Create app.py

app.py

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+import gradio as gr
+import pandas as pd
+import joblib
+
+# Load your Random Forest model
+loaded_model = joblib.load('random_forest_model.pkl')
+
+# Function to make predictions
+def predict(host_id, neighbourhood_group, neighbourhood, room_type, latitude, longitude, number_of_reviews, calculated_host_listings_count):
+    # Prepare input data as DataFrame
+    input_data = pd.DataFrame({
+        'host_id': [host_id],
+        'neighbourhood_group': [neighbourhood_group],
+        'neighbourhood': [neighbourhood],
+        'room_type': [room_type],
+        'latitude': [latitude],
+        'longitude': [longitude],
+        'number_of_reviews': [number_of_reviews],
+        'calculated_host_listings_count': [calculated_host_listings_count]
+    })
+
+    # One-hot encode the categorical features
+    input_data = pd.get_dummies(input_data, columns=['room_type', 'neighbourhood_group', 'neighbourhood'], drop_first=True)
+    
+    # Ensure the input data has the same columns as the training data
+    input_data = input_data.reindex(columns=X.columns, fill_value=0)
+
+    # Make the prediction
+    predicted_price = loaded_model.predict(input_data)
+    return predicted_price[0]
+
+# Create a Gradio interface
+iface = gr.Interface(
+    fn=predict,
+    inputs=[
+        gr.Number(label="Host ID"),
+        gr.Dropdown(["Manhattan", "Brooklyn", "Queens", "Bronx", "Staten Island"], label="Neighbourhood Group"),
+        gr.Dropdown(["Upper East Side", "Chelsea", "Williamsburg"], label="Neighbourhood"),
+        gr.Dropdown(["Entire home/apt", "Private room", "Shared room"], label="Room Type"),
+        gr.Number(label="Latitude"),
+        gr.Number(label="Longitude"),
+        gr.Number(label="Number of Reviews"),
+        gr.Number(label="Calculated Host Listings Count")
+    ],
+    outputs="number",
+    title="NYC Rental Price Prediction",
+    description="Predict the rental price of an Airbnb listing in NYC."
+)
+
+# Launch the interface
+iface.launch()