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app.py

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+import gradio as gr
+import numpy as np
+import joblib
+
+# =========================
+# Load model
+# =========================
+model = joblib.load("model.joblib")
+
+# =========================
+# Prediction function
+# =========================
+def predict_ev(year, make, model_code, ev_type, cafv, utility):
+    X = np.array([[year, make, model_code, ev_type, cafv, utility]])
+    prediction = model.predict(X)
+    return f"🔍 Electric Range dự đoán: {prediction[0]:.2f} km"
+
+# =========================
+# Gradio Interface
+# =========================
+with gr.Blocks(title="EV Performance Prediction") as demo:
+    gr.Markdown(
+        """
+        # 🔋 Electric Vehicle Performance Prediction
+        **Dự báo hiệu suất kỹ thuật xe điện (Electric Range)**
+        ---
+        """
+    )
+
+    gr.Markdown("### 📥 Nhập thông tin xe")
+
+    with gr.Row():
+        year = gr.Number(label="Model Year", value=2020)
+        make = gr.Number(label="Make (encoded)", value=10)
+        model_code = gr.Number(label="Model (encoded)", value=20)
+
+    with gr.Row():
+        ev_type = gr.Number(label="EV Type (encoded)", value=1)
+        cafv = gr.Number(label="CAFV Eligibility (encoded)", value=0)
+        utility = gr.Number(label="Electric Utility (encoded)", value=60)
+
+    predict_btn = gr.Button("🚀 Dự báo hiệu suất")
+    output = gr.Textbox(label="Kết quả dự báo")
+
+    predict_btn.click(
+        fn=predict_ev,
+        inputs=[year, make, model_code, ev_type, cafv, utility],
+        outputs=output
+    )
+
+    gr.Markdown(
+        """
+        ---
+        *Student Research Project – Academic Year 2024–2025*
+        """
+    )
+
+demo.launch()

requirements.txt

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+scikit-learn
+xgboost
+joblib
+numpy
+gardio