app.py

import gradio as gr
import joblib
import pandas as pd
import numpy as np
import shap
import matplotlib.pyplot as plt
from fpdf import FPDF
import tempfile

# --- Загрузка моделей ---
try:
    model = joblib.load('allostatic_model.pkl')
    imputer = joblib.load('imputer.pkl')
    explainer = joblib.load('shap_explainer.pkl')
    expected_features = imputer.feature_names_in_.tolist()
except Exception as e:
    print(f"Ошибка загрузки: {e}")

RU_NAMES = {
    'RIDAGEYR': 'Возраст', 'RIAGENDR': 'Пол', 'BMXBMI': 'ИМТ (BMI)',
    'BPXSY1': 'Сист. давление', 'LBXNEPCT': 'Нейтрофилы %', 'LBDLYMNO': 'Лимфоциты %',
    'LBXSATSI': 'АСТ (U/L)', 'LBXPLTSI': 'Тромбоциты', 'LBXSCR': 'Креатинин',
    'LBXGH': 'Гликированный Hb', 'RXDCOUNT': 'Кол-во лекарств', 'NLR': 'Индекс NLR',
    'APRI': 'Индекс APRI', 'eGFR': 'eGFR (фильтрация)', 'has_symptoms_for_stage4': 'Симптомы', 
    'simplified_AL_Index': 'Аллостатический индекс'
}

def calculate_metrics_dict(d):
    g_num = 1 if d.get('RIAGENDR') == "Male" else 2
    age = float(d.get('RIDAGEYR', 50))
    creat = float(d.get('LBXSCR', 0.9))
    
    nlr = d.get('LBXNEPCT', 60) / d.get('LBDLYMNO', 30) if d.get('LBDLYMNO', 0) > 0 else 0
    apri = (d.get('LBXSATSI', 25) / 40) / d.get('LBXPLTSI', 250) * 100 if d.get('LBXPLTSI', 0) > 0 else 0
    kappa = 0.7 if g_num == 2 else 0.9
    alpha = -0.241 if g_num == 2 else -0.302
    egfr = 142 * (min(creat/kappa, 1)**alpha) * (max(creat/kappa, 1)**-1.2) * (0.9938**age) * (1.012 if g_num == 2 else 1.0)
    
    symptoms = 1 if any([d.get('PFQ', False), d.get('MEM', False), d.get('CONC', False), d.get('WALK', False)]) else 0
    
    al_idx = sum([nlr > 2.5, d.get('LBXGH', 5.4) > 5.7, egfr < 90, d.get('RXDCOUNT', 0) >= 3, apri > 0.5, d.get('BMXBMI', 25) > 30, d.get('BPXSY1', 120) > 140])
    
    mapping = {
        'RIDAGEYR': age, 'RIAGENDR': g_num, 'BMXBMI': d.get('BMXBMI', 25), 
        'BPXSY1': d.get('BPXSY1', 120), 'LBXNEPCT': d.get('LBXNEPCT', 60), 'LBDLYMNO': d.get('LBDLYMNO', 30), 
        'LBXSATSI': d.get('LBXSATSI', 25), 'LBXPLTSI': d.get('LBXPLTSI', 250), 'LBXSCR': creat, 'LBXGH': d.get('LBXGH', 5.4), 
        'RXDCOUNT': d.get('RXDCOUNT', 0), 'NLR': nlr, 'APRI': apri, 'eGFR': egfr,
        'has_symptoms_for_stage4': symptoms, 'simplified_AL_Index': al_idx,
        'PFQ049': 1 if d.get('PFQ') else 0, 'PFQ051': 1 if d.get('PFQ') else 0, 
        'PFQ061Q': 1 if d.get('PFQ') else 0, 'PFQ090': 1 if d.get('PFQ') else 0
    }
    return mapping, al_idx, egfr

def main_analysis(age, gender, bmi, sys_bp, neutro, lympho, ast, plt_val, creat, hba1c, rxd, pfq, mem, conc, walk):
    input_data = {
        'RIDAGEYR': age, 'RIAGENDR': gender, 'BMXBMI': bmi, 'BPXSY1': sys_bp,
        'LBXNEPCT': neutro, 'LBDLYMNO': lympho, 'LBXSATSI': ast, 'LBXPLTSI': plt_val,
        'LBXSCR': creat, 'LBXGH': hba1c, 'RXDCOUNT': rxd, 'PFQ': pfq, 'MEM': mem, 'CONC': conc, 'WALK': walk
    }
    
    mapping, al_idx, egfr = calculate_metrics_dict(input_data)
    
    input_df = pd.DataFrame([{f: mapping.get(f, 0) for f in expected_features}])[expected_features]
    input_imputed = imputer.transform(input_df)
    raw_stage = int(model.predict(input_imputed)[0])
    stage = 1 if al_idx <= 1 and raw_stage >= 5 else raw_stage
    
