647 lines
24 KiB
Python
647 lines
24 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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本地多模态检索系统Web应用
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集成本地模型和FAISS向量数据库
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支持文搜文、文搜图、图搜文、图搜图四种检索模式
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"""
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import os
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import sys
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import logging
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import time
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import json
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import base64
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from io import BytesIO
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from pathlib import Path
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from datetime import datetime, timezone
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import numpy as np
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from PIL import Image
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from flask import Flask, request, jsonify, render_template, send_from_directory, send_file
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from werkzeug.utils import secure_filename
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import torch
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# 设置离线模式
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os.environ['TRANSFORMERS_OFFLINE'] = '1'
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# 导入本地模块
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from multimodal_retrieval_local import MultimodalRetrievalLocal
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from optimized_file_handler import OptimizedFileHandler
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# 设置日志
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logging.basicConfig(level=logging.INFO,
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format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
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logger = logging.getLogger(__name__)
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# 创建Flask应用
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app = Flask(__name__)
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# 配置
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app.config['UPLOAD_FOLDER'] = '/tmp/mmeb_uploads'
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app.config['MAX_CONTENT_LENGTH'] = 16 * 1024 * 1024 # 16MB
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app.config['MODEL_PATH'] = '/root/models/Ops-MM-embedding-v1-7B'
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app.config['INDEX_PATH'] = '/root/mmeb/local_faiss_index'
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app.config['ALLOWED_EXTENSIONS'] = {'txt', 'pdf', 'png', 'jpg', 'jpeg', 'gif'}
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# 确保上传目录存在
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os.makedirs(app.config['UPLOAD_FOLDER'], exist_ok=True)
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# 创建临时文件夹
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if not os.path.exists(app.config['UPLOAD_FOLDER']):
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os.makedirs(app.config['UPLOAD_FOLDER'], exist_ok=True)
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# 确保静态资源目录与 favicon 存在（方案二：静态文件）
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app.static_folder = os.path.join(os.path.dirname(__file__), 'static')
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os.makedirs(app.static_folder, exist_ok=True)
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favicon_path = os.path.join(app.static_folder, 'favicon.ico')
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if not os.path.exists(favicon_path):
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# 写入一个 1x1 透明 PNG 转 ICO 的简易占位图标（使用 PNG 作为内容也可被多数浏览器识别）
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# 这里直接写入一个极小的 PNG 文件，并命名为 .ico 以简化处理
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transparent_png_base64 = (
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"iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAQAAAC1HAwCAAAAC0lEQVR42mP8Xw8AAqMBhHqg7T8AAAAASUVORK5CYII="
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)
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with open(favicon_path, 'wb') as f:
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f.write(base64.b64decode(transparent_png_base64))
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# 创建文件处理器
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from optimized_file_handler import OptimizedFileHandler
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file_handler = OptimizedFileHandler(local_storage_dir=app.config['UPLOAD_FOLDER'])
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# 全局变量
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retrieval_system = None
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def allowed_file(filename):
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"""检查文件扩展名是否允许"""
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return '.' in filename and \
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filename.rsplit('.', 1)[1].lower() in app.config['ALLOWED_EXTENSIONS']
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def init_retrieval_system():
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"""初始化检索系统"""
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global retrieval_system
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if retrieval_system is not None:
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return retrieval_system
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logger.info("初始化多模态检索系统...")
