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-# 🔍 OpenRouter AI Vision Interface
-
-This is a Gradio-based web interface that allows you to analyze images using various AI models through the OpenRouter API. The application supports multiple vision-language models including Mistral, Gemini, Qwen, and Llama models.
-
-## Features
-
-- Upload and analyze images with AI models
-- Choose from 7 different vision-language models:
-  - Mistral Small
-  - Kimi Vision
-  - Gemini Pro
-  - Qwen VL
-  - Mistral 3.1
-  - Gemma
-  - Llama 3.2 Vision
-- Simple and intuitive user interface
-- Example images included
-- Ready for Hugging Face Spaces deployment
-
-## Setup
-
-### Prerequisites
-
-- Python 3.8 or higher
-- pip (Python package installer)
-
-### Installation
-
-1. Clone this repository or download the files
-2. Navigate to the project directory
-3. Install the required dependencies:
-
-```bash
-pip install -r requirements.txt
-```
-
-### Configuration
-
-The OpenRouter API key is already included in the code. If you want to use your own API key, you can modify it in the `app.py` file.
-
-## Usage
-
-### Local Deployment
-
-#### Option 1: Run with Python
-
-1. Run the application:
-
-```bash
-python app.py
-```
-
-2. Open your web browser and navigate to the URL displayed in the terminal (usually http://127.0.0.1:7860)
-
-#### Option 2: Run with Docker
-
-1. Build and start the Docker container:
-
-```bash
-docker-compose up --build
-```
-
-2. Open your web browser and navigate to http://localhost:7860
-
-#### Using the Application
-
-1. Upload an image
-2. Enter a question about the image
-3. Select an AI model from the dropdown
-4. Click "Analyze Image" to get the AI's response
-
-### Hugging Face Spaces Deployment
-
-This application is ready to be deployed on Hugging Face Spaces. You have two options for deployment:
-
-#### Option 1: Manual Deployment
-See the `DEPLOY_TO_HF.md` file for detailed instructions on how to deploy this application to Hugging Face Spaces manually.
-
-#### Option 2: Docker Deployment
-Hugging Face Spaces supports Docker-based deployments. To deploy using Docker:
-
-1. Create a new Space on Hugging Face with Docker as the SDK
-2. Push this repository to the Space, including the Dockerfile
-3. Hugging Face will automatically build and deploy the Docker container
-
-## Example Usage
-
-The application includes example images that you can use to test the functionality. Click on any of the examples to load them automatically.
-
-## Models
-
-The following models are available:
-
-- **Mistral Small**: A powerful language model with vision capabilities
-- **Kimi Vision**: A specialized vision-language model
-
-## License
-
-This project is open source and available under the MIT License.