This project is a simple chatbot implemented in Python using PyTorch and natural language processing techniques. The chatbot is trained to understand and respond to various user inputs based on predefined patterns and intents.
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Upload Files to Google Colab:
- Upload the following files to your Google Colab environment:
model.pynltk_utils.pyintents.json
- Upload the following files to your Google Colab environment:
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Create a new notebook:
- Run the following commands in a code cell to install the necessary libraries.
!pip install torch !pip install nltk !pip install sympy
- Run the following commands in a code cell to install the necessary libraries.
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Copy the code:
- Copy the code from the file
train.pyand execute it. - In another section of code, copy the code from
chat.pyand execute it. - Now you can interact with the chatbot named "Potato."
- Copy the code from the file
- If you want to train the chatbot with your dataset, modify the
intents.jsonfile with your patterns, responses, and tags. - Run the training script or notebook cell to train the model.
- Input your messages in the designated code cell and execute it to receive responses from the chatbot.
- Feel free to customize the chatbot's behavior by modifying the
intents.jsonfile, adding more patterns, or adjusting the model architecture inmodel.py.
- If you train the model, save the trained model (
data.pthor your specified filename). - When using the chatbot in the future, load the trained model for better performance.