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Chat Bot AI/ML Project

This project involves the development of a chatbot using Artificial Intelligence (AI) and Machine Learning (ML) techniques. The chatbot is trained to understand user queries and respond appropriately based on predefined intents.

File Structure

  • training.py: This file contains the code for training the chatbot model.
  • chatbot.py: This file contains the code for running the chatbot.

Data Preparation

  • The training data is stored in a JSON file named intent.json, which contains intents along with corresponding patterns and responses.
  • The NLTK library is used for text processing tasks such as tokenization, lemmatization, and bag-of-words creation.

Model Training (training.py)

  1. Data Loading: The training data is loaded from the intent.json file.
  2. Data Preprocessing: Patterns and responses from the intents are extracted and tokenized using NLTK.
  3. Word Lemmatization: Words are lemmatized to reduce them to their base forms.
  4. Word Vectorization: The bag-of-words technique is applied to convert words into numerical vectors.
  5. Model Architecture: A Sequential neural network model is built using Keras. The model consists of dense layers with ReLU activation and dropout for regularization.
  6. Model Compilation: The model is compiled using the Stochastic Gradient Descent (SGD) optimizer and categorical cross-entropy loss function.
  7. Model Training: The model is trained on the preprocessed data for a specified number of epochs.

Chatbot Implementation (chatbot.py)

  1. Model Loading: The pre-trained chatbot model is loaded from the chatbotmodel.h5 file.
  2. Data Loading: The words and classes used during training are loaded from the pickle files.
  3. Input Processing: User input is cleaned, tokenized, and converted into a bag-of-words representation.
  4. Intent Prediction: The model predicts the intent of the user input based on the bag-of-words representation.
  5. Response Generation: Based on the predicted intent, a response is selected randomly from the predefined responses associated with that intent.
  6. User Interaction: The chatbot continuously interacts with the user, predicting intents and generating responses based on user input.

Technologies Used

  • Python
  • Libraries: NLTK, TensorFlow, Keras, NumPy
  • File Serialization: Pickle (for storing words and classes)

Setup and Installation

  1. Install the required Python libraries (NLTK, TensorFlow, Keras).
  2. Download the training data (intent.json) and place it in the project directory.
  3. Run training.py to train the chatbot model and generate necessary pickle files and the trained model file.
  4. Run chatbot.py to start the chatbot interaction. Enter messages in the console to chat with the bot.

For any improvements or feedback, please feel free to contribute or reach out.

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