282 lines
17 KiB
Python
282 lines
17 KiB
Python
"""Render the prescription comparison window with synthetic, offline data."""
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from __future__ import annotations
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import os
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from copy import deepcopy
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from pathlib import Path
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from typing import Any
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os.environ.setdefault("QT_QPA_PLATFORM", "offscreen")
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from PySide6.QtWidgets import QApplication
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from doctor_workstation.ui.dialogs import issued_prescription_ai as ai
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from doctor_workstation.ui.theme import apply_theme
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def example_batch() -> dict[str, Any]:
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"""Three prescriptions that actually differ, so every state of the page has something to draw."""
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doctor = {"生地黄": 16, "天花粉": 15, "干石斛": 20, "醋五味子": 6,
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"生麦冬": 12, "茯苓": 10, "红参片": 6, "生牡丹皮": 10}
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candidates = {
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"qwen": {"生地黄": 15, "干石斛": 12, "醋五味子": 6, "生麦冬": 12, "茯苓": 15, "麸炒白术": 12, "丹参": 15},
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"openai": {"生地黄": 12, "醋五味子": 6, "生麦冬": 10, "茯苓": 12, "生白芍": 5, "炒酸枣仁": 6},
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}
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reports = {
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"qwen": {"summary": "界面演示数据：对照药味组成、剂量及用法，辅助医生逐项复核。",
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"diagnosis": "消渴病，气阴两虚兼血热。兼证需结合舌脉资料核实。",
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"analysis": "阅读药方对比页，查看同名药材的剂量差异、仅医生方药味与仅候选方药味。",
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"risk_assessment": [{"level": "high", "label": "血压数据缺失，影响补气药安全性评估"},
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{"level": "medium", "label": "肝肾功能具体指标未提供"}],
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"missing_information": ["甲状腺功能及眼底检查报告缺失", "舌象与脉诊仅有附件，无文本记录"]},
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"openai": {"summary": "界面演示数据：两份候选方独立生成，引用编号可回查原始资料。",
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"diagnosis": "已记录气阴两虚证；辨证依据需补充。",
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"analysis": "候选方以益气养阴为主，安神药味为本模型新增，需要医师确认。",
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"risk_assessment": [{"level": "high", "label": "缺少当前用药记录，无法排除配伍风险"}],
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"missing_information": ["舌象与脉诊仅有附件，无文本记录", "近期体重变化及 BMI 数据缺失"]},
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}
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models = {}
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for key, doses in candidates.items():
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herbs, rows = [], []
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for name in sorted(set(doctor) | set(doses), key=lambda item: (item not in doses, item)):
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common = {"name": name, "unit": "g", "dose_basis": "per_dose", "formula_type": "主方"}
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left, right = doctor.get(name), doses.get(name)
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if right is not None:
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herbs.append({**common, "dosage": right})
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rows.append({
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**common, "key": name,
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"doctor": {**common, "dosage": left} if left is not None else None,
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"candidate": {**common, "dosage": right, "source_rows": [len(herbs) - 1]} if right is not None else None,
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"doctor_dosage": left, "candidate_dosage": right,
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"match_type": "candidate_only" if left is None else "doctor_only" if right is None else "matched",
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"contribution": min(left, right) / max(left, right) if left and right else None,
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})
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matched = sum(row["match_type"] == "matched" for row in rows)
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denominator = len(doctor) + len(herbs)
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models[key] = {
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"status": "success", "report_id": 10 if key == "qwen" else 11,
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"candidate": {"status": "available_for_review", "prescription_name": "候选药方 · 界面示例",
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"herbs": herbs, "dose_basis": "per_dose", "prescription_type": "浓缩水丸",
