await saveExpedition(updated);
142:     navigate(`/report/${expedition.id}`);
149:           <h2 className="text-3xl font-extrabold capitalize text-neutral-800 tracking-tight">{expedition.missionType.replace('-', ' ')}</h2>
155:           <Check className="w-5 h-5"/> Generate Report
186:             <input type="file" accept="image/*" capture="environment" className="hidden" onChange={addPhoto} />
190:             <input 
208: function Report() {
218:           if (!exp.report) {
220:               const res = await fetch('http://localhost:8000/analyze', {
223:                 body: JSON.stringify({ missionType: exp.missionType, evidence: exp.evidence })
227:               const updated = { ...exp, status: 'complete' as const, report: data };
228:               await saveExpedition(updated);
232:               const mockReport: FieldReport = {
241:               const updated = { ...exp, status: 'complete' as const, report: mockReport };
242:               await saveExpedition(updated);
253:   if (!expedition || !expedition.report) return <div className="p-4 text-center mt-10">Generating report...</div>;
255:   const { report } = expedition;
259:       <h2 className="text-3xl font-bold mb-2">Field Report</h2>
270:         <p className="text-emerald-900 leading-relaxed">{report.summary}</p>
275:         {report.observations.map((obs: any, i: number) => (
293:             {report.missingEvidence.map((e: string, i: number) => <li key={i}>{e}</li>)}
298:           <p className="text-sm text-neutral-600">{report.suggestedNextStep}</p>
305: function About() {
319: export default function App() {
330:               <Link to="/missions" className="hover:text-emerald-600 flex items-center gap-1"><ClipboardList className="w-4 h-4 hidden sm:block"/> Missions</Link>
339:             <Route path="/missions" element={<Missions />} />
341:             <Route path="/report/:id" element={<Report />} />
<shellId: 9 completed with exit code 0>

{
  "command": "$i=0; Get-Content src\\App.tsx | ForEach-Object { $i++; '{0}: {1}' -f $i,$_ } | Select-Object -Last 220",
  "description": "Inspect lower frontend implementation"
}

Output too large to read at once (11.1 KB). Saved to: C:\Users\Lenovo\AppData\Local\Temp\1791603040604-copilot-tool-output-7892-30b1f507-073b-44fa-90a9-8e45dcdb65bc.txt
Consider using tools like rg (for searching), head/tail (for viewing start/end), view with view_range (for specific sections), or jq (for JSON) to examine portions of the output.

Preview (first 500 chars):
129:     setExpedition(updated);
130:     setNoteText('');
131:   };
132: 
133:   const deleteEvidence = async (evId: string) => {
134:     const updated = { ...expedition, evidence: expedition.evidence.filter(e => e.id !== evId) };
135:     await saveExpedition(updated);
136:     setExpedition(updated);
137:   };
138: 
139:   const completeExpedition = async () => {
140:     const updated = { ...expedition, status: 'queued' as const, completedAt: new Date().toISOString() };
141:     await saveE
<shellId: 11 completed with exit code 0>

{
  "command": "$i=0; Get-Content src\\App.tsx | ForEach-Object { $i++; '{0}: {1}' -f $i,$_ } | Select-Object -First 220",
  "description": "Inspect upper frontend implementation"
}

Output too large to read at once (10.0 KB). Saved to: C:\Users\Lenovo\AppData\Local\Temp\1791603040755-copilot-tool-output-7892-8e79c925-9c3c-4022-bc4b-d37f83d42ef9.txt
Consider using tools like rg (for searching), head/tail (for viewing start/end), view with view_range (for specific sections), or jq (for JSON) to examine portions of the output.

Preview (first 500 chars):
1: import { useState, useEffect } from 'react';
2: import { BrowserRouter as Router, Routes, Route, Link, useParams, useNavigate } from 'react-router-dom';
3: import { Leaf, Map, ClipboardList, Info, Camera, Trash, Plus, Check } from 'lucide-react';
4: import { getExpedition, saveExpedition } from './lib/db';
5: import type { Expedition, EvidenceItem, MissionType, FieldReport } from './lib/db';
6: 
7: function generateId() {
8:   return Math.random().toString(36).substr(2, 9);
9: }
10: 
11: func
<shellId: 10 completed with exit code 0>

{
  "command": "$lines=Get-Content src\\App.tsx; $lines[205..252]",
  "description": "Inspect report generation and fallback behavior"
}

}

function Report() {
  const { id } = useParams<{ id: string }>();
  const [expedition, setExpedition] = useState<Expedition | null>(null);

  const [errorMsg, setErrorMsg] = useState<string | null>(null);

  useEffect(() => {
    if (id) {
      getExpedition(id).then(async exp => {
        if (exp) {
          if (!exp.report) {
            try {
              const res = await fetch('http://localhost:8000/analyze', {
                method: 'POST',
                headers: { 'Content-Type': 'application/json' },
                body: JSON.stringify({ missionType: exp.missionType, evidence: exp.evidence })
              });
              if (!res.ok) throw new Error('Backend unavailable');
              const data = await res.json();
              const updated = { ...exp, status: 'complete' as const, report: data };
              await saveExpedition(updated);
              setExpedition(updated);
            } catch {
              setErrorMsg("AI analysis unavailable. Showing a locally generated evidence summary.");
              const mockReport: FieldReport = {
                summary: "A brief walk yielding partial insights into the local habitat (Mocked).",
                observations: [
                  { claim: "Found broadleaf patterns near the pathway.", confidence: "observed", supportingEvidenceIds: [] },
                  { claim: "Likely a moderately humid micro-climate.", confidence: "inferred", supportingEvidenceIds: [] },
                ],
                missingEvidence: ["Audio capturing ambient bird sounds.", "Temperature readings."],
                suggestedNextStep: "Return at dusk to capture nocturnal insect patterns."
              };
              const updated = { ...exp, status: 'complete' as const, report: mockReport };
              await saveExpedition(updated);
              setExpedition(updated);
            }
          } else {
            setExpedition(exp);
          }
        }
      });
    }
  }, [id]);

  if (!expedition || !expedition.report) return <div className="p-4 text-center mt-10">Generating report...</div>;
<shellId: 12 completed with exit code 0>

{
  "command": "git ls-files .env .env.example; Get-Content .gitignore",
  "description": "Check environment secret tracking"
}

.env.example
# Logs
logs
*.log
npm-debug.log*
yarn-debug.log*
yarn-error.log*
pnpm-debug.log*
lerna-debug.log*

node_modules
dist
dist-ssr
*.local

# Editor directories and files
.vscode/*
!.vscode/extensions.json
.idea
.DS_Store
*.suo
*.ntvs*
*.njsproj
*.sln
*.sw?

# Python
__pycache__/
*.py[cod]
*$py.class
.Python
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
parts/
sdist/
var/
wheels/
share/python-wheels/
*.egg-info/
.installed.cfg
*.egg
MANIFEST

# Environments
.env
.venv
env/
venv/
ENV/
env.bak/
venv.bak/
# Env vars
.env
.env.*
<shellId: 13 completed with exit code 0>

{
  "command": "$lines=Get-Content src\\App.tsx; $lines[80..205]",
  "description": "Inspect capture and completion flow"
}

);
}

function ActiveExpedition() {
  const { id } = useParams<{ id: string }>();
  const navigate = useNavigate();
  const [expedition, setExpedition] = useState<Expedition | null>(null);
  const [noteText, setNoteText] = useState('');

  useEffect(() => {
    if (id) {
      getExpedition(id).then(exp => {
        if (exp) setExpedition(exp);
      });
    }
  }, [id]);

  if (!expedition) return <div className="p-4 text-center mt-10">Loading expedition...</div>;

  const addPhoto = (e: React.ChangeEvent<HTMLInputElement>) => {
    if (e.target.files && e.target.files[0]) {
      const file = e.target.files[0];
      const reader = new FileReader();
      reader.onload = async (ev) => {
        const newEv: EvidenceItem = {
          id: generateId(),
          kind: 'photo',
          localUrl: ev.target?.result as string,
          capturedAt: new Date().toISOString(),
        };
        const updated = { ...expedition, evidence: [...expedition.evidence, newEv] };
        await saveExpedition(updated);
        setExpedition(updated);
      };
      reader.readAsDataURL(file);
    }
  };

  const addNote = async () => {
    if (!noteText.trim()) return;
    const newEv: EvidenceItem = {
      id: generateId(),
      kind: 'note',
      text: noteText,
      capturedAt: new Date().toISOString(),
    };
    const updated = { ...expedition, evidence: [...expedition.evidence, newEv] };
    await saveExpedition(updated);
    setExpedition(updated);
    setNoteText('');
  };

  const deleteEvidence = async (evId: string) => {
    const updated = { ...expedition, evidence: expedition.evidence.filter(e => e.id !== evId) };
    await saveExpedition(updated);
    setExpedition(updated);
  };

  const completeExpedition = async () => {
    const updated = { ...expedition, status: 'queued' as const, completedAt: new Date().toISOString() };
    await saveExpedition(updated);
    navigate(`/report/${expedition.id}`);
  };

  return (
    <div className="p-4 max-w-2xl mx-auto pb-24">
      <div className="mb-6 flex justify-between items-center">
        <div>
          <h2 className="text-3xl font-extrabold capitalize text-neutral-800 tracking-tight">{expedition.missionType.replace('-', ' ')}</h2>
          <span className="bg-amber-100 text-amber-800 px-3 py-1 rounded-full text-xs font-bold tracking-wider shadow-sm border border-amber-200 uppercase mt-2 inline-block">
            Status: {expedition.status}
          </span>
        </div>
        <button onClick={completeExpedition} className="bg-emerald-600 text-white px-5 py-3 rounded-xl shadow-lg hover:shadow-xl font-bold flex items-center gap-2 hover:bg-emerald-700 transition transform hover:-translate-y-0.5">
          <Check className="w-5 h-5"/> Generate Report
        </button>
      </div>

      <div className="space-y-4 mb-8">
        {expedition.evidence.length === 0 && (
          <div className="text-center p-8 border-2 border-dashed border-neutral-200 rounded-xl text-neutral-500">
            No evidence yet. Start capturing your surroundings!
          </div>
        )}
        {expedition.evidence.map(ev => (
          <div key={ev.id} className="flex items-start justify-between bg-white p-4 rounded-xl shadow-sm border border-neutral-200">
            <div className="flex-1">
              <span className="text-xs uppercase font-bold text-neutral-400 mb-1 block">{ev.kind}</span>
              {ev.kind === 'photo' && ev.localUrl && (
                <img src={ev.localUrl} alt="Evidence" className="w-full h-48 object-cover rounded mt-2 bg-neutral-100" />
              )}
              {ev.kind === 'note' && <p className="text-neutral-800 mt-1">{ev.text}</p>}
              <p className="text-xs text-neutral-400 mt-2">{new Date(ev.capturedAt).toLocaleTimeString()}</p>
            </div>
            <button onClick={() => deleteEvidence(ev.id)} className="text-red-500 p-2 ml-4 hover:bg-red-50 rounded">
              <Trash className="w-5 h-5"/>
            </button>
          </div>
        ))}
      </div>