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# 检查模型路径
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model_path = app.config['MODEL_PATH']
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if not os.path.exists(model_path):
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logger.error(f"模型路径不存在: {model_path}")
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raise FileNotFoundError(f"模型路径不存在: {model_path}")
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# 初始化检索系统
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retrieval_system = MultimodalRetrievalLocal(
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model_path=model_path,
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use_all_gpus=True,
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index_path=app.config['INDEX_PATH'],
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shard_model_across_gpus=True
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)
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logger.info("多模态检索系统初始化完成")
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return retrieval_system
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def get_image_base64(image_path):
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"""将图像转换为base64编码"""
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with open(image_path, "rb") as image_file:
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encoded_string = base64.b64encode(image_file.read()).decode('utf-8')
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return f"data:image/jpeg;base64,{encoded_string}"
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@app.route('/')
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def index():
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"""首页"""
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return render_template('local_index.html')
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@app.route('/favicon.ico')
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def favicon():
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"""提供静态 favicon（方案二）"""
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return send_from_directory(app.static_folder, 'favicon.ico')
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@app.route('/api/stats', methods=['GET'])
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def get_stats():
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"""获取系统统计信息"""
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try:
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retrieval = init_retrieval_system()
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stats = retrieval.get_stats()
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debug_info = {
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"server_time": datetime.now(timezone.utc).isoformat(),
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"vector_dimension": stats.get("vector_dimension"),
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"total_vectors": stats.get("total_vectors"),
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"model_path": stats.get("model_path"),
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"index_path": stats.get("index_path"),
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}
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return jsonify({"success": True, "stats": stats, "debug": debug_info})
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except Exception as e:
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logger.error(f"获取统计信息失败: {str(e)}")
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return jsonify({"success": False, "error": str(e)}), 500
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@app.route('/api/add_text', methods=['POST'])
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def add_text():
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"""添加文本"""
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try:
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data = request.json
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text = data.get('text')
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if not text:
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return jsonify({"success": False, "error": "文本不能为空"}), 400
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# 使用内存处理文本
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with file_handler.temp_file_context(text.encode('utf-8'), suffix='.txt') as temp_file:
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logger.info(f"处理文本: {temp_file}")
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# 初始化检索系统
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retrieval = init_retrieval_system()
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# 添加文本
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metadata = {
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"timestamp": time.time(),
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"source": "web_upload"
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}
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text_ids = retrieval.add_texts([text], [metadata])
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# 保存索引
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retrieval.save_index()
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stats = retrieval.get_stats()
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return jsonify({
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"success": True,
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"message": "文本添加成功",
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"text_id": text_ids[0] if text_ids else None,
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"debug": {
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"server_time": datetime.now(timezone.utc).isoformat(),
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"vector_dimension": stats.get("vector_dimension"),
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"total_vectors": stats.get("total_vectors"),
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}
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})
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except Exception as e:
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logger.error(f"添加文本失败: {str(e)}")
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return jsonify({"success": False, "error": str(e)}), 500
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finally:
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# 清理临时文件
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file_handler.cleanup_all_temp_files()
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@app.route('/api/add_images_batch', methods=['POST'])
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def add_images_batch():
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"""批量添加图像（多卡并行友好）"""
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try:
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# 读取多文件
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files = request.files.getlist('images')
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if not files:
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return jsonify({"success": False, "error": "没有上传文件"}), 400
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retrieval = init_retrieval_system()
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images = []
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metadatas = []
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image_paths = []
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for file in files:
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if file and allowed_file(file.filename):
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image_data = file.read()
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filename = secure_filename(file.filename)
