How to create simple chatbot in Gradio

Key takeaways:

  • Gradio simplifies chatbot development. It provides a user-friendly interface for creating chatbots without extensive web development knowledge.

  • Rapid prototyping is possible. Gradio allows for quick experimentation and iteration on chatbot functionalities.

  • Limitations exist for complex chatbots. While Gradio is suitable for basic chatbots, more advanced features might require additional tools.

In machine learning and natural language processing advancements, building a chatbot has become more accessible than ever. In this Answer, we’ll explore how to create a simple chatbot using Gradio, a Python library that facilitates the development of customizable user interfaces for machine learning models.

Gradio provides a powerful framework for building interactive applications, and developers can quickly prototype and deploy chatbots without extensive knowledge of web development or user interface design. We’ll start by defining the chatbot’s functionality and then use Gradio to set up a user-friendly interface for interacting with the chatbot. Let’s dive in!

Step-by-step procedure

Creating a simple chatbot using Gradio involves defining a function that processes user input and returns a response, then setting up a Gradio interface to interact with the chatbot. Here’s a step-by-step guide:

Step 1: Import necessary libraries

Import Gradio and transformers for the chatbot. 

import gradio as gr
from transformers import pipeline

  1. Gradio: Gradio is a Python library used to create user interfaces for machine learning models. It helps to visualize and interact with your models in a simple and intuitive way.

  2. Transformers: Transformers is a library developed by Hugging Face that provides pretrained models for various natural language processing tasks, including sentiment analysis.

Step 2: Define the chatbot function

First, create a function that takes user input and generates a response. It will analyze the sentiment of the text provided by the user and respond accordingly. Here’s a breakdown of how it will work:

  • The user inputs some text.

  • The chatbot analyzes the sentiment of the text.

  • It responds with a sentiment, such as “Positive,” “Negative,” or “Neutral.”

# Load sentiment analysis model
sentiment = pipeline("sentiment-analysis")
def analyze_sentiment(text, history):
    # Perform sentiment analysis
    result = sentiment(text)[0]
    label = result['label']
    
    # Format the response
    response = f"The sentiment of the text is {label}."
    return response

Step 3: Create the interface

Next, create a Gradio chatbot interface using a ChatInterface function: 

# Create Gradio chatbot interface
gradioChatbot = gr.ChatInterface(analyze_sentiment, title="Sentiment Analysis Chatbot", description="Enter some text and the chatbot will analyze its sentiment.")

The sentiment analysis function analyze_sentiment is used as a backend for the chatbot, and specifies the title and description of the interface.

Step 4: Launch the interface

Finally, launch the interface to start interacting with the chatbot. We can run the interface locally or deploy it to a web server. 

# Launch the chatbot interface
gradioChatbot.launch(server_name="0.0.0.0", server_port=7860)

Expected output

We’ll create a simple chatbot that can perform sentiment analysis on text inputs. Before starting, examine the output to get a better idea of the task at hand.

Code: Gradio chatbot

After following all the steps above, we can see the Gradio chatbot below in its working state. Run it to see it in action:

import gradio as gr
from transformers import pipeline

# Load sentiment analysis model
sentiment = pipeline("sentiment-analysis")

def analyze_sentiment(text, history):
    # Perform sentiment analysis
    result = sentiment(text)[0]
    label = result['label']
    
    # Format the response
    response = f"The sentiment of the text is {label}."
    return response

# Create Gradio chatbot interface
gradioChatbot = gr.ChatInterface(analyze_sentiment, title="Sentiment Analysis Chatbot", description="Enter some text and the chatbot will analyze its sentiment.")

# Launch the chatbot interface
gradioChatbot.launch(server_name="0.0.0.0", server_port=7860)
Code example for sentiment analysis on text inputs

Unlock the potential of Python and advanced technologies in the “Guide to Building Python and LLM-Based Multimodal Chatbots” course. Learn to integrate Gradio, Rasa, Gemini, and Whisper v3 to create multimodal, interactive AI chatbots and deploy them on Hugging Face.

Gradio simplifies the process of building chatbots by providing a user-friendly framework for creating interactive interfaces. With just a few lines of code, developers can quickly prototype and deploy chatbots without extensive web development knowledge. This accessibility allows for rapid experimentation and exploration of different chatbot functionalities and user experiences. While Gradio may have limitations for highly complex chatbots, it remains a valuable tool for building basic chatbots and creating user interfaces for machine learning models.

Frequently asked questions

Haven’t found what you were looking for? Contact Us

Is Gradio better than Streamlit?

Gradio and Streamlit are both excellent tools for creating interactive web applications for machine learning models. The best choice for you will depend on your specific project needs and preferences

Does chatbot use AI or ML?

Yes, chatbots typically use both AI and ML. In essence, AI provides the foundation for a chatbot’s intelligence, while ML enables it to learn and grow.

Free Resources

Copyright ©2025 Educative, Inc. All rights reserved