Update README.md

README.md

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----
-title: Visual Question Answering (VQA) System
-emoji: 🏞️
-colorFrom: blue
-colorTo: purple
-sdk: gradio
-sdk_version: 5.20.1
-app_file: run.py
-pinned: false
----
-# Visual Question Answering (VQA) System
-
-A multi-modal AI application that allows users to upload images and ask questions about them. This project uses pre-trained models from Hugging Face to analyze images and answer natural language questions.
-
-## Features
-
-- Upload images in common formats (jpg, png, etc.)
-- Ask questions about image content in natural language
-- Get AI-generated answers based on image content
-- User-friendly Streamlit interface
-- Support for various types of questions (objects, attributes, counting, etc.)
-
-## Technical Stack
-
-- **Python**: Main programming language
-- **PyTorch & Transformers**: Deep learning frameworks for running the models
-- **Streamlit**: Interactive web application framework
-- **HuggingFace Models**: Pre-trained visual question answering models
-- **PIL**: Image processing
-
-## Setup Instructions
-
-1. Clone this repository:
-   ```
-   git clone https://github.com/your-username/visual-question-answering.git
-   cd visual-question-answering
-   ```
-
-2. Create a virtual environment (recommended):
-   ```
-   python -m venv venv
-   # On Windows
-   venv\Scripts\activate
-   # On macOS/Linux
-   source venv/bin/activate
-   ```
-
-3. Install dependencies:
-   ```
-   pip install -r requirements.txt
-   ```
-
-4. Run the application:
-   ```
-   python run.py
-   ```
-   
-   Or directly with Streamlit:
-   ```
-   streamlit run app.py
-   ```
-
-5. Open a web browser and go to `http://localhost:8501`
-
-## Usage
-
-1. Upload an image using the file upload area
-2. Type your question about the image in the text field
-3. Select a model from the sidebar (BLIP or ViLT)
-4. Click "Get Answer" to get an AI-generated response
-5. View the answer displayed on the right side of the screen
-
-## Models Used
-
-This application uses the following pre-trained models from Hugging Face:
-- **BLIP**: For general visual question answering with free-form answers
-- **ViLT**: For detailed understanding of image content and yes/no questions
-
-## Project Structure
-
-- `app.py`: Main Streamlit application
-- `models/`: Contains model handling code
-- `utils/`: Utility functions for image processing and more
-- `static/`: Static files including uploaded images
-- `run.py`: Script to run the application
-
-## License
-
-This project is licensed under the MIT License - see the LICENSE file for details.
-
-## Acknowledgments
-
-- Hugging Face for their excellent pre-trained models
+---
+title: Visual Question Answering (VQA) System
+emoji: 🏞️
+colorFrom: blue
+colorTo: purple
+sdk: gradio
+sdk_version: 5.20.1
+app_file: app.py
+pinned: false
+---
+# Visual Question Answering (VQA) System
+
+A multi-modal AI application that allows users to upload images and ask questions about them. This project uses pre-trained models from Hugging Face to analyze images and answer natural language questions.
+
+## Features
+
+- Upload images in common formats (jpg, png, etc.)
+- Ask questions about image content in natural language
+- Get AI-generated answers based on image content
+- User-friendly Streamlit interface
+- Support for various types of questions (objects, attributes, counting, etc.)
+
+## Technical Stack
+
+- **Python**: Main programming language
+- **PyTorch & Transformers**: Deep learning frameworks for running the models
+- **Streamlit**: Interactive web application framework
+- **HuggingFace Models**: Pre-trained visual question answering models
+- **PIL**: Image processing
+
+## Setup Instructions
+
+1. Clone this repository:
+   ```
+   git clone https://github.com/your-username/visual-question-answering.git
+   cd visual-question-answering
+   ```
+
+2. Create a virtual environment (recommended):
+   ```
+   python -m venv venv
+   # On Windows
+   venv\Scripts\activate
+   # On macOS/Linux
+   source venv/bin/activate
+   ```
+
+3. Install dependencies:
+   ```
+   pip install -r requirements.txt
+   ```
+
+4. Run the application:
+   ```
+   python run.py
+   ```
+   
+   Or directly with Streamlit:
+   ```
+   streamlit run app.py
+   ```
+
+5. Open a web browser and go to `http://localhost:8501`
+
+## Usage
+
+1. Upload an image using the file upload area
+2. Type your question about the image in the text field
+3. Select a model from the sidebar (BLIP or ViLT)
+4. Click "Get Answer" to get an AI-generated response
+5. View the answer displayed on the right side of the screen
+
+## Models Used
+
+This application uses the following pre-trained models from Hugging Face:
+- **BLIP**: For general visual question answering with free-form answers
+- **ViLT**: For detailed understanding of image content and yes/no questions
+
+## Project Structure
+
+- `app.py`: Main Streamlit application
+- `models/`: Contains model handling code
+- `utils/`: Utility functions for image processing and more
+- `static/`: Static files including uploaded images
+- `run.py`: Script to run the application
+
+## License
+
+This project is licensed under the MIT License - see the LICENSE file for details.
+
+## Acknowledgments
+
+- Hugging Face for their excellent pre-trained models
 - The open-source community for various libraries used in this project