AI Document Generator

An AI-powered document processing application that allows users to upload documents and generate useful information such as summaries, important details, and skills using Artificial Intelligence.

Features

Upload documents through a web interface
Extract text from uploaded documents
Generate AI-powered document summaries
Extract important information from documents
Identify relevant skills and keywords
AI agent-based document processing
MCP (Model Context Protocol) integration
FastAPI backend
Simple and user-friendly web interface

Technologies Used

Python – Backend programming
FastAPI – Web framework and REST API
Google Gemini – AI/LLM for document analysis
MCP (Model Context Protocol) – AI agent and tool integration
HTML – Frontend structure
CSS – Frontend styling
JavaScript – Frontend functionality
Uvicorn – ASGI server
python-dotenv – Environment variable management

Project Structure

AI-Document-Generator/
│
├── app.py
├── agent.py
├── mcp_server.py
├── document_processor.py
├── prompts.py
├── requirements.txt
├── .gitignore
├── README.md
│
├── templates/
│   └── index.html
│
├── static/
│   ├── style.css
│   └── script.js
│
└── uploads/

File Description

app.py

The main FastAPI application.

It is responsible for:

Starting the web application
Handling document uploads
Calling document-processing functions
Communicating with the AI agent
Returning results to the frontend

agent.py

Contains the AI agent logic.

The agent processes the extracted document text and uses the configured AI model to generate useful information from the document.

mcp_server.py

Contains the MCP server implementation.

MCP is used to organize communication between the AI agent and the tools available in the application.

document_processor.py

Responsible for processing uploaded documents and extracting their text.

The extracted text is then passed to the AI agent for further analysis.

prompts.py

Contains the prompts used to instruct the AI model.

Keeping prompts in a separate file makes it easier to modify and improve the AI's responses.

templates/index.html

Contains the main HTML page and user interface of the application.

static/style.css

Contains the styling and layout of the web application.

static/script.js

Contains the JavaScript code responsible for frontend interactions and communication with the FastAPI backend.

requirements.txt

Contains the Python packages required to run the application.

Application Workflow

User
  │
  ▼
Upload Document
  │
  ▼
FastAPI Backend
  │
  ▼
Document Processor
  │
  ▼
Extract Document Text
  │
  ▼
AI Agent
  │
  ▼
Google Gemini / MCP
  │
  ▼
Generate Document Insights
  │
  ▼
Return Result
  │
  ▼
Display Result to User

Installation

1. Clone the Repository

git clone https://github.com/Nagashree13/AI-Document-Generator.git

Navigate to the project directory:

cd AI-Document-Generator

2. Create a Virtual Environment

python3 -m venv venv

Activate the virtual environment on Linux/macOS:

source venv/bin/activate

For Windows:

venv\Scripts\activate

3. Install Dependencies

pip install -r requirements.txt

API Key Configuration

The application requires an API key for the AI model.

Create a .env file in the project directory:

.env

Add your API key:

GEMINI_API_KEY=your_api_key_here

The application reads the API key from the environment variables.

Running the Application

Start the FastAPI server using:

uvicorn app:app --reload

After starting the server, open the application in your browser:

http://127.0.0.1:8000

How to Use

Open the AI Document Generator in your browser.
Upload a document.
The application extracts the text from the document.
The extracted text is processed by the AI agent.
Google Gemini analyzes the document.
The generated information is returned to the application.
The result is displayed on the web interface.

Example

For a resume uploaded to the application, the AI can generate information such as:

Summary:
MCA graduate with experience in Python, AI and web development.

Skills:
- Python
- SQL
- FastAPI
- MySQL
- JavaScript
- AI/ML

Future Improvements

Support for additional document formats
Chat with uploaded documents
Document question answering
Document comparison
Advanced keyword extraction
User authentication and authorization
Database integration
Cloud deployment
Additional MCP tools
Improved AI response generation

Project Objective

The objective of this project is to demonstrate the integration of Generative AI, AI agents, MCP, document processing, and web development to build a practical AI-based document analysis application.

This project demonstrates experience in:

Python development
FastAPI
Generative AI
Google Gemini
MCP
Document processing
Prompt engineering
REST APIs
Frontend-backend integration

Author

Nagashree M.K.

MCA Graduate

License

This project is created for educational and demonstration purposes.