chatbot-studio 1.0.6

A framework to design, train, and deploy AI chatbots.

chatbot_studio

chatbot_studio is a versatile Python framework designed to simplify the process of designing, training, and deploying AI-powered chatbots. Whether you're a business, an NLP developer, or a chatbot enthusiast, chatbot_studio provides all the tools you need to create robust conversational agents. if you're looking for the best machine learning tool for chatbot development, this tool has you covered.

Key Features

• Prebuilt Conversational Flows: Quickly build conversational flows with reusable templates for customer support, FAQs, and more.
• Integration with Popular NLP Models: Leverage Hugging Face Transformers and other popular NLP frameworks.
• Multi-Platform Deployment: Seamlessly deploy your chatbot to Telegram, Slack, WhatsApp, and other platforms.
• Custom Dataset Training: Easily train chatbots with your own datasets to suit specific use cases.
• Extensive Documentation: Clear and concise documentation with examples to help you get started quickly.

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Installation

Install chatbot_studio via pip:

pip install chatbot_studio

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Quick Start

1. Creating a Conversational Flow

from chatbot_studio.core.flow_builder import create_conversational_flow

steps = [
    {"question": "How can I assist you today?", "responses": ["Billing", "Technical Support"]},
    {"question": "Can you provide more details?", "responses": ["Yes", "No"]},
]

flow = create_conversational_flow("Customer Support", steps)
print(flow)

2. Integrating an NLP Model

from chatbot_studio.core.model_integration import integrate_model

model = integrate_model("distilbert-base-uncased", task="text-classification")
print(model("I love this product!"))

3. Training the Bot

from chatbot_studio.core.training import train_bot

trained_model = train_bot("path/to/dataset.json", "mock_model")

4. Deploying the Bot

from chatbot_studio.core.deployment import deploy_bot

status = deploy_bot("Telegram", {"api_key": "your_api_key"}, "my_bot")
print(status)

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Directory Structure

chatbot_studio/
|-- __init__.py
|-- core/
    |-- __init__.py
    |-- flow_builder.py
    |-- model_integration.py
    |-- training.py
    |-- deployment.py
|-- examples/
    |-- customer_support_flow.py
|-- tests/
    |-- test_flow_builder.py
    |-- test_model_integration.py
    |-- test_training.py
    |-- test_deployment.py
setup.py

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Running Tests

Run the test suite to verify that everything is working as expected:

pytest tests/

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Contributing

Contributions are welcome! Feel free to submit issues or pull requests to enhance chatbot_studio.

1. Fork the repository
2. Create your feature branch: git checkout -b feature-name
3. Commit your changes: git commit -m 'Add some feature'
4. Push to the branch: git push origin feature-name
5. Open a pull request

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License

This project is licensed under the MIT License. See the LICENSE file for details.

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