For some time now, I have been searching for frameworks suitable for prototyping. Or to put it more philosophically: I am trying to shorten the distance between an idea and a product. The shorter the time between concept and visible implementation, the easier it is to share thoughts with others.

For years, I worked with Django and Flask, but instead of focusing on my actual idea, I spent too much time creating an interactive interface for my users in HTML each time. That’s why Streamlit was a revelation for me: it was finally a framework that focused on turning data and models into a usable interface with minimal code.

But my journey did not end with Streamlit. Especially in AI projects, Gradio is increasingly being used to develop an interface, and this made me curios.

I used Gradio for the first time when I was developing a chatbot for a client. The goal was for users to directly ask questions to search their Word documents and stored data. I knew that Streamlit was suitable for quick prototypes, but Gradio offers a native chatbot interface, which greatly simplified the development.

In this article, I compare both frameworks and show when each tool is best suited.

Coding: Who Makes It Easier?

When I first worked with Streamlit, I was immediately impressed. You simply write the code, and the interface is automatically generated. It renders the code exactly as written – without additional effort for structure or control. Streamlit is like a wave that executes and arranges everything from top to bottom directly.

When you write st.sidebar, a sidebar immediately appears. A button? Just st.button(). And if this button is supposed to perform an action, you use a simple control structure.

🔍 Example: Use a Button in Streamlit

import streamlit as st

st.title("Streamlit Example")
st.sidebar.write("Here is a sidebar!")

button = st.button("Click me!")

if button:
    # Whether the button was clicked can be easily checked with an if statement.
    st.write("Button was clicked!")

# This line is displayed below the button but also below the text added by the clicked button.
st.write("This is a normal text.")

This is exactly what makes Streamlit special: Minimalist code with immediate effect.

This Streamlit code can be run directly using the start command from Streamlit:

streamlit run app.py

🔍 Example: A Button in Gradio

But when I worked with Gradio, it quickly became clear that a different philosophy is followed here. While Streamlit simply renders everything sequentially, Gradio forces you to consciously structure the interface.

Gradio operates on the “Prepare, Plan, Connect” principle. The components are defined from top to bottom like in Streamlit, but an interaction is defined through functions and connected to an output component.

import gradio as gr

# This is where the function is defined that is called when the button is clicked
def button_clicked():
    return "Button was clicked!"

# This is where the interface is defined
with gr.Blocks() as app:
    # The button is defined and added
    button = gr.Button("Click me!")
    
    # This is where the output text will be rendered
    output = gr.Textbox()

    # This is where the button is linked to the function "button_clicked"
    # The result of the function is directed to the component "output" via "outputs"
    button.click(button_clicked, outputs=output)

# This is where the app is started
app.launch()

This Gradio code can be executed directly like a normal Python file:

python app.py

Multi-User Usage: Where Do Gradio and Streamlit Stand?

In this regard, I must unfortunately say that both frameworks did not really impress me in my tests.

Of course, my main goal is to create a demo and share it with my clients or students. In this context, the limitations are not so dramatic. However, anyone who wants to use Gradio or Streamlit for a real multi-user application should be aware of the limitations.

Both frameworks run synchronously by default, meaning that all user requests are processed in a single server session. This can lead to delays when multiple users request the system simultaneously. Gradio and Streamlit do not block requests, but they are processed sequentially, which can cause users to wait under high load or – depending on the implementation – a previous session to be overwritten.

This does not mean that multi-user operation is impossible, but it requires additional configuration.

• Streamlit can be scaled with Streamlit Serverless or an external FastAPI backend.
• Gradio can be made more stable through Gradio State Handling or with FastAPI/Flask as middleware.

Nevertheless, both frameworks are not primarily designed for highly scalable applications, but rather for prototyping, data science dashboards, or AI demos.

Anyone who needs an application that is truly optimized for parallel user requests should take a look at Dash. Dash requires more work in creating interfaces, but it is also more powerful. For high scalability in production environments, Gunicorn with multiple Flask workers or Celery + Redis as a task queue are often used to better distribute background tasks.

Machine Learning and Input Processing: Where Does Gradio Really Shine?

This is where Gradio really starts to show its strengths. It was developed from the ground up for Machine Learning (ML) and thus offers an environment that clearly surpasses Streamlit in this category. Especially for Data Scientists, Gradio is a real game changer, as it offers a much greater variety of input options than Streamlit.

Streamlit is minimalist and very easy to use, but it quickly reaches its limits with input options. Essentially, you can mainly input text, numbers, or files there. Of course, you can create workarounds with other libraries, but Gradio offers out-of-the-box solutions that work directly.

