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app.py

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+# 建立 .streamlit/config.toml 避免 Hugging Face 權限錯誤
+import os
+os.makedirs(".streamlit", exist_ok=True)
+with open(".streamlit/config.toml", "w") as f:
+    f.write("""
+[server]
+headless = true
+port = 7860
+enableCORS = true
+""")
+
+import streamlit as st
+import joblib
+import pandas as pd
+import shap
+import matplotlib.pyplot as plt
+import platform
+from huggingface_hub import hf_hub_download
+
+# 跨平台字型設定
+if platform.system() == 'Windows':
+    plt.rcParams['font.family'] = 'Microsoft JhengHei'
+elif platform.system() == 'Darwin':  # macOS
+    plt.rcParams['font.family'] = 'AppleGothic'
+else:
+    plt.rcParams['font.family'] = 'Noto Sans CJK TC'  # Linux
+
+plt.rcParams['axes.unicode_minus'] = False  # 負號使用 ASCII 減號
+
+@st.cache_resource(show_spinner=True)
+def load_model_and_explainer():
+    # 下載模型與 LabelEncoder
+    model_path = hf_hub_download(
+        repo_id="jung-ming/Ocean-Meets-Forest",
+        filename="rf_model_with_encoder.pkl",
+        repo_type="model"
+    )
+    bundle = joblib.load(model_path)
+    model = bundle["model"]
+    le = bundle["label_encoder"]
+
+    # 建立 explainer（避免用 pickle 載入 Numba 編譯物件）
+    explainer = shap.TreeExplainer(model, feature_perturbation="interventional")
+
+    return model, le, explainer
+
+model, le, explainer = load_model_and_explainer()
+
+# 建立映射
+ship_type_to_code = dict(zip(le.classes_, le.transform(le.classes_)))
+
+st.title("🚢 台中港艘次預測系統")
+st.markdown("請輸入以下資訊，模型將預測該月艘次數")
+
+port_count = st.selectbox("航線組合數", list(range(1, 100)))
+year = st.selectbox("年", [2020, 2021, 2022, 2023, 2024, 2025])
+month = st.selectbox("月", list(range(1, 13)))
+ship_type = st.selectbox("船舶種類", list(ship_type_to_code.keys()))
+
+if st.button("🔮 開始預測"):
+    ship_type_encoded = ship_type_to_code[ship_type]
+    input_df = pd.DataFrame({
+        "航線組合數": [port_count],
+        "年": [year],
+        "月": [month],
+        "船舶種類_編碼": [ship_type_encoded]
+    })
+    pred = model.predict(input_df)[0]
+    st.success(f"預測結果：🚢 約為 {pred:.2f} 艘次")
+
+    st.subheader("🧠 模型決策解釋圖（SHAP Waterfall）")
+
+    shap_values = explainer(input_df)
+    fig, ax = plt.subplots(figsize=(8, 4))
+    shap.plots.waterfall(shap_values[0], show=False)
+
+    # 修正負號顯示問題
+    for text in ax.texts:
+        if text.get_text().startswith('\u2212'):
+            new_text = text.get_text().replace('\u2212', '-')
+            text.set_text(new_text)
+
+    st.pyplot(fig)
+    plt.close(fig)

requirements.txt

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+streamlit
+pandas
+joblib
+matplotlib
+shap
+scikit-learn
+huggingface_hub
+requests
+