Spaces:
Sleeping
Sleeping
| # app.py (robust, server-safe) | |
| import gradio as gr | |
| import pandas as pd | |
| import joblib | |
| import shap | |
| import numpy as np | |
| import matplotlib | |
| matplotlib.use("Agg") # 非交互后端，服务器端更稳 | |
| import matplotlib.pyplot as plt | |
| import warnings | |
| warnings.filterwarnings("ignore") | |
| # ====== 模型与背景数据 ====== | |
| MODEL_PATH = "models/SVM_pipeline.pkl" | |
| BG_PATH = "data/bg.csv" | |
| # 模型最终需要的10个特征（顺序必须与训练一致） | |
| feature_names = ["HGB", "HDL_C", "DBIL", "AST_ALT", "UA", "GFR", "PNI", "HALP", "AAPR", "conuts"] | |
| # 加载模型与背景 | |
| pipeline = joblib.load(MODEL_PATH) | |
| bg_df = pd.read_csv(BG_PATH) | |
| bg_array = bg_df[feature_names].to_numpy(dtype=np.float64) | |
| # 预测函数（供 KernelExplainer 调用） | |
| def _predict_proba_nd(x_nd: np.ndarray) -> np.ndarray: | |
| df = pd.DataFrame(x_nd, columns=feature_names) | |
| return pipeline.predict_proba(df) | |
| # 只初始化一次 explainer（性能更稳） | |
| explainer = shap.KernelExplainer(_predict_proba_nd, bg_array) | |
| def _render_force_plot(base_val: float, shap_1d: np.ndarray, feat_1d: np.ndarray, fnames): | |
| """返回 matplotlib Figure（旧接口，服务器端稳定）""" | |
| plt.close('all') | |
| shap.force_plot( | |
| base_val, np.asarray(shap_1d).reshape(-1), np.asarray(feat_1d).reshape(-1), | |
| feature_names=list(fnames), matplotlib=True, show=False | |
| ) | |
| fig = plt.gcf() | |
| fig.set_size_inches(8, 4) | |
| plt.tight_layout() | |
| return fig | |
| def predict_and_explain( | |
| HGB, HDL_C, DBIL, AST_ALT, UA, GFR, | |
| ALB, LYM, PLT, ALP, CHOL, | |
| nsamples=200 | |
| ): | |
| status = [] | |
| try: | |
| # ---- 1) 衍生指标（由原始输入计算）---- | |
| try: | |
| HGB = float(HGB); HDL_C = float(HDL_C); DBIL = float(DBIL); AST_ALT = float(AST_ALT) | |
| UA = float(UA); GFR = float(GFR) | |
| ALB = float(ALB); LYM = float(LYM); PLT = float(PLT) | |
| ALP = float(ALP); CHOL = float(CHOL) | |
| except Exception: | |
| return None, None, "Error: some inputs are not numeric." | |
| # 防极端值（避免除0） | |
| if PLT <= 0 or ALP <= 0: | |
| return None, None, "Error: PLT and ALP must be > 0." | |
| PNI = ALB + 5.0 * LYM | |
| HALP = HGB * ALB * LYM / PLT | |
| AAPR = ALB / ALP | |
| conuts = ( | |
| (0 if ALB >= 35 else 2 if ALB >= 30 else 4 if ALB >= 25 else 6) + | |
| (0 if LYM >= 1.6 else 1 if LYM >= 1.2 else 2 if LYM >= 0.8 else 3) + | |
| (0 if CHOL >= 4.65 else 1 if CHOL >= 3.10 else 2 if CHOL >= 2.59 else 3) | |
| ) | |
| x_row = np.array([[HGB, HDL_C, DBIL, AST_ALT, UA, GFR, PNI, HALP, AAPR, conuts]], dtype=np.float64) | |
| status.append(f"Derived: PNI={PNI:.3f}, HALP={HALP:.3f}, AAPR={AAPR:.3f}, CONUTS={conuts}") | |
| # ---- 2) 概率 ---- | |
| prob = float(pipeline.predict_proba(pd.DataFrame(x_row, columns=feature_names))[0, 1]) | |
| status.append(f"Pred prob computed: {prob:.3f}") | |
| # ---- 3) SHAP 计算 ---- | |
| ns = int(nsamples) if nsamples is not None else 200 | |
| shap_out = explainer.shap_values(x_row, nsamples=ns) | |
| # 统一提取“正类”一维向量 | |
| if isinstance(shap_out, list): | |
| sv = np.asarray(shap_out[1], dtype=np.float64) | |
| if sv.ndim == 2: | |
| sv = sv[0, :] | |
| else: | |
| sv = np.asarray(shap_out, dtype=np.float64) | |
| if sv.ndim == 3: # (1, n_features, n_classes) | |
