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app.py

@@ -3,11 +3,12 @@ import numpy as np
 import joblib
 
 # =========================
-# Load model (and scaler if needed)
+# Load model
 # =========================
-model = joblib.load("ev_model/model.joblib")
+model = joblib.load("model.joblib")
+
 # =========================
-# UI
+# Page config
 # =========================
 st.set_page_config(
     page_title="EV Performance Prediction",
@@ -15,31 +16,72 @@ st.set_page_config(
     layout="centered"
 )
 
-st.title("🔋 Electric Vehicle Performance Prediction")
-st.write("Predict **Electric Range** of an electric vehicle using a trained ML model.")
-
-st.divider()
+# =========================
+# Header
+# =========================
+st.markdown(
+    """
+    <h1 style='text-align: center;'>🔋 Electric Vehicle Performance Prediction</h1>
+    <p style='text-align: center; font-size:18px;'>
+        Dự báo hiệu suất kỹ thuật xe điện (Electric Range)
+    </p>
+    <hr>
+    """,
+    unsafe_allow_html=True
+)
 
 # =========================
-# Input features
-# (encoded giống như lúc train)
+# Sidebar (thầy cô rất thích)
 # =========================
-year = st.number_input("Model Year", min_value=1990, max_value=2035, value=2020)
-make = st.number_input("Make (encoded)", value=10)
-model_code = st.number_input("Model (encoded)", value=20)
-ev_type = st.number_input("Electric Vehicle Type (encoded)", value=1)
-cafv = st.number_input("CAFV Eligibility (encoded)", value=0)
-utility = st.number_input("Electric Utility (encoded)", value=60)
+st.sidebar.header("📌 Thông tin mô hình")
+st.sidebar.markdown(
+    """
+    - **Bài toán**: Hồi quy (Regression)  
+    - **Thuật toán**: XGBoost  
+    - **Đầu ra**: Electric Range  
+    - **Triển khai**: Hugging Face  
+    """
+)
 
 # =========================
-# Prediction
+# Input section
 # =========================
-if st.button("🔍 Predict"):
-    X = np.array([[year, make, model_code, ev_type, cafv, utility]])
+st.subheader("📥 Nhập thông tin xe")
+
+col1, col2 = st.columns(2)
+
+with col1:
+    year = st.number_input("Model Year", 1990, 2035, 2020)
+    make = st.number_input("Make (encoded)", value=10)
+    model_code = st.number_input("Model (encoded)", value=20)
 
-    # Nếu dùng scaler thì bật dòng này
-    # X = scaler.transform(X)
+with col2:
+    ev_type = st.number_input("EV Type (encoded)", value=1)
+    cafv = st.number_input("CAFV Eligibility (encoded)", value=0)
+    utility = st.number_input("Electric Utility (encoded)", value=60)
 
+# =========================
+# Predict button
+# =========================
+st.markdown("<br>", unsafe_allow_html=True)
+
+if st.button("🚀 Dự báo hiệu suất"):
+    X = np.array([[year, make, model_code, ev_type, cafv, utility]])
     prediction = model.predict(X)
 
-    st.success(f"🚗 Predicted Electric Range: **{prediction[0]:.2f} km**")
+    st.success(
+        f"🔍 **Electric Range dự đoán:** `{prediction[0]:.2f}` km"
+    )
+
+# =========================
+# Footer
+# =========================
+st.markdown(
+    """
+    <hr>
+    <p style='text-align: center; font-size:14px;'>
+        Student Research Project – Academic Year 2024–2025
+    </p>
+    """,
+    unsafe_allow_html=True
+)