Create app.py

app.py

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+import gradio as gr
+import pandas as pd
+import xgboost as xgb
+import os
+
+os.environ["MPLCONFIGDIR"] = "/tmp"  # Fix for fontconfig error
+
+# Load XGBoost booster from JSON
+booster = xgb.Booster()
+booster.load_model("xgb_model.json")
+
+# Define options
+makes = ['FORD', 'TOYOTA', 'HONDA', 'BMW', 'AUDI']
+transmissions = ['Automatic', 'Manual', 'CVT', 'Auto-Manual', 'Direct Drive']
+fuels = ['Regular Gasoline', 'Premium Gasoline', 'Diesel', 'Electricity', 'E85', 'CNG']
+classes = ['SUV', 'Compact Cars', 'Mid-size Cars', 'Large Cars', 'Pickup Trucks', 'Station Wagons']
+
+# Preprocess input into DMatrix
+def preprocess_input(year, cylinders, displ, make, trany, fuel, vclass):
+    df = pd.DataFrame([{
+        "year": year,
+        "cylinders": cylinders,
+        "displ": displ,
+        "make": make,
+        "trany": trany,
+        "fuelType": fuel,
+        "VClass": vclass
+    }])
+    return xgb.DMatrix(df)
+
+# Predict MPG
+def predict_mpg(year, cylinders, displ, make, trany, fuel, vclass):
+    input_dmatrix = preprocess_input(year, cylinders, displ, make, trany, fuel, vclass)
+    prediction = booster.predict(input_dmatrix)[0]
+    return f"🚗 Predicted Combined MPG: {prediction:.2f} MPG"
+
+# Gradio UI
+with gr.Blocks() as demo:
+    gr.Markdown("## 🚘 Vehicle MPG Predictor")
+    gr.Markdown("Enter vehicle details to predict Combined MPG")
+    with gr.Row():
+        with gr.Column():
+            year = gr.Slider(2000, 2025, value=2020, step=1, label="Year")
+            cylinders = gr.Slider(2, 16, value=4, step=1, label="Cylinders")
+            displ = gr.Slider(1.0, 8.0, value=2.0, step=0.1, label="Engine Displacement (L)")
+        with gr.Column():
+            make = gr.Dropdown(makes, label="Make")
+            trany = gr.Dropdown(transmissions, label="Transmission")
+            fuel = gr.Dropdown(fuels, label="Fuel Type")
+            vclass = gr.Dropdown(classes, label="Vehicle Class")
+    result = gr.Textbox(label="Prediction Result")
+    predict_btn = gr.Button("🚀 Predict MPG")
+    predict_btn.click(fn=predict_mpg, inputs=[year, cylinders, displ, make, trany, fuel, vclass], outputs=result)
+
+demo.launch()