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app.py

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+# -*- coding: utf-8 -*-
+"""gradio.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1Kn9JCj7NyEmbQZD_QTrBwgMiUd42MH-J
+"""
+
+import gradio as gr
+print("Gradio version:", gr.__version__)
+
+import gradio as gr
+
+def add_numbers(a, b):
+    return a + b
+
+demo = gr.Interface(
+    fn=add_numbers,
+    inputs=["number", "number"],
+    outputs="number")
+demo.launch()
+
+import gradio as gr
+
+def simple_app(name, photo, age, language):
+    greeting = f"Hello {name}!"
+    info = f"You are {age} years old and prefer {language}."
+    return greeting, photo, info
+
+demo = gr.Interface(
+    fn=simple_app,
+    inputs=[
+        gr.Textbox(label="Enter your name"),
+        gr.Image(label="Upload your photo"),
+        gr.Slider(0, 100, label="Your age"),
+        gr.Dropdown(["Python", "JavaScript", "C++"], label="Favorite language"), ],
+    outputs=[
+        gr.Textbox(label="Greeting"),
+        gr.Image(label="Your Photo"),
+        gr.Textbox(label="Your Info") ],
+)
+demo.launch()
+
+import gradio as gr
+
+def greet(name):
+    return f"Hello, {name}!"
+
+with gr.Blocks() as demo:
+    name = gr.Textbox(label="Enter your name")
+    button = gr.Button("Greet")
+    output = gr.Textbox(label="Greeting")
+    button.click(fn=greet, inputs=name, outputs=output)
+
+demo.launch()
+
+import gradio as gr
+
+def age_alert(age):
+    return "You are", age,"years old!"
+
+with gr.Blocks() as demo:
+    age_input = gr.Slider(0, 100, label="Select your age")
+    output = gr.Textbox(label="Age Info")
+    age_input.change(fn=age_alert, inputs=age_input, outputs=output)
+
+demo.launch()
+
+import gradio as gr
+import requests
+
+def get_joke():
+    response = requests.get("https://official-joke-api.appspot.com/random_joke")
+    if response.status_code == 200:
+        joke = response.json()
+        return f"{joke['setup']} {joke['punchline']}"
+    else:
+        return "Oops! Could not fetch a joke right now."
+
+with gr.Blocks() as demo:
+    btn = gr.Button("Tell me a joke!")
+    output = gr.Textbox(label="Here's your joke")
+    btn.click(fn=get_joke, inputs=None, outputs=output)
+
+demo.launch()
+
+"""Sentiment Analyzer UI"""
+
+import gradio as gr
+from transformers import pipeline
+
+sentiment_pipeline = pipeline("sentiment-analysis", model="nlptown/bert-base-multilingual-uncased-sentiment")
+
+def analyze_sentiment(text):
+    result = sentiment_pipeline(text)[0]
+    label = result['label']
+    score = result['score']
+    return f"Sentiment: {label}\nConfidence: {score:.2f}"
+
+gr.Interface(
+    fn=analyze_sentiment,
+    inputs=gr.Textbox(lines=3, placeholder="Type your sentence here..", label="Input Text"),
+    outputs=gr.Textbox(label="Sentiment Result"),
+    title="Sentiment Analyzer",
+    description="Enter a sentence and find out its sentiment based on score."
+).launch()
+
+"""Creating Interfaces For Classic ML Models
+
+Setup the Project
+"""
+
+pip install scikit-learn gradio pandas
+
+"""Train a Simple Model"""
+
+# Load dataset
+from sklearn.datasets import fetch_california_housing
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.pipeline import make_pipeline
+from sklearn.preprocessing import StandardScaler
+from sklearn.ensemble import RandomForestRegressor
+
+data = fetch_california_housing(as_frame=True)
+df = data.frame
+
+X = df.drop(columns=["MedHouseVal"])
+y = df["MedHouseVal"]
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+model = make_pipeline(StandardScaler(), RandomForestRegressor())
+model.fit(X_train, y_train)
+
+"""Create Prediction Function"""
+
+def predict_house_price(med_inc, house_age, ave_rooms, ave_bedrooms, population,
+                        ave_occup, latitude, longitude):
+    input_data = pd.DataFrame([[ med_inc, house_age, ave_rooms, ave_bedrooms,
+                                 population, ave_occup, latitude, longitude]], columns=X.columns)
+    prediction = model.predict(input_data)[0]
+    return f"Predicted Price: ${prediction * 100000:.2f}"
+
+"""Build the Gradio Interface"""
+
+import gradio as gr
+
+interface = gr.Interface(
+    fn=predict_house_price,
+    inputs=[
+        gr.Slider(0.0, 20.0, label="Median Income (10k$ units)"),
+        gr.Slider(1, 50, label="House Age"),
+        gr.Slider(1, 10, label="Average Rooms"),
+        gr.Slider(1, 5, label="Average Bedrooms"),
+        gr.Slider(100, 50000, label="Population"),
+        gr.Slider(1, 10, label="Average Occupancy"),
+        gr.Slider(32, 42, label="Latitude"),
+        gr.Slider(-125, -114, label="Longitude"),
+    ],
+    outputs="text",
+    title="California House Price Predictor",
+    description="Enter basic housing info to estimate price",
+)
+
+interface.launch(share=True)
+
+
+
+"""Deploying Gradio Apps
+Step 1: Prepare Your Files
+You’ll need 3 files in your project folder:
+
+1. app.py
+
+Save your code in a file called app.py. This is the main app file. Hugging Face Spaces will run this file to launch your Gradio UI.
+
+2. requirements.txt
+
+This file tells Hugging Face what Python libraries are needed to run your app. Create a requirements.txt file with this content:
+
+gradio
+pandas
+scikit-learn
+
+Hugging Face installs these packages in the background. If you miss one, your app might crash!
+
+3. README.md (Optional)
+
+It is a project description for Hugging Face viewers. Add a README.md with a short intro to your app. Example:
+
+California House Price Predictor
+
+This Gradio app predicts the median house price in California based on features like income, age, rooms, etc.
+
+Built using:   - Scikit-learn
+                       - Gradio
+                       - California Housing Dataset
+
+Step 2: Create a Hugging Face Space
+Go to Hugging Face Space and click on “Create new Space”.
+
+Fill the details:
+
+Space name: e.g., california-house-predictor
+Space SDK: Choose Gradio
+Visibility: Public or Private
+Click “Create Space”
+Step 3: Upload Your Files
+You’ll be taken to the Space repo view. Now:
+
+Click “Files”
+Click “Add file” --> Upload files
+Upload: app.py, requirements.txt and README.md (optional)
+Step 4: Wait for Build
+Once files are uploaded:
+
+Hugging Face will automatically install dependencies from requirements.txt
+Then it will run app.py, which launches the Gradio interface
+After a few seconds to minutes, your app will be live in the browser!
+"""
+

requirements.txt

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+gradio 
+pandas 
+scikit-learn