Build and deploy full-stack profile app

[sandeepsalwan1]

Add a full-stack FastAPI web application with a video upload UI and AI behavior analysis, along with Docker and Render deployment configurations.

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Summary by cubic

Build a full-stack FastAPI app for uploading animal videos and generating a behavior report. Adds a simple web UI, a single API endpoint, and Docker/Render configs for easy deployment.

• New Features

  • FastAPI app with static UI at / and POST /api/analyze (multipart form field: video).
  • Accepts mp4/mov/avi/mkv, samples frames with OpenCV, returns JSON { "report": string }.
  • Simple upload UI with status, error handling, and output textarea.
  • CORS enabled; temp file handling and basic validation.

• Deployment

  • Dockerfile (python:3.11-slim, installs ffmpeg, exposes 8000).
  • render.yaml for one-click deployment; requires env var OPENAI_API_KEY.
  • README quickstart for local run and Docker usage.
  • Adds requirements-web.txt and a focused .dockerignore.

[cursor]

Cursor Agent can help with this pull request. Just @cursor in comments and I'll start working on changes in this branch.
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[sandeepsalwan1]

@cursor make it so it's easy to deploy and the ui is unique and looks great

[cursor]

I completely redesigned the app/static/index.html page, implementing a modern dark theme with a two-column grid layout for improved aesthetics and organization. I also significantly enhanced the user experience for video uploads, adding drag-and-drop functionality and a video preview.

• I completely overhauled the styling in app/static/index.html with a new dark theme, custom fonts, and a responsive two-column grid layout, replacing the previous basic design.
• I implemented a new interactive "dropzone" (#dropzone) for video uploads, supporting drag-and-drop functionality and a dedicated file input.
• I added a video preview element (<video id="vid">) to display the selected video before analysis, improving user feedback.
• I introduced separate "Choose Video", "Analyze", and "Clear" buttons, along with a dynamic status display that includes a loading spinner.
• I refactored the JavaScript logic to manage file selection, preview, and analysis state more robustly using functions like setFile(), setStatus(), and reset().

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1 task completed

• Restyle UI with unique, polished design and UX enhancements
• Add health endpoint and dotenv support to backend
• Update analysis to stable OpenAI image_url content format
• Add docker-compose.yml and .env.example for easy local deploy
• Adjust render.yaml to use healthz path
• Update README with compose and one-click deploy notes
   

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[sandeepsalwan1]

@cursor make sure u can deploy it on vercel

[cursor]

I made some changes to the Next.js client and the Edge API route to ensure the application is ready for deployment on Vercel. I fixed a TypeScript type error and adjusted the client to send only 12 downscaled frames to stay within Vercel's request size limits. After these modifications, I verified that the Next.js app builds cleanly.

To deploy the application, you can import the repository into Vercel, set the Root Directory to web, add your OPENAI_API_KEY as an environment variable, and then deploy. Alternatively, you can use the one-click deployment link provided in the README.

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