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README.md

@@ -1,16 +1,5 @@
----
-title: Mô hình dự đoán hiệu suất xe điện
-emoji: 🔋
-colorFrom: blue
-colorTo: green
-sdk: gradio
-sdk_version: 6.3.0
-python_version: 3.10
-app_file: app.py
-pinned: false
----
 
-# 🔋 Mô hình dự đoán hiệu suất xe điện
-
-## 👨‍🎓 Tác giả
+# 🔋 Electric Vehicle Performance Prediction Model
 
+## 👨‍🎓 Author
+Student Research Project – Academic Year 2024–2025

app.py

@@ -5,7 +5,14 @@ import joblib
 # =========================
 # Load model
 # =========================
-model = joblib.load("model.joblib")
+from pathlib import Path
+
+MODEL_PATH = Path(__file__).parent / "model.joblib"
+
+def load_model():
+    return joblib.load(MODEL_PATH)
+
+model = load_model()
 
 # =========================
 # Prediction function

ev_model/README.md

@@ -0,0 +1,5 @@
+
+# 🔋 Electric Vehicle Performance Prediction Model
+
+## 👨‍🎓 Author
+Student Research Project – Academic Year 2024–2025

ev_model/app.py

@@ -0,0 +1,58 @@
+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()

ev_model/model.joblib

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2bb47892592702dc88f2ae782402df93cc4f3ae36b83cc42647262756474e63f
+size 70445

ev_model/requirements.txt

@@ -0,0 +1,5 @@
+scikit-learn
+xgboost
+joblib
+numpy
+gardio

requirements.txt

@@ -1,7 +1,5 @@
-gradio==6.3.0
-numpy
-pillow
-joblib
+scikit-learn
 xgboost
-tensorflow-cpu
-huggingface_hub
+joblib
+numpy
+gardio