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app.py

@@ -0,0 +1,1070 @@
+# -*- coding: utf-8 -*-
+"""
+PRA Analysis Web App
+上傳 xPONENT CSV → 自動分析 PRA Class I / II → 產生報告
+"""
+
+import io
+import os
+import re
+import sys
+import tempfile
+from pathlib import Path
+from collections import OrderedDict
+from datetime import datetime
+
+from flask import Flask, render_template, request, send_file, jsonify, redirect, url_for, session as flask_session
+
+# 匯入 PRA.py 核心邏輯
+from PRA import (
+    parse_xponent_csv, get_rxn, load_cutoffs, find_nc_sample,
+    NC_BEAD, PC_BEAD, CONTROL_BEADS,
+    BEAD_HLA_LOT21, _parse_allele_list,
+)
+
+app = Flask(__name__)
+app.config['MAX_CONTENT_LENGTH'] = 16 * 1024 * 1024  # 16MB
+app.secret_key = 'pra-analysis-2025'
+
+DEFAULT_USERS = {'NEPH': 'NEPH12345', 'okokyytt@gmail.com': '1234'}
+
+ADMIN_USER = 'okokyytt@gmail.com'
+
+import db as _db
+_db.seed_default_users(DEFAULT_USERS, admin_user=ADMIN_USER)
+
+
+def login_required(f):
+    from functools import wraps
+    @wraps(f)
+    def decorated(*args, **kwargs):
+        if not flask_session.get('logged_in'):
+            return redirect(url_for('login'))
+        return f(*args, **kwargs)
+    return decorated
+
+CUTOFFS = load_cutoffs()
+
+# ============================================================
+# PRA2 Lot 20 — LABScreen PRA Class II Bead-to-HLA Mapping
+# 35 HLA beads: 038-068, 090-094, 096, 098
+# ============================================================
+
+BEAD_HLA_LOT20 = OrderedDict([
+    ('038', {'sero': 'DR1, DR18, DR52, DQ4, DQ5, DP1',
+             'allele': 'DRB1*01:02, DRB1*03:02, DRB3*01:62, DQA1*01:01, DQA1*04:01, DQB1*04:02, DQB1*05:01, DPA1*02:01, DPA1*02:02, DPB1*01:01'}),
+    ('039', {'sero': 'DR1, DR16, DQ5, DP3, DP4',
+             'allele': 'DRB1*01:01, DRB1*16:01, DQA1*01:01, DQA1*01:02, DQB1*05:01, DQB1*05:02, DPA1*01:03, DPB1*03:01, DPB1*04:01'}),
+    ('042', {'sero': 'DR1, DR4, DQ7, DQ5, DP4',
+             'allele': 'DRB1*01:01, DRB1*04:01, DRB4*01:03:01:02N, DQA1*01:01, DQA1*03:03, DQB1*03:01/276N, DQB1*05:01, DPA1*01:03, DPB1*04:01'}),
+    ('043', {'sero': 'DR1, DR7, DR53, DR51, DQ2, DQ5, DP2, DP4',
+             'allele': 'DRB1*01:01, DRB1*07:01, DRB4*01:01, DRB5*01:01, DQA1*01:01, DQA1*02:01, DQB1*02:02, DQB1*05:01, DPA1*01:03, DPB1*02:01, DPB1*04:01'}),
+    ('044', {'sero': 'DR103, DR17, DR52, DQ2, DQ5, DP2, DP106',
+             'allele': 'DRB1*01:03, DRB1*03:01, DRB3*02:02, DQA1*01:01, DQA1*05:01, DQB1*02:01/163N, DQB1*05:01, DPA1*01:03, DPA1*02:01, DPB1*02:01, DPB1*106:01'}),
+    ('045', {'sero': 'DR103, DR13, DR52, DQ7, DP2, DP131',
+             'allele': 'DRB1*01:03, DRB1*13:04, DRB3*02:02, DQA1*05:05, DQB1*03:01, DQB1*03:19, DPA1*01:03, DPA1*02:01, DPB1*02:01, DPB1*131:01'}),
+    ('046', {'sero': 'DR4, DR11, DR52, DR53, DQ7, DQ8, DP2, DP4',
+             'allele': 'DRB1*04:02, DRB1*11:04, DRB3*02:02, DRB4*01:03, DQA1*03:01, DQA1*05:05, DQB1*03:01, DQB1*03:02, DPA1*01:03, DPA1*02:01, DPB1*02:01, DPB1*04:01'}),
+    ('047', {'sero': 'DR4, DR12, DR52, DR53, DQ7, DQ8, DP2, DP5',
+             'allele': 'DRB1*04:04, DRB1*12:02, DRB3*03:01, DRB4*01:03, DQA1*03:01, DQA1*06:01, DQB1*03:01, DQB1*03:02, DPA1*02:01, DPA1*02:02, DPB1*02:01, DPB1*05:01'}),
+    ('048', {'sero': 'DR4, DR14, DR52, DR53, DQ7, DQ5, DP1, DP3',
+             'allele': 'DRB1*04:01, DRB1*14:54, DRB3*02:02, DRB4*01:03, DQA1*01:04, DQA1*03:03, DQB1*03:01/276N, DQB1*05:03, DPA1*01:03, DPA1*02:01, DPB1*01:01, DPB1*03:01'}),
+    ('050', {'sero': 'DR7, DR11, DR52, DR53, DQ2, DQ7, DP1, DP4',
+             'allele': 'DRB1*07:01, DRB1*11:04, DRB3*02:02, DRB4*01:01, DQA1*02:01, DQA1*05:05, DQB1*02:02, DQB1*03:01, DPA1*01:03, DPA1*02:01, DPB1*01:01, DPB1*04:02'}),
+    ('051', {'sero': 'DR4, DR8, DR53, DQ8, DQ4, DP1, DP4',
+             'allele': 'DRB1*04:04, DRB1*08:01, DRB4*01:03, DQA1*03:01, DQA1*04:02, DQB1*03:02, DQB1*04:02, DPA1*01:03, DPA1*02:01, DPB1*01:01, DPB1*04:02'}),
+    ('052', {'sero': 'DR9, DR11, DR52, DR53, DQ7, DQ9, DP5',
+             'allele': 'DRB1*09:01, DRB1*11:01, DRB3*02:02, DRB4*01:03, DQA1*03:02, DQA1*05:05, DQB1*03:01, DQB1*03:03, DPA1*02:02, DPB1*05:01'}),
+    ('053', {'sero': 'DR9, DR10, DR53, DQ9, DQ5, DP5',
+             'allele': 'DRB1*09:01, DRB1*10:01, DRB4*01:03, DQA1*01:05, DQA1*03:02, DQB1*03:03, DQB1*05:01, DPA1*02:02, DPB1*05:01'}),
+    ('054', {'sero': 'DR7, DR12, DR52, DQ9, DQ5, DP3, DP14',
+             'allele': 'DRB1*07:01, DRB1*12:02, DRB3*03:01, DRB4*01:03:01:02N, DQA1*01:02, DQA1*02:01, DQB1*03:03, DQB1*05:02, DPA1*02:01, DPA1*02:02, DPB1*03:01, DPB1*14:01'}),
+    ('055', {'sero': 'DR7, DR15, DR53, DR51, DQ2, DQ6, DP4',
+             'allele': 'DRB1*07:01, DRB1*15:01, DRB4*01:03, DRB5*01:01, DQA1*01:02, DQA1*02:01, DQB1*02:02, DQB1*06:03, DPA1*01:03, DPB1*04:01, DPB1*04:02'}),
+    ('056', {'sero': 'DR17, DR7, DR52, DR53, DQ2, DP2',
+             'allele': 'DRB1*03:01, DRB1*07:01, DRB3*02:02, DRB4*01:01, DQA1*03:03, DQA1*05:01, DQB1*02:01/163N, DQB1*02:02, DPA1*01:03, DPB1*02:01'}),
+    ('057', {'sero': 'DR7, DR10, DR53, DQ2, DQ5, DP2, DP4',
+             'allele': 'DRB1*07:01, DRB1*10:01, DRB4*01:01, DQA1*01:05, DQA1*03:03, DQB1*02:02, DQB1*05:01, DPA1*01:03, DPB1*02:01, DPB1*04:01'}),
