Initial upload for secondary development
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142
chatlog_fastAPI/services/media_parser.py
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142
chatlog_fastAPI/services/media_parser.py
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import base64
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import logging
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import httpx
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from fastapi import HTTPException
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from services.ai_client import get_openai_client
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from services.media_resolver import resolve_media
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from services.runtime_settings import get_ai_settings
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log = logging.getLogger(__name__)
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async def _get_ai_client():
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return await get_openai_client()
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async def parse_media(kind: str, key: str) -> dict:
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"""
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Parse one chatlog media object into text.
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kind: voice, image, or video.
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key: chatlog media key.
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"""
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if kind not in {"voice", "image", "video"}:
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raise HTTPException(400, "不支持的媒体类型")
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if not key:
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raise HTTPException(400, "媒体 key 不能为空")
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ai = await get_ai_settings()
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if not ai.get("ai_api_key"):
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raise HTTPException(503, "AI 服务未配置，请在设置页填写 AI API Key")
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if kind == "voice" and not ai.get("voice_model"):
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raise HTTPException(503, "语音模型未配置，请在设置页填写语音模型名称，例如 paraformer-v2")
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if kind in ("image", "video") and not ai.get("vision_model"):
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raise HTTPException(503, "视觉模型未配置，请在设置页填写视觉模型名称，例如 qwen-vl-plus")
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media = await resolve_media(kind, key)
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if kind == "voice":
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return {"text": await _parse_voice(media.bytes, media.content_type)}
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return {"text": await _parse_visual(kind, media.bytes, media.content_type)}
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async def _parse_voice(media_bytes: bytes, content_type: str) -> str:
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b64_audio = base64.b64encode(media_bytes).decode()
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audio_ct = content_type.lower()
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if "silk" in audio_ct or "x-silk" in audio_ct:
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audio_mime = "audio/silk"
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elif "amr" in audio_ct:
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audio_mime = "audio/amr"
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elif "ogg" in audio_ct or "opus" in audio_ct:
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audio_mime = "audio/ogg"
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elif "wav" in audio_ct:
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audio_mime = "audio/wav"
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else:
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audio_mime = "audio/mpeg"
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data_uri = f"data:{audio_mime};base64,{b64_audio}"
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_, ai = await _get_ai_client()
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asr_headers = {
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"Authorization": f"Bearer {ai['ai_api_key']}",
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"Content-Type": "application/json",
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}
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async with httpx.AsyncClient(timeout=60) as http:
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submit = await http.post(
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"https://dashscope.aliyuncs.com/api/v1/services/audio/asr/transcription",
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headers={**asr_headers, "X-DashScope-Async": "enable"},
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json={
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"model": ai["voice_model"],
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"input": {"file_urls": [data_uri]},
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"parameters": {"language_hints": ["zh", "en"]},
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},
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timeout=30,
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)
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submit_data = submit.json()
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if submit.status_code not in (200, 201):
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raise HTTPException(500, f"提交识别任务失败: {submit_data.get('message', submit_data)}")
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task_id = submit_data.get("output", {}).get("task_id")
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if not task_id:
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raise HTTPException(500, f"未获取到 task_id: {submit_data}")
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for _ in range(30):
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import asyncio
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await asyncio.sleep(1)
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poll = await http.get(
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f"https://dashscope.aliyuncs.com/api/v1/tasks/{task_id}",
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headers=asr_headers,
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timeout=10,
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)
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poll_data = poll.json()
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status = poll_data.get("output", {}).get("task_status", "")
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if status == "SUCCEEDED":
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results = poll_data.get("output", {}).get("results", [])
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log.info("[media_parser] ASR SUCCEEDED results: %s", results)
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if not results:
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return "（识别结果为空）"
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trans_url = results[0].get("transcription_url", "")
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if trans_url:
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trans_resp = await http.get(trans_url, timeout=10)
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trans_data = trans_resp.json()
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log.info("[media_parser] transcription_url content: %s", str(trans_data)[:500])
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transcripts = trans_data.get("transcripts", [])
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text = transcripts[0].get("text", "") if transcripts else ""
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else:
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text = results[0].get("transcription", "")
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return text or "（识别结果为空）"
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if status in ("FAILED", "CANCELLED"):
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raise HTTPException(500, f"识别任务失败: {poll_data.get('output', {}).get('message', status)}")
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raise HTTPException(500, "语音识别超时（30秒）")
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async def _parse_visual(kind: str, media_bytes: bytes, content_type: str) -> str:
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b64 = base64.b64encode(media_bytes).decode()
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ct = content_type.lower()
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if "png" in ct:
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mime = "image/png"
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elif "webp" in ct:
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mime = "image/webp"
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else:
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mime = "image/jpeg"
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data_url = f"data:{mime};base64,{b64}"
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prompt = "请用中文简洁描述这张图片的内容。" if kind == "image" else "请用中文简洁描述这个视频截图的内容。"
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client, ai = await _get_ai_client()
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resp_ai = await client.chat.completions.create(
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model=ai["vision_model"],
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messages=[
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{
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"role": "user",
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"content": [
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{"type": "image_url", "image_url": {"url": data_url}},
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{"type": "text", "text": prompt},
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],
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}
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],
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max_tokens=300,
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)
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return resp_ai.choices[0].message.content or ""
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