      <div className="fixed bottom-0 left-0 right-0 bg-white border-t border-neutral-200 p-4 shadow-[0_-4px_6px_-1px_rgba(0,0,0,0.05)]">
        <div className="max-w-2xl mx-auto flex gap-2">
          <label className="flex-1 flex justify-center items-center gap-2 bg-neutral-100 hover:bg-neutral-200 text-neutral-700 py-3 rounded-lg cursor-pointer font-medium transition">
            <Camera className="w-5 h-5" /> Add Photo
            <input type="file" accept="image/*" capture="environment" className="hidden" onChange={addPhoto} />
          </label>
          
          <div className="flex-[2] flex gap-2">
            <input 
              type="text" 
              placeholder="Add a field note..." 
              className="flex-1 bg-neutral-100 border-none rounded-lg px-4 focus:ring-2 focus:ring-emerald-500"
              value={noteText}
              onChange={(e) => setNoteText(e.target.value)}
              onKeyDown={(e) => e.key === 'Enter' && addNote()}
            />
            <button onClick={addNote} className="bg-neutral-800 hover:bg-neutral-900 text-white px-4 rounded-lg flex items-center justify-center transition">
              <Plus className="w-5 h-5" />
            </button>
          </div>
        </div>
      </div>
    </div>
  );
}
<shellId: 15 completed with exit code 0>

{
  "command": "git status --short; git show --stat --oneline HEAD",
  "description": "Check working tree and latest change"
}

f51f1a9 chore: rename FieldProof to WildProof across entire stack
 README.md                |  8 ++++----
 api/main.py              |  4 ++--
 api/tabpfn_experiment.py |  2 +-
 api/temporal_worker.py   |  4 ++--
 index.html               |  2 +-
 src/App.tsx              | 10 +++++-----
 src/lib/db.ts            |  6 +++---
 7 files changed, 18 insertions(+), 18 deletions(-)
<shellId: 14 completed with exit code 0>

{
  "command": "git status --short; git log --oneline -8",
  "description": "Inspect latest changes"
}

a4a1f43 fix: address MVP judging gaps (TabPFN api, Temporal real integration, Fallback UI, CORS, CSS cleanup)
f51f1a9 chore: rename FieldProof to WildProof across entire stack
98ae635 docs: update README with correct working directory for backend
ae889c1 feat: Phase 8 UI Polish and README
fab4ab1 chore: ensure venv is ignored
52dcef8 fix: load .env using python-dotenv for Sentry
d0d9645 fix: IDE error in main.py and update gitignore
91699fa feat: Phase 7 Sentry Tracing integration across full stack
<shellId: 0 completed with exit code 0>

{
  "command": "Get-ChildItem src -Recurse -File | Select-Object FullName; Get-ChildItem api -Recurse -File | Select-Object FullName",
  "description": "List current implementation files"
}

FullName                                                                           
--------                                                                           
E:\HACKTOBERFEST WEEK 1 CHALLENGE\src\App.css                                      
E:\HACKTOBERFEST WEEK 1 CHALLENGE\src\App.tsx                                      
E:\HACKTOBERFEST WEEK 1 CHALLENGE\src\index.css                                    
E:\HACKTOBERFEST WEEK 1 CHALLENGE\src\main.tsx                                     
E:\HACKTOBERFEST WEEK 1 CHALLENGE\src\assets\hero.png                              
E:\HACKTOBERFEST WEEK 1 CHALLENGE\src\assets\react.svg                             
E:\HACKTOBERFEST WEEK 1 CHALLENGE\src\assets\vite.svg                              
E:\HACKTOBERFEST WEEK 1 CHALLENGE\src\lib\db.ts                                    
E:\HACKTOBERFEST WEEK 1 CHALLENGE\api\main.py                                      
E:\HACKTOBERFEST WEEK 1 CHALLENGE\api\requirements.txt                             
E:\HACKTOBERFEST WEEK 1 CHALLENGE\api\tabpfn_experiment.py                         
E:\HACKTOBERFEST WEEK 1 CHALLENGE\api\temporal_worker.py                           
E:\HACKTOBERFEST WEEK 1 CHALLENGE\api\temporal_workflow.py                         
E:\HACKTOBERFEST WEEK 1 CHALLENGE\api\__pycache__\main.cpython-311.pyc             
E:\HACKTOBERFEST WEEK 1 CHALLENGE\api\__pycache__\temporal_workflow.cpython-311.pyc
<shellId: 1 completed with exit code 0>

{
  "command": "Get-Content package.json; Get-Content README.md",
  "description": "Review current project claims"
}

{
  "name": "hacktoberfest-week-1-challenge",
  "private": true,
  "version": "0.0.0",
  "type": "module",
  "scripts": {
    "dev": "vite",
    "build": "tsc -b && vite build",
    "lint": "oxlint",
    "preview": "vite preview"
  },
  "dependencies": {
    "@sentry/react": "^11.6.0",
    "idb": "^8.0.4",
    "lucide-react": "^1.54.0",
    "react": "^19.2.8",
    "react-dom": "^19.2.8",
    "react-router-dom": "^7.18.4"
  },
  "devDependencies": {
    "@tailwindcss/vite": "^4.3.3",
    "@types/node": "^24.13.3",
    "@types/react": "^19.2.18",
    "@types/react-dom": "^19.2.7",
    "@vitejs/plugin-react": "^6.1.1",
    "autoprefixer": "^10.6.1",
    "oxlint": "^1.81.0",
    "postcss": "^8.5.29",
    "tailwindcss": "^4.3.3",
    "typescript": "~6.0.2",
    "vite": "^8.3.0"
  }
}
# WildProof 🌿

**WildProof** is an offline-first, uncertainty-aware field observation app designed to help researchers, hikers, and citizen scientists confidently log outdoor evidence. Built for the **Hacktoberfest DEV Challenge (Week 1: Touch Grass)**.

Unlike typical AI identification apps that confidently guess incorrect species, WildProof embraces uncertainty. It acts as an offline journal for your expedition (capturing photos, notes, and metadata via IndexedDB). When you return to civilization, a local AI (Gemma 2B) analyzes your evidence—currently processing the text notes and photographic metadata—generating a structured report that explicitly separates *observed facts* from *inferred deductions*.

## How it works

1. **Mission Selection**: Choose a mission type (e.g., Habitat Comparison, Pattern Hunt).
2. **Offline Evidence Capture**: Go outside! Log field notes and snap photos. Everything is saved locally via IndexedDB, meaning it works deep in the woods without cell service.
3. **Durable Analysis pipeline**: Back on Wi-Fi, the app submits your evidence to a local FastAPI backend. A Temporal workflow coordinates the AI analysis, ensuring no data is lost even if the AI model crashes or times out.
4. **Structured Report**: The local Gemma model structures the raw evidence into a rigid JSON schema, highlighting what was directly observed, what is missing, and suggesting your next actionable step in the field.

## Challenge Categories Targeted

We built WildProof to specifically target three prize categories:

### 1. Best Use of TabPFN
We integrated TabPFN directly into the API to predict "Expedition Quality" based on tabular features (number of photos, note length, mission duration). Because TabPFN is optimized for small tabular datasets, it perfectly captures the non-linear relationships in small field-data sets. It is fully wired up in the `/predict-quality` endpoint and displays the score dynamically in the UI. 
*See `api/tabpfn_experiment.py` for the reproducible baseline-vs-TabPFN experiment script!*

### 2. Best Use of Temporal
The AI analysis pipeline is notoriously flaky (GPU OOMs, local model timeouts). We wrapped the entire backend analysis in a **Temporal Workflow** (`api/temporal_workflow.py`). If the Gemma analysis step fails or times out, Temporal durably retries the specific activity without losing the user's hard-earned field data or restarting the entire expedition process.

### 3. Best Use of Sentry
We instrumented the entire stack for deep performance tracing and error tracking:
- **React Frontend**: `@sentry/react` captures browser interactions and session replays.
- **FastAPI Backend & Temporal Worker**: `sentry-sdk[fastapi]` tracks request latency and catches model timeouts. 
*(You can simulate a crash and generate a trace link using the `GET /sentry-debug` endpoint!)*

## Local Setup

### Prerequisites
- Node.js (v18+)
- Python 3.10+
- Local [Ollama](https://ollama.com/) with `gemma:2b` pulled (`ollama run gemma:2b`)
- (Optional) Temporal CLI for running the durable workflow locally.

### 1. Frontend Setup
```bash
npm install
npm run dev
```
The React app will be available at `http://localhost:5173`.

### 2. Backend Setup
Create a `.env` file from `.env.example` and add your Sentry DSN if you wish to track errors.
```bash
python -m venv venv
# Windows:
.\venv\Scripts\activate
# Mac/Linux:
# source venv/bin/activate

pip install -r api/requirements.txt
```

### 3. Running the Backend
To start the standard FastAPI server:
```bash
cd api
uvicorn main:app --reload
```

*(Optional)* To run the Temporal worker (requires Temporal running locally `temporal server start-dev`):
```bash
cd api
python temporal_worker.py
```

## License
MIT
<shellId: 3 completed with exit code 0>

{
  "command": "npm run build; npm run lint",
  "description": "Run frontend validation",
  "initial_wait": 120,
  "mode": "sync"
}

> [email protected] build
> tsc -b && vite build

vite v8.3.4 building client environment for production...
transforming...
✓ 2189 modules transformed.
rendering chunks...
computing gzip size...
dist/index.html                   0.49 kB │ gzip:   0.32 kB
dist/assets/index-Dt_fwG0i.css   23.84 kB │ gzip:   5.28 kB
dist/assets/index-IsZ2nT4l.js   566.10 kB │ gzip: 183.13 kB

[plugin builtin:vite-reporter] 
(!) Some chunks are larger than 500 kB after minification. Consider:
- Using dynamic import() to code-split the application
- Use build.rolldownOptions.output.codeSplitting to improve chunking: https://rolldown.rs/reference/OutputOptions.codeSplitting
- Adjust chunk size limit for this warning via build.chunkSizeWarningLimit.
✓ built in 8.37s
[PLUGIN_TIMINGS] JavaScript callbacks ran for 6.1s of this 8.3s build (74%).
The slowest callbacks, timed inside each callback (the wait before a callback starts is excluded, the time it awaits is included):
  - plugin @tailwindcss/vite:generate:build transform (53%, 4.4s, 1 call)
  - plugin vite:css-post renderChunk (17%, 1.4s, 1 call)
Additional callback time came from callbacks under 1s.
See https://rolldown.rs/reference/InputOptions.checks#bundlertimings for more details.


> [email protected] lint
> oxlint
<shellId: 2 completed with exit code 0>

{
  "command": "git show --stat --oneline a4a1f43; git show --format= --name-only a4a1f43",
  "description": "Inspect latest judging-gap fix"
}

a4a1f43 fix: address MVP judging gaps (TabPFN api, Temporal real integration, Fallback UI, CORS, CSS cleanup)
 README.md                |   4 +-
 api/main.py              |  57 ++++++++++++---
 api/temporal_workflow.py |  52 +++++++------
 src/App.css              | 185 +----------------------------------------------
 src/App.tsx              | 103 ++++++++++++++++++--------
 5 files changed, 154 insertions(+), 247 deletions(-)
README.md
api/main.py
api/temporal_workflow.py
src/App.css
src/App.tsx
<shellId: 4 completed with exit code 0>

{
  "command": "$i=0; Get-Content api\\main.py | ForEach-Object { $i++; '{0}: {1}' -f $i,$_ } | Select-Object -First 280",
  "description": "Review updated backend integration"
}

Output too large to read at once (9.3 KB). Saved to: C:\Users\Lenovo\AppData\Local\Temp\1791603496696-copilot-tool-output-19664-03c11a24-754a-4384-a7cb-7a1f38be5178.txt
Consider using tools like rg (for searching), head/tail (for viewing start/end), view with view_range (for specific sections), or jq (for JSON) to examine portions of the output.

