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-# ChatGPT Oasis Model Inference API - Hugging Face Spaces (Docker)
-
-A FastAPI-based inference server for vision models (Oasis 500M and ViT-L-20) deployed on Hugging Face Spaces using Docker SDK with local model files.
-
-## 🚀 Live Demo
-
-This API is deployed on Hugging Face Spaces and can be accessed at:
-```
-https://your-username-chatgpt-oasis.hf.space
-```
-
-## 📋 API Endpoints
-
-### Base URL
-```
-https://your-username-chatgpt-oasis.hf.space
-```
-
-### Available Endpoints
-
-#### 1. API Information
-- **GET** `/`
-- Returns API information and usage instructions
-
-#### 2. Health Check
-- **GET** `/health`
-- Returns server health status, model loading status, and model file presence
-
-#### 3. List Models
-- **GET** `/models`
-- Returns information about available models and their file status
-
-#### 4. Inference (Base64)
-- **POST** `/inference`
-- Accepts base64 encoded images
-- Request body:
-  ```json
-  {
-    "image": "base64_encoded_image_string",
-    "model_name": "oasis500m"  // or "vit-l-20"
-  }
-  ```
-
-#### 5. Inference (File Upload)
-- **POST** `/upload_inference`
-- Accepts image file uploads
-- Form data:
-  - `file`: Image file
-  - `model_name`: Model to use (optional, defaults to "oasis500m")
-
-#### 6. Simple Prediction (Gradio Compatible)
-- **POST** `/predict`
-- Simple file upload endpoint for easy integration
-
-## 🔧 Usage Examples
-
-### Using Python Requests
-
-```python
-import requests
-import base64
-from PIL import Image
-import io
-
-# Your Hugging Face Spaces URL
-SPACE_URL = "https://your-username-chatgpt-oasis.hf.space"
-
-# Method 1: File Upload
-def predict_with_file_upload(image_path, model_name="oasis500m"):
-    with open(image_path, 'rb') as f:
-        files = {'file': f}
-        data = {'model_name': model_name}
-        
-        response = requests.post(
-            f"{SPACE_URL}/upload_inference",
-            files=files,
-            data=data,
-            timeout=120
-        )
-        return response.json()
-
-# Method 2: Base64 Encoding
-def predict_with_base64(image_path, model_name="oasis500m"):
-    # Load and encode image
-    image = Image.open(image_path)
-    buffer = io.BytesIO()
-    image.save(buffer, format="JPEG")
-    image_base64 = base64.b64encode(buffer.getvalue()).decode()
-    
-    # Make request
-    response = requests.post(
-        f"{SPACE_URL}/inference",
-        json={
-            "image": image_base64,
-            "model_name": model_name
-        },
-        timeout=120
-    )
-    return response.json()
-
-# Example usage
-result = predict_with_file_upload("your_image.jpg", "oasis500m")
-print(result)
-```
-
-### Using cURL
-
-```bash
-# File upload inference
-curl -X POST "https://your-username-chatgpt-oasis.hf.space/upload_inference" \
-  -H "accept: application/json" \
-  -F "file=@your_image.jpg" \
-  -F "model_name=oasis500m"
-
-# Health check
-curl "https://your-username-chatgpt-oasis.hf.space/health"
-
-# API documentation
-curl "https://your-username-chatgpt-oasis.hf.space/docs"
-```
-
-### Using JavaScript/Fetch
-
-```javascript
-// File upload inference
-async function predictImage(file, modelName = 'oasis500m') {
-    const formData = new FormData();
-    formData.append('file', file);
-    formData.append('model_name', modelName);
-    
-    const response = await fetch('https://your-username-chatgpt-oasis.hf.space/upload_inference', {
-        method: 'POST',
-        body: formData
-    });
-    
-    return await response.json();
-}
-
-// Base64 inference
-async function predictImageBase64(imageBase64, modelName = 'oasis500m') {
-    const response = await fetch('https://your-username-chatgpt-oasis.hf.space/inference', {
-        method: 'POST',
-        headers: {
-            'Content-Type': 'application/json',
-        },
-        body: JSON.stringify({
-            image: imageBase64,
-            model_name: modelName
-        })
-    });
-    
-    return await response.json();
-}
-```
-
-## 📊 Response Format
-
-All inference endpoints return the same response format:
