fix: improve page quality vlm detection

fastapi_modules/fastapi_leaudit/leaudit_bridge/resilient_clients.py

@@ -239,7 +239,7 @@ class ResilientQwenVLMClient(QwenVLMClient):
         body = response.json()
         text = (body.get("choices") or [{}])[0].get("message", {}).get("content", "")
         parsed = _parse_json_loose(text)
-        return parsed if isinstance(parsed, dict) else {}
+        return parsed if isinstance(parsed, dict) else {"result": text, "reason": text}
 
 
 class ResilientChandraOCRClient(ChandraOCRClient):

fastapi_modules/fastapi_leaudit/services/impl/pageQualityServiceImpl.py

@@ -6,6 +6,7 @@ from typing import Any
 from pathlib import Path
 import tempfile
 import logging
+import json
 
 import fitz
 from leaudit.converters import doc2pdf
@@ -30,9 +31,11 @@ _PAGE_QUALITY_VLM_PROMPT = """
 你是文档扫描图片质量检测员。请判断这 1 页文档图片是否适合继续做 OCR 与合同/公文评查。
 
 判定标准：
-1. pass：文字主体清晰、方向正常、没有明显截断，能稳定阅读。
+1. 必须同时检查整页扫描质量，以及页面内所有内嵌照片、证据照片、现场照片、截图、印章和签名图片的清晰度。
-2. review：存在轻微模糊、倾斜、阴影、低对比度、局部遮挡、轻微截断，建议人工确认但仍可能可读。
+2. pass：文字主体清晰、方向正常、没有明显截断；页面内嵌照片/证据照片也能辨认关键视觉信息。
-3. reject：严重模糊、重影、过曝/过暗、页面大面积缺失、关键文字不可辨认、方向严重错误、空白页或非文档页，建议重拍。
+3. review：存在轻微模糊、倾斜、阴影、低对比度、局部遮挡、轻微截断；或内嵌照片/证据照片主体明显发虚、牌匾/场所/人物/关键物证不易辨认，建议人工确认但仍可能可用。
+4. reject：严重模糊、重影、过曝/过暗、页面大面积缺失、关键文字不可辨认、方向严重错误、空白页或非文档页；或内嵌证据照片主体无法辨认、关键证据信息不可用，建议重拍。
+5. 即使页面周边文字清楚，只要内嵌证据照片明显模糊，也不能判 pass，至少判 review，严重时判 reject。
 
 只输出 JSON，不要输出 Markdown，不要解释额外文本：
 {"status":"pass|review|reject","score":0.0到1.0,"reason":"20字以内中文原因"}
@@ -495,12 +498,28 @@ class PageQualityServiceImpl(IPageQualityService):
             logger.warning("VLM page quality detection failed: %s", exc)
             return "review", 0.5, "VLM图片质量检测失败，需人工确认"
 
-        status = str((result or {}).get("status") or "").strip().lower()
+        result_dict = self._coerce_vlm_result(result)
+        status = self._normalize_quality_status(
+            self._first_non_empty(
+                result_dict,
+                ("status", "quality_status", "qualityStatus", "result", "label", "decision", "conclusion"),
+            )
+        )
+        reason = self._normalize_quality_reason(
+            self._first_non_empty(
+                result_dict,
+                ("reason", "quality_reason", "qualityReason", "message", "msg", "detail", "explanation", "description"),
+            )
+        )
+        if status is None and reason:
+            status = self._normalize_quality_status(reason)
         if status not in {"pass", "review", "reject"}:
             return "review", 0.5, "VLM返回结果不可用，需人工确认"
 
-        score = self._normalize_quality_score((result or {}).get("score"), status)
+        score = self._normalize_quality_score(
-        reason = str((result or {}).get("reason") or "").strip() or None
+            self._first_non_empty(result_dict, ("score", "quality_score", "qualityScore", "confidence")),
+            status,
+        )
         if status != "pass" and not reason:
             reason = "页面图片质量需人工确认"
         return status, score, reason
@@ -526,6 +545,56 @@ class PageQualityServiceImpl(IPageQualityService):
             return defaults[status]
         return max(0.0, min(1.0, score))
 
