89 lines
3.1 KiB
Python
89 lines
3.1 KiB
Python
import base64
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import logging
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import httpx
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from io import BytesIO
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from app.config import settings
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async def vl_image_caption_func(image_data: bytes, prompt: str = "请详细描述这张图片") -> str:
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"""
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使用 VL 模型 (vLLM OpenAI API) 生成图片描述
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"""
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if not settings.VL_BINDING_HOST:
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return "[Image Processing Skipped: No VL Model Configured]"
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try:
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# 1. 编码图片为 Base64
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base64_image = base64.b64encode(image_data).decode('utf-8')
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# 2. 构造 OpenAI 格式请求
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# vLLM 支持 OpenAI Vision API
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url = f"{settings.VL_BINDING_HOST}/chat/completions"
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headers = {
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"Content-Type": "application/json",
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"Authorization": f"Bearer {settings.VL_KEY}"
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}
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payload = {
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"model": settings.VL_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": "text", "text": prompt},
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{
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"type": "image_url",
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"image_url": {
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"url": f"data:image/jpeg;base64,{base64_image}"
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}
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}
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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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async with httpx.AsyncClient(timeout=30.0) as client:
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response = await client.post(url, headers=headers, json=payload)
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response.raise_for_status()
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result = response.json()
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description = result['choices'][0]['message']['content']
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return f"[Image Description: {description}]"
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except Exception as e:
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logging.error(f"VL Caption failed: {str(e)}")
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return f"[Image Processing Failed: {str(e)}]"
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async def process_pdf_with_images(file_bytes: bytes) -> str:
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"""
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解析 PDF，提取文本并对图片进行 Caption
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"""
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import pypdf
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from PIL import Image
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text_content = ""
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pdf_file = BytesIO(file_bytes)
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reader = pypdf.PdfReader(pdf_file)
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for page_num, page in enumerate(reader.pages):
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# 1. 提取文本
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page_text = page.extract_text()
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text_content += f"--- Page {page_num + 1} Text ---\n{page_text}\n\n"
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# 2. 提取图片
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if False and settings.VL_BINDING_HOST:
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for count, image_file_object in enumerate(page.images):
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try:
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# 获取图片数据
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image_data = image_file_object.data
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# 简单验证图片有效性
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# Image.open(BytesIO(image_data)).verify()
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# 调用 VL 模型
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caption = await vl_image_caption_func(image_data)
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text_content += f"--- Page {page_num + 1} Image {count + 1} ---\n{caption}\n\n"
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except Exception as e:
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logging.warning(f"Failed to process image {count} on page {page_num}: {e}")
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return text_content
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