This is a Python-based chatbot built using Natural Language Processing (NLP) and machine learning techniques. The chatbot uses a deep learning model trained on user-defined intents and patterns. The project utilizes libraries such as NLTK, TensorFlow, Keras, and scikit-learn to process text data, train the model, and enable conversational AI.
✨ Key Features: Intelligent and accurate responses based on user input Natural Language Understanding using NLP techniques A training dataset that includes multiple intents and patterns for flexibility Model Saving: After training, the model is saved for future use and easy deployment User-friendly interaction with an intuitive interface
Make sure you have the following installed on your system:
Python 3.x pip (Python package installer) You will need the following Python libraries:
nltk tensorflow scikit-learn numpy keras json pickle
pip install nltk tensorflow scikit-learn numpy keras
chatbot_model.h5: The saved Keras model after training. This file is used for predicting the user’s intent based on their input.
training_data: A pickle file containing the vocabulary (words), classes (tags), and the training data (patterns and output labels).
intents.json: The JSON file containing user-defined intents. Each intent consists of patterns (possible user inputs) and tags (categories).
chatbot.py: The Python script containing the logic for training and interacting with the chatbot. It processes the intent data, trains a machine learning model, and predicts user intent.
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Prepare the Intent File Before starting, make sure the intents.json file is structured correctly. It should contain user input patterns along with their corresponding tags.
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Train the Model Run the chatbot.py script to train the model. The model will be trained on the data from intents.json, and the trained model will be saved as chatbot_model.h5. The training data will also be saved in the training_data file.
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Use the Chatbot Once the model is trained, you can use it to predict the intent of user inputs. Create a separate Python script to interact with the model. This script will use the trained model to predict the intent based on the user’s input.
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Interact with the Chatbot Run the interaction script to chat with the bot. Modify the interaction logic as necessary based on your project requirements.
Add more intents for better conversation handling. Improve the model using advanced NLP techniques like RNNs or BERT. Enable a more sophisticated response generation based on predicted intent.
This project is licensed under the MIT License - see the LICENSE file for details.
This project uses the NLTK and TensorFlow libraries. Special thanks to the developers of Keras and other open-source tools.
If you have any questions, feel free to open an issue or reach out directly!