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Chatbot Project

Python

This project implements a chatbot using natural language processing techniques and machine learning. The chatbot processes input from an Excel file, generates responses, and saves the results back to the file.

Features

  • Text normalization and lemmatization
  • Stop word removal
  • TF-IDF vectorization for text representation
  • Cosine similarity for finding the most relevant response
  • Excel file processing for batch input/output

Requirements

  • Python 3.x
  • NLTK
  • scikit-learn
  • pandas
  • numpy
  • matplotlib
  • seaborn
  • openpyxl

Installation

  1. Clone this repository: 
    git clone https://github.com/yourusername/chatbot-project.git
cd chatbot-project
  2. Install the required packages: 
    pip install -r requirements.txt
  3. Download the necessary NLTK resources: 
    import nltk
nltk.download('punkt')
nltk.download('averaged_perceptron_tagger')
nltk.download('wordnet')
nltk.download('stopwords')
nltk.download('omw-1.4')

Usage

  1. Prepare your input Excel file with an 'Input' column containing the user queries.
  2. Ensure you have a dialog_talk_agent.xlsx file containing the chatbot's knowledge base.
  3. Run the process_excel.py script: 
    python process_excel.py
    This will process the input file (default: "Book1.xlsx") and generate responses in the 'Output' column.

File Descriptions

  • final.py: Contains the main chatbot logic, including text preprocessing, TF-IDF vectorization, and response generation.
  • process_excel.py: Handles the Excel file processing, reading inputs, and writing outputs.
  • dialog_talk_agent.xlsx: The knowledge base for the chatbot (not provided in this repository).

How It Works

  1. The chatbot reads the input from an Excel file.
  2. For each input, it performs the following steps:
    • Normalizes the text (lowercase, remove special characters)
    • Tokenizes and lemmatizes the text
    • Removes stop words
    • Converts the text to TF-IDF vectors
    • Calculates cosine similarity with the knowledge base
    • Selects the most similar response
  3. The generated responses are written back to the Excel file.

Customization

You can customize the chatbot by modifying the dialog_talk_agent.xlsx file to include your own set of questions and responses.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Acknowledgments

  • NLTK team for providing essential NLP tools
  • scikit-learn developers for machine learning utilities

Contact

Project Link: https://github.com/Vishurdx-309/Chatbot