+    ('058', {'sero': 'DR8, DR12, DR52, DQ9, DQ4, DP5',
+             'allele': 'DRB1*08:02, DRB1*12:01, DRB3*01:01, DQA1*03:02, DQA1*04:01, DQB1*03:03, DQB1*04:02, DPA1*02:01, DPA1*02:02, DPB1*05:01'}),
+    ('059', {'sero': 'DR8, DR14, DR52, DQ4, DQ5, DP4',
+             'allele': 'DRB1*08:01, DRB1*14:01, DRB3*02:24, DQA1*01:04, DQA1*04:01, DQB1*04:02, DQB1*05:03, DPA1*01:03, DPB1*04:01=DPB1*105:01, DPB1*04:02=DPB1*126:01'}),
+    ('060', {'sero': 'DR9, DR12, DR52, DR53, DQ7, DQ9, DP5',
+             'allele': 'DRB1*09:01, DRB1*12:02, DRB3*02:02, DRB4*01:03, DQA1*03:02, DQA1*06:01, DQB1*03:01, DQB1*03:03, DPA1*02:02, DPB1*05:01'}),
+    ('061', {'sero': 'DR11, DR12, DR52, DQ7, DQ6, DP4, DP18',
+             'allele': 'DRB1*11:01, DRB1*12:02, DRB3*02:02, DRB3*03:01, DQA1*01:02, DQA1*06:01, DQB1*03:01, DQB1*06:02, DPA1*01:03, DPB1*04:01, DPB1*18:01'}),
+    ('062', {'sero': 'DR4, DR11, DR52, DR53, DQ7, DP2, DP17',
+             'allele': 'DRB1*04:03, DRB1*11:02, DRB3*02:02, DRB4*01:03, DQA1*03:01, DQA1*05:05, DQB1*03:04, DQB1*03:19, DPA1*01:03, DPA1*02:01, DPB1*02:01, DPB1*17:01'}),
+    ('063', {'sero': 'DR11, DR15, DR52, DR51, DQ6, DP2, DP18',
+             'allele': 'DRB1*11:01, DRB1*15:03, DRB3*02:02, DRB5*01:01, DQA1*01:02, DQB1*06:02, DPA1*01:03, DPB1*02:01, DPB1*18:01'}),
+    ('064', {'sero': 'DR18, DR12, DR52, DQ4, DQ5, DP1, DP18',
+             'allele': 'DRB1*03:02, DRB1*12:01, DRB3*01:01, DRB3*01:62, DQA1*01:05, DQA1*04:01, DQB1*04:02, DQB1*05:01, DPA1*01:03, DPA1*02:02, DPB1*01:01, DPB1*18:01'}),
+    ('065', {'sero': 'DR13, DR15, DR52, DR51, DQ6, DP3, DP13',
+             'allele': 'DRB1*13:02, DRB1*15:01, DRB3*03:01, DRB5*01:01, DQA1*01:02, DQB1*06:02, DQB1*06:04, DPA1*01:03, DPA1*02:01, DPB1*03:01, DPB1*13:01'}),
+    ('066', {'sero': 'DR17, DR13, DR52, DQ2, DQ5, DP1, DP13',
+             'allele': 'DRB1*03:01, DRB1*13:02, DRB3*02:02, DRB3*03:01, DQA1*01:02, DQA1*05:01, DQB1*02:01/163N, DQB1*05:01, DPA1*02:01, DPB1*01:01, DPB1*13:01'}),
+    ('067', {'sero': 'DR13, DR16, DR52, DR51, DQ7, DQ5, DP1, DP2',
+             'allele': 'DRB1*13:03, DRB1*16:01, DRB3*01:01, DRB5*02:02, DQA1*01:02, DQA1*05:05, DQB1*03:01, DQB1*05:02, DPA1*01:03, DPA1*02:02, DPB1*01:01, DPB1*02:02'}),
+    ('068', {'sero': 'DR13, DR14, DR52, DQ5, DQ6, DP1, DP5',
+             'allele': 'DRB1*13:01, DRB1*14:54, DRB3*01:01, DRB3*02:02, DQA1*01:03, DQA1*01:04, DQB1*05:03, DQB1*06:03, DPA1*02:02, DPB1*01:01, DPB1*05:01'}),
+    ('090', {'sero': 'DR14, DR16, DR52, DR51, DQ7, DP4, DP13',
+             'allele': 'DRB1*14:02, DRB1*16:02, DRB3*01:01, DRB5*02:02, DQA1*05:03, DQA1*05:05, DQB1*03:01, DPA1*01:03, DPA1*02:01, DPB1*04:01, DPB1*13:01'}),
+    ('091', {'sero': 'DR15, DR16, DR51, DQ5, DQ6, DP3, DP105',
+             'allele': 'DRB1*15:03, DRB1*16:02, DRB5*01:01, DRB5*02:21, DQA1*01:02, DQB1*05:02, DQB1*06:02, DPA1*01:03, DPA1*03:01, DPB1*03:01, DPB1*105:01'}),
+    ('092', {'sero': 'DR17, DR9, DR52, DR53, DQ2, DQ9, DP1, DP14',
+             'allele': 'DRB1*03:01, DRB1*09:01, DRB3*01:01, DRB4*01:03, DQA1*03:02, DQA1*05:01, DQB1*02:01, DQB1*03:03, DPA1*02:01, DPB1*01:01, DPB1*14:01'}),
+    ('093', {'sero': 'DR18, DR10, DR52, DQ4, DQ5, DP1, DP104',
+             'allele': 'DRB1*03:02, DRB1*10:01, DRB3*01:62, DQA1*01:05, DQA1*04:01, DQB1*04:02, DQB1*05:01, DPA1*01:03, DPA1*02:02, DPB1*01:01, DPB1*104:01'}),
+    ('094', {'sero': 'DR1, DR8, DQ4, DQ5, DP4, DP11',
+             'allele': 'DRB1*01:01, DRB1*08:01, DQA1*01:01, DQA1*04:01, DQB1*04:02, DQB1*05:01, DPA1*01:03, DPA1*02:01, DPB1*04:02, DPB1*11:01'}),
+    ('096', {'sero': 'DR17, DR13, DR52, DQ2, DQ6, DP2, DP5',
+             'allele': 'DRB1*03:01, DRB1*13:02, DRB3*02:02, DRB3*03:01, DQA1*01:02, DQA1*05:01, DQB1*02:01, DQB1*06:09, DPA1*02:01, DPB1*02:01, DPB1*05:01'}),
+    ('098', {'sero': 'DR9, DR15, DR53, DQ2, DQ5, DP13',
+             'allele': 'DRB1*09:01, DRB1*15:02, DRB4*01:01, DRB5*01:08:01N, DQA1*01:01, DQA1*03:03, DQB1*02:02, DQB1*05:01, DPA1*02:01, DPB1*13:01'}),
+])
+
+
+# ============================================================
+# 分析邏輯 (通用 PRA1 / PRA2)
+# ============================================================
+
+def clean_sero(sero_str):
+    """清理 sero 字串: 'A2, , B46, , Bw6, , Cw1,' → 'A2, B46, Cw1'"""
+    if not sero_str:
+        return ''
+    skip = {'Bw4', 'Bw6', 'DR51', 'DR52', 'DR53', ''}
+    parts = [s.strip() for s in sero_str.split(',')]
+    parts = [s for s in parts if s not in skip]
+    return ', '.join(parts)
+
+
+def detect_pra_class(metadata, bead_ids):
+    """根據 protocol 名稱或 bead ID 自動偵測 PRA Class"""
+    protocol = ''
+    if 'ProtocolName' in metadata:
+        protocol = metadata['ProtocolName'][0].upper()
+    if 'LS1PRA' in protocol or 'CLASS I' in protocol:
+        return 'PRA1'
+    if 'LS2PRA' in protocol or 'CLASS II' in protocol:
+        return 'PRA2'
+    # 用 bead ID 判斷
+    pra2_beads = set(BEAD_HLA_LOT20.keys())
+    pra1_beads = set(BEAD_HLA_LOT21.keys())
+    bid_set = set(bead_ids)
+    if bid_set & pra2_beads:
+        return 'PRA2'
+    if bid_set & pra1_beads:
+        return 'PRA1'
+    return 'PRA1'
+
+
+def get_bead_map(pra_class):
+    return BEAD_HLA_LOT20 if pra_class == 'PRA2' else BEAD_HLA_LOT21
+
+
