Building an AI-powered chatbot with Gradio

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Now, before you start rolling your eyes and thinking “oh great, another article on how to build a chatbot,” let me tell you why this one is different. First, we’re going to use Gradio instead of the usual Flask or Django frameworks. Why? Because it’s way easier to set up and has some pretty cool features that make building chatbots a breeze! ️

So, let’s get started with our first step installing Gradio. If you haven’t already done so, head over to their website (https://gradio.app/) and download the latest version for your operating system. Once it’s installed, open up a new terminal window or command prompt and type in:

# This line installs the gradio package using the pip command
# pip is a package manager for Python, used to install and manage software packages
# install is a pip command used to install packages
# gradio is the name of the package being installed

pip install gradio

That’s it! Now you can start building your chatbot using Gradio’s intuitive interface.

First, let’s create a new Python file called `chatbot.py`. This will be our main script that we’ll use to run the chatbot. Inside this file, add the following code:

# Import the necessary libraries
import os # Import the os library to access operating system functionalities
from gradio import Interface # Import the Gradio library for creating the chatbot interface

# Load your model here (e.g., using TensorFlow or PyTorch)
def load_model():
    # TODO: Implement loading of your model
    pass # Placeholder for the actual code to load the model

# Define a function to handle user input and return the bot's response
def chatbot(input):
    # Use your loaded model to generate a response based on the user's input
    # Return the generated response as output
    pass # Placeholder for the actual code to generate a response using the loaded model

# Create an interface for our chatbot using Gradio
if __name__ == "__main__":
    with Interface() as app: # Create a Gradio interface and assign it to the variable "app"
        # Define the inputs and outputs of your chatbot
        # In this case, we're just taking a single input (the user's message) and returning a single output (the bot's response)
        inp = app.Textbox(label="Enter your message") # Create a textbox component for the user to enter their message and assign it to the variable "inp"
        outp = app.Output() # Create an output component to display the bot's response and assign it to the variable "outp"
        
        # Define the function that will be called when the user submits their input
        def handle_input():
            # Load our model using the load_model function we defined earlier
            loaded_model = load_model() # Call the load_model function to load the model and assign it to the variable "loaded_model"
            
            # Use the chatbot function to generate a response based on the user's input
            response = chatbot(inp.value) # Call the chatbot function with the user's input as the argument and assign the generated response to the variable "response"
            
            # Return the generated response as output
            outp.set(response) # Set the output component to display the generated response
        
        # Add our handle_input function to the interface using the Button component
        app.Button("Submit", on_click=handle_input) # Create a button component with the label "Submit" and assign the handle_input function to be called when the button is clicked
        
        # Run the chatbot by running this script (e.g., from a terminal window or command prompt)
        if __name__ == "__main__":
            app() # Run the Gradio interface and start the chatbot

Now, let’s break down what we just did here:

1. We imported `os`, which is used to access the operating system’s environment variables (e.g., for loading our model from a file).
2. We defined two functions `load_model` and `chatbot`. The former will be responsible for loading our pre-trained machine learning model, while the latter will handle generating responses based on user input.
3. We created an interface using Gradio’s `Interface()` function. This allows us to define inputs (e.g., a text box) and outputs (e.g., a label or image). In this case, we’re just taking a single input the user’s message and returning a single output the bot’s response.
4. We defined our `handle_input` function, which will be called when the user submits their input using the “Submit” button. This function loads our model using the `load_model()` function we defined earlier and generates a response based on the user’s input using the `chatbot(inp.value)` call.
5. We added our handle_input function to the interface using Gradio’s `Button(“Submit”, on_click=handle_input)` syntax. This creates a button with the label “Submit” and sets its `on_click` property to our `handle_input()` function.
6. We ran the chatbot by running this script using Python’s `if __name__ == “__main__”: app()` syntax. This allows us to run the script as a standalone application (e.g., from a terminal window or command prompt) and automatically launch our Gradio interface when we execute it.

And that’s it! You now have an AI-powered chatbot using Gradio that can handle user input and generate responses based on your pre-trained machine learning model. Give it a try and let us know what you think in the comments below!

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