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# AI-Powered Web Application
This project is an AI-powered web application that provides four main functionalities:
1. **Document & Image Analysis**: Upload documents or images for AI-powered summarization and interpretation.
2. **Intelligent Question Answering**: Ask questions about your documents and images to get AI-powered answers.
3. **Data Visualization**: Generate visualizations from Excel data using natural language requests.
4. **Document Translation**: Translate your documents to different languages using AI.
## Project Structure
The project consists of two main parts:
1. **Frontend**: A vanilla JavaScript, HTML, and CSS application with a user-friendly interface for interacting with the AI functionalities.
2. **Backend**: A Python FastAPI application that serves as a RESTful API for the AI services.
## Technologies Used
### Frontend
- HTML5
- CSS3
- Vanilla JavaScript
### Backend
- Python
- FastAPI
- Hugging Face Transformers
- Document parsing libraries (Tika, PyPDF2, python-docx, pandas)
- Data visualization libraries (Matplotlib, Seaborn)
## Getting Started
### Prerequisites
- Python 3.8 or higher
- Docker (for deployment)
### Running the Application
```bash
# Navigate to the backend directory
cd backend
# Create a virtual environment (optional but recommended)
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
# Install dependencies
pip install -r requirements.txt
# Start the FastAPI server
uvicorn main:app --reload
```
### Running with Docker
```bash
# Build the Docker image
docker build -t ai-web-app .
# Run the container
docker run -p 8000:8000 ai-web-app
```
## Deployment on Hugging Face Spaces
This application can be deployed on Hugging Face Spaces using the Docker SDK. Follow these steps:
1. Create a new Space on Hugging Face Spaces.
2. Select Docker as the SDK.
3. Upload the project files to the Space.
4. The Space will automatically build and deploy the application.
## API Documentation
The API documentation is available at `/docs` when the backend server is running.
## Project Report
The project report should include the following sections:
1. **Backend Architecture and API Design**: Detailed description of the FastAPI backend structure, API endpoint specifications, request/response handling, and API documentation.
2. **Prompt Engineering and Optimization**: Detailed description of the prompt engineering process, including design, testing, and refinement of prompts for optimal performance.
3. **Frontend Design and User Experience**: Analysis of the frontend design choices, UI/UX considerations, user workflows, and implementation of interactive elements.
4. **Deployment and Scalability**: Discussion of Dockerization strategy, deployment process on Hugging Face Spaces, and considerations for web application scalability and performance.
## License
This project is licensed under the MIT License - see the LICENSE file for details. |