+def analyze_sample(sample_medians, nc_medians, bead_ids, bead_hla_map):
+    """分析單一樣本 (通用)"""
+    sample_nc_bead = sample_medians.get(NC_BEAD, 0)
+    nc_nc_bead = nc_medians.get(NC_BEAD, 0)
+    results = OrderedDict()
+    for bid in bead_ids:
+        if bid in CONTROL_BEADS or bid not in bead_hla_map:
+            continue
+        raw = sample_medians.get(bid, 0)
+        ns_raw = nc_medians.get(bid, 0)
+        normal = max(0.0, raw - sample_nc_bead - ns_raw + nc_nc_bead)
+        denom = ns_raw * sample_nc_bead
+        ratio = (raw * nc_nc_bead) / denom if denom > 0 else 0.0
+        rxn = get_rxn(normal)
+        results[bid] = {
+            'raw': raw, 'ns_raw': ns_raw,
+            'sample_nc': sample_nc_bead, 'nsnc': nc_nc_bead,
+            'normal': round(normal, 2), 'ratio': round(ratio, 2), 'rxn': rxn,
+        }
+    return results
+
+
+def calculate_pra(bead_results, threshold='X6'):
+    threshold_rxn = int(threshold[1:])
+    total = len(bead_results)
+    positive = sum(1 for r in bead_results.values() if r['rxn'] >= threshold_rxn)
+    pra = round(positive / total * 100) if total > 0 else 0
+    return pra, positive, total
+
+
+def get_confident_alleles(bead_results, bead_hla_map):
+    """
+    篩選 confident alleles：allele 層級檢查。
+    每個 allele 獨立判斷：只在 X6/X8 bead 出現、不在任何 X2/X4 bead 出現。
+    同一個 sero 的不同 allele 互不影響。
+    例: A*11:01 在 X2 → 排除 A*11:01
+        A*11:02 只在 X8 → 保留 A*11:02 → 報告寫 A11(A*11:02)
+    """
+    x6x8 = set()
+    x2x4 = set()
+    for bid, r in bead_results.items():
+        hla = bead_hla_map.get(bid, {})
+        alleles = _parse_allele_list(hla.get('allele', ''))
+        if r['rxn'] >= 6:
+            x6x8.update(alleles)
+        elif r['rxn'] in (2, 4):
+            x2x4.update(alleles)
+    return x6x8 - x2x4
+
+
+def _build_allele_to_sero(bead_hla_map):
+    """從 bead map 建立 allele→sero 對照"""
+    mapping = {}
+    for bid, hla in bead_hla_map.items():
+        sero_parts = [s.strip() for s in hla.get('sero', '').split(',')]
+        allele_parts = [a.strip() for a in hla.get('allele', '').split(',')]
+        skip = {'Bw4', 'Bw6', 'DR51', 'DR52', 'DR53', ''}
+        sero_by_locus = {}
+        for s in sero_parts:
+            if s in skip:
+                continue
+            # 判斷 locus
+            if s.startswith('A'):
+                sero_by_locus.setdefault('A', []).append(s)
+            elif s.startswith('B'):
+                sero_by_locus.setdefault('B', []).append(s)
+            elif s.startswith('Cw') or s.startswith('C'):
+                sero_by_locus.setdefault('C', []).append(s)
+            elif s.startswith('DR'):
+                sero_by_locus.setdefault('DR', []).append(s)
+            elif s.startswith('DQ'):
+                sero_by_locus.setdefault('DQ', []).append(s)
+            elif s.startswith('DP'):
+                sero_by_locus.setdefault('DP', []).append(s)
+
+        allele_by_locus = {}
+        for a in allele_parts:
+            if not a or a == '-':
+                continue
+            if a.startswith('A'):
+                allele_by_locus.setdefault('A', []).append(a)
+            elif a.startswith('B'):
+                allele_by_locus.setdefault('B', []).append(a)
+            elif a.startswith('C'):
+                allele_by_locus.setdefault('C', []).append(a)
+            elif a.startswith('DRB'):
+                allele_by_locus.setdefault('DR', []).append(a)
+            elif a.startswith('DQA') or a.startswith('DQB'):
+                allele_by_locus.setdefault('DQ', []).append(a)
+            elif a.startswith('DPA') or a.startswith('DPB'):
+                allele_by_locus.setdefault('DP', []).append(a)
+
+        for locus in sero_by_locus:
+            seros = sero_by_locus[locus]
+            alleles = allele_by_locus.get(locus, [])
+            for i, ag in enumerate(alleles):
+                if i < len(seros):
+                    mapping[ag] = seros[i]
+    return mapping
+
+
+def build_sero_mfi_stats(beads_detail, confident_alleles, bead_hla_map):
+    """
+    計算每個 confident sero 的 Max/Mean Normal MFI。
+    回傳 list of dict: [{sero, alleles, max_mfi, mean_mfi, count, beads}, ...]
+    按 max_mfi 降序排列。
+    """
+    a2s = _build_allele_to_sero(bead_hla_map)
+    skip_sero = {'Bw4', 'Bw6', 'DR51', 'DR52', 'DR53', ''}
+
+    # 找出 confident sero set
+    conf_seros = set()
+    sero_alleles = {}  # sero -> set of confident alleles
+    for ag in confident_alleles:
+        sero = a2s.get(ag)
+        if sero and sero not in skip_sero:
+            conf_seros.add(sero)
+            sero_alleles.setdefault(sero, set()).add(ag)
+
+    # 收集每個 confident sero 在正陽性 bead 上的 Normal 值
+    sero_normals = {}  # sero -> [normal values]
+    sero_beads = {}    # sero -> [bead ids]
+    for b in beads_detail:
+        if b['rxn'] < 6:
+            continue
+        hla = bead_hla_map.get(b['bead'], {})
+        sero_parts = [s.strip() for s in hla.get('sero', '').split(',')]
+        for s in sero_parts:
+            if s in skip_sero:
+                continue
+            if s in conf_seros:
+                sero_normals.setdefault(s, []).append(b['normal'])
+                sero_beads.setdefault(s, []).append(b['bead'])
+
+    # 組裝結果
+    stats = []
+    for sero in conf_seros:
