Gradio: Create Interactive ML Demos That Anyone Can Use in 5 Lines

import gradio as gr
from transformers import pipeline

classifier = pipeline("sentiment-analysis")

def analyze(text: str) -> dict:
    result = classifier(text)[0]
    return {"label": result["label"], "confidence": round(result["score"], 3)}

demo = gr.Interface(
    fn=analyze,
    inputs=gr.Textbox(label="Input text", placeholder="Type something..."),
    outputs=gr.JSON(label="Result"),
    title="Sentiment Analyzer",
    examples=["I love this product!", "This is terrible."],
)

demo.launch()

import gradio as gr

with gr.Blocks() as demo:
    gr.Markdown("# Image Classifier")

    with gr.Row():
        image_input = gr.Image(type="pil", label="Upload image")
        output_label = gr.Label(num_top_classes=5, label="Predictions")

    with gr.Row():
        classify_btn = gr.Button("Classify", variant="primary")
        clear_btn = gr.ClearButton([image_input, output_label])

    classify_btn.click(fn=classify_image, inputs=image_input, outputs=output_label)

demo.launch()

import gradio as gr
from anthropic import Anthropic

client = Anthropic()

def chat(message: str, history: list) -> str:
    messages = [{"role": h["role"], "content": h["content"]} for h in history]
    messages.append({"role": "user", "content": message})

    response = client.messages.create(
        model="claude-3-haiku-20240307",
        max_tokens=1024,
        messages=messages,
    )
    return response.content[0].text

demo = gr.ChatInterface(fn=chat, title="Claude Chat", type="messages")
demo.launch()

# Call your Gradio app's API
curl -X POST http://localhost:7860/api/predict   -H "Content-Type: application/json"   -d '{"data": ["Hello world"]}'

from gradio_client import Client

client = Client("https://your-app.hf.space")
result = client.predict("Hello world", api_name="/predict")

# Temporary public URL (72 hours)
demo.launch(share=True)

# Deploy to Hugging Face Spaces (permanent, free)
# 1. Create a Space at huggingface.co/spaces
# 2. Push your app.py + requirements.txt to the Space repo
# 3. HF Spaces builds and hosts it automatically