Update app.py

app.py

@@ -1,34 +1,68 @@
+# 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")
+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"
+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)
 
-def _predict_proba_nd(x_nd):
+# 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 predict_and_explain(HGB, HDL_C, DBIL, AST_ALT, UA, GFR, ALB, LYM, PLT, ALP, CHOL, nsamples=200):
+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:
-        # 自动派生变量
-        PNI = ALB + 5 * LYM
+        # ---- 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 = (
@@ -36,36 +70,79 @@ def predict_and_explain(HGB, HDL_C, DBIL, AST_ALT, UA, GFR, ALB, LYM, PLT, ALP,
             (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 = [[HGB, HDL_C, DBIL, AST_ALT, UA, GFR, PNI, HALP, AAPR, conuts]]
-        input_df = pd.DataFrame(x_row, columns=feature_names)
-
-        prob = float(pipeline.predict_proba(input_df)[0, 1])
-
-        shap_out = explainer.shap_values(np.array(x_row), nsamples=nsamples)
-        sv = shap_out[1][0] if isinstance(shap_out, list) else shap_out[0]
-
-        base_val = explainer.expected_value[1] if isinstance(explainer.expected_value, list) else explainer.expected_value
-
+        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')
-        shap.force_plot(base_val, sv, x_row[0], feature_names=feature_names, matplotlib=True, show=False)
-        fig = plt.gcf()
-        fig.set_size_inches(8, 4)
+        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()
-
-        return round(prob, 3), fig, "Success"
+        status.append("Rendered bar fallback.")
+        return round(prob, 3), fig, "\n".join(status)
 
     except Exception as e:
-        return None, None, f"Error: {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 = [137, 1.76, 8.6, 0.97, 310, 75.4, 33, 2.2, 164, 67.9, 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("### Logistic Regression Risk Prediction with SHAP Explanation")
+    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():
+        with gr.Column(scale=1):
             inputs = [
                 gr.Number(label="HGB (g/L)"),
                 gr.Number(label="HDL-C (mmol/L)"),
@@ -80,11 +157,24 @@ with gr.Blocks() as demo:
                 gr.Number(label="CHOL (mmol/L)")
             ]
             ns_slider = gr.Slider(100, 400, value=200, step=50, label="SHAP nsamples")
-            gr.Button("Fill Example").click(lambda: tuple(example_values), outputs=[*inputs, ns_slider])
-            gr.Button("Predict").click(fn=predict_and_explain,
-                                       inputs=[*inputs, ns_slider],
-                                       outputs=[gr.Number(label="Risk"), gr.Plot(), gr.Textbox(label="Status", lines=4)])
-        with gr.Column():
-            pass
-
-demo.launch()
+
+            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()