+        normals = sero_normals.get(sero, [])
+        if not normals:
+            continue
+        alleles_str = ', '.join(sorted(sero_alleles.get(sero, set())))
+        stats.append({
+            'sero': sero,
+            'alleles': alleles_str,
+            'max_mfi': round(max(normals), 1),
+            'mean_mfi': round(sum(normals) / len(normals), 1),
+            'count': len(normals),
+            'beads': ', '.join(sero_beads.get(sero, [])),
+        })
+
+    # 排序: 按 locus 分組, 再按 max_mfi 降序
+    def sort_key(x):
+        s = x['sero']
+        if s.startswith('A'):
+            locus = 0
+        elif s.startswith('B'):
+            locus = 1
+        elif s.startswith('Cw') or s.startswith('C'):
+            locus = 2
+        elif s.startswith('DR'):
+            locus = 0
+        elif s.startswith('DQ'):
+            locus = 1
+        elif s.startswith('DP'):
+            locus = 2
+        else:
+            locus = 9
+        return (locus, -x['max_mfi'])
+
+    stats.sort(key=sort_key)
+    return stats
+
+
+def generate_specificity(confident_alleles, bead_hla_map):
+    """
+    將 confident alleles 轉為 sero 格式的 Specificity 字串。
+    例: A11(A*11:02) A23 A24 B7 B62(B*15:01) Cw1
+    """
+    if not confident_alleles:
+        return '(-)'
+
+    a2s = _build_allele_to_sero(bead_hla_map)
+
+    # 統計每個 sero group 在整個 bead panel 上有哪些 allele
+    all_alleles_per_sero = {}
+    for bid, hla in bead_hla_map.items():
+        for ag in _parse_allele_list(hla.get('allele', '')):
+            sero = a2s.get(ag)
+            if sero:
+                all_alleles_per_sero.setdefault(sero, set()).add(ag)
+
+    # 將 confident alleles 按 sero 分組
+    sero_groups = {}
+    for ag in confident_alleles:
+        sero = a2s.get(ag)
+        if sero:
+            sero_groups.setdefault(sero, set()).add(ag)
+
+    # 排序
+    def sort_key(s):
+        if s.startswith('A'):
+            return (0, s)
+        elif s.startswith('B'):
+            return (1, s)
+        elif s.startswith('Cw'):
+            return (2, s)
+        elif s.startswith('DR'):
+            return (0, s)
+        elif s.startswith('DQ'):
+            return (1, s)
+        elif s.startswith('DP'):
+            return (2, s)
+        return (9, s)
+
+    parts = []
+    for sero in sorted(sero_groups.keys(), key=sort_key):
+        conf = sero_groups[sero]
+        total = all_alleles_per_sero.get(sero, set())
+        if conf >= total:
+            # 所有 allele 都是 confident → 只寫 sero
+            parts.append(sero)
+        else:
+            # 部分 → sero(allele1 allele2)
+            parts.append(f'{sero}({" ".join(sorted(conf))})')
+
+    return ' '.join(parts)
+
+
+def parse_xls_file(raw_bytes):
+    """
+    解析 HLA Fusion XLS 報告。
+    可能包含一個病人或 NC 報告。
+    回傳 dict: {sample_name, pra_class, date, beads_detail, ...}
+    """
+    import xlrd
+    wb = xlrd.open_workbook(file_contents=raw_bytes)
+    sh = wb.sheet_by_index(0)
+
+    # 讀取 metadata
+    sample_name = str(sh.cell_value(0, 0)).strip()
+    if not sample_name:
+        # Row 1 可能有 PATIENT: xxx
+        if sh.nrows > 1:
+            r1 = str(sh.cell_value(1, 0)).strip()
+            if r1.startswith('PATIENT'):
+                sample_name = str(sh.cell_value(1, 1)).strip() if sh.ncols > 1 else r1
+    session = ''
+    date_val = ''
+    catalog = ''
+    # 掃描 row 3 和 row 5 找 key-value (value 可能在 label 後面任何欄位)
+    for r in [3, 5]:
+        if r >= sh.nrows:
+            continue
+        row = [str(sh.cell_value(r, c)).strip() for c in range(min(sh.ncols, 15))]
+        for i, v in enumerate(row):
+            if v == 'SESSION :' or v == 'SESSION:':
+                for j in range(i + 1, len(row)):
+                    if row[j]:
+                        session = row[j]; break
+            if v == 'TEST DATE :' or v == 'TEST DATE:':
+                for j in range(i + 1, len(row)):
+                    if row[j]:
+                        date_val = row[j]; break
+            if v == 'CATALOG :' or v == 'CATALOG:':
+                for j in range(i + 1, len(row)):
+                    if row[j]:
+                        catalog = row[j]; break
+
+    # 偵測 PRA class
+    pra_class = 'PRA1'
+    if 'LS2PRA' in catalog.upper() or 'PRA2' in catalog.upper():
+        pra_class = 'PRA2'
+    elif 'PRA2' in session.upper():
+        pra_class = 'PRA2'
+    bead_map = get_bead_map(pra_class)
+
+    # 讀取 bead 資料 (col 0=BeadID, 3=Raw, 11=NS_Raw, 15=Normal, 20=Ratio, 22=Rxn, 26=Count, 29=Sero, 35=Allele)
+    beads_detail = []
+    nc_raw = 0
+    pc_raw = 0
+    for r in range(9, sh.nrows):
+        bid = str(sh.cell_value(r, 0)).strip()
+        if not bid or bid == 'BeadID':
+            continue
+        # 整數 bead ID → 補零到 3 位
+        try:
+            bid_int = int(float(bid))
+            bid = f'{bid_int:03d}'
+        except (ValueError, TypeError):
+            continue
+
+        raw_val = sh.cell_value(r, 3) if sh.ncols > 3 else 0
+        ns_raw = sh.cell_value(r, 11) if sh.ncols > 11 else 0
+        normal = sh.cell_value(r, 15) if sh.ncols > 15 else 0
+        ratio = sh.cell_value(r, 20) if sh.ncols > 20 else 0
+        rxn_val = sh.cell_value(r, 22) if sh.ncols > 22 else ''
