Updated readme

README.md

@@ -9,4 +9,145 @@ license: mit
 short_description: VQA API Endpoint
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+Check out the configuration reference at
+https://huggingface.co/docs/hub/spaces-config-reference
+
+# VizWiz Visual Question Answering API
+
+This repository contains a FastAPI backend for a Visual Question Answering (VQA)
+system trained on the VizWiz dataset.
+
+## Features
+
+- Upload images and ask questions about them
+- Get answers with confidence scores
+- Session management for asking multiple questions about the same image
+- Health check endpoint for monitoring
+- API documentation with Swagger UI
+
+## Project Structure
+
+```
+project_root/
+├── app/
+│   ├── main.py               # Main FastAPI application
+│   ├── models/               # Model definitions
+│   │   ├── __init__.py
+│   │   └── vqa_model.py      # VQA model implementation
+│   ├── routers/              # API route definitions
+│   │   ├── __init__.py
+│   │   └── vqa.py            # VQA-related endpoints
+│   ├── services/             # Business logic
+│   │   ├── __init__.py
+│   │   ├── model_service.py  # Model loading and inference
+│   │   └── session_service.py # Session management
+│   ├── utils/                # Utility functions
+│   │   ├── __init__.py
+│   │   └── image_utils.py    # Image processing utilities
+│   └── config.py             # Application configuration
+├── models/                   # Directory for model files
+├── uploads/                  # Directory for uploaded images
+├── .env                      # Environment variables
+└── requirements.txt          # Project dependencies
+```
+
+## Installation
+
+1. Clone the repository:
+
+```bash
+git clone https://github.com/dixisouls/vizwiz-vqa-api.git
+cd vizwiz-vqa-api
+```
+
+2. Create a virtual environment:
+
+```bash
+python -m venv venv
+source venv/bin/activate  # On Windows: venv\Scripts\activate
+```
+
+3. Install dependencies:
+
+```bash
+pip install -r requirements.txt
+```
+
+4. Create necessary directories:
+
+```bash
+mkdir -p models uploads
+```
+
+5. Place your trained model in the `models` directory.
+
+6. Update the `.env` file with your configuration.
+
+## Running the Application
+
+```bash
+uvicorn app.main:app --host 0.0.0.0 --port 8000 --reload
+```
+
+The API will be available at http://localhost:8000.
+
+API documentation is available at:
+
+- Swagger UI: http://localhost:8000/docs
+- ReDoc: http://localhost:8000/redoc
+
+## API Endpoints
+
+### Health Check
+
+```
+GET /health
+```
+
+Returns the health status of the API.
+
+### Upload Image
+
+```
+POST /api/vqa/upload
+```
+
+Upload an image and create a new session.
+
+### Ask Question
+
+```
+POST /api/vqa/ask
+```
+
+Ask a question about an uploaded image.
+
+### Get Session
+
+```
+GET /api/vqa/session/{session_id}
+```
+
+Get session information including question history.
+
+### Reset Session
+
+```
+DELETE /api/vqa/session/{session_id}
+```
+
+Reset a session to start fresh.
+
+## Environment Variables
+
+- `DEBUG`: Enable debug mode (default: False)
+- `MODEL_PATH`: Path to the trained model (default: ./models/vqa_model_best.pt)
+- `TEXT_MODEL`: Name of the text model (default: bert-base-uncased)
+- `VISION_MODEL`: Name of the vision model (default:
+  google/vit-base-patch16-384)
+- `HUGGINGFACE_TOKEN`: Hugging Face API token
+- `UPLOAD_DIR`: Directory for uploaded images (default: ./uploads)
+
+## License
+
+[MIT License](LICENSE)