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"usage_instruction": "服法由医生复核后确认。", "usage_days": 7, "times_per_day": 2,
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"rationale": "这是用于检查界面排版的模拟药方，不对应真实患者。",
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"risk_warnings": ["药味与剂量差异需要逐项复核。"]},
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"comparison": {"status": "comparable",
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"score": 200 * sum(row["contribution"] or 0 for row in rows) / denominator,
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"herb_score": 200 * matched / denominator, "matched_count": matched,
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"doctor_count": len(doctor), "candidate_count": len(herbs), "rows": rows,
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"reason": "药味与剂量可比；服法与疗程需单独复核。", "usage_differences": []},
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"report": reports[key],
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"coverage": {"status": "incomplete", "complete": False,
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"files": [{"file_id": f"a{index:04d}", "type": "image" if index % 3 else "document",
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"status": "processed", "transmitted": True, "version_verified": True}
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for index in range(20 if key == "qwen" else 17)]
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+ [{"file_id": f"z{index:04d}", "type": "document", "status": "restricted",
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"transmitted": False, "version_verified": False,
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"reason": "FILE_UNAVAILABLE_OR_UNSUPPORTED"} for index in range(2)]},
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"progress": {"stage_label": "处理完成", "elapsed_seconds": 135 if key == "qwen" else 591,
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"attempt": 1},
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"usage": {"total_calls": 3, "calls": [
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{"stage": "text:0", "ok": True, "latency_ms": 3120, "file_count": 0,
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"usage": {"completion_tokens": 980}, "error_code": ""},
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{"stage": "files:0", "ok": True, "latency_ms": 22400,
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"file_count": 20 if key == "qwen" else 17,
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"usage": {"completion_tokens": 2140}, "error_code": ""},
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{"stage": "final", "ok": True, "latency_ms": 18800, "file_count": 0,
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"usage": {"completion_tokens": 5617}, "error_code": ""}]},
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"review": {"status": "viewed", "comment": ""},
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"algorithm_version": "prescription-soft-dice-v1.1.0",
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"prompt_version": "manual-prescription-required-candidate-v4",
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}
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return {"id": 40, "prescription_id": 7556, "patient_id": 1391, "diagnosis_id": 1391,
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"prescription_revision": 1, "status": "success", "validity": "current",
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"comparison_type": "non_independent", "models": models, "coverage_status": "partial",
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"created_at": "2026-09-10 15:29:00", "cutoff_at": "2026-09-10 15:29:00",
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"source_summary": {"diagnoses_count": 2, "attachment_count": 22, "video_calls_count": 4,
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"chat_messages_count": 137, "source_record_count": 31},
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"doctor_snapshot": {"patient": {"name": "张卫君", "gender": 2, "gender_label": "女", "age": 58},
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"diagnosis": {"clinical_diagnosis": "2型糖尿病 消渴病 · 气阴两虚兼血热",
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"chief_complaint": "咳嗽反复1月余"},
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"prescription": {"herbs": [{"name": name, "dosage": dose, "unit": "g",
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"dose_basis": "per_dose", "formula_type": "主方"}
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for name, dose in doctor.items()],
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"prescription_type": "浓缩水丸", "dose_count": 1,
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"usage_instruction": "每日1剂，水煎分服。"}},
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"missing": [{"code": "TRANSCRIPT_NOT_VERIFIED_COMPLETE", "critical": True},
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{"code": "TRANSCRIPT_NOT_VERIFIED_COMPLETE", "critical": True},
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{"code": "ARCHIVE_SYNC_WATERMARK_UNAVAILABLE", "critical": False}]}
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def example_statistics() -> dict[str, Any]:
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"""Doctor-level shape the statistics window renders; synthetic, but structurally complete."""
