📚 PDF-Conv 💬

Welcome to PDF-Conv, a Streamlit-based application that allows you to upload multiple PDF files and interact with them through a conversational interface. 🚀

🌟 Features

• Upload multiple PDF documents 📄
• Extract text from PDFs and split into manageable chunks 🧩
• Create vector store from text chunks using Google Palm embeddings 🔍
• Conversational retrieval chain for interacting with PDF content 🤖
• Beautiful and user-friendly interface 🎨

📋 Prerequisites

Before you begin, ensure you have met the following requirements:

• Python 3.8 or later 🐍
• Streamlit
• PyPDF2
• Langchain
• Google Generative AI
• FAISS

🛠️ Installation

1. Clone the repo

   git clone https://github.com/silentkiller18/pdf-conv.git

2. Navigate to the project directory

   cd pdf-conv

3. Install required packages

   pip install -r requirements.txt

🚀 Usage

1. Set up your Google API key

   os.environ['GOOGLE_API_KEY'] = 'YOUR_GOOGLE_API_KEY'

2. Run the Streamlit application

   streamlit run app.py

3. Open your web browser and go to http://localhost:8501

4. Upload your PDF files and start interacting with the content! 🗂️

🌐 Deployment on AWS EC2

1. Launch an EC2 Instance

   • Go to the EC2 Dashboard and click "Launch Instance".
   • Choose an Amazon Machine Image (AMI), such as the latest Ubuntu Server.
   • Select an instance type, like t2.micro for free tier.
   • Configure instance details, add storage, and configure security groups to allow HTTP (port 80) and HTTPS (port 443).
   • Review and launch the instance.

2. Connect to Your Instance

   • Use SSH to connect to your instance.

   ssh -i /path/to/your-key-pair.pem ubuntu@your-ec2-public-dns

3. Install Dependencies

   • Update package lists and install Python, pip, and git.

   sudo apt-get update
   sudo apt-get install python3-pip git

4. Clone the Repository and Install Python Packages

   git clone https://github.com/silentkiller18/pdf-conv.git
   cd pdf-conv
   pip3 install -r requirements.txt

5. Set Up Environment Variable

   • Add your Google API key to the environment.

   echo "export GOOGLE_API_KEY='YOUR_GOOGLE_API_KEY'" >> ~/.bashrc
   source ~/.bashrc

6. Run the Streamlit Application

   streamlit run app.py --server.port 80

7. Access Your Application

   • Open your web browser and navigate to http://your-ec2-public-dns

📸 Screenshots

![Screenshot 1]

💡 How It Works

1. Upload PDFs: Users can upload multiple PDF files through the sidebar.
2. Text Extraction: Extracts text from each PDF file.
3. Text Splitting: Splits the extracted text into chunks for better processing.
4. Vector Store Creation: Creates a vector store using Google Palm embeddings.
5. Conversational Interface: Users can ask questions about the PDF content and receive responses.

🤝 Contributing

Contributions, issues, and feature requests are welcome! Feel free to check the issues page.

📜 License

Distributed under the MIT License. See LICENSE for more information.

🙏 Acknowledgements

• Streamlit
• PyPDF2
• Langchain
• Google Generative AI
• FAISS

📬 Contact

[email protected]

Project Link: https://github.com/silentkiller18/pdf-conv

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⭐️ Don't forget to give the project a star if you found it useful! ⭐️