📘 PDFLens - Smart Document Analysis

PDFLens is a powerful document analysis tool that allows you to upload PDF, Word, and Text files, then ask questions about their content with accurate page references.

🎥 Demo

Click the image above to watch the demo video

🛠️ Built With

• Core: Python, Streamlit
• AI & NLP: LangChain, Cerebras AI, Hugging Face Transformers
• Document Processing: PyPDF2, python-docx, Unstructured
• Vector Database: FAISS
• Embeddings: Sentence Transformers

🌟 Features

• 📄 Multi-format Support: Upload PDF, DOCX, or TXT files with ease
• 🔍 Accurate Page References: Get answers with precise page numbers (for PDFs) or sections (for other formats)
• 🤖 AI-Powered Insights: Advanced language understanding for comprehensive answers
• 📊 Document Structure View: Preview document layout and content organization
• ⚡ Fast Processing: Quick document analysis and response generation
• 🎯 Source Citations: See which pages the answers come from

Prerequisites

• Python 3.8 or higher
• Cerebras API key (Get one here)

Installation

1. Clone or download this repository

2. Create a virtual environment

   python -m venv venv

3. Activate the virtual environment

   • Windows (PowerShell):

     .\venv\Scripts\Activate.ps1

   • Windows (Command Prompt):

     .\venv\Scripts\activate.bat

   • macOS/Linux:

     source venv/bin/activate

4. Install dependencies

   pip install -r requirements.txt

5. Set up your API key

   Create a .env file in the project root and add your Cerebras API key:

   CEREBRAS_API_KEY=your_api_key_here
   

Usage

1. Start the application

   streamlit run app.py

   Or using the venv Python directly:

   .\venv\Scripts\python.exe -m streamlit run app.py

2. Open your browser

   The app will automatically open at http://localhost:8501

3. Upload a document

   Click "Browse files" and select a PDF, DOCX, or XLSX file

4. Ask questions

   Type your question in the chat input and get AI-powered answers based on your document

Project Structure

chatbot/
├── app.py                 # Main Streamlit application
├── requirements.txt       # Python dependencies
├── .env                   # Environment variables (API key)
├── README.md             # This file
└── venv/                 # Virtual environment (created after setup)

Technologies Used

• Streamlit: Web interface
• LangChain: LLM orchestration framework
• Cerebras AI: Language model API
• FAISS: Vector database for semantic search
• Sentence Transformers: Text embeddings
• PDFPlumber: PDF text extraction
• python-docx: Word document processing
• Pandas: Excel file handling

Configuration

Supported Models

The app currently uses qwen-3-235b-a22b-instruct-2507. You can change this in app.py:

llm = ChatCerebras(
    model="qwen-3-235b-a22b-instruct-2507",  # Change model here
    temperature=0,
    max_tokens=600,
)

Embedding Model

Default: sentence-transformers/all-MiniLM-L6-v2

You can change this in the create_vectorstore() function.

Troubleshooting

ModuleNotFoundError

Make sure you're using the virtual environment:

.\venv\Scripts\python.exe -m streamlit run app.py

API Key Error

• Verify your API key is correct in the .env file
• Ensure the .env file is in the same directory as app.py
• Restart the Streamlit app after changing the .env file

Import Errors

Reinstall dependencies in the virtual environment:

.\venv\Scripts\python.exe -m pip install -r requirements.txt

Security Notes

• Never commit your .env file to version control
• Keep your API key confidential
• Add .env to your .gitignore file

License

This project is open source and available for personal and educational use.

Contributing

Feel free to fork this project and submit pull requests for improvements!

Support

For issues with:

• Cerebras API: Visit Cerebras Documentation
• Streamlit: Check Streamlit Documentation
• LangChain: See LangChain Documentation