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def doctor(identifier: int, name: str, totals: tuple[int, int, int],
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qwen: tuple[int, float | None], openai: tuple[int, float | None],
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review: tuple[int, int]) -> dict[str, Any]:
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total, patients, paired = totals
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models = {}
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for key, (eligible, mean) in (("qwen", qwen), ("openai", openai)):
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share = (0.34, 0.38, 0.18, 0.07, 0.03)
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models[key] = {"eligible_count": eligible, "mean": mean,
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"median": None if mean is None else round(mean - 1.4, 1),
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"excluded_reasons": {"SOURCE_HISTORY_VERSIONS_UNAVAILABLE": max(0, total - eligible - 2),
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"incomplete_coverage": min(2, max(0, total - eligible))},
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"distribution": {name: round(eligible * fraction)
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for name, fraction in zip(
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("[0,20)", "[20,40)", "[40,60)", "[60,80)", "[80,100]"),
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share, strict=True)}}
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evaluated, qualified = review
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return {"doctor_id": identifier, "doctor_name": name, "total_count": total,
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"patient_count": patients, "paired_count": paired, "models": models,
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"review": {"evaluated_count": evaluated, "qualified_count": qualified,
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"qualified_rate": None if not evaluated else round(100 * qualified / evaluated, 1)}}
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doctors = [doctor(26, "何医生", (18, 14, 9), (12, 21.4), (9, 18.9), (6, 4)),
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doctor(31, "李医生", (11, 9, 4), (7, 26.8), (5, 24.1), (3, 1)),
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doctor(44, "王医生", (6, 5, 1), (2, 15.2), (0, None), (0, 0))]
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return {"total_count": sum(item["total_count"] for item in doctors),
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"patient_count": sum(item["patient_count"] for item in doctors), "doctors": doctors}
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class PreviewRepository:
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def __init__(self) -> None:
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self.batch = example_batch()
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def list_prescription_ai_reports(self, **_params: Any) -> dict[str, Any]:
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return {"enabled": True, "lists": deepcopy(self.history()), "count": len(self.history())}
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def history(self) -> list[dict[str, Any]]:
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"""The current batch plus the earlier ones it supersedes, newest first."""
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older = [
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{"id": 39, "created_at": "2026-09-10 15:18:00", "status": "success", "validity": "superseded",
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"comparison_type": "non_independent",
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"models": {"qwen": {"comparison": {"status": "comparable", "score": 48.9},
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"algorithm_version": "prescription-soft-dice-v1.0.1", "prompt_version": "v3"},
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"openai": {"comparison": {"status": "comparable", "score": 41.7},
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"algorithm_version": "prescription-soft-dice-v1.0.1", "prompt_version": "v3"}}},
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{"id": 38, "created_at": "2026-09-10 14:05:00", "status": "success", "validity": "superseded",
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"models": {"qwen": {"comparison": {"status": "comparable", "score": 33.4},
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"algorithm_version": "prescription-soft-dice-v1.0.1", "prompt_version": "v3"},
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"openai": {"comparison": {"status": "comparable", "score": 31.1},
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"algorithm_version": "prescription-soft-dice-v1.0.1", "prompt_version": "v3"}}},
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{"id": 37, "created_at": "2026-09-10 13:25:00", "status": "failed", "validity": "superseded",
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"models": {"qwen": {"error_message": "模型返回未通过校验",
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"algorithm_version": "prescription-soft-dice-v1.0.1"},
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"openai": {"comparison": {"status": "not_comparable"}}}},
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{"id": 36, "created_at": "2026-09-10 12:34:00", "status": "success", "validity": "superseded",
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"models": {"qwen": {"comparison": {"status": "not_comparable"},
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"algorithm_version": "prescription-soft-dice-v1.0.0"},
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"openai": {"comparison": {"status": "not_comparable"}}}},
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]
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return [self.batch, *older]
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def get_prescription_ai_report(self, _batch_id: int) -> dict[str, Any]:
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return deepcopy(self.batch)
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def prescription_ai_statistics(self, *_args: Any, **_params: Any) -> dict[str, Any]:
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return deepcopy(example_statistics())
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def main() -> None:
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application = QApplication.instance() or QApplication([])
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apply_theme(application)
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output = Path(__file__).resolve().parents[1] / "artifacts" / "issued_prescription_redesign"
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output.mkdir(parents=True, exist_ok=True)
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def immediate(function: Any, **callbacks: Any) -> None:
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result = function()
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if callbacks.get("on_success"):
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callbacks["on_success"](result)
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if callbacks.get("on_finished"):
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callbacks["on_finished"]()
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ai.run_async = immediate
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repository = PreviewRepository()
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dialog = ai.IssuedPrescriptionAiDialog(repository, ["*"], prescription_id=7556,
