Gradio ML Showcase

🎨 Gradio Orange 💙 Tech Blue 🌲 Forest Green 🌅 Sunset Red 🌊 Ocean Blue ⚫ Monochrome ☀️ Light Mode

Gradio ML Showcase Platform

Master the art of creating interactive machine learning demos with comprehensive tools, live examples, and deployment guides

Gradio Fundamentals: From Zero to Hero

What is Gradio?

Gradio is a Python library that transforms any function into an interactive web interface in just a few lines of code. Perfect for machine learning demos, data science prototypes, and sharing your models with the world.

Core Concepts

3

Lines of Code

∞

Possibilities

0

Frontend Skills

Key Benefits:

• Instant web interfaces for any Python function
• Share your work with a single public link
• No frontend development experience required
• Automatic mobile responsiveness
• Built-in authentication and analytics

Quick Start: Your First Gradio Interface

Live Python Code Editor

import gradio as gr def greet(name): return f"Hello {name}! Welcome to Gradio!" # Create interface demo = gr.Interface( fn=greet, inputs=gr.Textbox(label="Your Name"), outputs=gr.Textbox(label="Greeting"), title="My First Gradio App" ) # Launch the interface demo.launch()

Live Demo Result:

My First Gradio App

Enter your name and see the magic!

gr.Interface

The simplest way to create a Gradio app. Perfect for single-function demos.

gr.Interface(
  fn=my_function,
  inputs=...,
  outputs=...
)

gr.Blocks

Advanced layout control with custom designs, multiple functions, and complex interactions.

with gr.Blocks() as demo:
  # Custom layout
  btn.click(fn, inputs, outputs)

Deployment

Share instantly with public links or deploy to production with Docker, HuggingFace Spaces, and more.

demo.launch(
  share=True
)

Gradio Components Library

Interactive Component Explorer

Explore Gradio's extensive collection of input and output components. Click on any component to see code examples and try them live.

📝 Textbox

Single or multiline text input

gr.Textbox(
    label="Input Text",
    placeholder="Enter text...",
    lines=1
)

🔢 Number

Numerical input with validation

gr.Number(
    label="Input Number",
    minimum=0,
    maximum=100
)

🎚️ Slider

Range selector with visual feedback

gr.Slider(
    minimum=0,
    maximum=100,
    value=50,
    step=1
)

📋 Dropdown

Selection from predefined options

Option 1 Option 2 Option 3

gr.Dropdown(
    choices=["Option 1", "Option 2"],
    label="Select Option",
    value="Option 1"
)

☑️ Checkbox

Boolean input for yes/no questions

Check me

gr.Checkbox(
    label="Enable feature",
    value=False
)

📻 Radio

Single choice from multiple options

A B

gr.Radio(
    choices=["A", "B", "C"],
    label="Choose one"
)

📁 File

File upload with type filtering

gr.File(
    label="Upload file",
    file_types=[".pdf", ".txt"]
)

🖼️ Image

Image input with editing tools

Drop image here

gr.Image(
    label="Upload Image",
    type="pil"
)

🎵 Audio

Audio input with recording capability

🎤 Record

gr.Audio(
    label="Record Audio",
    type="numpy"
)

🎬 Video

Video input and playback

▶️

gr.Video(
    label="Upload Video"
)

📊 Dataframe

Interactive data table editor

A	B
1	2

gr.Dataframe(
    headers=["A", "B"],
    datatype=["number", "str"]
)

📈 Plot

Interactive charts and graphs

📈

gr.Plot(
    label="Data Visualization"
)

Component Code Generator

Click on any component above to see detailed code examples and usage patterns.

Visual Interface Builder

Design Your Interface

Interface Type: Simple Interface Advanced Blocks Chat Interface

Input Component: Textbox Number Slider Image Audio File

Output Component: Textbox Image Plot Dataframe Audio Video

App Title:

Description:

Generated Code

Python Code

import gradio as gr def predict(input_value): # Your processing function here return f"Processed: {input_value}" demo = gr.Interface( fn=predict, inputs=gr.Textbox(label="Input"), outputs=gr.Textbox(label="Output"), title="My Awesome Gradio App", description="Describe what your app does..." ) demo.launch()

Live Preview

My Awesome Gradio App

Describe what your app does...

Input

Output

Results will appear here...

Real-World Gradio Examples

📝 Text Generation Demo

Simulate a text generation model with customizable parameters

Prompt: Once upon a time, in a land far away

Temperature: 0.7

Max Length: 100

Generated Text:

Generating...

Click 'Generate Text' to see results

Implementation Code

import gradio as gr import random import time def generate_text(prompt, temperature, max_length): # Simulate text generation words = ["adventure", "mystery", "magical", "wonderful", "exciting", "journey", "discovery", "ancient", "hidden", "treasure"] result = prompt + " " for _ in range(int(max_length / 10)): if random.random() < temperature: result += random.choice(words) + " " else: result += "continuing " time.sleep(0.1) # Simulate processing time yield result demo = gr.Interface( fn=generate_text, inputs=[ gr.Textbox(label="Prompt", lines=3), gr.Slider(0, 1, value=0.7, label="Temperature"), gr.Slider(10, 500, value=100, label="Max Length") ], outputs=gr.Textbox(label="Generated Text", lines=8), title="Text Generation Demo", description="AI-powered text generation with customizable parameters" ) demo.launch()

🖼️ Image Processing Studio

Apply various filters and effects to images

Upload Image:

📸

Click or drag image here

Filter: None Blur Sharpen Vintage Black & White

Processed Result:

