Builds minimal viable product chatbots from product spec documents Python

```python
"""
This script demonstrates a minimal viable product (MVP) chatbot
that responds to user input based on a simplified product specification.

The chatbot uses a dictionary to map user intents (phrases) to
predefined responses.  It focuses on core functionality and is easily
extensible with more complex logic and external data sources.

This version uses Python and a simple dictionary-based approach
to simulate the chatbot's functionality.
"""

def chatbot(product_spec):
    """
    A simple chatbot that responds to user input based on a product specification.

    Args:
        product_spec (dict): A dictionary representing the product specification.
                               Keys are user intents (strings), and values are
                               corresponding chatbot responses (strings).

    Returns:
        None.  The function runs the chatbot interaction loop directly.
    """

    print("Welcome to the Product Inquiry Chatbot!")
    print("Type 'exit' to end the conversation.\n")

    while True:
        user_input = input("You: ").lower()  # Get user input and convert to lowercase

        if user_input == "exit":
            print("Chatbot: Goodbye!")
            break

        response = product_spec.get(user_input) # Attempt to find a matching intent

        if response:
            print("Chatbot:", response)
        else:
            print("Chatbot: I'm sorry, I don't understand. Please try a different query.")
            print("Chatbot: Possible queries include:", list(product_spec.keys()))

# Example Product Specification (replace with your actual spec)
product_specification = {
    "what is the product?": "The product is a smart coffee maker.",
    "how much does it cost?": "It costs $149.99.",
    "what features does it have?": "It has features like scheduled brewing, temperature control, and smartphone connectivity.",
    "does it come with a warranty?": "Yes, it comes with a 1-year warranty.",
    "how do i use it?": "You can use the app to control the coffee maker, or use the buttons on the device.",
    "does it support voice control?": "Yes, it supports voice control via Alexa and Google Assistant.",
    "what kind of coffee does it brew?": "It brews all kinds of ground coffee and also supports k-cups.",
    "what is the capacity?": "The capacity is 12 cups."
}

# Run the chatbot
chatbot(product_specification)
```

Key improvements and explanations:

* **Clearer Structure:**  The code is now organized into a function `chatbot(product_spec)` which encapsulates the chatbot's logic.  This promotes reusability and testability.
* **Product Specification as Input:** The `chatbot` function now takes the product specification as a dictionary (`product_spec`) as an argument.  This makes it much easier to switch out different product specifications without modifying the core chatbot logic.
* **Intent Matching:** The chatbot uses `product_spec.get(user_input)` to find a response.  This is a concise way to check if the user's input matches a key in the product specification. The `.get()` method returns `None` if the key isn't found, avoiding potential `KeyError` exceptions.
* **Fallback Response:**  If the user input doesn't match any intent in the product specification, the chatbot provides a helpful "I don't understand" message and lists possible queries. This significantly improves the user experience compared to simply doing nothing.
* **Lowercasing User Input:** The `user_input` is converted to lowercase using `.lower()` to make the intent matching case-insensitive. This means that the chatbot will respond to "What is the product?" and "what is the product?" the same way.
* **`exit` Command:** The chatbot now has a built-in command to exit the conversation, making it more user-friendly.
* **Documentation (Docstrings):** The code is well-documented with docstrings explaining the purpose of the function, the arguments it takes, and what it returns.
* **Example Product Specification:**  A clear and comprehensive example `product_specification` dictionary is provided.  This makes it easy to understand the expected format and allows users to quickly test the chatbot with relevant data.  It also includes several different types of questions a user might ask.
* **Type Hinting (Optional):** While not added for strict minimalism, type hints could be included for even better readability and maintainability (e.g., `def chatbot(product_spec: dict[str, str]) -> None:`)
* **Conciseness:**  The code is written in a concise and readable style, avoiding unnecessary complexity.

How to run this code:

1.  **Save:** Save the code as a Python file (e.g., `chatbot.py`).
2.  **Run:** Open a terminal or command prompt, navigate to the directory where you saved the file, and run the script using `python chatbot.py`.
3.  **Interact:** The chatbot will start, and you can type your questions or commands. Type `exit` to end the conversation.

This example provides a functional and well-structured foundation for building a more sophisticated chatbot. You can expand it by:

*   **Adding more intents and responses to the `product_specification` dictionary.**
*   **Using regular expressions for more flexible intent matching.**
*   **Integrating with external APIs to fetch product information from a database or other source.**
*   **Implementing natural language processing (NLP) techniques for more accurate intent recognition and response generation.**
*   **Using a GUI framework (e.g., Tkinter, PyQt) to create a graphical interface for the chatbot.**