Preview (first 500 chars):
1: from fastapi import FastAPI, HTTPException, Request
2: from fastapi.middleware.cors import CORSMiddleware
3: from pydantic import BaseModel, ValidationError, Field
4: from typing import List, Optional, Literal
5: import httpx
6: import json
7: import logging
8: from temporalio.client import Client
9: from temporal_workflow import EvidenceData
10: import sentry_sdk
11: import os
12: from dotenv import load_dotenv
13: 
14: # Optional TabPFN import
15: try:
16:     from tabpfn import TabPFNClass
<shellId: 5 completed with exit code 0>

{"command":"$i=0; Get-Content api\\temporal_workflow.py | ForEach-Object { $i++; '{0}: {1}' -f $i,$_ }; $i=0; Get-Content api\\tabpfn_experiment.py | ForEach-Object { $i++; '{0}: {1}' -f $i,$_ }","des...

1: from datetime import timedelta
2: import asyncio
3: from temporalio import activity, workflow
4: from typing import List, Dict, Any
5: 
6: # Define data classes used in the workflow
7: from dataclasses import dataclass
8: 
9: @dataclass
10: class EvidenceData:
11:     mission_type: str
12:     evidence_items: List[Dict[str, Any]]
13: 
14: @dataclass
15: class ReportData:
16:     summary: str
17:     observations: List[Dict[str, Any]]
18:     missing_evidence: List[str]
19:     suggested_next_step: str
20: 
21: # ---------------------------------------------------------
22: # ACTIVITIES
23: # ---------------------------------------------------------
24: 
25: @activity.defn
26: async def normalize_evidence(evidence: EvidenceData) -> EvidenceData:
27:     # Simulates preparing/cleaning local evidence payload for AI
28:     activity.logger.info("Normalizing evidence payload...")
29:     # Add dummy location normalization logic or size reduction
30:     return evidence
31: 
32: import json
33: import httpx
34: 
35: @activity.defn
36: async def run_gemma_analysis(evidence: EvidenceData) -> ReportData:
37:     activity.logger.info(f"Running REAL Gemma Analysis for {evidence.mission_type}...")
38:     
39:     prompt = f"Mission: {evidence.mission_type}. Analyze this field evidence:\n"
40:     for e in evidence.evidence_items:
41:         if e.get("kind") == "note":
42:             prompt += f"- Note: {e.get('text')}\n"
43:         elif e.get("kind") == "photo":
44:             prompt += "- Photo: [Image metadata omitted for text-only model]\n"
45: 
46:     prompt += "\nOutput JSON with summary, observations (with claim, confidence), missing_evidence, and suggested_next_step."
47: 
48:     # This will fail (and therefore trigger Temporal's durable retries) if Ollama is not running!
49:     async with httpx.AsyncClient(timeout=60.0) as client:
50:         response = await client.post(
51:             "http://localhost:11434/api/generate",
52:             json={"model": "gemma:2b", "prompt": prompt, "stream": False, "format": "json"}
53:         )
54:         response.raise_for_status()
55:         result = response.json()
56:         
57:         try:
58:             data = json.loads(result["response"])
59:             return ReportData(
60:                 summary=data.get("summary", "Fallback summary"),
61:                 observations=data.get("observations", []),
62:                 missing_evidence=data.get("missing_evidence", []),
63:                 suggested_next_step=data.get("suggested_next_step", "")
64:             )
65:         except Exception:
66:             raise RuntimeError("Failed to parse Gemma output into valid JSON")
67: 
68: @activity.defn
69: async def validate_report(report: ReportData) -> ReportData:
70:     activity.logger.info("Validating AI JSON Schema...")
71:     # Simulates Pydantic validation
72:     return report
73: 
74: @activity.defn
75: async def run_tabpfn_quality_prediction(report: ReportData) -> int:
76:     activity.logger.info("Running TabPFN tabular prediction on expedition quality...")
77:     return 95 # Dummy high quality score
78: 
79: @activity.defn
80: async def persist_report(report: ReportData) -> str:
81:     activity.logger.info("Persisting durable report to Database...")
82:     return "Report fully processed and saved!"
83: 
84: # ---------------------------------------------------------
85: # WORKFLOW
86: # ---------------------------------------------------------
87: 
88: @workflow.defn
89: class ExpeditionAnalysisWorkflow:
90:     @workflow.run
91:     async def analyze_expedition(self, evidence: EvidenceData) -> str:
92:         # Step 1: Normalize
93:         normalized = await workflow.execute_activity(
94:             normalize_evidence,
95:             evidence,
96:             start_to_close_timeout=timedelta(seconds=10),
97:         )
98: 
99:         # Step 2: Run Gemma Analysis
100:         # This activity is prone to "failure", but Temporal will automatically retry it
101:         # based on the default retry policy, ensuring the walk data isn't lost.
102:         raw_report = await workflow.execute_activity(
103:             run_gemma_analysis,
104:             normalized,
105:             start_to_close_timeout=timedelta(seconds=60),
106:         )
107: 
108:         # Step 3: Validate
109:         validated_report = await workflow.execute_activity(
110:             validate_report,
111:             raw_report,
112:             start_to_close_timeout=timedelta(seconds=10),
113:         )
114: 
115:         # Step 4: Quality Prediction
116:         quality_score = await workflow.execute_activity(
117:             run_tabpfn_quality_prediction,
118:             validated_report,
119:             start_to_close_timeout=timedelta(seconds=30),
120:         )
121: 
122:         # Step 5: Persist
123:         result = await workflow.execute_activity(
124:             persist_report,
125:             validated_report,
126:             start_to_close_timeout=timedelta(seconds=10),
127:         )
128: 
129:         return f"{result} (Quality Score: {quality_score})"
1: import pandas as pd
2: import numpy as np
3: from sklearn.model_selection import train_test_split
4: from sklearn.metrics import accuracy_score
5: from tabpfn import TabPFNClassifier
6: 
7: # 1. Generate Synthetic Dataset
8: # Features: num_photos, num_notes, total_note_length, duration_seconds
9: np.random.seed(42)
10: n_samples = 150
11: 
12: num_photos = np.random.randint(0, 5, n_samples)
13: num_notes = np.random.randint(0, 5, n_samples)
14: total_note_length = np.random.randint(0, 200, n_samples)
15: duration_seconds = np.random.randint(60, 3600, n_samples)
16: 
17: # A bit of noise
18: X = pd.DataFrame({
19:     'num_photos': num_photos,
20:     'num_notes': num_notes,
21:     'total_note_length': total_note_length,
22:     'duration_seconds': duration_seconds
23: })
24: 
25: # True labels (Is it a high-quality report?)
26: # Real world: determined by human review. Here: synthetically generated based on complex underlying rules.
27: y = ((num_photos >= 1) & (total_note_length >= 20) & (duration_seconds > 300)).astype(int)
28: # Add some random noise to the labels to make it non-deterministic
29: flip_mask = np.random.rand(n_samples) < 0.1
30: y = np.where(flip_mask, 1 - y, y)
31: 
32: X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
33: 
34: # 2. Baseline Model
35: # Rule: evidence_count >= 3 && note_length >= 40
36: def baseline_predict(df):
37:     evidence_count = df['num_photos'] + df['num_notes']
38:     return ((evidence_count >= 3) & (df['total_note_length'] >= 40)).astype(int)
39: 
40: baseline_preds = baseline_predict(X_test)
41: baseline_acc = accuracy_score(y_test, baseline_preds)
42: 
43: # 3. TabPFN Model
44: # TabPFN is a pre-trained transformer for tabular data that needs no hyperparameter tuning for small datasets
45: classifier = TabPFNClassifier(device='cpu', N_ensemble_configurations=32)
46: classifier.fit(X_train, y_train)
47: tabpfn_preds = classifier.predict(X_test)
48: tabpfn_acc = accuracy_score(y_test, tabpfn_preds)
49: 
50: print("=== WildProof TabPFN Quality Prediction Experiment ===")
51: print(f"Dataset Size: {n_samples} samples")
52: print(f"Features: {list(X.columns)}")
53: print(f"Baseline Accuracy: {baseline_acc * 100:.2f}%")
54: print(f"TabPFN Accuracy:   {tabpfn_acc * 100:.2f}%")
55: print("\nLimitations: TabPFN is optimized for small tabular datasets (<10k samples) and can be memory intensive for inference in a lightweight API compared to a simple heuristic. However, it perfectly captures non-linear relationships in the field data.")
<shellId: 7 completed with exit code 0>

{"command":"$i=0; Get-Content src\\App.tsx | ForEach-Object { $i++; if($_ -match 'AI|quality|predict|API|fallback|error|report|localhost|VITE|Temporal') { '{0}: {1}' -f $i,$_ } }","description":"Revie...

5: import type { Expedition, EvidenceItem, MissionType, FieldReport } from './lib/db';
19:         An offline-first open-AI companion for turning ordinary walks into trustworthy field notes.
45:     await saveExpedition(newExp);
59:         <p className="text-emerald-200/90 text-lg max-w-md">Uncertainty-aware field logging for the rigorous outdoor researcher.</p>
112:         await saveExpedition(updated);
128:     await saveExpedition(updated);
135:     await saveExpedition(updated);
141:     await saveExpedition(updated);
142:     navigate(`/report/${expedition.id}`);
149:           <h2 className="text-3xl font-extrabold capitalize text-neutral-800 tracking-tight">{expedition.missionType.replace('-', ' ')}</h2>
155:           <Check className="w-5 h-5"/> Generate Report
208: function Report() {
212:   const [errorMsg, setErrorMsg] = useState<string | null>(null);
213:   const [quality, setQuality] = useState<{score: number, source: string} | null>(null);
215:   const fetchReport = async (exp: Expedition) => {
216:     setErrorMsg(null);
218:       const baseUrl = import.meta.env.VITE_API_BASE_URL || 'http://localhost:8000';
219:       const res = await fetch(`${baseUrl}/analyze`, {
224:       if (!res.ok) throw new Error('Backend unavailable');
225:       const data = await res.json();
227:       // Fetch quality score
230:         const qRes = await fetch(`${baseUrl}/predict-quality`, {
239:         if (qRes.ok) qData = await qRes.json();
240:       } catch (e) { console.warn("Quality endpoint failed", e); }
242:       const updated = { ...exp, status: 'complete' as const, report: data };
243:       await saveExpedition(updated);
245:       setQuality({ score: qData.score, source: qData.model });
247:       setErrorMsg("Local AI Offline. Viewing Mock Fallback Data.");
248:       const mockReport: FieldReport = {
249:         summary: "[MOCK REPORT] A brief walk yielding partial insights into the local habitat.",
251:           { claim: "Found broadleaf patterns near the pathway.", confidence: "observed", supportingEvidenceIds: [] },
252:           { claim: "Likely a moderately humid micro-climate.", confidence: "inferred", supportingEvidenceIds: [] },
257:       setExpedition({ ...exp, report: mockReport });
265:           if (!exp.report || exp.status !== 'complete') {
266:             await fetchReport(exp);
275:   if (!expedition || !expedition.report) return <div className="p-4 text-center mt-10">Generating report...</div>;
277:   const { report } = expedition;
281:       <h2 className="text-3xl font-bold mb-2">Field Report</h2>
284:       {errorMsg && (
289:               <p className="font-bold uppercase text-xs tracking-wider mb-1">Mock Fallback Mode</p>
290:               <p className="text-sm">{errorMsg}</p>
294:             onClick={() => fetchReport(expedition)}
297:             Retry AI Analysis
302:       {quality && !errorMsg && (
305:             <p className="text-xs font-bold text-blue-500 uppercase tracking-wide">Data Quality Score</p>
306:             <p className="text-sm text-blue-800">Scored via {quality.source === 'tabpfn' ? 'TabPFN Model' : 'Baseline Heuristic'}</p>
308:           <div className="text-3xl font-extrabold text-blue-600">{quality.score.toFixed(0)}/100</div>
313:         <p className="text-emerald-900 leading-relaxed">{report.summary}</p>
318:         {report.observations.map((obs: any, i: number) => (
327:             <p className="text-neutral-800 flex-1">{obs.claim}</p>
336:             {report.missingEvidence.map((e: string, i: number) => <li key={i}>{e}</li>)}
341:           <p className="text-sm text-neutral-600">{report.suggestedNextStep}</p>
353:         WildProof turns a 20-minute walk into structured, evidence-backed field research using local AI.
379:         <main className="flex-1 w-full max-w-4xl mx-auto">
384:             <Route path="/report/:id" element={<Report />} />
387:         </main>
<shellId: 6 completed with exit code 0>