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current_user={"name": "张医生"})
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dialog.show()
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names = {dialog.tabs.tabText(index): index for index in range(dialog.tabs.count())}
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for filename, width, height, tab in (
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("prescription-1440.png", 1440, 940, "对比总览"),
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("prescription-1280.png", 1280, 860, "对比总览"),
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("prescription-1024.png", 1024, 700, "对比总览"),
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("prescription-940.png", 940, 640, "对比总览"),
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("original-1280.png", 1280, 860, "原方记录"),
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("analysis-1280.png", 1280, 860, "完整报告"),
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("candidate-1280.png", 1280, 860, "方义与用法"),
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("per-herb-1440.png", 1440, 940, "候选与逐味"),
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("sources-1440.png", 1440, 940, "资料与缺口"),
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("history-1440.png", 1440, 940, "历史与趋势"),
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("pipeline-1440.png", 1440, 940, "处理进度"),
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):
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dialog.resize(width, height)
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dialog.tabs.setCurrentIndex(names[tab])
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for _ in range(4):
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application.processEvents()
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assert (dialog.width(), dialog.height()) == (width, height), (filename, dialog.size())
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assert dialog.grab().save(str(output / filename))
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print(filename, "window", width, height, "workspace", dialog.tabs.width(), dialog.tabs.height())
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dialog.tabs.setCurrentIndex(names["完整报告"])
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dialog._set_report_mode("differences")
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application.processEvents()
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assert dialog.grab().save(str(output / "report-differences-1440.png"))
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dialog._set_report_mode("both")
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dialog.tabs.setCurrentIndex(names["对比总览"])
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dialog.resize(1280, 860)
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for _ in range(4):
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application.processEvents()
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dialog.review_model.setCurrentIndex(1)
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application.processEvents()
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assert dialog.grab().save(str(output / "openai-1280.png"))
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dialog.review_model.setCurrentIndex(0)
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repository.batch["status"] = "running"
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repository.batch["models"]["openai"].update(status="running", candidate=None, comparison=None, report=None)
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dialog.refresh()
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application.processEvents()
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assert dialog.grab().save(str(output / "partial-1280.png"))
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repository.batch.update(validity="superseded", status="success")
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dialog.refresh()
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application.processEvents()
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assert dialog.grab().save(str(output / "historical-1280.png"))
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repository.batch.update(validity="current", status="running")
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repository.batch["models"]["qwen"].update(status="running", candidate=None, comparison=None, report=None)
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dialog.refresh()
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application.processEvents()
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assert dialog.grab().save(str(output / "pending-1280.png"))
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# A failed model must state the reason and offer its own retry on the card itself.
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repository.batch.update(validity="current", status="partial")
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repository.batch["models"]["qwen"].update(
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status="failed", error_code="upstream_timeout", error_message="上游模型超时，未返回结果",
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candidate=None, comparison=None, report=None, retry_count=1, max_retry=3)
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repository.batch["models"]["openai"] = deepcopy(example_batch()["models"]["openai"])
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dialog.refresh()
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application.processEvents()
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assert dialog.grab().save(str(output / "failed-1280.png"))
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# The light theme is the same window with the other palette; capture it once.
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dialog.resize(1440, 940)
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dialog._switch_theme()
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for _ in range(12):
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application.processEvents()
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dialog.repaint()
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assert dialog.grab().save(str(output / "light-1440.png"))
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dialog._switch_theme()
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for _ in range(4):
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application.processEvents()
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dialog.close()
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statistics = ai.PrescriptionAiStatisticsDialog(repository, ["*"])
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statistics.resize(1240, 880)
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statistics.show()
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for _ in range(4):
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application.processEvents()
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assert statistics.grab().save(str(output / "statistics-1240.png"))
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statistics.close()
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print("statistics-1240.png", statistics.panel.doctors.rowCount(), "doctors")
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print(output)
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if __name__ == "__main__":
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main()
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