Upload app.py

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", "Location", "CYFRA21-1", "CA125", "LDH"]
+
+# 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, CA125, LDH):
+    input_data = pd.DataFrame(
+        [[CT_value, Tumor_size, ctDNA, CEA, Location, CYFRA21_1, CA125, LDH]], 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})
+    # 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="CA125 (U/mL) Normal range: 0-35"),
+        gr.Number(label="LDH (U/L) Normal range: 120-250"),
+    ],
+    outputs=[
+        gr.Textbox(label="预测结果"),
+        gr.Image(type="filepath", label="SHAP Waterfall Plot"),
+    ],
+    title="淋巴结转移预测",
+    description="输入变量，获取预测阳性概率及SHAP解释图",
+)
+
+demo.launch(share=True)