{
  "command": "$lines=Get-Content api\\main.py; $lines[1..180]",
  "description": "Inspect API quality implementation"
}

from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel, ValidationError, Field
from typing import List, Optional, Literal
import httpx
import json
import logging
from temporalio.client import Client
from temporal_workflow import EvidenceData
import sentry_sdk
import os
from dotenv import load_dotenv

# Optional TabPFN import
try:
    from tabpfn import TabPFNClassifier
    import pandas as pd
    import numpy as np
    
    # Train dummy model on startup for demo purposes
    np.random.seed(42)
    n_samples = 150
    X = pd.DataFrame({
        'num_photos': np.random.randint(0, 5, n_samples),
        'num_notes': np.random.randint(0, 5, n_samples),
        'total_note_length': np.random.randint(0, 200, n_samples),
        'duration_seconds': np.random.randint(60, 3600, n_samples)
    })
    y = ((X['num_photos'] >= 1) & (X['total_note_length'] >= 20) & (X['duration_seconds'] > 300)).astype(int)
    
    tabpfn_model = TabPFNClassifier(device='cpu', N_ensemble_configurations=8)
    tabpfn_model.fit(X, y)
    TABPFN_AVAILABLE = True
except ImportError:
    TABPFN_AVAILABLE = False

load_dotenv()

sentry_dsn = os.environ.get("SENTRY_DSN")
if sentry_dsn:
    sentry_sdk.init(
        dsn=sentry_dsn,
        traces_sample_rate=1.0,
        profiles_sample_rate=1.0,
    )

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

app = FastAPI(title="WildProof API")

# Allow frontend to access the API
app.add_middleware(
    CORSMiddleware,
    allow_origins=["http://localhost:5173", "https://wildproof.onrender.com"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

OLLAMA_API_URL = "http://localhost:11434/api/generate"
MODEL_NAME = "gemma:2b" # Or "gemma", "llama3.2-vision", depending on what is installed locally

class EvidencePayload(BaseModel):
    id: str
    kind: str
    text: Optional[str] = None
    capturedAt: str
    localUrl: Optional[str] = None # Base64 representation if sent

class AnalyzeRequest(BaseModel):
    missionType: str
    evidence: List[EvidencePayload]

class Observation(BaseModel):
    claim: str
    confidence: Literal["observed", "inferred", "unknown"]
    supportingEvidenceIds: List[str]

class FieldReport(BaseModel):
    summary: str
    observations: List[Observation]
    missingEvidence: List[str] = Field(alias="missing_evidence")
    suggestedNextStep: str = Field(alias="suggested_next_step")

    class Config:
        populate_by_name = True

@app.get("/health")
def health_check():
    return {"status": "ok"}

@app.get("/sentry-debug")
def trigger_error():
    logger.error("Simulated performance issue / timeout for Sentry Tracing.")
    raise Exception("Simulated timeout for Sentry!")

async def generate_from_ollama(prompt: str) -> str:
    async with httpx.AsyncClient(timeout=60.0) as client:
        try:
            response = await client.post(
                OLLAMA_API_URL,
                json={
                    "model": MODEL_NAME,
                    "prompt": prompt,
                    "stream": False,
                    "format": "json"
                }
            )
            response.raise_for_status()
            return response.json().get("response", "")
        except Exception as e:
            logger.error(f"Failed to communicate with Ollama: {e}")
            raise HTTPException(status_code=503, detail="Local AI model is unavailable.")

@app.post("/analyze", response_model=FieldReport)
async def analyze_expedition(request: AnalyzeRequest):
    logger.info(f"Analyzing expedition: {request.missionType} with {len(request.evidence)} items.")
    
    # Formulate prompt from evidence
    text_evidence = []
    for ev in request.evidence:
        if ev.kind == "note" and ev.text:
            text_evidence.append(f"Note ID {ev.id}: {ev.text}")
        elif ev.kind == "photo":
            text_evidence.append(f"Photo Evidence ID {ev.id} captured.")

    evidence_str = "\n".join(text_evidence)
    
    prompt = f"""
You are an expert field researcher analyzing data from an outdoor expedition.
Mission Type: {request.missionType}

Here is the collected evidence:
{evidence_str}

Analyze this evidence and provide a structured JSON response. Do NOT output markdown, ONLY valid JSON.
Your JSON must match this structure exactly:
{{
  "summary": "A 1-2 sentence overall summary of the expedition.",
  "observations": [
    {{
      "claim": "Specific observation here.",
      "confidence": "observed" | "inferred" | "unknown",
      "supportingEvidenceIds": ["id1", "id2"]
    }}
  ],
  "missing_evidence": ["What evidence is missing to make a stronger conclusion?"],
  "suggested_next_step": "A single actionable next step."
}}

Rule for confidence:
- "observed": Only if directly stated in a note or directly visible in a photo (if provided).
- "inferred": A logical deduction based on the evidence.
- "unknown": Something guessed or speculative.

Ensure the "supportingEvidenceIds" match the Note IDs or Photo IDs provided above.
"""
    
    raw_response = await generate_from_ollama(prompt)
    
    try:
        data = json.loads(raw_response)
        report = FieldReport(**data)
        return report
    except (json.JSONDecodeError, ValidationError) as e:
        logger.warning(f"First parse attempt failed: {e}. Attempting repair.")
        
        repair_prompt = f"""
You previously returned invalid JSON. Fix the following JSON string so it matches the expected schema perfectly.
Schema:
{{
  "summary": "...",
  "observations": [
    {{
      "claim": "...",
      "confidence": "observed" | "inferred" | "unknown",
      "supportingEvidenceIds": ["id1"]
    }}
  ],
  "missing_evidence": ["..."],
<shellId: 8 completed with exit code 0>

{
  "command": "git diff HEAD~1..HEAD -- api\\main.py src\\App.tsx README.md",
  "description": "Review latest code diff"
}

Output too large to read at once (11.0 KB). Saved to: C:\Users\Lenovo\AppData\Local\Temp\1791603502888-copilot-tool-output-19664-d47e7c99-0c59-41ba-bb68-4837d59d46d3.txt
Consider using tools like rg (for searching), head/tail (for viewing start/end), view with view_range (for specific sections), or jq (for JSON) to examine portions of the output.

Preview (first 500 chars):
diff --git a/README.md b/README.md
index e759aa0..9e68c3f 100644
--- a/README.md
+++ b/README.md
@@ -2,7 +2,7 @@
 
 **WildProof** is an offline-first, uncertainty-aware field observation app designed to help researchers, hikers, and citizen scientists confidently log outdoor evidence. Built for the **Hacktoberfest DEV Challenge (Week 1: Touch Grass)**.
 
-Unlike typical AI identification apps that confidently guess incorrect species, WildProof embraces uncertainty. It acts as an offline journal 
<shellId: 9 completed with exit code 0>

{
  "filePaths": [
    "E:\\HACKTOBERFEST WEEK 1 CHALLENGE\\src",
    "E:\\HACKTOBERFEST WEEK 1 CHALLENGE\\api"
  ]
}

No errors found.

{
  "resourcePath": "E:\\HACKTOBERFEST WEEK 1 CHALLENGE",
  "pythonPath": "E:\\HACKTOBERFEST WEEK 1 CHALLENGE\\venv\\Scripts\\python.exe"
}

A Python Environment has been configured.  
Following is the information about the Python environment:
1. Environment Type: venv
2. Version: 3.11.9

3. Command Prefix to run Python in a terminal is: `"e:/HACKTOBERFEST WEEK 1 CHALLENGE/venv/Scripts/python.exe"`
Instead of running `Python sample.py` in the terminal, you will now run: `"e:/HACKTOBERFEST WEEK 1 CHALLENGE/venv/Scripts/python.exe" sample.py`
Similarly instead of running `Python -c "import sys;...."` in the terminal, you will now run: `"e:/HACKTOBERFEST WEEK 1 CHALLENGE/venv/Scripts/python.exe" -c "import sys;...."`

{"command":"& 'E:\\HACKTOBERFEST WEEK 1 CHALLENGE\\venv\\Scripts\\python.exe' -m py_compile api\\main.py api\\temporal_workflow.py api\\temporal_worker.py api\\tabpfn_experiment.py","description":"Val...

<shellId: 10 completed with exit code 0>

{
  "command": "$lines=Get-Content api\\main.py; $lines[180..300]",
  "description": "Inspect quality endpoint implementation"
}

"missing_evidence": ["..."],
  "suggested_next_step": "..."
}}

Invalid JSON:
{raw_response}
"""
        repair_response = await generate_from_ollama(repair_prompt)
        
        try:
            data = json.loads(repair_response)
            report = FieldReport(**data)
            return report
        except Exception as e:
            logger.error(f"Repair attempt failed: {e}")
            raise HTTPException(status_code=500, detail="AI returned malformed data. Please try again.")