+        count_val = sh.cell_value(r, 26) if sh.ncols > 26 else 0
+        sero_raw = str(sh.cell_value(r, 29)).strip() if sh.ncols > 29 else ''
+        sero = clean_sero(sero_raw)
+        allele = str(sh.cell_value(r, 35)).strip() if sh.ncols > 35 else ''
+
+        # 處理數值
+        try:
+            raw_val = float(raw_val) if raw_val != '' else 0
+        except (ValueError, TypeError):
+            raw_val = 0
+        try:
+            ns_raw = float(ns_raw) if ns_raw != '' else 0
+        except (ValueError, TypeError):
+            ns_raw = 0
+        try:
+            normal = float(normal) if normal != '' else 0
+        except (ValueError, TypeError):
+            normal = 0
+        try:
+            ratio = float(ratio) if ratio != '' else 0
+        except (ValueError, TypeError):
+            ratio = 0
+        try:
+            count_val = int(float(count_val)) if count_val != '' else 0
+        except (ValueError, TypeError):
+            count_val = 0
+
+        # Rxn: 可能是數字 1/2/4/6/8 或 'NC'/'PC'
+        rxn_str = str(rxn_val).strip()
+        if rxn_str in ('NC', 'nc'):
+            nc_raw = raw_val
+            continue
+        elif rxn_str in ('PC', 'pc'):
+            pc_raw = raw_val
+            continue
+
+        try:
+            rxn_int = int(float(rxn_val))
+        except (ValueError, TypeError):
+            continue
+
+        if bid not in bead_map:
+            continue
+
+        beads_detail.append({
+            'bead': bid, 'rxn': rxn_int,
+            'raw': round(raw_val, 1), 'ns_raw': round(ns_raw, 1),
+            'normal': round(normal, 2), 'ratio': round(ratio, 2),
+            'count': count_val,
+            'sero': sero,
+            'allele': allele,
+        })
+
+    if not beads_detail:
+        return None  # 沒有有效的 HLA bead 資料
+
+    # 計算 PRA%
+    total = len(beads_detail)
+    pra_all = {}
+    for t in ['X2', 'X4', 'X6', 'X8']:
+        t_rxn = int(t[1:])
+        pos = sum(1 for b in beads_detail if b['rxn'] >= t_rxn)
+        pra_all[t] = round(pos / total * 100) if total > 0 else 0
+
+    pra6 = pra_all['X6']
+    overall = 'Positive' if pra6 > 0 else 'Negative'
+
+    # Confident alleles (sero 層級檢查)
+    # 將 beads_detail 轉為 get_confident_alleles 需要的格式
+    br_dict = OrderedDict()
+    for b in beads_detail:
+        br_dict[b['bead']] = {'rxn': b['rxn']}
+    confident = get_confident_alleles(br_dict, bead_map)
+
+    sero_mfi = build_sero_mfi_stats(beads_detail, confident, bead_map) if overall == 'Positive' else []
+
+    return {
+        'name': sample_name,
+        'overall': overall,
+        'pra': pra6,
+        'pra_all': pra_all,
+        'beads': beads_detail,
+        'confident_alleles': sorted(confident),
+        'specificity': generate_specificity(confident, bead_map) if overall == 'Positive' else '(-)',
+        'sero_mfi': sero_mfi,
+        '_pra_class': pra_class,
+        '_date': date_val,
+        '_batch': session,
+        '_nc_raw': nc_raw,
+        '_pc_raw': pc_raw,
+    }
+
+
+def full_analyze_xls(raw_bytes_list, filenames):
+    """解析一個或多個 XLS 檔，回傳與 full_analyze 相同格式的結果"""
+    patients = []
+    pra_class = 'PRA1'
+    date_val = ''
+    batch = ''
+    nc_signal = 0
+    pc_signal = 0
+
+    errors = []
+    for raw_bytes, fname in zip(raw_bytes_list, filenames):
+        try:
+            pt = parse_xls_file(raw_bytes)
+        except Exception as e:
+            errors.append(f'{fname}: {type(e).__name__}: {e}')
+            continue
+        if pt is None:
+            errors.append(f'{fname}: 無 bead 資料 (可能非 HLA Fusion 報告)')
+            continue
+        # 若 sample name 為空，用檔名
+        if not pt.get('name'):
+            pt['name'] = Path(fname).stem
+        pra_class = pt.pop('_pra_class', 'PRA1')
+        if pt['_date']:
+            date_val = pt.pop('_date')
+        else:
+            pt.pop('_date')
+        if pt['_batch']:
+            batch = pt.pop('_batch')
+        else:
+            pt.pop('_batch')
+        nc_signal = pt.pop('_nc_raw', 0) or nc_signal
+        pc_signal = pt.pop('_pc_raw', 0) or pc_signal
+        patients.append(pt)
+
+    if not patients:
+        err_detail = '; '.join(errors) if errors else '無 bead 資料'
+        fnames = ', '.join(filenames)
+        return None, f'無法從 XLS 中讀取病人資料 [{fnames}] ({err_detail})'
+
+    return {
+        'pra_class': pra_class,
+        'class_label': 'PRA Class I' if pra_class == 'PRA1' else 'PRA Class II',
+        'date': date_val,
+        'batch': batch,
+        'nc_name': 'NC',
+        'pc_signal': round(pc_signal, 0),
+        'nc_signal': round(nc_signal, 0),
+        'patients': patients,
+        'filename': ', '.join(filenames),
+    }, None
+
+
+def full_analyze(csv_content, filename='upload.csv'):
+    """完整分析流程 (CSV)，回傳結構化結果"""
+    # 寫入暫存檔 (以 UTF-8 寫入)
+    tmp = tempfile.NamedTemporaryFile(delete=False, suffix='.csv', mode='wb')
+    tmp.write(csv_content.encode('utf-8-sig'))
+    tmp.close()
+
+    try:
+        metadata, bead_ids, data_blocks = parse_xponent_csv(tmp.name)
+    finally:
+        os.unlink(tmp.name)
+
+    pra_class = detect_pra_class(metadata, bead_ids)
+    bead_map = get_bead_map(pra_class)
+
+    median_data = data_blocks.get('Median') or data_blocks.get('Avg Net MFI')