Example: Image Processing with Gradio vs. Streamlit

Suppose we want to upload an image and process it directly in a model, for example, to classify or edit it.

With Streamlit, you first have to upload the image, then convert the file to the desired format, and then process it further:

from PIL import Image, ImageOps, ImageFilter
import streamlit as st

def apply_filter(image):
    # Additional filters can be defined here

    # Applying a gray-scale filter
    return ImageOps.grayscale(image)
    
st.markdown(
    """
    # Simple image processing demo to apply a grayscale filter
    """
)

# Definition of a generic file upload
uploaded_file = st.file_uploader("Upload Image", type=["jpg", "jpeg", "png"])

if uploaded_file:
    # The uploaded file is loaded as an image
    image = Image.open(uploaded_file)

    # Display the uploaded image
    st.image(image, caption="Uploaded Image")

    if st.button("Apply Filter"):
         # The filter is applied
        filtered_image = apply_filter(image)

        # Display the result
        st.image(filtered_image, caption="Filtered Image")

Here we have to handle the image processing ourselves, which interrupts the workflow.

With Gradio, it’s easier because Gradio directly supports various input types like images, audio, or videos. You can simply define an image as input, and Gradio handles the conversion itself:

import gradio as gr

def apply_filter(image):
    # Additional filters can be defined here
    
    # Applying a gray-scale filter
    return image.convert("L")
    
with gr.Blocks() as app:
    gr.Markdown(
        """
        # Simple image processing demo to apply a grayscale filter
        """
    )

    # Filter section
    with gr.Row(equal_height=True): # equal_height: Same height for columns
        with gr.Column(scale=1):
            gr.Markdown("### Image Processing - Input")

            # Definition of an explicit image input
            image_input = gr.Image(type="pil", label="Upload Image", scale=4)

            apply_button = gr.Button("Apply Filter")
        
        with gr.Column(scale=1):

            gr.Markdown("### Output")
            # The result of the image processing is displayed here
            image_output = gr.Image(type="pil", label="Processed Image", scale=4)

        # Linking the button with the filter function.
        apply_button.click(
            apply_filter,
            inputs=[image_input, filter_selector],
            outputs=image_output
        )

# This is where the app is started
app.launch()

Here the image is directly converted into a PIL object, without having to handle the parsing yourself. This saves time and is another task you don’t have to worry about.

Another Example: Audio Input

Streamlit cannot directly use audio files as input – you have to save the uploaded audio as a file first and then process it further.

Gradio, on the other hand, allows you to process audio files directly as a waveform or NumPy array:

import gradio as gr

def process_audio(audio):
    return audio

gr.Interface(
    fn=process_audio,
    inputs=gr.Audio(type="numpy"),  # Audio file directly as NumPy array
    outputs="audio"
).launch()

This widget not only allows the upload of an audio file but also direct recording via a microphone. The uploaded audio file can also be played directly. All these functions are packed into this simple Gradio component!

This makes Gradio particularly attractive for machine learning models that require audio or image processing. Hugging Face offers a more detailed example of using Gradio and the audio component to implement automatic audio transcription using an AI model: huggingface-nlp-course-chapter-9.

Visualizations: Streamlit or Gradio?

📊 Visualizations are possible in both frameworks – but with different focuses.

👑 Streamlit: Perfect for Dashboards and Interactive Data Analysis

Streamlit was originally developed for data science and data-driven applications. This is particularly evident in its strong integration with Plotly, Matplotlib, Seaborn, and Altair.

But the most important thing: Streamlit is not just for static visualizations!

It offers numerous interactive widgets like st.slider(), st.button(), or st.selectbox(), which can be easily linked to make plots quickly interactive.

🔍 An Example of an Interactive Dashboard with Streamlit:

In this example, a bar chart is created. The values can be scaled via a slider. With each change of the slider, the plot is automatically updated. While this may not be the most relevant example, it is nicely compact to illustrate the principle.

import streamlit as st
import pandas as pd
import matplotlib.pyplot as plt

# Dummy data
df = pd.DataFrame({"Category": ["A", "B", "C"], "Values": [10, 20, 15]})

# Slider for dynamic scaling
scale = st.slider("Scaling", 1, 10, 5)

# Matplotlib plot
# Since the scale is given directly as a parameter, the plot is updated with each change
fig, ax = plt.subplots()
ax.bar(df["Category"], df["Values"] * scale)

# Render plot in Streamlit
st.pyplot(fig)

Creating visualizations in Gradio is a bit more complex. A function (plot) is needed again to connect the slider and the plot. This is an advantage for larger apps and makes structuring easier – but for smaller projects, Streamlit remains easier.