@app.post("/analyze-durable")
async def analyze_expedition_durable(request: AnalyzeRequest):
    logger.info("Triggering durable analysis via Temporal...")
    try:
        client = await Client.connect("localhost:7233")
        
        # Convert Pydantic request to workflow dataclass
        evidence_data = EvidenceData(
            mission_type=request.missionType,
            evidence_items=[ev.model_dump() for ev in request.evidence]
        )
        
        # Execute workflow
        result = await client.execute_workflow(
            "ExpeditionAnalysisWorkflow",
            evidence_data,
            id=f"expedition-analysis-{request.missionType}-{len(request.evidence)}",
            task_queue="wildproof-analysis-queue",
        )
        return {"status": "Workflow completed", "result": result}
    except Exception as e:
        logger.error(f"Temporal execution failed: {e}")
        raise HTTPException(status_code=500, detail="Temporal workflow failed or server is not running.")

class QualityPredictionRequest(BaseModel):
    num_photos: int
    num_notes: int
    total_note_length: int
    duration_seconds: int

@app.post("/predict-quality")
def predict_quality(features: QualityPredictionRequest):
    if TABPFN_AVAILABLE:
        try:
            df = pd.DataFrame({
                'num_photos': [features.num_photos],
                'num_notes': [features.num_notes],
                'total_note_length': [features.total_note_length],
                'duration_seconds': [features.duration_seconds]
            })
            probs = tabpfn_model.predict_proba(df)[0]
            score = int(probs[1] * 100) # Convert probability to 0-100 score
            return {"score": score, "model": "tabpfn"}
        except Exception as e:
            logger.error(f"TabPFN prediction failed: {e}")
            
    # Baseline heuristic fallback
    evidence_count = features.num_photos + features.num_notes
    if evidence_count >= 3 and features.total_note_length >= 40:
        score = 85
    elif evidence_count > 0:
        score = 50
    else:
        score = 10
        
    return {"score": score, "model": "baseline_heuristic"}
<shellId: 11 completed with exit code 0>

{
  "command": "$lines=Get-Content src\\App.tsx; $lines[208..365]",
  "description": "Inspect report UI and quality wiring"
}

const { id } = useParams<{ id: string }>();
  const [expedition, setExpedition] = useState<Expedition | null>(null);

  const [errorMsg, setErrorMsg] = useState<string | null>(null);
  const [quality, setQuality] = useState<{score: number, source: string} | null>(null);

  const fetchReport = async (exp: Expedition) => {
    setErrorMsg(null);
    try {
      const baseUrl = import.meta.env.VITE_API_BASE_URL || 'http://localhost:8000';
      const res = await fetch(`${baseUrl}/analyze`, {
        method: 'POST',
        headers: { 'Content-Type': 'application/json' },
        body: JSON.stringify({ missionType: exp.missionType, evidence: exp.evidence })
      });
      if (!res.ok) throw new Error('Backend unavailable');
      const data = await res.json();
      
      // Fetch quality score
      let qData = { score: 85, model: 'baseline' };
      try {
        const qRes = await fetch(`${baseUrl}/predict-quality`, {
          method: 'POST',
          headers: { 'Content-Type': 'application/json' },
          body: JSON.stringify({
            num_photos: exp.evidence.filter(e => e.kind === 'photo').length,
            num_notes: exp.evidence.filter(e => e.kind === 'note').length,
            duration_seconds: 1200
          })
        });
        if (qRes.ok) qData = await qRes.json();
      } catch (e) { console.warn("Quality endpoint failed", e); }

      const updated = { ...exp, status: 'complete' as const, report: data };
      await saveExpedition(updated);
      setExpedition(updated);
      setQuality({ score: qData.score, source: qData.model });
    } catch {
      setErrorMsg("Local AI Offline. Viewing Mock Fallback Data.");
      const mockReport: FieldReport = {
        summary: "[MOCK REPORT] A brief walk yielding partial insights into the local habitat.",
        observations: [
          { claim: "Found broadleaf patterns near the pathway.", confidence: "observed", supportingEvidenceIds: [] },
          { claim: "Likely a moderately humid micro-climate.", confidence: "inferred", supportingEvidenceIds: [] },
        ],
        missingEvidence: ["Audio capturing ambient bird sounds.", "Temperature readings."],
        suggestedNextStep: "Return at dusk to capture nocturnal insect patterns."
      };
      setExpedition({ ...exp, report: mockReport });
    }
  };

  useEffect(() => {
    if (id) {
      getExpedition(id).then(async exp => {
        if (exp) {
          if (!exp.report || exp.status !== 'complete') {
            await fetchReport(exp);
          } else {
            setExpedition(exp);
          }
        }
      });
    }
  }, [id]);

  if (!expedition || !expedition.report) return <div className="p-4 text-center mt-10">Generating report...</div>;

  const { report } = expedition;

  return (
    <div className="p-4 max-w-2xl mx-auto mt-4">
      <h2 className="text-3xl font-bold mb-2">Field Report</h2>
      <p className="text-neutral-500 mb-6">{new Date(expedition.completedAt || expedition.startedAt).toLocaleString()}</p>

      {errorMsg && (
        <div className="bg-amber-100 border border-amber-300 text-amber-900 p-4 rounded-xl mb-6 flex items-start justify-between gap-4 shadow-sm">
          <div className="flex items-start gap-2">
            <Info className="w-5 h-5 flex-shrink-0 mt-0.5 text-amber-600" />
            <div>
              <p className="font-bold uppercase text-xs tracking-wider mb-1">Mock Fallback Mode</p>
              <p className="text-sm">{errorMsg}</p>
            </div>
          </div>
          <button 
            onClick={() => fetchReport(expedition)}
            className="bg-amber-200 hover:bg-amber-300 text-amber-900 px-4 py-2 rounded-lg text-sm font-bold shadow-sm transition"
          >
            Retry AI Analysis
          </button>
        </div>
      )}

      {quality && !errorMsg && (
        <div className="bg-blue-50 border border-blue-100 p-4 rounded-xl mb-6 flex items-center justify-between shadow-sm">
          <div>
            <p className="text-xs font-bold text-blue-500 uppercase tracking-wide">Data Quality Score</p>
            <p className="text-sm text-blue-800">Scored via {quality.source === 'tabpfn' ? 'TabPFN Model' : 'Baseline Heuristic'}</p>
          </div>
          <div className="text-3xl font-extrabold text-blue-600">{quality.score.toFixed(0)}/100</div>
        </div>
      )}

      <div className="bg-emerald-50 border border-emerald-100 p-6 rounded-xl mb-6">
        <p className="text-emerald-900 leading-relaxed">{report.summary}</p>
      </div>

      <h3 className="text-xl font-semibold mb-4">Observations</h3>
      <div className="space-y-3 mb-8">
        {report.observations.map((obs: any, i: number) => (
          <div key={i} className="flex items-start gap-3 bg-white p-4 border border-neutral-200 rounded-lg shadow-sm">
            <span className={`text-xs px-2 py-1 rounded font-bold uppercase tracking-wide mt-0.5
              ${obs.confidence === 'observed' ? 'bg-blue-100 text-blue-700' : ''}
              ${obs.confidence === 'inferred' ? 'bg-amber-100 text-amber-700' : ''}
              ${obs.confidence === 'unknown' ? 'bg-neutral-100 text-neutral-600' : ''}
            `}>
              {obs.confidence}
            </span>
            <p className="text-neutral-800 flex-1">{obs.claim}</p>
          </div>
        ))}
      </div>

      <div className="grid md:grid-cols-2 gap-4">
        <div className="bg-white p-5 border border-neutral-200 rounded-xl shadow-sm">
          <h4 className="font-semibold text-neutral-900 mb-2">Missing Evidence</h4>
          <ul className="list-disc pl-5 text-sm text-neutral-600 space-y-1">
            {report.missingEvidence.map((e: string, i: number) => <li key={i}>{e}</li>)}
          </ul>
        </div>
        <div className="bg-white p-5 border border-neutral-200 rounded-xl shadow-sm">
          <h4 className="font-semibold text-neutral-900 mb-2">Suggested Next Step</h4>
          <p className="text-sm text-neutral-600">{report.suggestedNextStep}</p>
        </div>
      </div>
    </div>
  );
}

function About() {
  return (
    <div className="p-4 max-w-xl mx-auto mt-8 text-neutral-800">
      <h2 className="text-3xl font-bold mb-4">About WildProof</h2>
      <p className="text-lg leading-relaxed mb-4">
        WildProof turns a 20-minute walk into structured, evidence-backed field research using local AI.
      </p>
      <p className="leading-relaxed">
        Instead of telling you to go outside and guessing what you took a photo of, we structure your observation, identify missing data, and make explicit distinctions between what is directly observed versus what is inferred.
      </p>
    </div>
  );
}

export default function App() {
  return (
    <Router>
      <div className="min-h-screen flex flex-col font-sans bg-neutral-50 pb-20 md:pb-0">
        <header className="bg-white border-b border-neutral-200 sticky top-0 z-10 shadow-sm">
<shellId: 13 completed with exit code 0>

{
  "command": "git diff HEAD~1..HEAD -- src\\App.tsx | Select-String -Pattern '^\\+|^-' | Select-Object -First 180",
  "description": "Review frontend fixes"
}

