mirror of
https://github.com/linyqh/NarratoAI.git
synced 2026-09-09 21:49:06 +00:00
8e4271c2ce
优化了视频剪辑的FFmpeg命令参数顺序，将原本后置`-ss`的慢搜索改为前置`-ss`的快速搜索模式，大幅减少长视频剪辑时的不必要解码开销。重构了时间处理逻辑，新增辅助函数统一转换时间格式与计算裁剪时长，更新了所有兼容降级的编码命令以适配新参数格式，同时新增单元测试验证命令参数的正确性。
242 lines
10 KiB
Python
242 lines
10 KiB
Python
import json
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import unittest
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from unittest import mock
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from app.services.llm.migration_adapter import SubtitleAnalyzerAdapter
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from app.services.llm.unified_service import UnifiedLLMService
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from app.services.prompts import PromptManager
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class SubtitleAnalyzerAdapterPipelineTests(unittest.TestCase):
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def test_generate_narration_copy_uses_plain_text_prompt_with_selected_type(self):
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adapter = SubtitleAnalyzerAdapter(
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api_key="sk-test",
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model="test-model",
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base_url="https://example.test/v1",
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provider="openai",
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)
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with mock.patch.object(adapter, "_run_async_safely", return_value="她被家人逼到绝路，反击从这一刻开始。") as call:
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result = adapter.generate_narration_copy(
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short_name="测试短剧",
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plot_analysis="女主被家人误会后反击。",
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subtitle_content="# 视频 1: 1.mp4\n00:00:01,000 --> 00:00:04,000\n女主被误会。",
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temperature=0.7,
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narration_language="简体中文（中国）",
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drama_genre="家庭伦理",
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)
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self.assertEqual("success", result["status"])
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self.assertIn("反击", result["narration_copy"])
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self.assertIn("家庭伦理", call.call_args.kwargs["prompt"])
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self.assertNotIn("response_format", call.call_args.kwargs)
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def test_generate_narration_copy_can_use_film_tv_prompt_category(self):
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self.assertTrue(PromptManager.exists("film_tv_narration", "narration_copy"))
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adapter = SubtitleAnalyzerAdapter(
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api_key="sk-test",
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model="test-model",
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base_url="https://example.test/v1",
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provider="openai",
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prompt_category="film_tv_narration",
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)
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with mock.patch.object(adapter, "_run_async_safely", return_value="他发现证据不对，真正的凶手另有其人。") as call:
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result = adapter.generate_narration_copy(
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short_name="测试电影",
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plot_analysis="主角发现证据疑点。",
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subtitle_content="# 视频 1: 1.mp4\n00:00:01,000 --> 00:00:04,000\n证据不对。",
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temperature=0.7,
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narration_language="简体中文（中国）",
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drama_genre="悬疑/犯罪",
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)
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self.assertEqual("success", result["status"])
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self.assertIn("影视解说正文创作任务", call.call_args.kwargs["prompt"])
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self.assertIn("用户选择的影视类型", call.call_args.kwargs["prompt"])
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self.assertNotIn("短剧解说正文创作任务", call.call_args.kwargs["prompt"])
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def test_film_tv_script_prompts_exclude_intro_outro_and_ads(self):
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base_parameters = {
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"drama_name": "测试电影",
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"drama_genre": "悬疑/犯罪",
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"plot_analysis": "主角发现证据疑点。",
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"subtitle_content": "# 视频 1: 1.mp4\n00:00:01,000 --> 00:00:04,000\n证据不对。",
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"narration_language": "简体中文（中国）",
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}
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prompt_parameters = {
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"segment_planning": base_parameters,
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"script_matching": {
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**base_parameters,
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"narration_copy": "他发现证据不对，真正的凶手另有其人。",
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"original_sound_ratio": 30,
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},
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"script_generation": {
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**base_parameters,
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"segment_plan": '{"segments": []}',
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},
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"script_repair": {
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**base_parameters,
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"invalid_script": '{"items": []}',
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"validation_errors": "片段包含广告",
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},
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}
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for prompt_name, parameters in prompt_parameters.items():
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with self.subTest(prompt_name=prompt_name):
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prompt = PromptManager.get_prompt(
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category="film_tv_narration",
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name=prompt_name,
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parameters=parameters,
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)
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self.assertIn("片头", prompt)
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self.assertIn("片尾", prompt)
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self.assertIn("广告", prompt)
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self.assertIn("绝对不能", prompt)
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def test_match_narration_copy_to_script_uses_json_prompt_with_selected_type(self):
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adapter = SubtitleAnalyzerAdapter(
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api_key="sk-test",
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model="test-model",
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base_url="https://example.test/v1",
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provider="openai",
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)
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matched = json.dumps(
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{
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"items": [
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{
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"_id": 1,
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"video_id": 1,
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"video_name": "1.mp4",
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"timestamp": "00:00:01,000-00:00:04,000",
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"picture": "女主被家人误会",
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"narration": "她被家人逼到绝路，反击从这一刻开始。",
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"OST": 0,
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}
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]
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},
