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Chatbots - Using Natural Language Processing and TensorFlow Overview This repository contains a project aimed at building a sophisticated chatbot using Natural Language Processing (NLP) and TensorFlow. The chatbot is designed to understand the context of user inputs and respond appropriately, providing an engaging and interactive user experience.

Project Highlights

1. Transforming Conversational Intents into a TensorFlow Model NLP Techniques: Preprocess and understand conversational intents using various NLP techniques. Neural Network: Build and train a neural network using TFLEARN, a high-level API for TensorFlow. Model Saving: Save the trained model and relevant data as a pickle file for easy reuse and deployment.
2. Building the Chatbot Framework Loading the Model: Load the saved model and pickle file to create a framework for processing user inputs. Response Generation: Utilize the trained neural network to generate context-aware responses, ensuring meaningful interactions.
3. Input Processing and Response Demonstration Input Analysis: Demonstrate how user inputs are processed by the framework. Contextual Responses: Show the chatbot's ability to provide appropriate responses based on learned conversational patterns.

Getting Started

Prerequisites

Python 3.x TensorFlow TFLEARN NLTK (Natural Language Toolkit) NumPy

Installation

Clone the repository:

Copy code- git clone https://github.com/Payalsahb20220/CHATBOTS---Using-Natural-Language-Processing-and-TensorFlow.git

cd chatbot-nlp-tensorflow

Install the required packages:

pip install -r requirements.txt

Usage

Training the Model: Run the Jupyter Notebook to preprocess the data, train the model, and save it as a pickle file. Building the Framework: Load the saved model and pickle file to create the chatbot framework. Testing the Chatbot: Interact with the chatbot to see how it processes inputs and generates responses. Repository Structure data/: Contains the dataset and intents used for training the chatbot. models/: Directory to save the trained models and pickle files. notebooks/: Google colab Notebooks for training and testing the chatbot. scripts/: Python scripts for building and running the chatbot framework. README.md: Project overview and setup instructions.

Contributing Contributions are welcome! Please open an issue or submit a pull request for any improvements or new features.

License This project is licensed under the Payal Sah License - see the LICENSE file for details.

Acknowledgements TensorFlow TFLEARN NLTK