metadata

license: mit
title: Data Extractor Using Gemini
sdk: docker
emoji: 🏃
colorFrom: yellow
colorTo: blue

📊 Financial Data Extractor Using Gemini

A powerful AI-driven financial document analysis system that automatically extracts, organizes, and generates professional Excel reports from financial documents using Google's Gemini AI models.

🚀 Features

Core Functionality

📄 Multi-format Document Support: PDF, DOCX, TXT, and image files
🔍 Intelligent Data Extraction: AI-powered extraction of financial data points
📊 Smart Data Organization: Automatic categorization into 12+ financial categories
💻 Excel Report Generation: Professional multi-worksheet Excel reports with charts
🎯 Real-time Processing: Live streaming interface with progress tracking

Advanced Capabilities

🤖 Multi-Agent Workflow: Specialized AI agents for extraction, arrangement, and code generation
💾 Session Management: Persistent storage with SQLite caching
🔄 Auto-shutdown: Intelligent resource management for cloud deployments
📱 Modern UI: Beautiful Gradio-based web interface
🌐 Cross-platform: Works on Windows, Mac, and Linux
🐳 Docker Support: Containerized deployment ready

🏗️ Architecture

The system uses a sophisticated multi-agent workflow powered by the Agno framework:

📄 Document Upload
    ↓
🔍 Data Extractor Agent
    ↓ (Structured Financial Data)
📊 Data Arranger Agent  
    ↓ (Organized Categories)
💻 Code Generator Agent
    ↓ (Python Excel Code)
📊 Excel Report Output

Agent Specialization

Data Extractor: Extracts financial data points with confidence scoring
Data Arranger: Organizes data into 12+ professional categories
Code Generator: Creates Python code for Excel report generation

📋 Requirements

System Requirements

Python 3.8+
Google API Key (for Gemini models)
2GB+ RAM recommended
Cross-platform compatible

Dependencies

agno>=1.7.4
gradio
google-generativeai
PyPDF2
Pillow
python-dotenv
pandas
matplotlib
openpyxl
python-docx
lxml
markdown
requests
seaborn
sqlalchemy
websockets

🚀 Quick Start

1. Clone the Repository

git clone <repository-url>
cd Data_Extractor_Using_Gemini

2. Install Dependencies

pip install -r requirements.txt

3. Configure Environment

Create a .env file:

GOOGLE_API_KEY=your_gemini_api_key_here

4. Run the Application

python app.py

The application will be available at http://localhost:7860

🐳 Docker Deployment

Build and Run

docker build -t financial-extractor .
docker run -p 7860:7860 --env-file .env financial-extractor

Environment Variables

GOOGLE_API_KEY: Your Google Gemini API key
INACTIVITY_TIMEOUT_MINUTES: Auto-shutdown timeout (default: 30)

📖 Usage Guide

1. Upload Document

Drag and drop or select your financial document
Supported formats: PDF, DOCX, TXT, PNG, JPG, JPEG

2. Select Processing Mode

Quick Analysis: Standard extraction and organization
Custom Prompts: Use predefined prompt templates for specific document types

3. Monitor Progress

Real-time streaming interface shows each processing step
Progress indicators for all workflow stages
Live terminal output for code execution

4. Download Results

Professional Excel report with multiple worksheets
Organized data categories with charts and formatting
All intermediate files available for download

📊 Output Structure

The generated Excel reports include:

Worksheets

Summary: Executive overview with key metrics
Revenue: Income and revenue streams
Expenses: Operating and non-operating expenses
Assets: Current and non-current assets
Liabilities: Short-term and long-term liabilities
Equity: Shareholder equity components
Cash Flow: Cash flow statements
Ratios: Financial ratio analysis
Charts: Visual representations of key data
Raw Data: Original extracted data points

Features

Professional formatting with consistent styling
Interactive charts and visualizations
Dynamic period handling (auto-detects years/quarters)
Cross-referenced data validation
Print-ready layouts

🔧 Configuration

Model Settings

Configure AI models in config/settings.py:

Data Extractor Model
Data Arranger Model
Code Generator Model
Thinking budgets and retry settings

Prompt Customization

Customize agent instructions in instructions/agents/:

data_extractor.md: Data extraction prompts
data_arranger.md: Data organization prompts
code_generator.md: Excel generation prompts

Workflow Configuration

Modify workflow behavior in workflow/financial_workflow.py:

Agent configurations
Tool assignments
Output formats

🛠️ Development

Project Structure

├── app.py                 # Main Gradio application
├── workflow/              # Core workflow implementation
├── instructions/          # Agent instruction templates
├── prompts/              # Prompt gallery configurations
├── config/               # Application settings
├── utils/                # Utility functions
├── static/               # Static assets
├── models/               # Data models
└── terminal_stream.py    # Real-time terminal streaming

Key Components

WorkflowUI: Main interface controller
FinancialDocumentWorkflow: Core processing pipeline
AutoShutdownManager: Resource management
TerminalLogHandler: Real-time logging
PromptGallery: Template management

🔒 Security & Privacy

Local Processing: All document processing happens locally
No Data Storage: Documents are processed and cleaned up automatically
API Key Security: Environment-based configuration
Session Isolation: Each session has isolated temporary directories

🌐 Deployment Options

Local Development

python app.py

Production (Gunicorn)

gunicorn -w 4 -b 0.0.0.0:7860 app:app

Cloud Platforms

Hugging Face Spaces: Ready for deployment
Google Cloud Run: Containerized deployment
AWS/Azure: Standard container deployment

🤝 Contributing

Fork the repository
Create a feature branch
Make your changes
Add tests if applicable
Submit a pull request

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

🆘 Support

Common Issues

API Key Errors: Ensure your Google API key is valid and has Gemini access
Memory Issues: Increase system RAM or reduce document size
Processing Timeouts: Check network connectivity and API quotas