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image_path = os.path.join(app.config['UPLOAD_FOLDER'], filename)
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with open(image_path, 'wb') as f:
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f.write(image_data)
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try:
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img = Image.open(BytesIO(image_data))
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if img.mode != 'RGB':
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img = img.convert('RGB')
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except Exception as e:
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return jsonify({"success": False, "error": f"图像格式不支持: {filename}: {str(e)}"}), 400
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images.append(img)
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metadatas.append({
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"filename": filename,
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"timestamp": time.time(),
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"source": "web_upload_batch",
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"size": len(image_data),
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"local_path": image_path
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})
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image_paths.append(image_path)
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else:
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return jsonify({"success": False, "error": f"不支持的文件类型: {file.filename}"}), 400
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image_ids = retrieval.add_images(images, metadatas, image_paths)
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retrieval.save_index()
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stats = retrieval.get_stats()
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return jsonify({
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"success": True,
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"message": f"批量添加成功: {len(image_ids)}/{len(images)}",
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"image_ids": image_ids,
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"debug": {
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"server_time": datetime.now(timezone.utc).isoformat(),
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"vector_dimension": stats.get("vector_dimension"),
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"total_vectors": stats.get("total_vectors"),
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}
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})
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except Exception as e:
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logger.error(f"批量添加图像失败: {str(e)}")
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return jsonify({"success": False, "error": str(e)}), 500
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finally:
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file_handler.cleanup_all_temp_files()
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@app.route('/api/add_texts_batch', methods=['POST'])
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def add_texts_batch():
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"""批量添加文本（多卡并行友好）"""
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try:
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data = request.json
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if not data or 'texts' not in data:
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return jsonify({"success": False, "error": "缺少 texts 数组"}), 400
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texts = [t for t in data.get('texts', []) if isinstance(t, str) and t.strip()]
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if not texts:
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return jsonify({"success": False, "error": "文本数组为空"}), 400
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retrieval = init_retrieval_system()
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metadatas = [{"timestamp": time.time(), "source": "web_upload_batch"} for _ in texts]
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text_ids = retrieval.add_texts(texts, metadatas)
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retrieval.save_index()
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stats = retrieval.get_stats()
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return jsonify({
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"success": True,
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"message": f"批量添加成功: {len(text_ids)}/{len(texts)}",
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"text_ids": text_ids,
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"debug": {
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"server_time": datetime.now(timezone.utc).isoformat(),
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"vector_dimension": stats.get("vector_dimension"),
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"total_vectors": stats.get("total_vectors"),
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}
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})
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except Exception as e:
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logger.error(f"批量添加文本失败: {str(e)}")
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return jsonify({"success": False, "error": str(e)}), 500
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@app.route('/api/add_image', methods=['POST'])
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def add_image():
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"""添加图像"""
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try:
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# 检查是否有文件
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if 'image' not in request.files:
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return jsonify({"success": False, "error": "没有上传文件"}), 400
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file = request.files['image']
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# 检查文件名
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if file.filename == '':
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return jsonify({"success": False, "error": "没有选择文件"}), 400
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if file and allowed_file(file.filename):
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# 读取图像数据
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image_data = file.read()
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file_size = len(image_data)
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# 使用文件处理器处理图像
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logger.info(f"处理图像: {file.filename} ({file_size} 字节)")
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# 初始化检索系统
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retrieval = init_retrieval_system()
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# 创建临时文件
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file_obj = BytesIO(image_data)
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filename = secure_filename(file.filename)
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# 保存到本地文件系统
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image_path = os.path.join(app.config['UPLOAD_FOLDER'], filename)
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with open(image_path, 'wb') as f:
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f.write(image_data)
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# 加载图像
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try:
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image = Image.open(BytesIO(image_data))
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# 确保图像是RGB模式