--- a/src/App.tsx
+++ b/src/App.tsx
+  const [quality, setQuality] = useState<{score: number, source: string} | null>(null);
+
+  const fetchReport = async (exp: Expedition) => {
+    setErrorMsg(null);
+    try {
+      const baseUrl = import.meta.env.VITE_API_BASE_URL || 'http://localhost:8000';
+      const res = await fetch(`${baseUrl}/analyze`, {
+        method: 'POST',
+        headers: { 'Content-Type': 'application/json' },
+        body: JSON.stringify({ missionType: exp.missionType, evidence: exp.evidence })
+      });
+      if (!res.ok) throw new Error('Backend unavailable');
+      const data = await res.json();
+      
+      // Fetch quality score
+      let qData = { score: 85, model: 'baseline' };
+      try {
+        const qRes = await fetch(`${baseUrl}/predict-quality`, {
+          method: 'POST',
+          headers: { 'Content-Type': 'application/json' },
+          body: JSON.stringify({
+            num_photos: exp.evidence.filter(e => e.kind === 'photo').length,
+            num_notes: exp.evidence.filter(e => e.kind === 'note').length,
+            duration_seconds: 1200
+          })
+        });
+        if (qRes.ok) qData = await qRes.json();
+      } catch (e) { console.warn("Quality endpoint failed", e); }
+
+      const updated = { ...exp, status: 'complete' as const, report: data };
+      await saveExpedition(updated);
+      setExpedition(updated);
+      setQuality({ score: qData.score, source: qData.model });
+    } catch {
+      setErrorMsg("Local AI Offline. Viewing Mock Fallback Data.");
+      const mockReport: FieldReport = {
+        summary: "[MOCK REPORT] A brief walk yielding partial insights into the local habitat.",
+        observations: [
+          { claim: "Found broadleaf patterns near the pathway.", confidence: "observed", supportingEvidenceIds: [] },
+          { claim: "Likely a moderately humid micro-climate.", confidence: "inferred", supportingEvidenceIds: [] },
+        ],
+        missingEvidence: ["Audio capturing ambient bird sounds.", "Temperature readings."],
+        suggestedNextStep: "Return at dusk to capture nocturnal insect patterns."
+      };
+      setExpedition({ ...exp, report: mockReport });
+    }
+  };
-          if (!exp.report) {
-            try {
-              const res = await fetch('http://localhost:8000/analyze', {
-                method: 'POST',
-                headers: { 'Content-Type': 'application/json' },
-                body: JSON.stringify({ missionType: exp.missionType, evidence: exp.evidence })
-              });
-              if (!res.ok) throw new Error('Backend unavailable');
-              const data = await res.json();
-              const updated = { ...exp, status: 'complete' as const, report: data };
-              await saveExpedition(updated);
-              setExpedition(updated);
-            } catch {
-              setErrorMsg("AI analysis unavailable. Showing a locally generated evidence summary.");
-              const mockReport: FieldReport = {
-                summary: "A brief walk yielding partial insights into the local habitat (Mocked).",
-                observations: [
-                  { claim: "Found broadleaf patterns near the pathway.", confidence: "observed", 
supportingEvidenceIds: [] },
-                  { claim: "Likely a moderately humid micro-climate.", confidence: "inferred", supportingEvidenceIds: 
[] },
-                ],
-                missingEvidence: ["Audio capturing ambient bird sounds.", "Temperature readings."],
-                suggestedNextStep: "Return at dusk to capture nocturnal insect patterns."
-              };
-              const updated = { ...exp, status: 'complete' as const, report: mockReport };
-              await saveExpedition(updated);
-              setExpedition(updated);
-            }
+          if (!exp.report || exp.status !== 'complete') {
+            await fetchReport(exp);
-        <div className="bg-amber-100 border border-amber-300 text-amber-800 p-4 rounded-xl mb-6 flex items-start 
gap-2">
-          <Info className="w-5 h-5 flex-shrink-0 mt-0.5" />
-          <p>{errorMsg}</p>
+        <div className="bg-amber-100 border border-amber-300 text-amber-900 p-4 rounded-xl mb-6 flex items-start 
justify-between gap-4 shadow-sm">
+          <div className="flex items-start gap-2">
+            <Info className="w-5 h-5 flex-shrink-0 mt-0.5 text-amber-600" />
+            <div>
+              <p className="font-bold uppercase text-xs tracking-wider mb-1">Mock Fallback Mode</p>
+              <p className="text-sm">{errorMsg}</p>
+            </div>
+          </div>
+          <button 
+            onClick={() => fetchReport(expedition)}
+            className="bg-amber-200 hover:bg-amber-300 text-amber-900 px-4 py-2 rounded-lg text-sm font-bold 
shadow-sm transition"
+          >
+            Retry AI Analysis
+          </button>
+        </div>
+      )}
+
+      {quality && !errorMsg && (
+        <div className="bg-blue-50 border border-blue-100 p-4 rounded-xl mb-6 flex items-center justify-between 
shadow-sm">
+          <div>
+            <p className="text-xs font-bold text-blue-500 uppercase tracking-wide">Data Quality Score</p>
+            <p className="text-sm text-blue-800">Scored via {quality.source === 'tabpfn' ? 'TabPFN Model' : 'Baseline 
Heuristic'}</p>
+          </div>
+          <div className="text-3xl font-extrabold text-blue-600">{quality.score.toFixed(0)}/100</div>
<shellId: 12 completed with exit code 0>

{"command":"& 'E:\\HACKTOBERFEST WEEK 1 CHALLENGE\\venv\\Scripts\\python.exe' -c \"import sys; sys.path.insert(0, 'api'); import main; print('imported', main.TABPFN_AVAILABLE)\"","description":"Smoke-...

imported False
<shellId: 14 completed with exit code 0>

{
  "resourcePath": "E:\\HACKTOBERFEST WEEK 1 CHALLENGE"
}

Following is the information about the Python environment:
1. Environment Type: venv
2. Version: 3.11.9

3. Command Prefix to run Python in a terminal is: `"e:/HACKTOBERFEST WEEK 1 CHALLENGE/venv/Scripts/python.exe"`
Instead of running `Python sample.py` in the terminal, you will now run: `"e:/HACKTOBERFEST WEEK 1 CHALLENGE/venv/Scripts/python.exe" sample.py`
Similarly instead of running `Python -c "import sys;...."` in the terminal, you will now run: `"e:/HACKTOBERFEST WEEK 1 CHALLENGE/venv/Scripts/python.exe" -c "import sys;...."`
4. Below is a list of the Python packages, each in the format <name> or <name> (<version>). The version may be omitted if unknown: 
- annotated-doc (0.0.5)
- annotated-types (0.8.0)
- anyio (4.15.1)
- certifi (2026.7.22)
- charset-normalizer (3.5.2)
- click (8.5.0)
- cloudpickle (3.1.2)
- colorama (0.4.6)
- dnspython (2.9.0)
- email-validator (2.3.0)
- fastapi (0.111.0)
- fastapi-cli (0.0.32)
- filelock (4.1.0)
- fsspec (2026.9.0)
- h11 (0.16.0)
- httpcore (1.0.9)
- httptools (0.9.0)
- httpx (0.27.0)
- idna (3.20)
- jinja2 (3.1.6)
- joblib (1.6.0)
- markdown-it-py (4.2.0)
- markupsafe (3.0.4)
- mdurl (0.1.2)
- mpmath (1.3.0)
- networkx (3.6.1)
- nexus-rpc (1.4.0)
- numpy (1.26.4)
- orjson (3.13.0)
- pandas (2.2.2)
- pip (24.0)
- protobuf (7.36.2)
- pydantic (2.8.2)
- pydantic-core (2.20.1)
- pygments (2.21.0)
- python-dateutil (2.9.0.post0)
- python-dotenv (1.2.4)
- python-multipart (0.0.32)
- pytz (2026.5)
- pyyaml (6.0.3)
- requests (2.34.2)
- rich (15.0.0)
- rich-toolkit (0.20.6)
- scikit-learn (1.5.0)
- scipy (1.17.1)
- sentry-sdk (2.71.0)
- setuptools (84.0.0)
- shellingham (1.5.4)
- six (1.17.0)
- sniffio (1.3.1)
- starlette (0.37.2)
- sympy (1.14.0)
- tabpfn (0.1.10)
- temporalio (1.34.0)
- threadpoolctl (3.7.0)
- torch (2.14.1)
- typer (0.27.3)
- types-protobuf (7.35.1.20260906)
- typing-extensions (4.16.0)
- tzdata (2026.5)
- ujson (6.0.0)
- urllib3 (2.8.0)
- uvicorn (0.30.1)
- watchfiles (1.3.0)
- websockets (17.2)

{"command":"& 'E:\\HACKTOBERFEST WEEK 1 CHALLENGE\\venv\\Scripts\\python.exe' -c \"from tabpfn import TabPFNClassifier; print('class ok'); import pandas as pd, numpy as np; X=pd.DataFrame({'num_photos...

Traceback (most recent call last):
  File "<string>", line 1, in <module>
  File "E:\HACKTOBERFEST WEEK 1 CHALLENGE\venv\Lib\site-packages\tabpfn\__init__.py", line 1, in <module>
    from tabpfn.scripts.transformer_prediction_interface import TabPFNClassifier
  File "E:\HACKTOBERFEST WEEK 1 CHALLENGE\venv\Lib\site-packages\tabpfn\scripts\transformer_prediction_interface.py", line 20, in <module>
    from tabpfn.scripts.model_builder import load_model, load_model_only_inference
  File "E:\HACKTOBERFEST WEEK 1 CHALLENGE\venv\Lib\site-packages\tabpfn\scripts\model_builder.py", line 4, in <module>
    from tabpfn.transformer import TransformerModel
  File "E:\HACKTOBERFEST WEEK 1 CHALLENGE\venv\Lib\site-packages\tabpfn\transformer.py", line 9, in <module>
    from tabpfn.layer import TransformerEncoderLayer, _get_activation_fn
  File "E:\HACKTOBERFEST WEEK 1 CHALLENGE\venv\Lib\site-packages\tabpfn\layer.py", line 5, in <module>
    from torch.nn.modules.transformer import _get_activation_fn, Module, Tensor, Optional, MultiheadAttention, Linear, Dropout, LayerNorm
ImportError: cannot import name 'Optional' from 'torch.nn.modules.transformer' (E:\HACKTOBERFEST WEEK 1 CHALLENGE\venv\Lib\site-packages\torch\nn\modules\transformer.py)
<shellId: 15 completed with exit code 1>

{
  "command": "git status --short; git log --oneline -6",
  "description": "Inspect latest commit state"
}

8ac6eaa fix: address final MVP judging gaps (TabPFN honesty, App fallback persistence, Temporal switch)
a4a1f43 fix: address MVP judging gaps (TabPFN api, Temporal real integration, Fallback UI, CORS, CSS cleanup)
f51f1a9 chore: rename FieldProof to WildProof across entire stack
98ae635 docs: update README with correct working directory for backend
ae889c1 feat: Phase 8 UI Polish and README
fab4ab1 chore: ensure venv is ignored
<shellId: 16 completed with exit code 0>

{"command":"$i=0; Get-Content api\\main.py | ForEach-Object { $i++; if($_ -match 'TABPFN|predict_quality|tabpfn|fallback|CORS|allow_origins|analyze-durable|validate_report|persist') { '{0}: {1}' -f $i...

2: from fastapi.middleware.cors import CORSMiddleware
31:     CORSMiddleware,
32:     allow_origins=["http://localhost:5173", "https://wildproof.onrender.com"],
175: @app.post("/analyze-durable")
206: def predict_quality(features: QualityPredictionRequest):
207:     # Live TabPFN integration requires older torch versions and heavy memory,
209:     # (See api/tabpfn_experiment.py for the reproducible TabPFN experiment!)
218:     return {"score": score, "model": "baseline_heuristic (tabpfn_unavailable)"}
<shellId: 17 completed with exit code 0>

{"command":"$i=0; Get-Content src\\App.tsx | ForEach-Object { $i++; if($_ -match 'qData|quality|analyze-durable|VITE_API|fallback|Mock|Temporal|saveExpedition') { '{0}: {1}' -f $i,$_ } }","description...

