Delete website.py

website.py

@@ -1,73 +0,0 @@
-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)