| # 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. |