Create app.py

app.py

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+import gradio as gr
+import pandas as pd
+from sklearn.linear_model import LinearRegression
+
+# Sample data
+data = pd.DataFrame({
+    "sqft": [1500, 1800, 2400, 3000, 3500],
+    "bedrooms": [3, 4, 3, 5, 4],
+    "bathrooms": [2, 2, 3, 4, 3],
+    "price": [300000, 350000, 400000, 500000, 450000]
+})
+
+# Train model
+X = data[["sqft", "bedrooms", "bathrooms"]]
+y = data["price"]
+model = LinearRegression().fit(X, y)
+
+# Prediction function
+def predict_price(sqft, bedrooms, bathrooms):
+    try:
+        input_df = pd.DataFrame([[sqft, bedrooms, bathrooms]], columns=X.columns)
+        pred = model.predict(input_df)[0]
+        return f"🏠 Estimated Price: ${pred:,.0f}"
+    except Exception as e:
+        return f"⚠️ Error: {e}"
+
+# Gradio UI
+demo = gr.Interface(
+    fn=predict_price,
+    inputs=[
+        gr.Slider(500, 5000, value=2000, label="Square Footage"),
+        gr.Slider(1, 6, value=3, label="Bedrooms"),
+        gr.Slider(1, 5, value=2, label="Bathrooms")
+    ],
+    outputs=gr.Textbox(label="Prediction"),
+    title="🏡 House Price Predictor"
+)
+
+demo.launch()