Upload Mars pressure prediction model and documentation

app.py

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+import gradio as gr
+import joblib
+import numpy as np
+import pandas as pd
+
+# Load the model and scaler
+model_info = joblib.load('model/mars_pressure_model.joblib')
+model = model_info['model']
+scaler = model_info['scaler']
+feature_columns = model_info['feature_columns']
+
+def predict_pressure(max_ground_temp, min_ground_temp, max_air_temp, min_air_temp, month):
+    # Create a feature dictionary
+    features = {
+        'max_ground_temp(°C)': float(max_ground_temp),
+        'min_ground_temp(°C)': float(min_ground_temp),
+        'max_air_temp(°C)': float(max_air_temp),
+        'min_air_temp(°C)': float(min_air_temp),
+        'earth_month': float(month),
+        'earth_day': 15  # Use middle of month as default
+    }
+    
+    # Add season features
+    season = (month % 12) // 3
+    seasons = ['Winter', 'Spring', 'Summer', 'Fall']
+    for s in seasons:
+        features[f'season_{s}'] = 1.0 if seasons[season] == s else 0.0
+    
+    # Create DataFrame with correct column order
+    X = pd.DataFrame([features])[feature_columns]
+    
+    # Scale features
+    X_scaled = scaler.transform(X)
+    
+    # Make prediction
+    prediction = model.predict(X_scaled)[0]
+    
+    # Calculate uncertainty
+    uncertainty = model_info['metrics']['prediction_std']
+    
+    return (
+        f"{prediction:.1f} Pa",
+        f"({prediction-uncertainty:.1f} to {prediction+uncertainty:.1f} Pa)",
+        f"±{uncertainty:.1f} Pa"
+    )
+
+# Create the interface
+iface = gr.Interface(
+    fn=predict_pressure,
+    inputs=[
+        gr.Slider(-70, 20, value=-13, label="Maximum Ground Temperature (°C)"),
+        gr.Slider(-100, -50, value=-75, label="Minimum Ground Temperature (°C)"),
+        gr.Slider(-60, 30, value=2, label="Maximum Air Temperature (°C)"),
+        gr.Slider(-140, -5, value=-80, label="Minimum Air Temperature (°C)"),
+        gr.Slider(1, 12, step=1, value=6, label="Month (1-12)")
+    ],
+    outputs=[
+        gr.Label(label="Predicted Atmospheric Pressure"),
+        gr.Label(label="Prediction Range"),
+        gr.Label(label="Uncertainty")
+    ],
+    title="Mars Atmospheric Pressure Predictor",
+    description="""
+    Predict atmospheric pressure on Mars based on environmental measurements.
+    Default values are set to typical conditions observed in the dataset.
+    The model has an R² score of 0.397 and provides uncertainty estimates.
+    """,
+    examples=[
+        [-13, -75, 2, -80, 6],  # Average conditions
+        [11, -52, 24, -8, 3],   # Warmest conditions
+        [-67, -100, -61, -136, 12]  # Coldest conditions
+    ]
+)
+
+# Launch the app with specific configurations for HuggingFace Spaces
+if __name__ == "__main__":
+    iface.launch(
+        server_name="0.0.0.0",
+        server_port=7860,  # Default port for HuggingFace Spaces
+        share=False,
+        show_error=True
+    )

model/mars_pressure_model.joblib

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-size 1196118
+oid sha256:6008323411642a4e5e47ccd3548f08724bebc0c0ef562b96937900077f2fc111
+size 6899580

requirements.txt

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+gradio==3.41.0
+numpy==1.24.1
+pandas==1.5.3
+scikit-learn==1.2.1
+joblib==1.2.0
+typing-extensions==4.5.0
+python-multipart==0.0.6