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| import gradio as gr | |
| import pandas as pd | |
| import joblib | |
| import shap | |
| import numpy as np | |
| import matplotlib | |
| matplotlib.use("Agg") | |
| 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" | |
| feature_names = ["HGB", "HDL_C", "DBIL", "AST_ALT", "UA", "GFR", "PNI", "HALP", "AAPR", "conuts"] | |
| 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): | |
| df = pd.DataFrame(x_nd, columns=feature_names) | |
| return pipeline.predict_proba(df) | |
| 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): | |
| try: | |
| # 自动派生变量 | |
| PNI = ALB + 5 * LYM | |
| HALP = HGB * ALB * LYM / PLT | |
| AAPR = ALB / ALP | |
| conuts = ( | |
| (0 if ALB >= 35 else 2 if ALB >= 30 else 4 if ALB >= 25 else 6) + | |
| (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 | |
| 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) | |
| plt.tight_layout() | |
| return round(prob, 3), fig, "Success" | |
| except Exception as e: | |
| return None, None, f"Error: {e}" | |
| example_values = [137, 1.76, 8.6, 0.97, 310, 75.4, 33, 2.2, 164, 67.9, 2.8, 200] | |
| with gr.Blocks() as demo: | |
| gr.Markdown("### Logistic Regression Risk Prediction with SHAP Explanation") | |
| with gr.Row(): | |
| with gr.Column(): | |
| inputs = [ | |
| gr.Number(label="HGB (g/L)"), | |
| gr.Number(label="HDL-C (mmol/L)"), | |
| gr.Number(label="DBIL (μmol/L)"), | |
| gr.Number(label="AST/ALT"), | |
| gr.Number(label="UA (μmol/L)"), | |
| gr.Number(label="GFR (mL/min/1.73 m²)"), | |
| gr.Number(label="ALB (g/L)"), | |
| gr.Number(label="LYM (×10⁹/L)"), | |
| gr.Number(label="PLT (×10⁹/L)"), | |
| gr.Number(label="ALP (U/L)"), | |
| 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_vals), 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() | |