Chatbot with Intent Recognition and Response Generation

This repository contains Python code for building a chatbot that can understand user intents and generate appropriate responses. It leverages the power of TensorFlow and natural language processing (NLP) techniques.

Getting Started

1. Activate virtual environment

source venv/bin/activate

2. Install dependencies

pip install -r requirements.txt

3. Start uvicorn server

uvicorn main:app --reload

3. Open local API docs http://localhost:8000/docs

Key Features:

• Intent Recognition: Accurately classifies user input into predefined intents using a trained neural network model.
• Response Generation: Provides relevant responses based on the identified intent, drawing from a set of pre-defined responses.
• Data-Driven Training: Trains the model on a dataset of intents and patterns, enabling continuous improvement.

Dependencies:

• Python 3.x
• TensorFlow
• NumPy
• nltk
• pickle
• json

Project Structure:

• intents.json: Contains the dataset of intents and their corresponding patterns and responses.
• words.pkl: Stores the processed vocabulary of words used in the training data.
• classes.pkl: Stores the list of intent classes identified in the dataset.
• chatbotmodel.keras: The trained TensorFlow Keras model for intent classification.
• training.py: Script for training the chatbot model.
• chatbot.py: Script for interacting with the trained chatbot and generating responses.