app.py

import gradio as gr
import pandas as pd
import numpy as np
from sklearn.linear_model import LinearRegression

# Load dataset (online use kar rahe hain taake HF pe chale)
url = "https://raw.githubusercontent.com/ageron/handson-ml/master/datasets/housing/housing.csv"
df = pd.read_csv(url)

# Select features
df = df[['total_rooms', 'households', 'population', 'total_bedrooms']].dropna()

# Split data
X = df[['total_rooms', 'households', 'population']]
y = df['total_bedrooms']

# Train model
model = LinearRegression()
model.fit(X, y)

# Prediction function
def predict(total_rooms, households, population):
    data = np.array([[total_rooms, households, population]])
    prediction = model.predict(data)[0]
    return float(prediction)

# Gradio UI
interface = gr.Interface(
    fn=predict,
    inputs=[
        gr.Number(label="Total Rooms"),
        gr.Number(label="Households"),
        gr.Number(label="Population")
    ],
    outputs=gr.Number(label="Predicted Bedrooms"),
    title="Housing Bedrooms Prediction",
    description="Predict total bedrooms using linear regression"
)