ADaptiv

Frontend (Next.js) + backend (Flask) for adaptive post‑production product placement.

Structure

frontend/   # Next.js app
backend/    # Flask API + prompt logic

Development

Frontend:

cd frontend
npm install
npm run dev

Backend:

cd backend
pip install -r requirements.txt
python main.py

Backend API

The backend uses the Gemini API (via GOOGLE_API_KEY) to generate contextual text and drive the video workflow.

The Flask server listens on http://localhost:8000 by default and exposes three endpoints:

GET /health

• Returns 200 with {"status":"healthy"} when the API is up.
• 500 on internal errors.

POST /analyze

• Targeted by the frontend “Process items” button.
• Request: multipart/form-data with
  • video: required video file (mp4, avi, mov, mkv).
  • image: required reference image (png, jpg, jpeg, webp).
  • text: optional textual instructions/prompts.
• Response: binary stream of the generated video (Content-Type inferred from the file). The Gemini summary is echoed in the X-Generated-Text header when available. Errors return JSON {"error": "..."} with status 400 (bad input) or 500 (processing failure).

Example:

curl -o output.mp4 \
  -H "Accept: video/mp4" \
  -F "video=@/path/input.mp4" \
  -F "image=@/path/reference.jpg" \
  -F "text=Swap the soda can for sparkling water" \
  http://localhost:8000/analyze

POST /process-video

• Same payload as /analyze.
• Response JSON:

  {
    "video": "<base64-encoded binary>",
    "text": "<Gemini narrative>",
    "filename": "processed_<uuid>.mp4"
  }

• Errors mirror /analyze (400/500 with {"error": "..."}).

Temporary uploads and generated outputs are deleted automatically once the response is sent, so callers should persist the response locally if needed.