Customer Support Chatbot

A Customer Support Chatbot built using Natural Language Processing (NLP) techniques and Logistic Regression. The chatbot understands user queries and responds based on predefined intents and their associated responses.

Project Overview

This project implements a simple customer support chatbot that can classify user input into predefined categories (intents) and generate appropriate responses. The chatbot uses Logistic Regression for intent classification and TF-IDF Vectorization for text feature extraction. The user interface is built with Streamlit, allowing for an interactive web-based experience.

Key Features:

• Intent Recognition: Identifies the intent of the user's input (e.g., greeting, inquiry, complaint).
• Response Generation: Provides pre-defined responses based on the identified intent.
• Conversation History: Logs user inputs, chatbot responses, and timestamps to a CSV file for later reference.
• Web Interface: A Streamlit-based interface to interact with the chatbot.

Technologies Used

• Python 3.x
• Streamlit: A framework for building web apps.
• Scikit-learn: For implementing machine learning models (Logistic Regression, TF-IDF).
• NLTK: For natural language processing tasks (tokenization).
• Pandas: For handling CSV-based conversation history.
• JSON: For storing predefined intents and responses.

Requirements

To run this project, you need to install the required dependencies. You can do this by creating a virtual environment and installing the dependencies via pip.

1. Create a Virtual Environment (optional but recommended):

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

2. Install Dependencies: Create a requirements.txt file with the following content:

   streamlit
   scikit-learn
   nltk
   pandas
   
   Then run the following command to install the dependencies:
   ```bash
   pip install -r requirements.txt
   

Installation

1. Clone the Repository

git clone <repository-url>
cd <repository-directory>

2. Create a Virtual Environment (Optional but Recommended)

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

3. Install Required Packages

pip install -r requirements.txt

4. Download NLTK Data

import nltk
nltk.download('punkt')

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Usage

To run the chatbot application, execute the following command:

streamlit run app.py

Once the application is running, you can interact with the chatbot through the web interface. Type your message in the input box and press Enter to see the chatbot's response.

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Intents Data

The chatbot's behavior is defined by the intents.json file, which contains various tags, patterns, and responses. You can modify this file to add new intents or change existing ones.

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Conversation History

The chatbot saves the conversation history in a CSV file (chat_log.csv). You can view past interactions by selecting the "Conversation History" option in the sidebar.

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Contributing

Contributions to this project are welcome! If you have suggestions for improvements or features, feel free to open an issue or submit a pull request.

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License

This project is licensed under the MIT License. See the LICENSE file for details.

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Acknowledgments

• NLTK for natural language processing.
• Scikit-learn for machine learning algorithms.
• Streamlit for building the web interface.

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