Tender Analysis AI

A powerful Streamlit-based web application that uses open-source AI models to analyze multiple tender documents simultaneously. Built with LangChain, Streamlit, and Hugging Face models for comprehensive procurement document analysis.

Features

• Multiple Document Upload: Upload up to 10 PDF tender documents simultaneously
• Cross-Document Analysis: Analyze and compare requirements across multiple tender documents
• Executive Summary Generation: AI-generated comprehensive summaries covering all uploaded documents
• Compliance Requirements Extraction: Automated extraction of key eligibility and compliance criteria
• Interactive Q&A with Sources: Chat interface with source document references for answers
• Document Statistics: Overview of processed files and text chunks

Technology Stack

• Frontend: Streamlit
• AI/ML:
  • LLM: OpenAI GPT-OSS-20B (via Hugging Face API)
  • Embeddings: sentence-transformers/all-MiniLM-L6-v2
  • Vector Store: FAISS (in-memory)
• Document Processing: PyPDF, LangChain
• Backend: Python 3.8+

Quick Start

Prerequisites

• Python 3.8 or higher
• Hugging Face API token (free)

Installation

1. Clone the repository

   git clone https://github.com/RiddheshMore/Tender-Analysis-AI.git
   cd Tender-Analysis-AI

2. Create virtual environment

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies

   pip install -r requirements.txt

4. Set up environment variables

   # Create .env file in the root directory
   echo 'HUGGINGFACEHUB_API_TOKEN="your_huggingface_token_here"' > .env

5. Run the application

   streamlit run app.py

6. Open your browser to http://localhost:8501

Usage

1. Upload Documents: Select up to 10 PDF tender documents using the file uploader
2. View Document Overview: Review statistics showing files processed and text chunks created
3. Analysis Results: Get comprehensive executive summary and compliance requirements covering all documents
4. Interactive Q&A: Ask specific questions about any of the documents (answers include source references)

Example Questions You Can Ask:

• "What is the submission deadline?"
• "What are the minimum experience requirements?"
• "What certifications are needed?"
• "What is the project scope?"
• "What are the payment terms?"

Project Structure

Tender-Analysis-AI/
├── app.py                    # Main Streamlit application
├── requirements.txt          # Python dependencies
├── README.md                # Project documentation
├── .env                     # Environment variables (create this)
├── .gitignore              # Git ignore rules
└── sample-data/            # Sample tender documents (optional)

Configuration

Environment Variables

Create a .env file in the root directory:

HUGGINGFACEHUB_API_TOKEN="your_huggingface_token_here"

Getting Hugging Face API Token

1. Go to Hugging Face
2. Sign up for a free account
3. Go to Settings → Access Tokens
4. Create a new token with "Read" permissions
5. Copy and paste it into your .env file

Supported Document Types

• Government Tenders: Public procurement documents from any country
• Corporate RFPs: Request for Proposals from private companies
• International Tenders: Cross-border procurement documents
• Construction Bids: Infrastructure and construction project tenders
• IT/Software Tenders: Technology procurement documents
• Service Contracts: Professional services tender documents

📄 License

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

🆘 Support

If you encounter any issues or have questions:

1. Check the existing Issues
2. Create a new issue with detailed information
3. Join our discussions in the Discussions tab

🙏 Acknowledgments

• Streamlit for the amazing web framework
• LangChain for the powerful AI orchestration
• Hugging Face for open-source AI models
• FAISS for efficient vector search

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