4: import { getExpedition, saveExpedition } from './lib/db';
45:     await saveExpedition(newExp);
112:         await saveExpedition(updated);
128:     await saveExpedition(updated);
135:     await saveExpedition(updated);
141:     await saveExpedition(updated);
213:   const [quality, setQuality] = useState<{score: number, source: string} | null>(null);
214:   const [useTemporal, setUseTemporal] = useState<boolean>(true);
219:       const baseUrl = import.meta.env.VITE_API_BASE_URL || 'http://localhost:8000';
220:       const endpoint = useTemporal ? '/analyze-durable' : '/analyze';
230:       if (useTemporal && data.result) {
231:         data = data.result; // Unwrap Temporal result
234:       // Fetch quality score
235:       let qData = null;
237:         const qRes = await fetch(`${baseUrl}/predict-quality`, {
247:         if (qRes.ok) qData = await qRes.json();
248:       } catch (e) { console.warn("Quality endpoint failed", e); }
251:       await saveExpedition(updated);
253:       if (qData) {
254:         setQuality({ score: qData.score, source: qData.model });
257:       setErrorMsg(`Local AI or Temporal Offline. Viewing Mock Fallback Data.`);
258:       const mockReport: FieldReport = {
259:         summary: "[MOCK REPORT] A brief walk yielding partial insights into the local habitat.",
267:       // Persist fallback with failed status so it doesn't infinitely loop
268:       const updated = { ...exp, status: 'failed' as const, report: mockReport };
269:       await saveExpedition(updated);
278:           // If no report, or if it failed previously, fetch it unless we already have the fallback loaded
299:           <span className="text-sm font-bold text-neutral-500">Temporal Workflow</span>
301:             onClick={() => setUseTemporal(!useTemporal)}
302:             className={`w-12 h-6 rounded-full transition-colors relative flex items-center ${useTemporal ? 'bg-emerald-500' : 'bg-neutral-300'}`}
304:             <div className={`w-4 h-4 bg-white rounded-full absolute shadow transition-transform ${useTemporal ? 'translate-x-7' : 'translate-x-1'}`} />
314:               <p className="font-bold uppercase text-xs tracking-wider mb-1">Mock Fallback Mode</p>
327:       {quality && !errorMsg && (
330:             <p className="text-xs font-bold text-blue-500 uppercase tracking-wide">Data Quality Score</p>
331:             <p className="text-sm text-blue-800">Scored via {quality.source === 'tabpfn' ? 'TabPFN Model' : 'Baseline Heuristic'}</p>
333:           <div className="text-3xl font-extrabold text-blue-600">{quality.score.toFixed(0)}/100</div>
<shellId: 18 completed with exit code 0>

{
  "command": "npm run build; npm run lint",
  "description": "Run frontend validation",
  "initial_wait": 120,
  "mode": "sync"
}

> [email protected] build
> tsc -b && vite build

vite v8.3.4 building client environment for production...
transforming...
✓ 2189 modules transformed.
rendering chunks...
computing gzip size...
dist/index.html                   0.49 kB │ gzip:   0.32 kB
dist/assets/index-DZffQ7u8.css   25.02 kB │ gzip:   5.34 kB
dist/assets/index-BlPAb4MV.js   566.86 kB │ gzip: 183.32 kB

[plugin builtin:vite-reporter] 
(!) Some chunks are larger than 500 kB after minification. Consider:
- Using dynamic import() to code-split the application
- Use build.rolldownOptions.output.codeSplitting to improve chunking: https://rolldown.rs/reference/OutputOptions.codeSplitting
- Adjust chunk size limit for this warning via build.chunkSizeWarningLimit.
✓ built in 6.71s
[PLUGIN_TIMINGS] JavaScript callbacks ran for 6.4s of this 6.7s build (96%).
The slowest callbacks, timed inside each callback (the wait before a callback starts is excluded, the time it awaits is included):
  - plugin @tailwindcss/vite:generate:build transform (81%, 5.4s, 1 call)
Additional callback time came from callbacks under 1s.
See https://rolldown.rs/reference/InputOptions.checks#bundlertimings for more details.


> [email protected] lint
> oxlint

src/App.tsx:280:19: warning react-hooks(exhaustive-deps): React Hook useEffect has a missing dependency: 'fetchReport' help: Either include it or remove the dependency array.
<shellId: 19 completed with exit code 0>

{
  "filePaths": [
    "E:\\HACKTOBERFEST WEEK 1 CHALLENGE\\src",
    "E:\\HACKTOBERFEST WEEK 1 CHALLENGE\\api"
  ]
}

No errors found.

{
  "command": "$lines=Get-Content api\\main.py; $lines[195..225]",
  "description": "Inspect honest quality fallback"
}

logger.error(f"Temporal execution failed: {e}")
        raise HTTPException(status_code=500, detail="Temporal workflow failed or server is not running.")

class QualityPredictionRequest(BaseModel):
    num_photos: int
    num_notes: int
    total_note_length: int
    duration_seconds: int

@app.post("/predict-quality")
def predict_quality(features: QualityPredictionRequest):
    # Live TabPFN integration requires older torch versions and heavy memory,
    # so the API gracefully falls back to a baseline heuristic. 
    # (See api/tabpfn_experiment.py for the reproducible TabPFN experiment!)
    evidence_count = features.num_photos + features.num_notes
    if evidence_count >= 3 and features.total_note_length >= 40:
        score = 85
    elif evidence_count > 0:
        score = 50
    else:
        score = 10
        
    return {"score": score, "model": "baseline_heuristic (tabpfn_unavailable)"}
<shellId: 20 completed with exit code 0>

{
  "command": "$lines=Get-Content src\\App.tsx; $lines[208..285]",
  "description": "Inspect Temporal and fallback flow"
}

const { id } = useParams<{ id: string }>();
  const [expedition, setExpedition] = useState<Expedition | null>(null);

  const [errorMsg, setErrorMsg] = useState<string | null>(null);
  const [quality, setQuality] = useState<{score: number, source: string} | null>(null);
  const [useTemporal, setUseTemporal] = useState<boolean>(true);

  const fetchReport = async (exp: Expedition) => {
    setErrorMsg(null);
    try {
      const baseUrl = import.meta.env.VITE_API_BASE_URL || 'http://localhost:8000';
      const endpoint = useTemporal ? '/analyze-durable' : '/analyze';
      
      const res = await fetch(`${baseUrl}${endpoint}`, {
        method: 'POST',
        headers: { 'Content-Type': 'application/json' },
        body: JSON.stringify({ missionType: exp.missionType, evidence: exp.evidence })
      });
      if (!res.ok) throw new Error('Backend unavailable');
      
      let data = await res.json();
      if (useTemporal && data.result) {
        data = data.result; // Unwrap Temporal result
      }
      
      // Fetch quality score
      let qData = null;
      try {
        const qRes = await fetch(`${baseUrl}/predict-quality`, {
          method: 'POST',
          headers: { 'Content-Type': 'application/json' },
          body: JSON.stringify({
            num_photos: exp.evidence.filter(e => e.kind === 'photo').length,
            num_notes: exp.evidence.filter(e => e.kind === 'note').length,
            duration_seconds: 1200,
            total_note_length: exp.evidence.filter(e => e.kind === 'note').reduce((acc, e) => acc + (e.text?.length || 0), 0)
          })
        });
        if (qRes.ok) qData = await qRes.json();
      } catch (e) { console.warn("Quality endpoint failed", e); }

      const updated = { ...exp, status: 'complete' as const, report: data };
      await saveExpedition(updated);
      setExpedition(updated);
      if (qData) {
        setQuality({ score: qData.score, source: qData.model });
      }
    } catch {
      setErrorMsg(`Local AI or Temporal Offline. Viewing Mock Fallback Data.`);
      const mockReport: FieldReport = {
        summary: "[MOCK REPORT] A brief walk yielding partial insights into the local habitat.",
        observations: [
          { claim: "Found broadleaf patterns near the pathway.", confidence: "observed", supportingEvidenceIds: [] },
          { claim: "Likely a moderately humid micro-climate.", confidence: "inferred", supportingEvidenceIds: [] },
        ],
        missingEvidence: ["Audio capturing ambient bird sounds.", "Temperature readings."],
        suggestedNextStep: "Return at dusk to capture nocturnal insect patterns."
      };
      // Persist fallback with failed status so it doesn't infinitely loop
      const updated = { ...exp, status: 'failed' as const, report: mockReport };
      await saveExpedition(updated);
      setExpedition(updated);
    }
  };

  useEffect(() => {
    if (id) {
      getExpedition(id).then(async exp => {
        if (exp) {
          // If no report, or if it failed previously, fetch it unless we already have the fallback loaded
          if (!exp.report || (exp.status !== 'complete' && exp.status !== 'failed')) {
            await fetchReport(exp);
          } else {
            setExpedition(exp);
          }
        }
      });
    }
<shellId: 21 completed with exit code 0>

{
  "command": "$lines=Get-Content api\\temporal_workflow.py; $lines[30..135]",
  "description": "Inspect workflow realism"
}

import json
import httpx

@activity.defn
async def run_gemma_analysis(evidence: EvidenceData) -> ReportData:
    activity.logger.info(f"Running REAL Gemma Analysis for {evidence.mission_type}...")
    
    prompt = f"Mission: {evidence.mission_type}. Analyze this field evidence:\n"
    for e in evidence.evidence_items:
        if e.get("kind") == "note":
            prompt += f"- Note: {e.get('text')}\n"
        elif e.get("kind") == "photo":
            prompt += "- Photo: [Image metadata omitted for text-only model]\n"

    prompt += "\nOutput JSON with summary, observations (with claim, confidence), missing_evidence, and suggested_next_step."

    # This will fail (and therefore trigger Temporal's durable retries) if Ollama is not running!
    async with httpx.AsyncClient(timeout=60.0) as client:
        response = await client.post(
            "http://localhost:11434/api/generate",
            json={"model": "gemma:2b", "prompt": prompt, "stream": False, "format": "json"}
        )
        response.raise_for_status()
        result = response.json()
        
        try:
            data = json.loads(result["response"])
            return ReportData(
                summary=data.get("summary", "Fallback summary"),
                observations=data.get("observations", []),
                missing_evidence=data.get("missing_evidence", []),
                suggested_next_step=data.get("suggested_next_step", "")
            )
        except Exception:
            raise RuntimeError("Failed to parse Gemma output into valid JSON")

@activity.defn
async def validate_report(report: ReportData) -> ReportData:
    activity.logger.info("Validating AI JSON Schema...")
    # Simulates Pydantic validation
    return report

@activity.defn
async def run_tabpfn_quality_prediction(report: ReportData) -> int:
    activity.logger.info("Running TabPFN tabular prediction on expedition quality...")
    async with httpx.AsyncClient(timeout=10.0) as client:
        try:
            # We call our own API to use the exact same TabPFN/fallback logic
            response = await client.post(
                "http://localhost:8000/predict-quality",
                json={"num_photos": 1, "num_notes": len(report.observations), "total_note_length": len(report.summary), "duration_seconds": 600}
            )
            return response.json().get("score", 85)
        except Exception:
            return 85

@activity.defn
async def persist_report(report: ReportData) -> ReportData:
    activity.logger.info("Persisting durable report to Database...")
    return report

# ---------------------------------------------------------
# WORKFLOW
# ---------------------------------------------------------

@workflow.defn
class ExpeditionAnalysisWorkflow:
    @workflow.run
    async def analyze_expedition(self, evidence: EvidenceData) -> Dict[str, Any]:
        # Step 1: Normalize
        normalized = await workflow.execute_activity(
            normalize_evidence,
            evidence,
            start_to_close_timeout=timedelta(seconds=10),
        )

        # Step 2: Run Gemma Analysis
        raw_report = await workflow.execute_activity(
            run_gemma_analysis,
            normalized,
            start_to_close_timeout=timedelta(seconds=60),
        )

        # Step 3: Validate
        validated_report = await workflow.execute_activity(
            validate_report,
            raw_report,
            start_to_close_timeout=timedelta(seconds=10),
        )

        # Step 4: Quality Prediction
        quality_score = await workflow.execute_activity(
            run_tabpfn_quality_prediction,
            validated_report,
            start_to_close_timeout=timedelta(seconds=30),
        )

        # Step 5: Persist
        final_report = await workflow.execute_activity(
            persist_report,
            validated_report,
            start_to_close_timeout=timedelta(seconds=10),
        )

        # Return as a dictionary that matches the frontend FieldReport schema
<shellId: 23 completed with exit code 0>

{"command":"& 'E:\\HACKTOBERFEST WEEK 1 CHALLENGE\\venv\\Scripts\\python.exe' -c \"import sys; sys.path.insert(0, 'api'); import main; print('TABPFN_AVAILABLE=', main.TABPFN_AVAILABLE); print(main.pre...

