logic.py

import numpy as np
import plotly.graph_objects as go
from datetime import datetime

# --- 嘗試匯入 FPDF (防呆機制) ---
try:
    from fpdf import FPDF
    HAS_FPDF = True
except ImportError:
    HAS_FPDF = False

# --- PDF 生成引擎 ---
if HAS_FPDF:
    class PDFReport(FPDF):
        def header(self):
            self.set_font('Arial', 'B', 15)
            self.cell(0, 10, 'RadShield AI - Clinical Decision Report', 0, 1, 'C')
            self.ln(5)

        def footer(self):
            self.set_y(-15)
            self.set_font('Arial', 'I', 8)
            self.cell(0, 10, f'Page {self.page_no()}', 0, 0, 'C')

    def create_pdf(patient_data, risk_result):
        pdf = PDFReport()
        pdf.add_page()
        pdf.set_font("Arial", size=12)

        # 1. Patient Info
        pdf.set_font("Arial", 'B', 12)
        pdf.cell(0, 10, "1. Patient Information", 0, 1)
        pdf.set_font("Arial", size=10)
        pdf.cell(0, 6, f"Name: {patient_data['name_en']} (ID: {patient_data['id']})", 0, 1)
        pdf.cell(0, 6, f"Age: {patient_data['age']} | Date: {datetime.now().strftime('%Y-%m-%d')}", 0, 1)
        pdf.ln(5)

        # 2. Risk Assessment
        pdf.set_font("Arial", 'B', 12)
        pdf.cell(0, 10, "2. Risk Stratification", 0, 1)
        pdf.set_font("Arial", size=10)
        
        # Acute
        if risk_result['acute_level'] == 'High':
            pdf.set_text_color(220, 53, 69) 
        else:
            pdf.set_text_color(0, 0, 0)
        pdf.cell(0, 6, f"Acute Toxicity Risk: {risk_result['acute_level']} (Score: {risk_result['acute_score']}/100)", 0, 1)
        
        # Chronic
        if risk_result['chronic_level'] == 'High':
            pdf.set_text_color(220, 53, 69) 
        else:
            pdf.set_text_color(0, 0, 0)
        pdf.cell(0, 6, f"Late Toxicity Risk: {risk_result['chronic_level']} (Score: {risk_result['chronic_score']}/100)", 0, 1)
        pdf.set_text_color(0, 0, 0)
        pdf.ln(5)

        # 3. Drivers
        pdf.set_font("Arial", 'B', 12)
        pdf.cell(0, 10, "3. Key Risk Drivers (Concept 4)", 0, 1)
        pdf.set_font("Arial", size=10)
        
        if risk_result['drivers_en']:
            for driver in risk_result['drivers_en']:
                pdf.cell(0, 6, f"- {driver}", 0, 1)
        else:
            pdf.cell(0, 6, "- No significant risk drivers identified.", 0, 1)
        pdf.ln(5)

        # 4. Recommendations
        pdf.set_font("Arial", 'B', 12)
        pdf.cell(0, 10, "4. Optimization Plan (Concept 3)", 0, 1)
        pdf.set_font("Arial", size=10)
        
        if risk_result['recommendations_en']:
            for rec in risk_result['recommendations_en']:
                pdf.multi_cell(0, 6, f"- {rec}")
        else:
            pdf.cell(0, 6, "- Standard of Care (SoC) recommended.", 0, 1)
            
        pdf.ln(10)
        pdf.set_font("Arial", 'I', 8)
        pdf.cell(0, 6, "Disclaimer: This report is generated by a prototype AI system.", 0, 1)

        return pdf.output(dest='S').encode('latin-1')
else:
    def create_pdf(patient_data, risk_result):
        raise ImportError("FPDF package not installed")


# --- 風險計算核心邏輯（整合版）---
def calculate_risk(patient_data):
    """
    整合的風險計算函數，結合了兩個應用的邏輯
    """
    acute_score = 10
    chronic_score = 5
    drivers = []     
    drivers_en = []  
    recommendations = []    
    recommendations_en = [] 

    # 1. 臨床因子
    if patient_data.get('ibd', False):
        acute_score += 25
        chronic_score += 30
        drivers.append('發炎性腸道疾病 (IBD)')
        drivers_en.append('Inflammatory Bowel Disease (IBD)')
    if patient_data.get('diabetes', False):
        chronic_score += 10
    if patient_data.get('anticoagulants', False):
        acute_score += 15
        chronic_score += 25
        drivers.append('使用抗凝血劑 (Bleeding Risk)')
        drivers_en.append('Anticoagulant Usage (High Bleeding Risk)')
    
    # 2. 基因組因子 (Concept 4)
    if patient_data.get('atm_mutation', False):
        acute_score += 20
        chronic_score += 25
        drivers.append('ATM 基因突變 (Radiosensitivity)')
        drivers_en.append('ATM Gene Mutation (Radiosensitivity)')
    if patient_data.get('nbn_mutation', False):
        acute_score += 15
        chronic_score += 20
        drivers.append('NBN 基因突變')
        drivers_en.append('NBN Gene Mutation')
    if patient_data.get('lig1_mutation', False):
        acute_score += 12
        chronic_score += 18
        drivers.append('LIG1 基因突變 (複製修復異常)')
        drivers_en.append('LIG1 Mutation (Replication Repair Defect)')
    if patient_data.get('lig4_mutation', False):
        acute_score += 30
        chronic_score += 40
        drivers.append('LIG4 基因表現異常 (DNA Repair Defect)')
        drivers_en.append('LIG4 Expression Abnormality (DNA Repair Defect)')
    if patient_data.get('pcna_mutation', False):
        acute_score += 10
        chronic_score += 15
        drivers.append('PCNA 變異 (複製壓力升高)')
        drivers_en.append('PCNA Mutation (Replication Stress)')
    if patient_data.get('rev3l_mutation', False):
        acute_score += 18
        chronic_score += 25
        drivers.append('REV3L 變異 (Translesion 合成風險)')
        drivers_en.append('REV3L Mutation (TLS Risk)')
    if patient_data.get('polh_mutation', False):
        acute_score += 16
        chronic_score += 20
        drivers.append('POLH 變異 (損傷修復不足)')
        drivers_en.append('POLH Mutation (TLS Inefficiency)')
    if patient_data.get('xpc_mutation', False):
        acute_score += 14
        chronic_score += 15
        drivers.append('XPC 變異 (NER 缺陷)')
        drivers_en.append('XPC Mutation (NER Defect)')

