Parveenshakya969/My_Rent_Predictor

app.py

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import gradio as gr import joblib import spaces

We load the model once when the app starts

deployed_lr = joblib.load('my_first_ml_model.pkl')

--- ZERO-GPU DECORATOR AND PREDICTION LOGIC ---

@spaces.GPU def predict_rent(size_of_prop): # The model expects a 2D array: [[size]] prediction = deployed_lr.predict([[size_of_prop]]) # Extract the single prediction value and format it return f"Estimated Rent: {prediction[0]:.2f}"

Create the web interface

interface = gr.Interface( fn=predict_rent, inputs=gr.Number(label="Please Enter the Size of Your Property for rent"), outputs=gr.Text(label="Predicted Rent"), title="Property Rent Predictor", description="Enter the property size to get a rent estimate powered by Machine Learning." )

if name == "main": interface.launch()

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