Create app.py

app.py

@@ -0,0 +1,64 @@
+import pandas as pd
+import joblib
+import gradio as gr
+
+# Load the model
+model = joblib.load('forest_model.joblib')
+
+def predict(elevation, horizontal_distance_to_roadways, horizontal_distance_to_fire_points, 
+            horizontal_distance_to_hydrology, vertical_distance_to_hydrology):
+    
+    # Create a DataFrame with all features (initialized to 0)
+    input_data = pd.DataFrame(0, index=[0], columns=model.feature_names_in_)
+    
+    # Set the values for our important features
+    feature_values = {
+        'Elevation': elevation,
+        'Horizontal_Distance_To_Roadways': horizontal_distance_to_roadways,
+        'Horizontal_Distance_To_Fire_Points': horizontal_distance_to_fire_points,
+        'Horizontal_Distance_To_Hydrology': horizontal_distance_to_hydrology,
+        'Vertical_Distance_To_Hydrology': vertical_distance_to_hydrology
+    }
+    
+    for feature, value in feature_values.items():
+        input_data[feature] = value
+    
+    # Make prediction
+    prediction = model.predict(input_data)[0]
+    
+    forest_types = {
+        1: "Spruce/Fir",
+        2: "Lodgepole Pine",
+        3: "Ponderosa Pine",
+        4: "Cottonwood/Willow",
+        5: "Aspen",
+        6: "Douglas-fir",
+        7: "Krummholz"
+    }
+    
+    return forest_types[prediction]
+
+# Create Gradio interface
+inputs = [
+    gr.Number(label="Elevation (meters)", minimum=1800, maximum=4000),
+    gr.Number(label="Distance to Roadways (meters)", minimum=0, maximum=8000),
+    gr.Number(label="Distance to Fire Points (meters)", minimum=0, maximum=8000),
+    gr.Number(label="Distance to Hydrology (meters)", minimum=0, maximum=1000),
+    gr.Number(label="Vertical Distance to Hydrology (meters)", minimum=-500, maximum=500)
+]
+
+output = gr.Text(label="Predicted Forest Cover Type")
+
+interface = gr.Interface(
+    fn=predict,
+    inputs=inputs,
+    outputs=output,
+    title="Forest Cover Type Prediction",
+    description="Predict forest cover type using the most important environmental features.",
+    examples=[
+        [2596, 510, 6279, 258, 0]  # Sample values for the top 5 features
+    ]
+)
+
+if __name__ == "__main__":
+    interface.launch()