Create app.py

app.py

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+import gradio as gr
+import pandas as pd
+from huggingface_hub import hf_hub_download
+import joblib
+
+# Load the model
+repo_id = "rmaitest/mlmodel2"
+model_file = "house_price_model.pkl"  # Adjust as necessary
+
+# Download and load the model
+model_path = hf_hub_download(repo_id, model_file)
+model = joblib.load(model_path)
+
+def predict_price(size, bedrooms, age):
+    # Create a DataFrame from the input
+    input_data = pd.DataFrame({
+        'Size (sq ft)': [size],
+        'Number of Bedrooms': [bedrooms],
+        'Age of House (years)': [age]
+    })
+    
+    # Make prediction
+    prediction = model.predict(input_data)
+    return prediction[0]
+
+# Define the Gradio interface
+iface = gr.Interface(
+    fn=predict_price,
+    inputs=[
+        gr.inputs.Number(label="Size (sq ft)"),
+        gr.inputs.Number(label="Number of Bedrooms"),
+        gr.inputs.Number(label="Age of House (years)")
+    ],
+    outputs=gr.outputs.Number(label="Predicted Price ($)"),
+    title="House Price Prediction",
+    description="Enter the size, number of bedrooms, and age of the house to get the predicted price."
+)
+
+# Launch the interface
+if __name__ == "__main__":
+    iface.launch()