Processed image will appear here

Implementation Code

import gradio as gr from PIL import Image, ImageFilter, ImageOps import numpy as np def process_image(image, filter_type): if image is None: return None # Apply different filters if filter_type == "blur": return image.filter(ImageFilter.GaussianBlur(radius=2)) elif filter_type == "sharpen": return image.filter(ImageFilter.SHARPEN) elif filter_type == "vintage": # Apply vintage effect img_array = np.array(image) img_array[:,:,0] = np.clip(img_array[:,:,0] * 1.2, 0, 255) # Enhance red img_array[:,:,2] = np.clip(img_array[:,:,2] * 0.8, 0, 255) # Reduce blue return Image.fromarray(img_array.astype(np.uint8)) elif filter_type == "black-white": return ImageOps.grayscale(image) else: return image demo = gr.Interface( fn=process_image, inputs=[ gr.Image(label="Upload Image", type="pil"), gr.Dropdown( choices=["none", "blur", "sharpen", "vintage", "black-white"], value="none", label="Filter Type" ) ], outputs=gr.Image(label="Processed Image", type="pil"), title="Image Processing Studio", description="Apply various filters and effects to your images" ) demo.launch()

💬 AI Chat Assistant

Interactive chatbot with conversation memory

Assistant: Hello! I'm your AI assistant. How can I help you today?

Implementation Code

import gradio as gr import random def chatbot_response(message, history): # Simple rule-based responses responses = { "hello": "Hi there! How can I help you?", "how are you": "I'm doing well, thank you for asking!", "what can you do": "I can help answer questions and have conversations.", "bye": "Goodbye! Have a great day!", "default": [ "That's interesting! Tell me more.", "I see what you mean.", "Can you elaborate on that?", "That's a good point!", "I understand your perspective." ] } msg_lower = message.lower() for key in responses: if key in msg_lower and key != "default": return responses[key] return random.choice(responses["default"]) # Using ChatInterface for conversation flow demo = gr.ChatInterface( fn=chatbot_response, title="AI Chat Assistant", description="Chat with an AI assistant that remembers context", examples=[ "Hello, how are you?", "What can you help me with?", "Tell me a joke" ] ) demo.launch()

Deployment Strategies & Best Practices

🏠 Local Development

Start developing and testing your Gradio apps locally

# Install Gradio
pip install gradio

# Run your app
python app.py

⚡

Fast Setup

🔄

Hot Reload

🌐 Public Sharing

Share your app instantly with a public link

demo.launch(
  share=True
)

72h

Link Duration

🔗

Public URL

🤗 HuggingFace Spaces

Deploy permanently on HuggingFace's free hosting platform

# requirements.txt
gradio==4.0.0
torch
transformers

# app.py
import gradio as gr
demo.launch()

FREE

Cost

∞

Uptime

🚀 Deployment Simulator

Simulate different deployment scenarios and see the generated configurations

Deployment Target: Local Development Public Share Link HuggingFace Spaces Docker Container Cloud Platform

Enable Authentication

Enable Analytics

Enable Request Queue

Generated Configuration:

Select deployment options and click Generate Configuration

⚡ Performance Tips

Memory Management:

• Use generators for streaming responses
• Clear GPU memory after inference
• Implement model caching strategies

Queue Configuration:

• Set appropriate max_size for concurrent users
• Use default_concurrency_limit
• Monitor queue status in real-time

demo.queue(
  max_size=50,
  default_concurrency_limit=2
).launch()

🔒 Security Best Practices

Input Validation:

• Sanitize all user inputs
• Set file size and type limits
• Implement rate limiting

Authentication:

• Use built-in authentication
• Integrate with OAuth providers
• Set up HTTPS for production

demo.launch(
  auth=[("admin", "password")],
  ssl_verify=False
)

Advanced Gradio Techniques

🎨 Custom Themes & Styling

Create beautiful, branded interfaces with custom CSS and themes

Theme Base: Default Soft Monochrome Glass

Primary Color:

Secondary Color:

Generated theme code will appear here

🔌 API Integration Patterns

Connect your Gradio apps with external APIs and services

API Type: OpenAI API HuggingFace API REST API GraphQL

Select an API type and generate integration code

🔄 Real-time Data Streaming

Implement streaming responses and real-time updates

Streaming Demo:

Click 'Start Streaming' to see real-time text generation

Streaming Implementation:

def stream_response(prompt):
  words = prompt.split() + ["generating", "more", "content"]
  result = ""
  
  for word in words:
    result += word + " "
    time.sleep(0.1)
    yield result

demo = gr.Interface(
  fn=stream_response,
  inputs=gr.Textbox(),
  outputs=gr.Textbox(),
  live=True
)

💾 State Management

Manage persistent state across user interactions

with gr.Blocks() as demo:
  state = gr.State(value=0)
  
  def update_counter(current):
    return current + 1
  
  btn.click(
    update_counter,
    state,
    state
  )

🧩 Custom Components

Build reusable custom components

class CustomComponent:
  def __init__(self, **kwargs):
    super().__init__(**kwargs)
  
  def preprocess(self, x):
    return x
  
  def postprocess(self, x):
    return x

📊 Analytics & Monitoring

Track usage and monitor app performance

demo.launch(
  analytics_enabled=True,
  enable_queue=True,
  show_api=True
)

🚀 Performance Benchmarking Tool

Test and optimize your Gradio app's performance

Concurrent Users: 10

Request Duration: 30s

0

Requests/sec

0ms

Avg Latency

100%

Success Rate