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import pandas as pd
import numpy as np
from sklearn.ensemble import RandomForestClassifier
import gradio as gr
def load_and_train_model():
# Load data
train_data = pd.read_csv('train.csv.zip')
# Select important features
important_features = ['Elevation', 'Horizontal_Distance_To_Roadways',
'Horizontal_Distance_To_Fire_Points',
'Horizontal_Distance_To_Hydrology',
'Vertical_Distance_To_Hydrology']
X = train_data[important_features]
y = train_data['Cover_Type']
# Train model
model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(X, y)
return model
def predict(elevation, distance_to_roads, distance_to_fire, distance_to_water, vertical_distance):
input_data = np.array([[
elevation,
distance_to_roads,
distance_to_fire,
distance_to_water,
vertical_distance
]])
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"
}
confidence = max(model.predict_proba(input_data)[0]) * 100
return f"{forest_types[prediction]} (Confidence: {confidence:.1f}%)"
# Train model at startup
print("Loading and training model...")
model = load_and_train_model()
print("Model training completed!")
# Create Gradio interface with improved inputs
inputs = [
gr.Slider(
minimum=1800, maximum=4000, step=10,
label="Elevation (meters above sea level)",
info="Height of the location above sea level",
value=2500
),
gr.Slider(
minimum=0, maximum=8000, step=50,
label="Distance to Nearest Road (meters)",
info="How far is the nearest road?",
value=500
),
gr.Slider(
minimum=0, maximum=8000, step=50,
label="Distance to Fire Points (meters)",
info="Distance to nearest fire ignition points",
value=1000
),
gr.Slider(
minimum=0, maximum=1000, step=10,
label="Distance to Water (meters)",
info="Distance to nearest water source",
value=200
),
gr.Slider(
minimum=-500, maximum=500, step=10,
label="Vertical Distance to Water (meters)",
info="Vertical distance to nearest water source (negative means below)",
value=0
)
]
output = gr.Text(
label="Predicted Forest Type",
info="Prediction with confidence level"
)
interface = gr.Interface(
fn=predict,
inputs=inputs,
outputs=output,
title="🌲 Forest Cover Type Predictor",
description="""
Predict the type of forest cover based on environmental features.
Just adjust the sliders to match your location's characteristics!
""",
examples=[
[2596, 510, 6279, 258, 0],
[2300, 300, 1500, 100, -50],
[3500, 1000, 2000, 500, 200]
],
theme="soft"
)
if __name__ == "__main__":
interface.launch() |