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Browse files- app.py +153 -0
- requirements.txt +3 -0
app.py
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# conda create -n IMH-XGBoost conda-forge::huggingface_hub
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# pip install -r requirements.txt -i https://mirrors.tuna.tsinghua.edu.cn/pypi/web/simple
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import os
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# 获取模型
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if not os.path.exists('xgb.baseline.model.json'):
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from huggingface_hub import login, snapshot_download
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login(token=os.environ.get("HF_TOKEN"))
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snapshot_download(repo_id='Limour-blog/IMH-XGBoost', local_dir=r'.', allow_patterns='xgb.baseline.model.json')
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import xgboost as xgb
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import numpy as np
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clf = xgb.XGBClassifier(enable_categorical=True)
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clf.load_model(r"xgb.baseline.model.json")
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def limit(_value, _min, _max):
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return min(max(_value, _min), _max)
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def args2Array(
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BSA=1.824,
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CTNT=4.715, # _0
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CK_MB=200.5, # _0
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CRP=18.01, # _1
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PD_DIMER=1.047,
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NT_PROBNP=883.6, # _3
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ARRHYTHMIA=0,
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APOE=36.76,
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MHR=0.8378
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):
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BSA = limit(BSA, 1.401, 2.231)
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BSA = (BSA - 1.824) / 0.1654
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CTNT = limit(CTNT, -9.566, 19.58)
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CTNT = (CTNT - 4.715) / 3.877
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CK_MB = limit(CK_MB, -213, 571)
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CK_MB = (CK_MB - 200.5) / 154.3
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CRP = limit(CRP, -25.04, 55.86)
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CRP = (CRP - 18.01) / 17.53
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PD_DIMER = limit(PD_DIMER, -1.131, 2.959)
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PD_DIMER = (PD_DIMER - 1.047) / 0.8045
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NT_PROBNP = limit(NT_PROBNP, -610.1, 2106)
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NT_PROBNP = (NT_PROBNP - 883.6) / 625.8
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APOE = limit(APOE, 3.625, 68.62)
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APOE = (APOE - 36.76) / 13.85
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MHR = limit(MHR, -0.06439, 1.683)
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MHR = (MHR - 0.8378) / 0.3103
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return np.array([[BSA, CTNT, CK_MB,
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CRP, PD_DIMER, NT_PROBNP,
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ARRHYTHMIA, APOE, MHR]])
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def predict(_array):
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return float(clf.predict_proba(_array)[0,1])
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# 测试模型预测阳性正确
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assert predict(args2Array(
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BSA=1.99,
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CTNT=10, # _0
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CK_MB=374, # _0
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CRP=14.4, # _1
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PD_DIMER=0.88,
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NT_PROBNP=463.7, # _3
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ARRHYTHMIA=0,
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APOE=37,
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MHR=0.8378
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)) >= 0.72
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# 测试模型预测阴性正确
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assert predict(args2Array(
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BSA=1.51,
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CTNT=1.53, # _0
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CK_MB=95, # _0
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CRP=4.9, # _1
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PD_DIMER=1.4,
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NT_PROBNP=519.2, # _3
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ARRHYTHMIA=0,
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APOE=36.76,
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MHR=0.5581
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)) < 0.72
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import gradio as gr
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# ========== 完整版的模型 ==========
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with gr.Blocks() as complete_model:
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with gr.Row():
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g_BSA = gr.Number(label="BSA", scale=1, value=1.824,
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info="患者的体表面积, 缺失请保持默认值",
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interactive=True)
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g_ARRHYTHMIA = gr.Checkbox(label="ARRHYTHMIA", scale=1, value=False,
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info="患者是否发生恶性心律失常或传导阻滞, 缺失请保持默认值",
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interactive=True)
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g_PD_DIMER = gr.Number(label="PD_DIMER", scale=1, value=1.047,
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info="PCI术后D-二聚体峰值, 缺失请保持默认值",
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interactive=True)
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with gr.Row():
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g_CTNT = gr.Number(label="CTNT", scale=1, value=4.715,
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info="PCI术后即刻的CTNT值, 缺失请保持默认值",
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interactive=True)
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g_CK_MB = gr.Number(label="CK_MB", scale=1, value=200.5,
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info="PCI术后即刻的CK_MB值, 缺失请保持默认值",
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interactive=True)
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g_NT_PROBNP = gr.Number(label="NT_PROBNP", scale=1, value=883.6,
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info="PCI术后36小时的NT_PROBNP值, 缺失请保持默认值",
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interactive=True)
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with gr.Row():
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g_CRP = gr.Number(label="CRP", scale=1, value=18.01,
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info="PCI术后24小时的CRP值, 缺失请保持默认值",
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interactive=True)
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g_APOE = gr.Number(label="APOE", scale=1, value=36.76,
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info="患者血脂APOE值, 缺失请保持默认值",
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interactive=True)
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g_MHR = gr.Number(label="MHR", scale=1, value=0.8378,
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info="单核细胞与高密度脂蛋白胆固醇比值, 缺失请保持默认值",
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interactive=True)
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with gr.Row():
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g_output1 = gr.Number(label="XGB.predict_proba", scale=1, interactive=False, info="cutoff值为0.72")
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g_output2 = gr.Textbox(label="结论", scale=1, interactive=False, info="预测患者IMH为阳性或阴性")
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g_calc = gr.Button("计算", variant="primary", size='lg')
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def btn_calc(
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BSA, CTNT, CK_MB,
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CRP, PD_DIMER, NT_PROBNP,
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ARRHYTHMIA, APOE, MHR
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):
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res1 = predict(args2Array(
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BSA=BSA,
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CTNT=CTNT, # _0
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CK_MB=CK_MB, # _0
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CRP=CRP, # _1
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PD_DIMER=PD_DIMER,
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NT_PROBNP=NT_PROBNP, # _3
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ARRHYTHMIA = (1 if ARRHYTHMIA else 0),
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APOE=APOE,
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MHR=MHR
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))
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if res1 >= 0.72:
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res2 = '阳性'
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else:
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res2 = '阴性'
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return round(res1, 4), res2
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g_calc.click(
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fn = btn_calc,
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inputs=[g_BSA, g_CTNT, g_CK_MB,
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g_CRP, g_PD_DIMER, g_NT_PROBNP,
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g_ARRHYTHMIA, g_APOE, g_MHR],
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outputs=[g_output1, g_output2]
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)
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# ========== 开始运行 ==========
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demo = gr.TabbedInterface([complete_model],
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["complete_model"])
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gr.close_all()
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demo.queue(api_open=False, max_size=1).launch(
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server_name = "0.0.0.0",
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share=False, show_error=True, show_api=False)
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requirements.txt
ADDED
@@ -0,0 +1,3 @@
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scikit-learn==1.5.2
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xgboost-cpu==2.1.3
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gradio==5.9.1
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