wren
142 lines
4.5 KiB
Python
142 lines
4.5 KiB
Python
import pytest
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import httpx
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from fastapi_modules.fastapi_leaudit.leaudit_bridge.resilient_clients import ResilientQwenVLMClient
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from fastapi_modules.fastapi_leaudit.services.impl.pageQualityServiceImpl import PageQualityServiceImpl
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class _FakeVlmClient:
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def __init__(self, response):
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self.response = response
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self.prompts = []
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async def extract_multifield(self, *, prompt, images_data_urls, max_tokens=800):
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self.prompts.append((prompt, images_data_urls, max_tokens))
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if isinstance(self.response, Exception):
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raise self.response
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return self.response
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@pytest.mark.asyncio
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async def test_vlm_page_quality_reject_result_is_used():
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service = PageQualityServiceImpl()
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service.VlmClient = _FakeVlmClient(
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{
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"status": "reject",
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"score": 0.18,
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"reason": "页面文字严重模糊，无法稳定辨认关键内容",
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}
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)
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status, score, reason = await service._classify_page_image_by_vlm(b"image-bytes")
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assert status == "reject"
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assert score == 0.18
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assert "严重模糊" in reason
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assert "只输出 JSON" in service.VlmClient.prompts[0][0]
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assert "内嵌照片" in service.VlmClient.prompts[0][0]
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assert "即使页面周边文字清楚" in service.VlmClient.prompts[0][0]
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@pytest.mark.asyncio
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async def test_vlm_page_quality_embedded_evidence_blur_cannot_pass():
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service = PageQualityServiceImpl()
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service.VlmClient = _FakeVlmClient(
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{
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"quality_status": "疑似模糊",
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"quality_score": "0.42",
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"message": "内嵌证据照片主体发虚，门头文字不易辨认",
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}
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)
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status, score, reason = await service._classify_page_image_by_vlm(b"image-bytes")
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assert status == "review"
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assert score == 0.42
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assert "内嵌证据照片" in reason
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@pytest.mark.asyncio
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async def test_vlm_page_quality_chinese_reject_status_is_supported():
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service = PageQualityServiceImpl()
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service.VlmClient = _FakeVlmClient(
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{
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"result": "不通过",
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"confidence": 0.1,
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"detail": "证据照片严重模糊，关键场所无法辨认",
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}
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)
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status, score, reason = await service._classify_page_image_by_vlm(b"image-bytes")
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assert status == "reject"
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assert score == 0.1
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assert "严重模糊" in reason
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@pytest.mark.asyncio
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async def test_vlm_page_quality_json_string_result_is_supported():
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service = PageQualityServiceImpl()
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service.VlmClient = _FakeVlmClient(
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'{"status":"review","score":0.33,"reason":"页面内照片模糊"}'
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)
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status, score, reason = await service._classify_page_image_by_vlm(b"image-bytes")
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assert status == "review"
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assert score == 0.33
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assert reason == "页面内照片模糊"
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@pytest.mark.asyncio
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async def test_vlm_page_quality_invalid_result_falls_back_to_review_not_pass():
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service = PageQualityServiceImpl()
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service.VlmClient = _FakeVlmClient({"status": "unknown", "reason": ""})
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status, score, reason = await service._classify_page_image_by_vlm(b"image-bytes")
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assert status == "review"
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assert score == 0.5
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assert "VLM返回结果不可用" in reason
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@pytest.mark.asyncio
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async def test_vlm_page_quality_error_falls_back_to_review_not_pass():
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service = PageQualityServiceImpl()
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service.VlmClient = _FakeVlmClient(RuntimeError("vlm down"))
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status, score, reason = await service._classify_page_image_by_vlm(b"image-bytes")
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assert status == "review"
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assert score == 0.5
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assert "VLM图片质量检测失败" in reason
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@pytest.mark.asyncio
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async def test_resilient_vlm_extract_multifield_keeps_raw_text_when_json_parse_fails(monkeypatch):
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client = ResilientQwenVLMClient(base_url="http://example.test", api_key="x", model="vlm-test")
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async def fake_post_with_retry(payload):
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return httpx.Response(
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200,
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json={
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"choices": [
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{
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"message": {
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"content": "疑似模糊：内嵌证据照片主体发虚，建议人工复核",
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}
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}
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]
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},
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)
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monkeypatch.setattr(client, "_post_with_retry", fake_post_with_retry)
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result = await client.extract_multifield(
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prompt="图片质量检测",
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images_data_urls=["data:image/png;base64,xxx"],
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)
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assert result["result"].startswith("疑似模糊")
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assert "内嵌证据照片" in result["reason"]
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