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PDFChatBotApp is an interactive web application that allows users to have dynamic conversations with a chatbot using content from PDF files. The application utilizes NLP techniques to process PDF text, create embeddings, and retrieve relevant information based on user queries.

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PDF Chat Bot App

PDF Chat Bot App is an innovative web application that enables users to engage in interactive conversations with a chatbot using content extracted from PDF files. This project leverages natural language processing (NLP) and machine learning to create an engaging and insightful experience.

Features

  • PDF Content Extraction: The app can process PDF files, extract text, and break it down into manageable chunks for efficient interaction.

  • Dynamic Conversations: Engage in dynamic and context-aware conversations with the chatbot, powered by advanced NLP techniques.

  • Question and Answering: Utilize question-answering capabilities to retrieve relevant information from PDF documents.

  • Chat History: Keep track of your interactions with the chatbot through a chat history feature displaying a chronological conversation record.

  • User-Friendly Interface: The user interface is designed to be intuitive, making it easy for users to upload PDF files, input queries, and receive responses.

How to Use

  1. Clone this repository to your local machine using git clone https://github.com/yourusername/PDFChatBotApp.git

  2. Install the required dependencies by running pip install -r requirements.txt

  3. Obtain an OpenAI API key:

    • Visit the OpenAI Platform to sign up or log in.
    • Create a new API key in your OpenAI account.
  4. Create a .env file in the root directory of the project and add your OpenAI API key:

  5. Launch the app locally by executing streamlit run app.py

  6. Upload your PDF file and start chatting with the chatbot!

About

PDFChatBotApp is an interactive web application that allows users to have dynamic conversations with a chatbot using content from PDF files. The application utilizes NLP techniques to process PDF text, create embeddings, and retrieve relevant information based on user queries.

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