This is a minimal AI-integrated web application that takes a user prompt, generates a short AI-created video using the Vadoo AI API, and displays the result in the browser.
Built with FastAPI for the backend and vanilla HTML/CSS/JS for the frontend. Deployed publicly on Render.

• Prompt to Video – Enter text and get a generated short video (5–10 seconds+ depending on API).
• Vadoo API Integration – Uses the generate_video endpoint.
• Webhook Support – Receives video completion callbacks from Vadoo.
• Polling Fallback – UI polls backend /job-status for updates.
• Bonus Features:
  • Loading spinner while video is generating
  • Prompt template enhancement (cinematic, high quality appended automatically)
  • Video history so users can rewatch
  • Caching to avoid API rate-limit hits (optional)

.
├── backend/
│   ├── main.py           # FastAPI backend
│   ├── requirements.txt  # Backend dependencies
│   └── .env              # Environment variables (NOT committed to GitHub)
│
├── frontend/
│   ├── index.html        # Main UI
│   ├── style.css         # Basic styles
│   └── script.js         # API calls, UI updates
│
├── README.md
└── LICENSE

Create a .env file in backend/ with:

VADOO_API_KEY=your_vadoo_api_key_here
BACKEND_BASE_URL=https://<your-render-service>.onrender.com

Important:

• Do not commit .env to GitHub.
• On Render, set these in Environment → Environment Variables.

1. Clone the repo

   git clone https://github.com/adityawalture/Text-to-Video-webapp.git
   cd ai-video-webapp

2. Install backend dependencies

   cd backend
   pip install -r requirements.txt

3. Run FastAPI server

   uvicorn main:app --reload --host 0.0.0.0 --port 8000

4. Open frontend locally

   • Open frontend/index.html in your browser.
   • Make sure BACKEND_URL in script.js points to http://localhost:8000.

1. Push code to GitHub.
2. Create a Web Service in Render:
   • Root Directory: backend
   • Build Command: pip install -r requirements.txt
   • Start Command: uvicorn main:app --host 0.0.0.0 --port 10000
3. Set VADOO_API_KEY and BACKEND_BASE_URL in Render Environment.
4. Deploy.
5. Set Webhook URL in Vadoo dashboard:

   https://<your-render-service>.onrender.com/webhook/vadoo
   

6. App url: https://adityawalture.github.io/Text-to-Video-webapp/frontend/

1. User Prompt

   • Enter text in UI → POST to /generate-video.

2. Video Generation

   • Backend sends request to Vadoo API with API key.
   • Returns job_id to frontend.

3. Webhook Update

   • Vadoo calls /webhook/vadoo when video is ready.
   • Backend stores video_url in memory/cache.

4. Frontend Polling

   • UI calls /job-status?job_id=... until status is "complete".
   • Displays video player with generated video.

You can simulate a completed job:

curl -X POST https://<your-render-service>.onrender.com/webhook/vadoo   -H "Content-Type: application/json"   -d '{"vid":"test-123","status":"complete","url":"https://sample-videos.com/video321/mp4/720/big_buck_bunny_720p_1mb.mp4"}'

Then:

curl "https://<your-render-service>.onrender.com/job-status?job_id=test-123"

• Vadoo free tier has limited generations — you may hit "Generation limits over" errors.
• The mock video fallback allows UI testing without consuming credits.
• No API keys are stored in the frontend — all calls go through the backend.