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if image.mode != 'RGB':
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logger.info(f"将图像从 {image.mode} 转换为 RGB")
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image = image.convert('RGB')
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logger.info(f"成功加载图像: {filename}, 格式: {image.format}, 模式: {image.mode}, 大小: {image.size}")
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except Exception as e:
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logger.error(f"加载图像失败: {filename}, 错误: {str(e)}")
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return jsonify({"success": False, "error": f"图像格式不支持: {str(e)}"}), 400
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# 添加图像
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metadata = {
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"filename": filename,
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"timestamp": time.time(),
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"source": "web_upload",
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"size": file_size,
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"local_path": image_path
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}
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# 添加到检索系统
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image_ids = retrieval.add_images([image], [metadata], [image_path])
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# 保存索引
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retrieval.save_index()
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stats = retrieval.get_stats()
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return jsonify({
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"success": True,
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"message": "图像添加成功",
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"image_id": image_ids[0] if image_ids else None,
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"debug": {
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"server_time": datetime.now(timezone.utc).isoformat(),
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"vector_dimension": stats.get("vector_dimension"),
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"total_vectors": stats.get("total_vectors"),
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"image_size": [image.width, image.height] if 'image' in locals() else None,
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}
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})
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else:
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return jsonify({"success": False, "error": "不支持的文件类型"}), 400
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except Exception as e:
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logger.error(f"添加图像失败: {str(e)}")
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return jsonify({"success": False, "error": str(e)}), 500
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finally:
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# 清理临时文件
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file_handler.cleanup_all_temp_files()
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@app.route('/api/search_by_text', methods=['POST'])
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def search_by_text():
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"""文本搜索"""
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try:
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data = request.json
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query = data.get('query')
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k = int(data.get('k', 5))
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filter_type = data.get('filter_type') # "text", "image" 或 null
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if not query:
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return jsonify({"success": False, "error": "查询文本不能为空"}), 400
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# 初始化检索系统
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retrieval = init_retrieval_system()
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# 执行搜索
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results = retrieval.search_by_text(query, k, filter_type)
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# 处理结果
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processed_results = []
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for result in results:
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item = {
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"score": result.get("score", 0),
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"type": result.get("type")
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}
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if result.get("type") == "text":
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item["text"] = result.get("text", "")
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elif result.get("type") == "image":
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if "path" in result and os.path.exists(result["path"]):
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item["image"] = get_image_base64(result["path"])
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item["filename"] = os.path.basename(result["path"])
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if "description" in result:
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item["description"] = result["description"]
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processed_results.append(item)
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stats = retrieval.get_stats()
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return jsonify({
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"success": True,
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"results": processed_results,
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"query": query,
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"filter_type": filter_type,
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"debug": {
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"server_time": datetime.now(timezone.utc).isoformat(),
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"vector_dimension": stats.get("vector_dimension"),
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"total_vectors": stats.get("total_vectors"),
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}
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})
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except Exception as e:
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logger.error(f"文本搜索失败: {str(e)}")
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return jsonify({"success": False, "error": str(e)}), 500
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@app.route('/api/search_by_image', methods=['POST'])
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def search_by_image():
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"""图像搜索"""
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try:
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# 检查是否有文件
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if 'image' not in request.files:
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return jsonify({"success": False, "error": "没有上传文件"}), 400
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file = request.files['image']
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k = int(request.form.get('k', 5))
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filter_type = request.form.get('filter_type') # "text", "image" 或 null
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# 检查文件名
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if file.filename == '':
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return jsonify({"success": False, "error": "没有选择文件"}), 400