import gradio as gr
import pandas as pd
import matplotlib.pyplot as plt

# Dummy data
df = pd.DataFrame({"Category": ["A", "B", "C"], "Values": [10, 20, 15]})

def plot(scale):
    # Creating the Matplotlib plot
    fig, ax = plt.subplots()

    # Setting the values and applying the selected scale value
    ax.bar(df["Category"], df["Values"] * scale)
    return fig

with gr.Blocks() as app:
    scale = gr.Slider(minimum=1, maximum=10, value=5, label="Scaling")
    plot_output = gr.Plot()

    scale.change(fn=plot, inputs=scale, outputs=plot_output)

app.launch()

You can find more about Gradio and Streamlit in my comparison repository on GitHub: Python-Dashboard-Frameworks---Educational-Repository

Chatbot Integration: Gradio vs. Streamlit – Which is Better?

Now we come to the main reason why I am writing this article. I recently had a project for a client where I wanted to create a platform that allows users not only to search for information in documents but also to ask specific questions on these documents.

So, I needed a user interface for a chatbot. Of course, I knew that a chatbot could be implemented with Streamlit, but the templates were not ideal. Then I came across Gradio – and it turned out to be exactly what I was looking for.

Chatbot with Gradio

Creating a chatbot with Gradio is very simple because the ChatInterface function offers a pre-made solution that looks almost like the ChatGPT interface. All you have to do is define a response function that processes the conversation.

🔍 A simple example of a chatbot with Gradio:

---

import gradio as gr

def chat_response(message, history):
    # Code to generate the assistant's response goes here
    return "This is a test response."

chatbot = gr.ChatInterface(
    fn=chat_response,
    type="messages",
    chatbot=gr.Chatbot(placeholder="Write a question ...", type="messages")
)

chatbot.launch()

Advantages of Gradio:

• Ready-made Chat UI: Looks professional without having to build a design yourself.

• Automatically saves message history: No manual session handling required.

• Simple trigger mechanisms: Each message is directly passed to the response function.

Chatbot with Streamlit

Streamlit has also offered built-in chat components for some time. However, you have to manage the message history yourself and ensure that the bot can follow the conversation.

🔍 A simple example of a chatbot with Streamlit:

import streamlit as st

# Save message history
if "messages" not in st.session_state:
    st.session_state.messages = []

# Display message history
for message in st.session_state.messages:
    with st.chat_message(message["role"]):
        st.write(message["content"])


def process_prompt(prompt):    
    # Render the user's message
    with st.chat_message("user"):
        st.write(prompt)

    # Code to generate the assistant's response goes here

    # Render the bot's response
    with st.chat_message("assistant"):
        st.write("This is a test response.")  

    # Append the bot's response to the message history
    st.session_state.messages.append({"role": "assistant", "content": "This is a test response."})
    
# Process user input
if prompt := st.chat_input("Ask a question ..."):
    
    # Append the user's input to the message history
    st.session_state.messages.append({"role": "user", "content": prompt})

    process_prompt(prompt)

The above Streamlit code is significantly more complex than the Gradio implementation. While it is possible to implement a chatbot in Streamlit, Gradio is much simpler if that’s the only goal.

Nachteile von Streamlit:

• Manual message handling required: Each message must be saved and loaded.

• No ready-made chat UI: You have to design where each element appears yourself.

Community & Ecosystem – Who Offers More Support?

A technology can be great – but without a community to help, you can quickly feel left alone. Both Gradio and Streamlit have active communities, but the differences are noticeable.

👑 Streamlit has a large and established community. No wonder – it has been on the market longer, has more tutorials, an official community forum, and direct integration with Streamlit Cloud, where you can deploy apps with one click. You can find a solution for almost any problem, and most data science teams have tried Streamlit at least once.

🖥️ Gradio is growing rapidly – especially in the field of machine learning. Since Hugging Face acquired Gradio, the ecosystem has been booming. Particularly in the ML and AI scene, Gradio is becoming more and more of a standard. Many large open-source models (Stable Diffusion, BLOOM, LLaMA) use Gradio as an interface, which means more and more developers are adopting it.

In short: When to use which tool?

Feature	👑 Streamlit	🖥️ Gradio
Data Visualization	Perfect for dashboards	Supported, but less focused
Interactive Widgets	Many options (st.slider(), st.button())	Optimized for ML inputs
Machine Learning	Manual Integration	Optimized for ML out of the box
Use Case	Dashboards, business apps	AI demos, image & text models

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