    # 3. 劑量學因子
    v70 = patient_data.get('rectum_v70', 0)
    v50 = patient_data.get('rectum_v50', 0)
    
    if v70 > 20:
        chronic_score += (v70 - 20) * 2
        drivers.append(f'Rectum V70 過高 ({v70}%)')
        drivers_en.append(f'High Rectum V70 ({v70}%)')
    if v50 > 50:
        acute_score += (v50 - 50) * 1.5

    # 4. 治療模式因子（來自 HTML 版本）
    modality_multiplier = patient_data.get('modality', 1.0)
    acute_score = acute_score * modality_multiplier
    chronic_score = chronic_score * modality_multiplier

    # 5. 年齡因子
    age = patient_data.get('age', 60)
    if age > 75:
        chronic_score += 5
    if age > 70:
        acute_score += 3

    acute_score = min(acute_score, 100)
    chronic_score = min(chronic_score, 100)

    def get_level(score):
        if score < 30: return 'Low', 'green'
        if score < 60: return 'Intermediate', 'orange'
        return 'High', 'red'

    acute_level, acute_color = get_level(acute_score)
    chronic_level, chronic_color = get_level(chronic_score)

    # 產生建議
    if chronic_level == 'High' or v70 > 20:
        recommendations.append("💡 建議使用直腸隔離凝膠 (SpaceOAR) 以增加物理距離")
        recommendations.append("💡 需重新優化治療計畫：限制 Rectum V70 < 15%")
        recommendations_en.append("Apply Rectal Spacer (SpaceOAR) to increase physical separation.")
        recommendations_en.append("Re-optimize treatment plan: Tighten Rectum V70 constraint to < 15%.")
    
    if any([patient_data.get('atm_mutation', False),
            patient_data.get('lig4_mutation', False),
            patient_data.get('rev3l_mutation', False),
            patient_data.get('polh_mutation', False)]):
        recommendations.append("🧬 基因組顯示高輻射敏感性：考慮減少單次劑量或轉用質子治療 (Proton)")
        recommendations_en.append("Genomic Radiosensitivity: Consider hypofractionation reduction or Proton Therapy.")
    
    if patient_data.get('anticoagulants', False) and acute_score > 40:
        recommendations.append("💊 出血風險高：建議會診心臟科評估暫停抗凝血劑")
        recommendations_en.append("High Bleeding Risk: Consult cardiology regarding temporary suspension of anticoagulants.")

    # 計算綜合風險分數（用於族群定位圖表）
    overall_score = (acute_score + chronic_score) / 2

    return {
        "acute_score": acute_score,
        "chronic_score": chronic_score,
        "overall_score": overall_score,
        "acute_level": acute_level,
        "chronic_level": chronic_level,
        "drivers": drivers,
        "drivers_en": drivers_en,
        "recommendations": recommendations,
        "recommendations_en": recommendations_en
    }

# --- 生成族群風險定位圖表（來自 HTML 版本）---
def create_population_chart(patient_score):
    """
    創建族群風險分佈圖表，顯示病患在族群中的位置
    """
    # 生成常態分佈數據（模擬 1200 例病患）
    mean = 40
    std = 15
    x = np.linspace(0, 100, 100)
    y = (1 / (std * np.sqrt(2 * np.pi))) * np.exp(-0.5 * ((x - mean) / std) ** 2)
    
    # 標準化 y 值以便視覺化
    y_normalized = y / y.max() * 100
    
    fig = go.Figure()
    
    # 族群分佈曲線（藍色主題）
    fig.add_trace(go.Scatter(
        x=x,
        y=y_normalized,
        mode='lines',
        name='族群分佈 (Population)',
        line=dict(color='#3b82f6', width=3),
        fill='tozeroy',
        fillcolor='rgba(59, 130, 246, 0.15)'
    ))
    
    # 目前病患位置（深藍色標記）
    patient_y = (1 / (std * np.sqrt(2 * np.pi))) * np.exp(-0.5 * ((patient_score - mean) / std) ** 2)
    patient_y_normalized = patient_y / y.max() * 100
    
    fig.add_trace(go.Scatter(
        x=[patient_score],
        y=[patient_y_normalized],
        mode='markers',
        name='目前病患 (Current Patient)',
        marker=dict(
            size=18,
            color='#1e40af',
            symbol='circle',
            line=dict(width=3, color='white')
        ),
        hovertemplate=f'風險分數: {patient_score:.1f}<extra></extra>'
    ))
    
    fig.update_layout(
        # title="族群風險定位 (Population Study)", # 移除重複標題
        xaxis_title="預測風險分數 (Risk Score)",
        yaxis_title="相對頻率",
        height=300,
        hovermode='x unified',
        legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1),
        margin=dict(l=0, r=0, t=40, b=0),
        plot_bgcolor='white',
        paper_bgcolor='white',
        font=dict(color='#1e293b')
    )
    
    return fig