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if file and allowed_file(file.filename):
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# 读取图像数据
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image_data = file.read()
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file_size = len(image_data)
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# 根据文件大小选择处理方式
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if file_size <= 5 * 1024 * 1024: # 5MB
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# 小文件使用内存处理
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logger.info(f"使用内存处理搜索图像: {file.filename} ({file_size} 字节)")
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image = Image.open(BytesIO(image_data))
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# 初始化检索系统
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retrieval = init_retrieval_system()
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# 执行搜索
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results = retrieval.search_by_image(image, k, filter_type)
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else:
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# 大文件使用临时文件处理
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with file_handler.temp_file_context(image_data, suffix=os.path.splitext(file.filename)[1]) as temp_file:
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logger.info(f"使用临时文件处理搜索图像: {temp_file} ({file_size} 字节)")
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# 初始化检索系统
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retrieval = init_retrieval_system()
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# 加载图像
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image = Image.open(temp_file)
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# 执行搜索
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results = retrieval.search_by_image(image, k, filter_type)
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# 处理结果
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processed_results = []
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for result in results:
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item = {
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"score": result.get("score", 0),
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"type": result.get("type")
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}
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||
|
||
if result.get("type") == "text":
|
||
item["text"] = result.get("text", "")
|
||
elif result.get("type") == "image":
|
||
if "path" in result and os.path.exists(result["path"]):
|
||
item["image"] = get_image_base64(result["path"])
|
||
item["filename"] = os.path.basename(result["path"])
|
||
if "description" in result:
|
||
item["description"] = result["description"]
|
||
|
||
processed_results.append(item)
|
||
|
||
stats = retrieval.get_stats()
|
||
return jsonify({
|
||
"success": True,
|
||
"results": processed_results,
|
||
"filter_type": filter_type,
|
||
"debug": {
|
||
"server_time": datetime.now(timezone.utc).isoformat(),
|
||
"vector_dimension": stats.get("vector_dimension"),
|
||
"total_vectors": stats.get("total_vectors"),
|
||
}
|
||
})
|
||
else:
|
||
return jsonify({"success": False, "error": "不支持的文件类型"}), 400
|
||
|
||
except Exception as e:
|
||
logger.error(f"图像搜索失败: {str(e)}")
|
||
return jsonify({"success": False, "error": str(e)}), 500
|
||
finally:
|
||
# 清理临时文件
|
||
file_handler.cleanup_all_temp_files()
|
||
|
||
@app.route('/uploads/<filename>')
|
||
def uploaded_file(filename):
|
||
"""提供上传文件的访问"""
|
||
return send_from_directory(app.config['UPLOAD_FOLDER'], filename)
|
||
|
||
@app.route('/temp/<filename>')
|
||
def temp_file(filename):
|
||
"""提供临时文件的访问"""
|
||
return send_from_directory(app.config['UPLOAD_FOLDER'], filename)
|
||
|
||
@app.route('/api/save_index', methods=['POST'])
|
||
def save_index():
|
||
"""保存索引"""
|
||
try:
|
||
# 初始化检索系统
|
||
retrieval = init_retrieval_system()
|
||
|
||
# 保存索引
|
||
retrieval.save_index()
|
||
|
||
stats = retrieval.get_stats()
|
||
return jsonify({
|
||
"success": True,
|
||
"message": "索引保存成功",
|
||
"debug": {
|
||
"server_time": datetime.now(timezone.utc).isoformat(),
|
||
"vector_dimension": stats.get("vector_dimension"),
|
||
"total_vectors": stats.get("total_vectors"),
|
||
}
|
||
})
|
||
|
||
except Exception as e:
|
||
logger.error(f"保存索引失败: {str(e)}")
|
||
return jsonify({"success": False, "error": str(e)}), 500
|
||
|
||
@app.route('/api/clear_index', methods=['POST'])
|
||
def clear_index():
|
||
"""清空索引"""
|
||
try:
|
||
# 初始化检索系统
|
||
retrieval = init_retrieval_system()
|
||
|
||
# 清空索引
|
||
retrieval.clear_index()
|
||
|
||
stats = retrieval.get_stats()
|
||
return jsonify({
|
||
"success": True,
|
||
"message": "索引已清空",
|
||
"debug": {
|
||
"server_time": datetime.now(timezone.utc).isoformat(),
|
||
"vector_dimension": stats.get("vector_dimension"),
|
||
"total_vectors": stats.get("total_vectors"),
|
||
}
|
||
})
|
||
|
||
except Exception as e:
|
||
logger.error(f"清空索引失败: {str(e)}")
|
||
return jsonify({"success": False, "error": str(e)}), 500
|
||
|
||
@app.route('/api/list_items', methods=['GET'])
|
||
def list_items():
|
||
"""列出所有索引项"""
|
||
try:
|
||
# 初始化检索系统
|
||
retrieval = init_retrieval_system()
|
||
|
||
# 获取所有项
|
||
items = retrieval.list_items()
|
||
|
||
stats = retrieval.get_stats()
|
||
return jsonify({
|
||
"success": True,
|
||
"items": items,
|
||
"debug": {
|
||
"server_time": datetime.now(timezone.utc).isoformat(),
|
||
"vector_dimension": stats.get("vector_dimension"),
|
||
"total_vectors": stats.get("total_vectors"),
|
||
}
|
||
})
|
||
|
||
except Exception as e:
|
||
logger.error(f"列出索引项失败: {str(e)}")
|
||
return jsonify({"success": False, "error": str(e)}), 500
|
||
|
||
@app.route('/api/system_info', methods=['GET', 'POST'])
|
||
def system_info():
|
||
"""获取系统信息"""
|
||
try:
|
||
# GPU信息
|
||
gpu_info = []
|
||
if torch.cuda.is_available():
|
||
for i in range(torch.cuda.device_count()):
|
||
gpu_info.append({
|
||
"id": i,
|
||
"name": torch.cuda.get_device_name(i),
|
||
"memory_total": torch.cuda.get_device_properties(i).total_memory / (1024 ** 3),
|
||
"memory_allocated": torch.cuda.memory_allocated(i) / (1024 ** 3),
|
||
"memory_reserved": torch.cuda.memory_reserved(i) / (1024 ** 3)
|
||
})
|
||
|
||
# 检索系统信息
|
||
retrieval_info = {}
|
||
if retrieval_system is not None:
|
||
retrieval_info = retrieval_system.get_stats()
|
||
|
||
return jsonify({
|
||
"success": True,
|
||
"gpu_info": gpu_info,
|
||
"retrieval_info": retrieval_info,
|
||
"model_path": app.config['MODEL_PATH'],
|
||
"index_path": app.config['INDEX_PATH'],
|
||
"debug": {"server_time": datetime.now(timezone.utc).isoformat()}
|
||
})
|
||
except Exception as e:
|
||
logger.error(f"获取系统信息失败: {str(e)}")
|
||
return jsonify({"success": False, "error": str(e)}), 500
|
||
|
||
@app.route('/api/health', methods=['GET'])
|
||
def health():
|
||
"""健康检查：模型可用性、索引状态、GPU 信息"""
|
||
try:
|
||
retrieval = init_retrieval_system()
|
||
stats = retrieval.get_stats()
|
||
health_info = {
|
||
"model_loaded": True,
|
||
"vector_dimension": stats.get("vector_dimension"),
|
||
"total_vectors": stats.get("total_vectors"),
|
||
"gpu_available": torch.cuda.is_available(),
|
||
"cuda_device_count": torch.cuda.device_count() if torch.cuda.is_available() else 0,
|
||
"server_time": datetime.now(timezone.utc).isoformat(),
|
||
}
|
||
return jsonify({"success": True, "health": health_info})
|
||
except Exception as e:
|
||
logger.error(f"健康检查失败: {str(e)}")
|
||
return jsonify({"success": False, "error": str(e), "health": {"model_loaded": False, "server_time": datetime.now(timezone.utc).isoformat()}}), 500
|
||
|
||
if __name__ == '__main__':
|
||
try:
|
||
# 预初始化检索系统
|
||
init_retrieval_system()
|
||
|
||
# 启动Web应用
|
||
app.run(host='0.0.0.0', port=5000, debug=False)
|
||
except Exception as e:
|
||
logger.error(f"启动Web应用失败: {str(e)}")
|
||
sys.exit(1)
|