| sv = sv[0, :, 1] | |
| elif sv.ndim == 2: # (1, n_features) | |
| sv = sv[0, :] | |
| else: | |
| sv = sv.reshape(-1) | |
| status.append(f"SHAP 1D shape: {sv.shape}; features: {x_row.shape[1:]}") | |
| # base value 取正类 | |
| ev = explainer.expected_value | |
| if isinstance(ev, (list, np.ndarray)): | |
| ev = np.asarray(ev).reshape(-1) | |
| base_val = float(ev[1] if len(ev) > 1 else ev[0]) | |
| else: | |
| base_val = float(ev) | |
| # ---- 4) 绘图：优先力图；失败则条形图兜底 ---- | |
| try: | |
| fig = _render_force_plot(base_val, sv, x_row[0, :], feature_names) | |
| status.append("Rendered force plot (matplotlib).") | |
| return round(prob, 3), fig, "\n".join(status) | |
| except Exception as e_force: | |
| status.append(f"Force-plot failed: {repr(e_force)}; fallback=bar") | |
| order = np.argsort(np.abs(sv))[::-1] | |
| topk = order[:min(10, sv.shape[0])] | |
| plt.close('all') | |
| fig = plt.figure(figsize=(8, 5), dpi=160) | |
| plt.barh(np.array(feature_names)[topk], sv[topk]) | |
| plt.xlabel("SHAP value") | |
| plt.title("Top features (single-sample contribution)") | |
| plt.gca().invert_yaxis() | |
| plt.tight_layout() | |
| status.append("Rendered bar fallback.") | |
| return round(prob, 3), fig, "\n".join(status) | |
| except Exception as e: | |
| return None, None, f"Fatal error: {repr(e)}" | |
| # ====== 示例：一组“原始指标”可复现你之前的 PNI/HALP/AAPR/CONUTS ====== | |
| # 对应：PNI=44, HALP≈60.8, AAPR≈0.486, CONUTS=4 | |
| example_values = [167, 1.76, 8.6, 0.97, 310, 75, 33, 2.2, 164, 68, 2.8, 200] | |
| # 顺序：HGB, HDL_C, DBIL, AST_ALT, UA, GFR, ALB, LYM, PLT, ALP, CHOL, nsamples | |
| # ====== Gradio 界面 ====== | |
| with gr.Blocks() as demo: | |
| gr.Markdown( | |
| "### Meige Risk Prediction (SVM) with SHAP Explanation\n" | |
| "Enter **original clinical indicators**; the app will derive PNI/HALP/AAPR/CONUTS internally.\n\n" | |
| "**Units**: HGB (g/L), HDL‑C (mmol/L), DBIL (μmol/L), AST/ALT (ratio), UA (μmol/L), " | |
| "GFR (mL/min/1.73 m²), ALB (g/L), LYM (×10⁹/L), PLT (×10⁹/L), ALP (U/L), CHOL (mmol/L)." | |
| ) | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| inputs = [ | |
| gr.Number(label="HGB (g/L)"), | |
| gr.Number(label="HDL-C (mmol/L)"), | |
| gr.Number(label="DBIL (μmol/L)"), | |
| gr.Number(label="AST/ALT"), | |
| gr.Number(label="UA (μmol/L)"), | |
| gr.Number(label="GFR (mL/min/1.73 m²)"), | |
| gr.Number(label="ALB (g/L)"), | |
| gr.Number(label="LYM (×10⁹/L)"), | |
| gr.Number(label="PLT (×10⁹/L)"), | |
| gr.Number(label="ALP (U/L)"), | |
| gr.Number(label="CHOL (mmol/L)") | |
| ] | |
| ns_slider = gr.Slider(100, 400, value=200, step=50, label="SHAP nsamples") | |
| btn_fill = gr.Button("Fill Example") | |
| btn_predict = gr.Button("Predict") | |
| with gr.Column(scale=1): | |
| out_prob = gr.Number(label="Predicted Probability") | |
| out_plot = gr.Plot(label="SHAP Force Plot (fallback: bar)") | |
| out_log = gr.Textbox(label="Status", lines=6) | |
| def _fill_example(): | |
| return tuple(example_values) | |
| btn_fill.click(fn=_fill_example, outputs=[*inputs, ns_slider]) | |
| btn_predict.click( | |
| fn=predict_and_explain, | |
| inputs=[*inputs, ns_slider], | |
| outputs=[out_prob, out_plot, out_log] | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch() | |