Create app.py

app.py

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+import gradio as gr
+import pandas as pd
+import joblib
+from huggingface_hub import hf_hub_download
+
+# Download the model from Hugging Face hub
+model_filename = hf_hub_download(repo_id="poudel/fuel-burn-predictor", filename="fuel_burn_model.pkl")
+
+# Load the model
+model = joblib.load(model_filename)
+
+# Define the prediction function
+def predict_fuel_burn_kg(truck_id, kms, litros):
+    # Map truck ID input to match the expected format
+    truck_ids = {'Truck_ID_MTP3482': 0, 'Truck_ID_MTP5052': 1, 'Truck_ID_MTP5126': 2}
+    truck_id_num = truck_ids.get(truck_id, -1)  # Convert Truck ID to numerical representation
+    
+    # Create a dataframe with the input data
+    input_data = pd.DataFrame({
+        'Truck_ID': [truck_id_num],
+        'Kms': [kms],
+        'Litros': [litros]
+    })
+    
+    # Predict fuel burn in liters
+    prediction_litros = model.predict(input_data)
+    
+    # Convert liters to kilograms (using diesel density of 0.835 kg/liter)
+    density_kg_per_liter = 0.835
+    prediction_kg = prediction_litros[0] * density_kg_per_liter
+    
+    return round(prediction_kg, 2)
+
+# Create the Gradio interface
+app = gr.Interface(
+    fn=predict_fuel_burn_kg,
+    inputs=[
+        gr.Dropdown(['Truck_ID_MTP3482', 'Truck_ID_MTP5052', 'Truck_ID_MTP5126'], label="Truck ID"),
+        gr.Number(label="Kilometers Driven"),
+        gr.Number(label="Fuel Consumed (Liters)")
+    ],
+    outputs=gr.Number(label="Predicted Fuel Burn (kg)"),
+    title="Truck Fuel Burn Predictor",
+    description="Enter the truck ID, kilometers driven, and fuel consumed in liters to predict fuel burn in kilograms."
+)
+
+# Launch the Gradio app
+app.launch()