📚 EZ GenAI – Smart Assistant for Research Summarization

An AI-powered research assistant built with Streamlit, LangChain, and Groq API, designed to intelligently summarize, query, and challenge users with MCQs based on uploaded PDF/TXT research documents.

🎥 Demo Video

Video Link: Watch the demo video on YouTube

Use this GenAI

🔗 Live App: https://ezgenaiproject-rajeevkumar.streamlit.app/)

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📜 License:

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🚀 Features

• ✅ Upload and process .pdf or .txt documents
• ✨ AI-generated summary
• 🔍 Ask custom questions with source references
• 🧠 "Challenge Me" mode with logic-based MCQs and answer feedback
• 💬 Conversational Q&A using memory buffer
• ⚡ Fast response powered by Groq’s LLaMA3 LLMs

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🧱 Architecture & Reasoning Flow

flowchart TD
    A[User Uploads PDF/TXT] --> B[Text Extraction]
    B --> C[Text Chunking & Embedding via FAISS]
    C --> D[Summarization with LLM]
    C --> E[Q&A with RetrievalQA]
    C --> F[Logic-Based MCQ Generation]
    F --> G[User Response Evaluation]
    E --> H[Answer + Source Snippets]
    G --> I[Feedback on Answers]

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⚙️ Setup Instructions

1. Clone the repository

git clone https://github.com/0221csds146/EZ_Gen_Ai_project.git
cd EZ_Gen_Ai_project

2. Create and activate a virtual environment (optional but recommended)

# Linux/macOS
python -m venv venv
source venv/bin/activate

# Windows
python -m venv venv
venv\Scripts\activate

3. Install the dependencies

pip install -r requirements.txt

4. Configure your API key

You need a Groq API Key for LLaMA3 access.

Option A – Create a .env file:

GROQ_API_KEY=your_groq_key_here

Option B – Use Streamlit Secrets:

Create a file at .streamlit/secrets.toml:

GROQ_API_KEY = "your_groq_key_here"

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5. Run the Streamlit app

streamlit run main.py

📂 Project Structure

EZ_Gen_Ai_project/
├── main.py                 # Streamlit frontend
├── backend.py              # LangChain logic + prompt handling
├── prompts.py              # Prompt templates
├── requirements.txt
├── .env / secrets.toml     # API keys
└── README.md

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🧰 Tech Stack

Technology	Description
Python	Core backend language used to build the application
Streamlit	UI framework for creating the interactive web app
LangChain	Framework for managing LLM chains (Q&A, summarization, MCQs)
Groq API (LLaMA3)	Fast LLM used for all generative tasks
FAISS	Vector database for efficient semantic search
PyPDF2	PDF text extraction utility
dotenv / Secrets	Secure handling of API keys using .env or Streamlit secrets

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💡 Acknowledgements

• Groq LLaMA3 API – For blazing fast LLM inference
• LangChain – For orchestration of LLM applications
• Streamlit – For building interactive web interfaces
• FAISS – For efficient vector searchs
• OpenAI ChatGPT – For brainstorming, debugging, and generating ideas
• Anthropic Claude – For logic testing and prompt design

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🤝 License

This project is licensed under the MIT License.