This is a simple chatbot implemented using Python and Streamlit. The chatbot uses a logistic regression classifier with TF-IDF vectorization to classify user input and generate appropriate responses. It can handle various intents such as greetings, goodbyes, thanks, and provides information on topics like budgets and credit scores.
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Clone the repository:
git clone https://github.com/shaadclt/Chatbot.git
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Change to the project directory:
cd chatbot -
Install the required dependencies:
pip install -r requirements.txt
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Run the chatbot:
streamlit run app.py
Once the chatbot is running, a web interface will open in your browser. Type a message in the input box and press Enter to start the conversation. The chatbot will respond based on the input and display the response in the chat area.
You can have a conversation with the chatbot by typing messages and pressing Enter.
If you want to customize the chatbot's behavior or add new intents, you can modify the intents list in the app.py file. Each intent consists of a tag, patterns (user input variations), and corresponding responses.
You can also modify the training data, add more features, or use more sophisticated models to enhance the chatbot's capabilities.
Contributions to this project are welcome. If you encounter any issues or have suggestions for improvement, please open an issue or submit a pull request on the GitHub repository.
This project is licensed under the MIT License. See the LICENSE file for more information.
- The chatbot code is based on the Streamlit library and the scikit-learn machine learning library.
- The project uses the Natural Language Toolkit (NLTK) for text processing and tokenization.
Feel free to customize and improve the README file based on your specific needs and additional information about the project.