LearnSmart 😎

This Streamlit application allows you to upload PDF files, process their text, and interact with the content using natural language queries powered by Google Gemini AI.

Features

• Upload PDF Files: Upload multiple PDF files and process them to extract text.
• Text Chunking: Automatically splits the extracted text into manageable chunks for better processing.
• Vector Store Creation: Uses Google Generative AI embeddings to create a vector store for efficient similarity searches.
• Conversational Chain: Utilizes Google Gemini AI to answer questions based on the content of the uploaded PDFs.
• Interactive UI: Simple and user-friendly interface to interact with your PDFs.

Installation

Prerequisites

• Python 3.7+

Setup

1. Clone the repository:

   git clone https://github.com/thevishvammoliya/Learn-Smart
   cd Learn-Smart

2. Create a virtual environment and activate it:

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

3. Install the required packages:

   pip install -r requirements.txt

4. Set up environment variables:

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

   GOOGLE_API_KEY=your-google-api-key

Usage

1. Run the Streamlit app:

   streamlit run app.py

2. Upload PDFs:

   • Use the sidebar to upload multiple PDF files.
   • Click on the "Submit & Process" button to extract and process the text from the PDFs.

3. Ask Questions:

   • Enter your question in the text input field and press Enter.
   • The application will use the content of the uploaded PDFs to answer your question.

How It Works

1. PDF Text Extraction:

   The get_pdf_text function extracts text from the uploaded PDF files.

2. Text Chunking:

   The get_text_chunks function splits the extracted text into smaller chunks using RecursiveCharacterTextSplitter.

3. Vector Store Creation:

   The get_vector_store function creates a vector store from the text chunks using Google Generative AI embeddings and FAISS.

4. Conversational Chain:

   The get_conversational_chain function sets up a QA chain using Google Gemini AI to answer questions based on the processed PDF content.

5. User Input Handling:

   The user_input function processes the user's question, performs a similarity search on the vector store, and retrieves the most relevant chunks to generate an answer.

Example

1. Upload PDFs: Use the sidebar to upload your PDF files.
2. Ask a Question: Enter a question like "What is the main topic of the document?" in the input field.
3. Get an Answer: The app will display a detailed answer based on the content of the uploaded PDFs.

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Enjoy chatting with your PDFs using LearnSmart! 🚀

Requirements

Here is the content for your requirements.txt file:

streamlit
google-generativeai
python-dotenv
langchain
PyPDF2
chromadb
faiss-cpu
langchain_google_genai
langchain_community

Environment Variables

Replace the API key in env.example file and rename it to .env:

GOOGLE_API_KEY="YOUR_API_KEY_HERE"

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Technologies Used