+    def _coerce_vlm_result(self, result: Any) -> dict[str, Any]:
+        if isinstance(result, dict):
+            return result
+        if isinstance(result, str):
+            text_result = result.strip()
+            if not text_result:
+                return {}
+            try:
+                parsed = json.loads(text_result)
+            except json.JSONDecodeError:
+                return {"result": text_result, "reason": text_result}
+            return parsed if isinstance(parsed, dict) else {"result": text_result}
+        return {}
+
+    def _first_non_empty(self, payload: dict[str, Any], keys: tuple[str, ...]) -> Any:
+        for key in keys:
+            value = payload.get(key)
+            if value is not None and str(value).strip():
+                return value
+        return None
+
+    def _normalize_quality_status(self, raw_status: Any) -> str | None:
+        text_status = str(raw_status or "").strip().lower()
+        if not text_status:
+            return None
+        compact_status = text_status.replace(" ", "").replace("_", "").replace("-", "")
+        if compact_status in {"pass", "passed", "ok", "good", "clear", "readable"}:
+            return "pass"
+        if compact_status in {"review", "warn", "warning", "manual", "uncertain", "suspect", "suspicious"}:
+            return "review"
+        if compact_status in {"reject", "rejected", "fail", "failed", "bad", "unreadable", "retake"}:
+            return "reject"
+
+        reject_keywords = ("不通过", "拒绝", "重拍", "不可读", "无法辨认", "无法识别", "严重", "大面积缺失", "空白页")
+        review_keywords = ("复核", "人工", "疑似", "轻微", "建议确认", "建议人工", "模糊", "不清晰", "低对比", "发虚")
+        pass_keywords = ("通过", "合格", "清晰", "可读")
+        if any(keyword in text_status for keyword in reject_keywords):
+            return "reject"
+        if any(keyword in text_status for keyword in review_keywords):
+            return "review"
+        if any(keyword in text_status for keyword in pass_keywords):
+            return "pass"
+        return None
+
+    def _normalize_quality_reason(self, raw_reason: Any) -> str | None:
+        reason = str(raw_reason or "").strip()
+        if not reason:
+            return None
+        return reason[:80]
+
     def _document_service(self):
         if self.DocumentService is None:
             from fastapi_modules.fastapi_leaudit.services.impl.documentServiceImpl import DocumentServiceImpl

tests/test_page_quality_vlm.py

@@ -1,5 +1,7 @@
 import pytest
+import httpx
 
+from fastapi_modules.fastapi_leaudit.leaudit_bridge.resilient_clients import ResilientQwenVLMClient
 from fastapi_modules.fastapi_leaudit.services.impl.pageQualityServiceImpl import PageQualityServiceImpl
 
 
@@ -32,6 +34,58 @@ async def test_vlm_page_quality_reject_result_is_used():
     assert score == 0.18
     assert "严重模糊" in reason
     assert "只输出 JSON" in service.VlmClient.prompts[0][0]
+    assert "内嵌照片" in service.VlmClient.prompts[0][0]
+    assert "即使页面周边文字清楚" in service.VlmClient.prompts[0][0]
+
+
+@pytest.mark.asyncio
+async def test_vlm_page_quality_embedded_evidence_blur_cannot_pass():
+    service = PageQualityServiceImpl()
+    service.VlmClient = _FakeVlmClient(
+        {
+            "quality_status": "疑似模糊",
+            "quality_score": "0.42",
+            "message": "内嵌证据照片主体发虚，门头文字不易辨认",
+        }
+    )
+
+    status, score, reason = await service._classify_page_image_by_vlm(b"image-bytes")
+
+    assert status == "review"
+    assert score == 0.42
+    assert "内嵌证据照片" in reason
+
+
+@pytest.mark.asyncio
+async def test_vlm_page_quality_chinese_reject_status_is_supported():
+    service = PageQualityServiceImpl()
+    service.VlmClient = _FakeVlmClient(
+        {
+            "result": "不通过",
+            "confidence": 0.1,
+            "detail": "证据照片严重模糊，关键场所无法辨认",
+        }
+    )
+
+    status, score, reason = await service._classify_page_image_by_vlm(b"image-bytes")
+
+    assert status == "reject"
+    assert score == 0.1
+    assert "严重模糊" in reason
+
+
+@pytest.mark.asyncio
+async def test_vlm_page_quality_json_string_result_is_supported():
+    service = PageQualityServiceImpl()
+    service.VlmClient = _FakeVlmClient(
+        '{"status":"review","score":0.33,"reason":"页面内照片模糊"}'
+    )
+
+    status, score, reason = await service._classify_page_image_by_vlm(b"image-bytes")
+
+    assert status == "review"
+    assert score == 0.33
+    assert reason == "页面内照片模糊"
 
 
 @pytest.mark.asyncio
@@ -56,3 +110,32 @@ async def test_vlm_page_quality_error_falls_back_to_review_not_pass():
     assert status == "review"
     assert score == 0.5
     assert "VLM图片质量检测失败" in reason
+
+
+@pytest.mark.asyncio
+async def test_resilient_vlm_extract_multifield_keeps_raw_text_when_json_parse_fails(monkeypatch):
+    client = ResilientQwenVLMClient(base_url="http://example.test", api_key="x", model="vlm-test")
+
+    async def fake_post_with_retry(payload):
+        return httpx.Response(
+            200,
+            json={
+                "choices": [
+                    {
+                        "message": {
+                            "content": "疑似模糊：内嵌证据照片主体发虚，建议人工复核",
+                        }
+                    }
+                ]
+            },
+        )
+
+    monkeypatch.setattr(client, "_post_with_retry", fake_post_with_retry)
+
+    result = await client.extract_multifield(
+        prompt="图片质量检测",
+        images_data_urls=["data:image/png;base64,xxx"],
+    )
+
+    assert result["result"].startswith("疑似模糊")
+    assert "内嵌证据照片" in result["reason"]