    # SHAP
    plt.close('all')
    plt.figure(figsize=(10, 5))
    s_vals = explainer.shap_values(input_imputed)
    s_array = np.array(s_vals)
    c_idx = min(stage, s_array.shape[0]-1) if s_array.ndim == 3 else 0
    final_shap = s_array[c_idx, 0, :] if s_array.ndim == 3 else (s_array[0] if s_array.ndim > 1 else s_array)
    shap.bar_plot(final_shap, feature_names=[RU_NAMES.get(f, f) for f in expected_features], show=False)
    plt.tight_layout()
    plot_path = tempfile.NamedTemporaryFile(delete=False, suffix=".png").name
    plt.savefig(plot_path)
    
    # PDF
    pdf = FPDF()
    pdf.add_page()
    pdf.set_font("Arial", 'B', size=16)
    pdf.cell(200, 10, txt="Allostatic Risk Report", ln=True, align='C')
    pdf.set_font("Arial", size=12)
    pdf.ln(10)
    pdf.cell(200, 10, txt=f"Stage: {stage}", ln=True)
    pdf.cell(200, 10, txt=f"AL Index: {al_idx}", ln=True)
    pdf.cell(200, 10, txt=f"eGFR: {egfr:.2f}", ln=True)
    pdf.image(plot_path, x=10, y=70, w=180)
    pdf_path = tempfile.NamedTemporaryFile(delete=False, suffix=".pdf").name
    pdf.output(pdf_path)
    
    # Цветовая индикация
    color = "green" if stage == 1 else "orange" if stage < 4 else "red"
    res_label = f"СТАДИЯ {stage}"
    
    return res_label, f"Индекс нагрузки: {al_idx} | eGFR: {egfr:.1f}", plot_path, pdf_path

with gr.Blocks(theme=gr.themes.Soft()) as demo:
    gr.Markdown("# 🏥 ProPharm Dashboard")
    
    with gr.Row():
        with gr.Column(scale=1):
            gr.Markdown("### 🎚️ Параметры пациента")
            with gr.Group():
                age = gr.Slider(18, 95, label="Возраст", value=50)
                gender = gr.Radio(["Male", "Female"], label="Пол", value="Male")
                bmi = gr.Slider(10, 60, label="ИМТ (BMI)", value=24.5, step=0.1)
                sys_bp = gr.Slider(80, 220, label="Систолическое АД", value=120)
            
            with gr.Group():
                gr.Markdown("🧪 **Лаборатория**")
                neutro = gr.Slider(10, 90, label="Нейтрофилы %", value=60)
                lympho = gr.Slider(5, 70, label="Лимфоциты %", value=30)
                ast = gr.Slider(5, 150, label="АСТ (U/L)", value=25)
                plt_val = gr.Slider(50, 600, label="Тромбоциты", value=250)
                creat = gr.Slider(0.3, 5.0, label="Креатинин (mg/dL)", value=0.9, step=0.1)
                hba1c = gr.Slider(3, 15, label="Гликированный Hb %", value=5.4, step=0.1)
                rxd = gr.Slider(0, 15, label="Кол-во лекарств", value=0)
            
            with gr.Group():
                gr.Markdown("🧠 **Гериатрический статус**")
                with gr.Row():
                    pfq = gr.Checkbox(label="Физ. огр.")
                    mem = gr.Checkbox(label="Память")
                    conc = gr.Checkbox(label="Конц.")
                    walk = gr.Checkbox(label="Ходьба")
            
            btn = gr.Button("🚀 ПРОВЕСТИ АНАЛИЗ", variant="primary")

        with gr.Column(scale=1):
            gr.Markdown("### 📊 Заключение")
            out_stage = gr.Label(label="Результат")
            out_ind = gr.Textbox(label="Ключевые показатели")
            out_plot = gr.Image(label="Вклад факторов (SHAP)")
            out_pdf = gr.File(label="📥 Отчет для печати (PDF)")

    btn.click(main_analysis, [age, gender, bmi, sys_bp, neutro, lympho, ast, plt_val, creat, hba1c, rxd, pfq, mem, conc, walk], [out_stage, out_ind, out_plot, out_pdf])

demo.launch()