-
-```json
-{
-  "predictions": [
-    {
-      "label": "predicted_class_name",
-      "confidence": 0.95
-    },
-    {
-      "label": "second_predicted_class",
-      "confidence": 0.03
-    }
-  ],
-  "model_used": "oasis500m",
-  "confidence_scores": [0.95, 0.03, 0.01, 0.005, 0.005]
-}
-```
-
-## 🤖 Available Models
-
-### Oasis 500M
-- **Type**: Vision Transformer
-- **Size**: ~500M parameters
-- **File**: `oasis500m.safetensors`
-- **Use Case**: General image classification
-- **Performance**: High accuracy on ImageNet
-
-### ViT-L-20
-- **Type**: Vision Transformer Large
-- **Size**: ~300M parameters  
-- **File**: `vit-l-20.safetensors`
-- **Use Case**: High-performance image classification
-- **Performance**: State-of-the-art on many benchmarks
-
-## 🔍 API Documentation
-
-Once deployed, you can access:
-- **Interactive API Docs**: `https://your-username-chatgpt-oasis.hf.space/docs`
-- **Alternative API Docs**: `https://your-username-chatgpt-oasis.hf.space/redoc`
-
-## 🚀 Deployment on Hugging Face Spaces (Docker SDK)
-
-### Prerequisites
-1. Hugging Face account
-2. Local model files (`.safetensors`)
-3. Git repository with your code
-
-### Steps to Deploy
-
-1. **Create a new Space on Hugging Face**
-   - Go to [Hugging Face Spaces](https://huggingface.co/spaces)
-   - Click "Create new Space"
-   - Choose **"Docker"** as the SDK
-   - Set visibility (public/private)
-
-2. **Prepare your files**
-   - `Dockerfile` - Container configuration
-   - `app.py` - Main FastAPI application
-   - `requirements.txt` - Python dependencies
-   - `README.md` - This documentation
-   - `oasis500m.safetensors` - Oasis model weights
-   - `vit-l-20.safetensors` - ViT model weights
-
-3. **Upload files to your Space**
-   - Upload all files to the Space repository
-   - The Dockerfile will copy the model files into the container
-
-4. **Configure the Space**
-   - Set appropriate hardware requirements (CPU/GPU)
-   - Ensure sufficient memory for model loading
-
-5. **Deploy**
-   - Push your code to the Space repository
-   - Hugging Face will automatically build the Docker image and deploy
-
-### Space Configuration
-
-Your Space will need:
-- **Hardware**: CPU (or GPU for faster inference)
-- **Memory**: At least 8GB RAM (for both models)
-- **Storage**: Sufficient space for model files (~3GB)
-
-## 📁 File Structure
-
-```
-your-space/
-├── Dockerfile              # Container configuration
-├── app.py                  # FastAPI application
-├── requirements.txt        # Python dependencies
-├── README.md              # Documentation
-├── .dockerignore          # Docker ignore file
-├── oasis500m.safetensors  # Oasis model weights
-└── vit-l-20.safetensors   # ViT model weights
-```
-
-## ⚡ Performance Tips
-
-- **Model Loading**: Models are loaded once when the container starts
-- **Local Files**: Using local `.safetensors` files avoids download time
-- **Caching**: Consider implementing response caching for repeated requests
-- **Batch Processing**: For multiple images, send them sequentially
-- **Image Size**: Optimize image size before sending (models expect specific dimensions)
-
-## 🔧 Troubleshooting
-
-### Common Issues
-
-1. **Model Loading Time**
-   - First request may take longer as models load from local files
-   - Check `/health` endpoint for model status
-
-2. **Memory Issues**
-   - Use smaller images
-   - Process one image at a time
-   - Consider using only one model at a time
-
-3. **Model File Issues**
-   - Ensure `.safetensors` files are uploaded to the Space
-   - Check `/health` endpoint for file presence status
-
-4. **Timeout Errors**
-   - Increase timeout settings in your client
-   - Check Space logs for errors
-
-### Getting Help
-
-- Check the Space logs in Hugging Face dashboard
-- Use the `/health` endpoint to verify model and file status
-- Test with the `/docs` interactive interface
-
-## 📝 License
-
-This project is for inference purposes. Please respect the licenses of the underlying models (Oasis and ViT).
-
-## 🤝 Contributing
-
-Feel free to submit issues and enhancement requests!