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15-feature model

app.py

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+import numpy as np
+import pandas as pd
+import xgboost as xgb
+import shap
+import gradio as gr
+import matplotlib.pyplot as plt
+import matplotlib
+
+matplotlib.use("Agg")  # 防止服务器无图形界面时报错
+
+# 指定输入特征顺序
+feature_names = [
+    "CT value(HU)",
+    "Tumor size(cm)",
+    "ctDNA",
+    "CEA",
+    "CYFRA21-1",
+    "AAPR",
+    "Location",
+    "CYFRA21-1",
+    "CA125",
+    "LDH",
+    "ANC",
+    "ALT",
+    "GGT",
+    "CREA",
+    "UREA",
+    "Pleural indentation",
+]
+
+# 加载模型（确保是用 sklearn API 训练并保存的）
+model = xgb.XGBClassifier()
+model.load_model("./xgb_model.json")
+model.get_booster().feature_names = feature_names
+
+# 初始化 SHAP 解释器
+explainer = shap.Explainer(model)
+
+
+# 预测函数
+def predict_probability(
+    CT_value,
+    Tumor_size,
+    ctDNA,
+    CEA,
+    Location,
+    CYFRA21_1,
+    AAPR,
+    CA125,
+    LDH,
+    ANC,
+    ALT,
+    GGT,
+    CREA,
+    UREA,
+    Pleural_indentation,
+):
+    input_data = pd.DataFrame(
+        [
+            [
+                CT_value,
+                Tumor_size,
+                ctDNA,
+                CEA,
+                Location,
+                CYFRA21_1,
+                AAPR,
+                CA125,
+                LDH,
+                ANC,
+                ALT,
+                GGT,
+                CREA,
+                UREA,
+                Pleural_indentation,
+            ]
+        ],
+        columns=feature_names,
+    )
+
+    # 将 Location 和 ctDNA 转换为数值型
+    input_data["Location"] = input_data["Location"].map({"Central": 1, "Peripheral": 0})
+    input_data["ctDNA"] = input_data["ctDNA"].map({"Positive": 1, "Negative": 0})
+    input_data["Pleural_indentation"] = input_data["Pleural_indentation"].map({"Positive": 1, "Negative": 0})
+    # 预测
+    try:
+        prob = model.predict_proba(input_data)[0][1]
+    except Exception as e:
+        return f"预测出错: {e}", None
+
+    # 计算 SHAP 值
+    try:
+        shap_values = explainer(input_data)
+
+        # 绘图
+        shap.plots.waterfall(shap_values[0], show=False)
+        plt.title("SHAP Waterfall Plot")
+        plt.savefig("shap_plot.png", bbox_inches="tight", dpi=300)
+        plt.close()
+    except Exception as e:
+        return f"SHAP 图生成失败: {e}", None
+
+    return f"阳性概率: {prob:.2%}", "shap_plot.png"
+
+
+demo = gr.Interface(
+    fn=predict_probability,
+    inputs=[
+        gr.Number(label="CT value(HU)"),
+        gr.Number(label="Tumor size(cm)"),
+        gr.Dropdown(choices=["Positive", "Negative"], label="ctDNA"),
+        gr.Number(label="CEA (ng/mL) Normal range: 0-5"),
+        gr.Dropdown(choices=["Central", "Peripheral"], label="Location"),  # 修改为 Dropdown 类型
+        gr.Number(label="CYFRA21-1 (ng/mL) Normal range: 0-5"),
+        gr.Number(label="AAPR (ng/mL) Normal range: 0-5"),
+        gr.Number(label="CA125 (U/mL) Normal range: 0-35"),
+        gr.Number(label="LDH (U/L) Normal range: 120-250"),
+        gr.Number(label="ANC (10^9/L) Normal range: 1.8-6.3"),
+        gr.Number(label="ALT (U/L) Normal range: 7-40"),
+        gr.Number(label="GGT (U/L) Normal range: 7-45"),
+        gr.Number(label="CREA (μmol/L) Normal range: 41-81"),
+        gr.Number(label="UREA (mmol/L) Normal range: 2.6-8.8"),
+        gr.Dropdown(choices=["Positive", "Negative"], label="Pleural indentation"),
+    ],
+    outputs=[
+        gr.Textbox(label="Results of prediction"),
+        gr.Image(type="filepath", label="SHAP Waterfall Plot"),
+    ],
+    title="Prediction of Lymph Node Metastasis",
+    description="Variables were entered to obtain the predicted positive probability and SHAP interpretation map",
+)
+
+demo.launch(share=True)

xgb_model.json

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