+    if not median_data:
+        return None, 'CSV 中找不到 Median 資料'
+
+    count_data = data_blocks.get('Count', {})
+    sample_names = list(median_data.keys())
+    nc_name = find_nc_sample(sample_names)
+    if not nc_name:
+        return None, '找不到 NC 樣本'
+
+    nc_medians = median_data[nc_name]
+    date_val = metadata.get('Date', [''])[0] if 'Date' in metadata else ''
+    batch = metadata.get('Batch', [''])[0] if 'Batch' in metadata else ''
+
+    # PC/NC QC
+    pc_val = nc_medians.get(PC_BEAD, 0)
+    nc_val = nc_medians.get(NC_BEAD, 0)
+
+    patient_samples = [s for s in sample_names if s != nc_name]
+    patients = []
+
+    for sname in patient_samples:
+        br = analyze_sample(median_data[sname], nc_medians, bead_ids, bead_map)
+        pra6, pos6, tot = calculate_pra(br, 'X6')
+        overall = 'Positive' if pra6 > 0 else 'Negative'
+        confident = get_confident_alleles(br, bead_map)
+
+        # bead 明細
+        beads_detail = []
+        sample_counts = count_data.get(sname, {})
+        for bid, r in br.items():
+            hla = bead_map.get(bid, {})
+            beads_detail.append({
+                'bead': bid, 'rxn': r['rxn'],
+                'raw': round(r['raw'], 1), 'ns_raw': round(r['ns_raw'], 1),
+                'normal': r['normal'], 'ratio': r['ratio'],
+                'count': int(sample_counts.get(bid, 0)),
+                'sero': clean_sero(hla.get('sero', '')),
+                'allele': hla.get('allele', ''),
+            })
+
+        # PRA at all thresholds
+        pra_all = {}
+        for t in ['X2', 'X4', 'X6', 'X8']:
+            p, _, _ = calculate_pra(br, t)
+            pra_all[t] = p
+
+        patients.append({
+            'name': sname,
+            'overall': overall,
+            'pra': pra6,
+            'pra_all': pra_all,
+            'beads': beads_detail,
+            'confident_alleles': sorted(confident),
+            'specificity': generate_specificity(confident, bead_map) if overall == 'Positive' else '(-)',
+            'sero_mfi': build_sero_mfi_stats(beads_detail, confident, bead_map) if overall == 'Positive' else [],
+        })
+
+    result = {
+        'pra_class': pra_class,
+        'class_label': 'PRA Class I' if pra_class == 'PRA1' else 'PRA Class II',
+        'date': date_val,
+        'batch': batch,
+        'nc_name': nc_name,
+        'pc_signal': round(pc_val, 0),
+        'nc_signal': round(nc_val, 0),
+        'patients': patients,
+        'filename': filename,
+    }
+    return result, None
+
+
+# ============================================================
+# Flask Routes
+# ============================================================
+
+@app.route('/login', methods=['GET', 'POST'])
+def login():
+    if request.method == 'POST':
+        username = request.form.get('username', '').strip()
+        password = request.form.get('password', '')
+        import db
+        if db.check_user(username, password):
+            flask_session['logged_in'] = True
+            flask_session['username'] = username
+            flask_session['role'] = db.get_user_role(username)
+            flask_session['display_name'] = db.get_user_display_name(username)
+            return redirect(url_for('dashboard'))
+        return render_template('login.html', error='帳號或密碼錯誤')
+    return render_template('login.html')
+
+
+@app.route('/register', methods=['GET', 'POST'])
+def register():
+    if request.method == 'POST':
+        display_name = request.form.get('display_name', '').strip()
+        username = request.form.get('username', '').strip()
+        password = request.form.get('password', '')
+        password2 = request.form.get('password2', '')
+        if not display_name:
+            return render_template('register.html', error='請輸入姓名')
+        if not username:
+            return render_template('register.html', error='請輸入帳號')
+        if not password or len(password) < 4:
+            return render_template('register.html', error='密碼至少 4 碼')
+        if password != password2:
+            return render_template('register.html', error='兩次密碼不一致')
+        import db
+        ok, msg = db.register_user(username, password, display_name)
+        if ok:
+            return render_template('login.html', success='註冊成功，請登入')
+        return render_template('register.html', error=msg)
+    return render_template('register.html')
+
+
+@app.route('/logout')
+def logout():
+    flask_session.clear()
+    return redirect(url_for('login'))
+
+
+@app.route('/')
+@login_required
+def dashboard():
+    return render_template('dashboard.html',
+                           username=flask_session.get('username', ''),
+                           is_admin=flask_session.get('role') == 'admin')
+
+
+@app.route('/new')
+@login_required
+def index():
+    return render_template('index.html')
+
+
+@app.route('/analyze', methods=['POST'])
+@login_required
+def analyze():
+    files = request.files.getlist('file')
+    if not files or not files[0].filename:
+        return render_template('index.html', error='請選擇檔案')
+
+    # 收集所有結果，按 PRA class 分組
+    all_patients = {'PRA1': [], 'PRA2': []}
+    meta = {'PRA1': {}, 'PRA2': {}}
+    errors = []
+
+    for f in files:
