Create app.py

app.py

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+import numpy as np
+import joblib
+!pip install gradio
+import gradio as gr
+from huggingface_hub import hf_hub_download
+
+# Download the model file if it doesn't exist locally
+model_filename = "knn_house_model.pkl"
+try:
+    model_path = hf_hub_download(repo_id="Tahani1/Houses-Prices-Prediction", filename=model_filename)
+except Exception as e:
+    print(f"Error downloading '{model_filename}' from Hugging Face Hub: {e}")
+    raise  # Re-raise the exception to stop execution
+
+# Load the trained model and preprocessing tools
+model = joblib.load(model_path) # Load the model from the downloaded path
+scaler = joblib.load(hf_hub_download(repo_id="Tahani1/Houses-Prices-Prediction", filename="scaler.pkl"))
+label_encoder = joblib.load(hf_hub_download(repo_id="Tahani1/Houses-Prices-Prediction", filename="label_encoder.pkl"))
+
+# Function to predict house price
+def predict_price(num_rooms, distance, country, build_quality):
+    country_encoded = label_encoder.transform([country])[0]
+    features = np.array([[num_rooms, distance, country_encoded, build_quality]])
+    features_scaled = scaler.transform(features)
+    predicted_price = model.predict(features_scaled)[0]
+    return f"Predicted House Price: ${predicted_price:,.2f}"
+
+# Gradio Interface
+inputs = [
+    gr.Number(label="Number of Rooms"),
+    gr.Number(label="Distance to Center (km)"),
+    gr.Dropdown(label="Country", choices=label_encoder.classes_.tolist()),
+    gr.Slider(minimum=1, maximum=10, label="Build Quality")
+]
+
+outputs = gr.Textbox(label="Prediction Result")
+
+# Create and launch Gradio app
+app = gr.Interface(fn=predict_price, inputs=inputs, outputs=outputs, title="House Price Prediction")
+app.launch()