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ensure_ascii=False,
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)
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with mock.patch.object(adapter, "_run_async_safely", return_value=matched) as call:
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result = adapter.match_narration_copy_to_script(
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short_name="测试短剧",
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plot_analysis="女主被家人误会后反击。",
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subtitle_content="# 视频 1: 1.mp4\n00:00:01,000 --> 00:00:04,000\n女主被误会。",
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narration_copy="她被家人逼到绝路，反击从这一刻开始。",
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temperature=0.7,
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narration_language="简体中文（中国）",
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drama_genre="家庭伦理",
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original_sound_ratio=60,
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)
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self.assertEqual("success", result["status"])
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self.assertEqual(1, json.loads(result["narration_script"])["items"][0]["_id"])
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self.assertIn("家庭伦理", call.call_args.kwargs["prompt"])
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self.assertIn("60%", call.call_args.kwargs["prompt"])
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self.assertEqual("json", call.call_args.kwargs["response_format"])
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def test_match_narration_copy_to_script_uses_streaming_when_callback_exists(self):
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adapter = SubtitleAnalyzerAdapter(
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api_key="sk-test",
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model="test-model",
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base_url="https://example.test/v1",
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provider="openai",
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)
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matched = json.dumps({"items": []}, ensure_ascii=False)
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with mock.patch.object(adapter, "_run_async_safely", return_value=matched) as call:
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result = adapter.match_narration_copy_to_script(
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short_name="测试短剧",
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plot_analysis="女主被家人误会后反击。",
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subtitle_content="# 视频 1: 1.mp4",
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narration_copy="她被家人逼到绝路，反击从这一刻开始。",
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stream_callback=lambda _event: None,
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)
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self.assertEqual("success", result["status"])
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self.assertIs(UnifiedLLMService.generate_text_stream, call.call_args.args[0])
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self.assertIn("on_chunk", call.call_args.kwargs)
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def test_generate_narration_script_plans_segments_before_copywriting(self):
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adapter = SubtitleAnalyzerAdapter(
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api_key="sk-test",
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model="test-model",
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base_url="https://example.test/v1",
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provider="openai",
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)
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responses = iter(
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[
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json.dumps(
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{
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"segments": [
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{
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"_id": 1,
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"video_id": 1,
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"video_name": "1.mp4",
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"timestamp": "00:00:01,000-00:00:04,000",
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"OST": 0,
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"intent": "开场钩子",
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}
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]
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},
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ensure_ascii=False,
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),
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json.dumps(
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{
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"items": [
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{
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"_id": 1,
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"video_id": 1,
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"video_name": "1.mp4",
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"timestamp": "00:00:01,000-00:00:04,000",
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"picture": "女主被误会",
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"narration": "她被所有人误会，真正的反击却刚刚开始。",
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"OST": 0,
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}
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]
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},
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ensure_ascii=False,
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),
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]
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)
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with mock.patch.object(adapter, "_run_async_safely", side_effect=lambda *_args, **_kwargs: next(responses)) as call:
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result = adapter.generate_narration_script(
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short_name="测试短剧",
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plot_analysis="女主被误会后反击。",
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subtitle_content="# 视频 1: 1.mp4\n00:00:01,000 --> 00:00:04,000\n女主被误会。",
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temperature=0.7,
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narration_language="简体中文（中国）",
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)
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self.assertEqual("success", result["status"])
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self.assertEqual(2, call.call_count)
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self.assertEqual(1, json.loads(result["narration_script"])["items"][0]["_id"])
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def test_repair_narration_script_returns_repaired_json(self):
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adapter = SubtitleAnalyzerAdapter(
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api_key="sk-test",
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model="test-model",
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base_url="https://example.test/v1",
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provider="openai",
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)
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repaired = json.dumps({"items": []}, ensure_ascii=False)
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with mock.patch.object(adapter, "_run_async_safely", return_value=repaired):
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result = adapter.repair_narration_script(
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short_name="测试短剧",
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plot_analysis="",
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subtitle_content="# 视频 1: 1.mp4",
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invalid_script="{bad}",
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validation_errors="时间戳错误",
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narration_language="简体中文（中国）",
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)
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self.assertEqual("success", result["status"])
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self.assertEqual(repaired, result["narration_script"])
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if __name__ == "__main__":
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unittest.main()
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