+        raw_bytes = f.read()
+        fname = f.filename or 'unknown'
+        ext = Path(fname).suffix.lower()
+        is_xls = ext in ('.xls', '.xlsx') or (
+            len(raw_bytes) > 8 and raw_bytes[:8] == b'\xd0\xcf\x11\xe0\xa1\xb1\x1a\xe1')
+
+        if is_xls:
+            try:
+                pt = parse_xls_file(raw_bytes)
+            except Exception as e:
+                errors.append(f'{fname}: {e}')
+                continue
+            if pt is None:
+                errors.append(f'{fname}: 無 bead 資料')
+                continue
+            if not pt.get('name'):
+                pt['name'] = Path(fname).stem
+            pc = pt.pop('_pra_class', 'PRA1')
+            date_val = pt.pop('_date', '')
+            batch = pt.pop('_batch', '')
+            nc_raw = pt.pop('_nc_raw', 0)
+            pc_raw = pt.pop('_pc_raw', 0)
+            all_patients[pc].append(pt)
+            if date_val:
+                meta[pc]['date'] = date_val
+            if batch:
+                meta[pc]['batch'] = batch
+            meta[pc]['nc_signal'] = nc_raw
+            meta[pc]['pc_signal'] = pc_raw
+        else:
+            # CSV
+            content = None
+            for enc in ['utf-8-sig', 'utf-8', 'cp1252', 'latin-1', 'big5', 'cp950']:
+                try:
+                    content = raw_bytes.decode(enc)
+                    if '"Results"' in content or '"Median"' in content:
+                        break
+                except (UnicodeDecodeError, UnicodeError):
+                    continue
+            if content is None:
+                content = raw_bytes.decode('latin-1')
+            result, error = full_analyze(content, fname)
+            if error:
+                errors.append(f'{fname}: {error}')
+                continue
+            pc = result['pra_class']
+            all_patients[pc].extend(result['patients'])
+            meta[pc] = {
+                'date': result['date'], 'batch': result['batch'],
+                'nc_signal': result['nc_signal'], 'pc_signal': result['pc_signal'],
+                'nc_name': result['nc_name'],
+            }
+
+    # 組裝兩邊結果
+    def build_result(pc):
+        pts = all_patients[pc]
+        if not pts:
+            return None
+        m = meta.get(pc, {})
+        return {
+            'pra_class': pc,
+            'class_label': 'PRA Class I' if pc == 'PRA1' else 'PRA Class II',
+            'date': m.get('date', ''),
+            'batch': m.get('batch', ''),
+            'nc_name': m.get('nc_name', 'NC'),
+            'pc_signal': round(m.get('pc_signal', 0)),
+            'nc_signal': round(m.get('nc_signal', 0)),
+            'patients': pts,
+        }
+
+    result_pra1 = build_result('PRA1')
+    result_pra2 = build_result('PRA2')
+
+    if not result_pra1 and not result_pra2:
+        err_msg = '; '.join(errors) if errors else '無法辨識檔案格式'
+        return render_template('index.html', error=err_msg)
+
+    patient_name = request.form.get('patient_name', '').strip()
+    patient_id = request.form.get('patient_id', '').strip()
+
+    return render_template('index.html',
+                           result_pra1=result_pra1,
+                           result_pra2=result_pra2,
+                           patient_name=patient_name,
+                           patient_id=patient_id,
+                           errors=errors if errors else None)
+
+
+@app.route('/export_docx', methods=['POST'])
+@login_required
+def export_docx():
+    """匯出 DOCX 報告"""
+    from docx import Document
+    from docx.shared import Pt, Cm
+    from docx.enum.text import WD_ALIGN_PARAGRAPH
+
+    doc = Document()
+    style = doc.styles['Normal']
+    style.font.name = 'Calibri'
+    style.font.size = Pt(11)
+
+    data = request.json
+    pra_class_label = data.get('class_label', 'PRA Class I')
+    date_str = data.get('date', '')
+    pra_tag = 'PRA1' if 'I' in pra_class_label else 'PRA2'
+
+    # 標題
+    p = doc.add_paragraph(f'{date_str} {pra_tag}')
+    p.runs[0].bold = True
+    p.runs[0].font.size = Pt(14)
+
+    for pt in data.get('patients', []):
+        doc.add_paragraph('')
+        p = doc.add_paragraph(pt['name'])
+        p.runs[0].bold = True
+
+        doc.add_paragraph(pra_class_label)
+        doc.add_paragraph(f'Overall: {pt["overall"]}')
+        doc.add_paragraph(f'%SA (or %PRA): {pt["pra"]}')
+        doc.add_paragraph('Specificity:')
+
+        spec = pt.get('specificity', '').strip()
+        if not spec or spec == '':
+            spec = '(-)' if pt['overall'] == 'Negative' else '(-)'
+        doc.add_paragraph(spec)
+        doc.add_paragraph('COMMENT:')
+
+    buf = io.BytesIO()
+    doc.save(buf)
+    buf.seek(0)
+
+    filename = f'{date_str.replace("/", "")}_{pra_tag}_report.docx'
+    return send_file(buf, as_attachment=True, download_name=filename,
+                     mimetype='application/vnd.openxmlformats-officedocument.wordprocessingml.document')
+
+
+@app.route('/save', methods=['POST'])
+@login_required
+def save():
+    """儲存分析結果到資料庫"""
+    import db
+    data = request.json
+    patient_name = data.get('patient_name', '').strip()
+    chart_no = data.get('chart_no', '').strip()
+    if not chart_no:
+        return jsonify({'error': '請輸入病歷號'}), 400
