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| import os | |
| os.environ["HF_HOME"] = "/tmp/.cache/huggingface" | |
| os.environ["MPLCONFIGDIR"] = "/tmp/.config/matplotlib" | |
| 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 | |
| import matplotlib.font_manager as fm | |
| # 自訂函數：嘗試找系統中可用的中文字型路徑 | |
| def find_chinese_font(): | |
| for font_path in fm.findSystemFonts(fontext='ttf'): | |
| # 這裡可依你系統字型名關鍵字調整 | |
| if ("NotoSans" in font_path and ("TC" in font_path or "TraditionalChinese" in font_path)) \ | |
| or "STHeiti" in font_path or "Heiti" in font_path or "LiHei" in font_path: | |
| return font_path | |
| return None | |
| chinese_font_path = find_chinese_font() | |
| if chinese_font_path: | |
| chinese_font_prop = fm.FontProperties(fname=chinese_font_path) | |
| print(f"使用中文字型檔: {chinese_font_path}") | |
| else: | |
| chinese_font_prop = None | |
| print("找不到適合的中文字型檔，請確認系統已安裝中文字型") | |
| # matplotlib 全局字型設定（Fallback用） | |
| if platform.system() == 'Windows': | |
| plt.rcParams['font.family'] = 'Microsoft JhengHei' | |
| elif platform.system() == 'Darwin': # macOS | |
| plt.rcParams['font.family'] = 'AppleGothic' | |
| else: | |
| # Linux 預設用找到的字型名稱，沒找到就用 DejaVu Sans | |
| if chinese_font_prop: | |
| plt.rcParams['font.family'] = chinese_font_prop.get_name() | |
| else: | |
| plt.rcParams['font.family'] = 'DejaVu Sans' | |
| plt.rcParams['axes.unicode_minus'] = False # 負號用 ASCII 減號 | |
| def load_model_and_explainer(): | |
| 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"] | |
| explainer = shap.TreeExplainer(model, feature_perturbation="interventional") | |
| return model, le, explainer | |
| model, le, explainer = load_model_and_explainer() | |
| 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 用中文欄位名，符合模型訓練時格式 | |
| 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 plot）") | |
| shap_values = explainer(input_df) | |
| # 把SHAP特徵名稱轉成英文，但不改input_df欄位名稱 | |
| feature_name_map = { | |
| "航線組合數": "Route Count", | |
| "年": "Year", | |
| "月": "Month", | |
| "船舶種類_編碼": "Ship Type Code" | |
| } | |
| shap_values.feature_names = [feature_name_map.get(f, f) for f in shap_values.feature_names] | |
| ax = shap.plots.waterfall(shap_values[0], show=False) | |
| # 負號替換（避免顯示問題） | |
| for text in ax.texts: | |
| if text.get_text().startswith('\u2212'): | |
| text.set_text(text.get_text().replace('\u2212', '-')) | |
| st.pyplot(ax.figure) | |
| plt.close(ax.figure) |