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README.md

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-# Multi-Modal AI Demo
-
-This project demonstrates the use of multi-modal AI capabilities using Hugging Face pretrained models. The application provides the following features:
-
-1. **Image Captioning**: Generate descriptive captions for images
-2. **Visual Question Answering**: Answer questions about the content of images
-3. **Sentiment Analysis**: Analyze the sentiment of text inputs
-
-## Requirements
-
-- Python 3.8+
-- Dependencies listed in `requirements.txt`
-
-## Installation
-
-1. Clone this repository
-2. Install dependencies and setup the application:
-   ```
-   python run.py
-   ```
-   Then select option 5 to perform full setup (install requirements, fix dependencies, and download sample images)
-
-## Known Issues and Solutions
-
-If you encounter errors related to package compatibility (Pydantic, FastAPI, or Gradio errors), use:
-```
-python fix_dependencies.py
-```
-This will install compatible versions of all dependencies to ensure the application runs correctly.
-
-## Usage
-
-Run the web interface:
-```
-python app.py
-```
-
-Then open your browser and navigate to the URL shown in the terminal (typically http://127.0.0.1:7860).
-
-## Deploying to Hugging Face Spaces
-
-To deploy this project to Hugging Face Spaces:
-
-1. Create a new Space on [Hugging Face Spaces](https://huggingface.co/spaces)
-2. Choose the "Gradio" SDK
-3. Set up GitHub repository with these files:
-   - `app.py`
-   - `model_utils.py`
-   - `requirements.txt`
-   - `README.md`
-4. Push to the repository connected to your Space
-5. Hugging Face will automatically deploy your application
-
-## Models Used
-
-This demo uses the following pretrained models from Hugging Face:
-- Image Captioning: `nlpconnect/vit-gpt2-image-captioning`
-- Visual Question Answering: `nlpconnect/vit-gpt2-image-captioning` (simplified)
-- Sentiment Analysis: `distilbert-base-uncased-finetuned-sst-2-english`
-
-## License
-
-MIT