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()