+
+    patient_id = db.get_or_create_patient(patient_name, chart_no)
+    is_submitted = data.get('submitted', False)
+    status = 'submitted' if is_submitted else 'draft'
+    saved = []
+
+    for r in data.get('reports', []):
+        report_date = r.get('report_date', '')
+        pra_class = r.get('pra_class', '')
+        pra_percent = r.get('pra_percent', 0)
+        overall = r.get('overall', '')
+        specificity = r.get('specificity', '')
+        comment = r.get('comment', '')
+        sero_mfi = r.get('sero_mfi', [])
+
+        submitted_by = flask_session.get('display_name', flask_session.get('username', ''))
+        rid = db.save_report(patient_id, report_date, pra_class, pra_percent,
+                             overall, specificity, comment, sero_mfi, status, submitted_by)
+        saved.append({'report_id': rid, 'pra_class': pra_class})
+
+    return jsonify({'ok': True, 'patient_id': patient_id, 'saved': saved})
+
+
+@app.route('/history')
+@login_required
+def history():
+    """顯示所有報告紀錄"""
+    import db
+    reports = db.get_all_reports()
+    return render_template('history.html', reports=reports)
+
+
+@app.route('/history/<chart_no>')
+@login_required
+def patient_history(chart_no):
+    """顯示單一病人的報告歷史 + MFI 比較"""
+    import db
+    patient, reports = db.get_patient_reports(chart_no)
+    if not patient:
+        return render_template('history.html', reports=db.get_all_reports(),
+                               error=f'找不到病歷號 {chart_no}')
+
+    # MFI comparison (Class I and Class II)
+    dates1, antigens1, pra1 = db.get_mfi_comparison(chart_no, 'PRA Class I')
+    dates2, antigens2, pra2 = db.get_mfi_comparison(chart_no, 'PRA Class II')
+
+    class Comp:
+        def __init__(self, dates, antigens, pra_by_date):
+            self.dates = dates
+            self.antigens = antigens
+            self.pra_by_date = pra_by_date
+
+    comp1 = Comp(dates1, antigens1, pra1) if dates1 else None
+    comp2 = Comp(dates2, antigens2, pra2) if dates2 else None
+
+    return render_template('patient.html', patient=patient, reports=reports,
+                           comparison_class1=comp1, comparison_class2=comp2)
+
+
+@app.route('/delete_report/<int:report_id>', methods=['POST'])
+@login_required
+def delete_report_route(report_id):
+    import db
+    db.delete_report(report_id)
+    return jsonify({'ok': True})
+
+
+@app.route('/admin')
+@login_required
+def admin():
+    if flask_session.get('role') != 'admin':
+        return redirect(url_for('dashboard'))
+    import db
+    users = db.get_all_users()
+    return render_template('admin.html', users=users,
+                           username=flask_session.get('username', ''))
+
+
+@app.route('/admin/delete_user/<int:user_id>', methods=['POST'])
+@login_required
+def admin_delete_user(user_id):
+    if flask_session.get('role') != 'admin':
+        return jsonify({'error': '無權限'}), 403
+    import db
+    db.delete_user(user_id)
+    return jsonify({'ok': True})
+
+
+@app.route('/admin/update_user/<int:user_id>', methods=['POST'])
+@login_required
+def admin_update_user(user_id):
+    if flask_session.get('role') != 'admin':
+        return jsonify({'error': '無權限'}), 403
+    import db
+    data = request.json
+    db.update_user(user_id, data.get('display_name'), data.get('username'),
+                   data.get('password'), data.get('role'))
+    return jsonify({'ok': True})
+
+
+@app.route('/save_donor_hla', methods=['POST'])
+@login_required
+def save_donor_hla_route():
+    import db
+    data = request.json
+    chart_no = data.get('chart_no', '')
+    donor_hla = data.get('donor_hla', '')
+    if not chart_no:
+        return jsonify({'error': 'missing chart_no'}), 400
+    db.save_donor_hla(chart_no, donor_hla)
+    return jsonify({'ok': True})
+
+
+@app.route('/analysis')
+@login_required
+def analysis():
+    """統計分析頁面 — 選擇病人查看 MFI 趨勢"""
+    import db
+    patients = db.get_all_patients()
+    chart_no = request.args.get('chart_no', '')
+
+    patient = None
+    reports = []
+    comp1 = None
+    comp2 = None
+
+    if chart_no:
+        patient, reports = db.get_patient_reports(chart_no)
+        if patient:
+            dates1, antigens1, pra1 = db.get_mfi_comparison(chart_no, 'PRA Class I')
+            dates2, antigens2, pra2 = db.get_mfi_comparison(chart_no, 'PRA Class II')
+
+            class Comp:
+                def __init__(self, dates, antigens, pra_by_date):
+                    self.dates = dates
+                    self.antigens = antigens
+                    self.pra_by_date = pra_by_date
+
+            comp1 = Comp(dates1, antigens1, pra1) if dates1 else None
+            comp2 = Comp(dates2, antigens2, pra2) if dates2 else None
+
+    donor_hla = ''
+    if chart_no:
+        donor_hla = db.get_donor_hla(chart_no)
+
+    return render_template('analysis.html', patients=patients, chart_no=chart_no,
+                           patient=patient, reports=reports,
+                           comparison_class1=comp1, comparison_class2=comp2,
+                           donor_hla=donor_hla)
+
+
+if __name__ == '__main__':
+    print('PRA Analysis Web App')
+    print('http://127.0.0.1:5000')
+    app.run(debug=True, port=5000)