Traceback (most recent call last):
  File "<string>", line 1, in <module>
AttributeError: module 'main' has no attribute 'TABPFN_AVAILABLE'
Sentry is attempting to send 2 pending events
Waiting up to 2 seconds
Press Ctrl-Break to quit
<shellId: 22 completed with exit code 1>

{
  "command": "$lines=Get-Content api\\temporal_workflow.py; $lines[120..170]",
  "description": "Inspect final workflow return"
}

# Step 4: Quality Prediction
        quality_score = await workflow.execute_activity(
            run_tabpfn_quality_prediction,
            validated_report,
            start_to_close_timeout=timedelta(seconds=30),
        )

        # Step 5: Persist
        final_report = await workflow.execute_activity(
            persist_report,
            validated_report,
            start_to_close_timeout=timedelta(seconds=10),
        )

        # Return as a dictionary that matches the frontend FieldReport schema
        return {
            "summary": final_report.summary,
            "observations": final_report.observations,
            "missing_evidence": final_report.missing_evidence,
            "suggested_next_step": final_report.suggested_next_step,
            "quality_score": quality_score
        }
<shellId: 24 completed with exit code 0>

{
  "command": "git status --short; git log --oneline -6",
  "description": "Inspect latest changes"
}

0590c66 fix: normalize schema, fix Temporal quality input, update health check
8ac6eaa fix: address final MVP judging gaps (TabPFN honesty, App fallback persistence, Temporal switch)
a4a1f43 fix: address MVP judging gaps (TabPFN api, Temporal real integration, Fallback UI, CORS, CSS cleanup)
f51f1a9 chore: rename FieldProof to WildProof across entire stack
98ae635 docs: update README with correct working directory for backend
ae889c1 feat: Phase 8 UI Polish and README
<shellId: 25 completed with exit code 0>

{"command":"$i=0; Get-Content src\\App.tsx | ForEach-Object { $i++; if($_ -match 'normalize|missing_evidence|missingEvidence|suggested_next|suggestedNext|analyze-durable|quality|useEffect|fetchReport'...

1: import { useState, useEffect, useCallback } from 'react';
90:   useEffect(() => {
213:   const [quality, setQuality] = useState<{score: number, source: string} | null>(null);
216:   const fetchReport = useCallback(async (exp: Expedition) => {
220:       const endpoint = useTemporal ? '/analyze-durable' : '/analyze';
234:       const normalizedReport: FieldReport = {
237:         missingEvidence: data.missingEvidence ?? data.missing_evidence ?? [],
238:         suggestedNextStep: data.suggestedNextStep ?? data.suggested_next_step ?? ''
241:       // Fetch quality score
244:         const qRes = await fetch(`${baseUrl}/predict-quality`, {
255:       } catch (e) { console.warn("Quality endpoint failed", e); }
257:       const updated = { ...exp, status: 'complete' as const, report: normalizedReport };
261:         setQuality({ score: qData.score, source: qData.model });
271:         missingEvidence: ["Audio capturing ambient bird sounds.", "Temperature readings."],
272:         suggestedNextStep: "Return at dusk to capture nocturnal insect patterns."
281:   useEffect(() => {
287:             await fetchReport(exp);
294:   }, [id, fetchReport]);
326:             onClick={() => fetchReport(expedition)}
334:       {quality && !errorMsg && (
337:             <p className="text-xs font-bold text-blue-500 uppercase tracking-wide">Data Quality Score</p>
338:             <p className="text-sm text-blue-800">Scored via {quality.source === 'tabpfn' ? 'TabPFN Model' : 'Baseline Heuristic'}</p>
340:           <div className="text-3xl font-extrabold text-blue-600">{quality.score.toFixed(0)}/100</div>
368:             {report.missingEvidence.map((e: string, i: number) => <li key={i}>{e}</li>)}
373:           <p className="text-sm text-neutral-600">{report.suggestedNextStep}</p>
<shellId: 26 completed with exit code 0>

{"command":"$i=0; Get-Content api\\temporal_workflow.py | ForEach-Object { $i++; if($_ -match 'Quality|quality|num_photos|num_notes|duration|return 85|EvidenceData|ReportData') { '{0}: {1}' -f $i,$_ }...

10: class EvidenceData:
15: class ReportData:
22: class QualityInput:
23:     evidence: EvidenceData
24:     report: ReportData
31: async def normalize_evidence(evidence: EvidenceData) -> EvidenceData:
41: async def run_gemma_analysis(evidence: EvidenceData) -> ReportData:
64:             return ReportData(
74: async def validate_report(report: ReportData) -> ReportData:
80: async def run_tabpfn_quality_prediction(inputs: QualityInput) -> Dict[str, Any]:
81:     activity.logger.info("Running TabPFN tabular prediction on expedition quality...")
83:     num_photos = sum(1 for e in inputs.evidence.evidence_items if e.get("kind") == "photo")
84:     num_notes = sum(1 for e in inputs.evidence.evidence_items if e.get("kind") == "note")
91:                 "http://localhost:8000/predict-quality",
93:                     "num_photos": num_photos, 
94:                     "num_notes": num_notes, 
96:                     "duration_seconds": 600
103:             activity.logger.error(f"Quality endpoint failed: {e}")
105:             return {"score": None, "model": "unavailable", "reason": "quality endpoint unavailable"}
108: async def persist_report(report: ReportData) -> ReportData:
119:     async def analyze_expedition(self, evidence: EvidenceData) -> Dict[str, Any]:
141:         # Step 4: Quality Prediction
142:         quality_score_data = await workflow.execute_activity(
143:             run_tabpfn_quality_prediction,
144:             QualityInput(evidence=normalized, report=validated_report),
161:             "quality_score": quality_score_data.get("score") if quality_score_data else None
<shellId: 27 completed with exit code 0>

{"command":"$i=0; Get-Content api\\main.py | ForEach-Object { $i++; if($_ -match 'health|quality_model|tabpfn|missing_evidence|suggested_next|model') { '{0}: {1}' -f $i,$_ } }","description":"Check ba...

3: from pydantic import BaseModel, ValidationError, Field
39: MODEL_NAME = "gemma:2b" # Or "gemma", "llama3.2-vision", depending on what is installed locally
41: class EvidencePayload(BaseModel):
48: class AnalyzeRequest(BaseModel):
52: class Observation(BaseModel):
57: class FieldReport(BaseModel):
60:     missingEvidence: List[str] = Field(alias="missing_evidence")
61:     suggestedNextStep: str = Field(alias="suggested_next_step")
66: @app.get("/health")
67: def health_check():
70:         "quality_model": "baseline_heuristic",
71:         "tabpfn": "unavailable"
85:                     "model": MODEL_NAME,
95:             raise HTTPException(status_code=503, detail="Local AI model is unavailable.")
97: @app.post("/analyze", response_model=FieldReport)
129:   "missing_evidence": ["What evidence is missing to make a stronger conclusion?"],
130:   "suggested_next_step": "A single actionable next step."
162:   "missing_evidence": ["..."],
163:   "suggested_next_step": "..."
188:             evidence_items=[ev.model_dump() for ev in request.evidence]
203: class QualityPredictionRequest(BaseModel):
211:     # Live TabPFN integration requires older torch versions and heavy memory,
213:     # (See api/tabpfn_experiment.py for the reproducible TabPFN experiment!)
222:     return {"score": score, "model": "baseline_heuristic (tabpfn_unavailable)"}
<shellId: 28 completed with exit code 0>

{
  "command": "npm run build; npm run lint",
  "description": "Run current frontend validation",
  "initial_wait": 120,
  "mode": "sync"
}

> [email protected] build
> tsc -b && vite build

vite v8.3.4 building client environment for production...
transforming...
✓ 2189 modules transformed.
rendering chunks...
computing gzip size...
dist/index.html                   0.49 kB │ gzip:   0.32 kB
dist/assets/index-DZffQ7u8.css   25.02 kB │ gzip:   5.34 kB
dist/assets/index-BPhIAsN9.js   567.06 kB │ gzip: 183.37 kB

[plugin builtin:vite-reporter] 
(!) Some chunks are larger than 500 kB after minification. Consider:
- Using dynamic import() to code-split the application
- Use build.rolldownOptions.output.codeSplitting to improve chunking: https://rolldown.rs/reference/OutputOptions.codeSplitting
- Adjust chunk size limit for this warning via build.chunkSizeWarningLimit.
✓ built in 753ms

> [email protected] lint
> oxlint
<shellId: 29 completed with exit code 0>

{
  "filePaths": [
    "E:\\HACKTOBERFEST WEEK 1 CHALLENGE\\src",
    "E:\\HACKTOBERFEST WEEK 1 CHALLENGE\\api"
  ]
}

No errors found.

{
  "command": "$lines=Get-Content api\\temporal_workflow.py; $lines[75..112]",
  "description": "Verify Temporal metric calculation"
}

# Simulates Pydantic validation
    return report

@activity.defn
async def run_tabpfn_quality_prediction(inputs: QualityInput) -> Dict[str, Any]:
    activity.logger.info("Running TabPFN tabular prediction on expedition quality...")
    
    num_photos = sum(1 for e in inputs.evidence.evidence_items if e.get("kind") == "photo")
    num_notes = sum(1 for e in inputs.evidence.evidence_items if e.get("kind") == "note")
    total_note_length = sum(len(e.get("text", "")) for e in inputs.evidence.evidence_items if e.get("kind") == "note")
    
    async with httpx.AsyncClient(timeout=10.0) as client:
        try:
            # We call our own API to use the exact same TabPFN/fallback logic
            response = await client.post(
                "http://localhost:8000/predict-quality",
                json={
                    "num_photos": num_photos, 
                    "num_notes": num_notes, 
                    "total_note_length": total_note_length, 
                    "duration_seconds": 600
                }
            )
            response.raise_for_status()
            data = response.json()
            return {"score": data.get("score"), "model": data.get("model")}
        except Exception as e:
            activity.logger.error(f"Quality endpoint failed: {e}")
            # Raise an error to allow Temporal to retry, or fallback honestly.
            return {"score": None, "model": "unavailable", "reason": "quality endpoint unavailable"}

@activity.defn
async def persist_report(report: ReportData) -> ReportData:
    activity.logger.info("Persisting durable report to Database...")
    return report

# ---------------------------------------------------------
# WORKFLOW
<shellId: 30 completed with exit code 0>

{
  "command": "$lines=Get-Content api\\main.py; $lines[60..78]",
  "description": "Verify backend health output"
}

suggestedNextStep: str = Field(alias="suggested_next_step")

    class Config:
        populate_by_name = True

@app.get("/health")
def health_check():
    return {
        "status": "ok",
        "quality_model": "baseline_heuristic",
        "tabpfn": "unavailable"
    }

@app.get("/sentry-debug")
def trigger_error():
    logger.error("Simulated performance issue / timeout for Sentry Tracing.")
    raise Exception("Simulated timeout for Sentry!")

async def generate_from_ollama(prompt: str) -> str:
<shellId: 32 completed with exit code 0>

{"command":"& 'E:\\HACKTOBERFEST WEEK 1 CHALLENGE\\venv\\Scripts\\python.exe' -m py_compile api\\main.py api\\temporal_workflow.py api\\temporal_worker.py api\\tabpfn_experiment.py","description":"Val...

<shellId: 31 completed with exit code 0>