修改数据结构 #1
71
ESConnect.py
71
ESConnect.py
@@ -15,7 +15,7 @@ AUTH = None # 如需认证则改为("用户名","密码")
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es = Elasticsearch(["http://localhost:9200"])
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# 定义索引名称和类型名称
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index_name = "wordsearch2"
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index_name = "wordsearch2666"
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def create_index_with_mapping():
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"""修正后的索引映射配置"""
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@@ -23,22 +23,12 @@ def create_index_with_mapping():
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mapping = {
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"mappings": {
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"properties": {
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"id": {
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"type": "text", # 改为text类型支持分词
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"data": {
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"type": "text", # 存储转换后的字符串，支持分词搜索
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"analyzer": "ik_max_word",
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"search_analyzer": "ik_smart"
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},
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"name": {
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"type": "text",
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"analyzer": "ik_max_word",
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"search_analyzer": "ik_smart"
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},
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"students": {"type": "keyword"}, # 仅保留type参数
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"teacher": {"type": "keyword"}, # 仅保留type参数
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"timestamp": {
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"type": "date",
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"format": "strict_date_optional_time||epoch_millis"
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}
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"image": {"type": "keyword"}, # 存储图片路径或标识
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}
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}
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}
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@@ -54,8 +44,7 @@ def create_index_with_mapping():
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def get_doc_id(data):
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"""
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根据关键字段生成唯一ID（用于去重）
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可以根据实际需求调整字段组合
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根据数据内容生成唯一ID（用于去重）
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参数:
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data (dict): 包含文档数据的字典
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@@ -63,8 +52,10 @@ def get_doc_id(data):
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返回:
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str: 基于数据内容生成的MD5哈希值作为唯一ID
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"""
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# 组合关键字段生成唯一字符串
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unique_str = f"{data['id']}{data['name']}{data['students']}{data['teacher']}"
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# 使用data字段的内容生成唯一字符串
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data_str = data.get('data', '')
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image_str = data.get('image', '')
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unique_str = f"{data_str}{image_str}"
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# 使用MD5哈希生成唯一ID
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return hashlib.md5(unique_str.encode('utf-8')).hexdigest()
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@@ -184,47 +175,3 @@ def batch_write_data(data):
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except requests.exceptions.HTTPError as e:
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print(f"文档写入失败: {e.response.text}, 数据: {data}")
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return False
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def update_mapping():
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# 定义新的映射配置
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new_mapping = {
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"properties": {
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"id": {
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"type": "text",
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"analyzer": "ik_max_word",
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"search_analyzer": "ik_smart"
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},
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"name": {
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"type": "text",
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"analyzer": "ik_max_word"
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},
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"students": {
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"type": "keyword"
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},
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"teacher": {
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"type": "keyword"
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}
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}
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}
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# 执行PUT请求更新映射
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try:
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response = requests.put(
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f"{ES_URL}/{index_name}/_mapping",
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auth=AUTH,
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json=new_mapping,
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headers={"Content-Type": "application/json"}
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)
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response.raise_for_status()
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print("索引映射更新成功")
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print(response.json())
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# 验证映射更新结果
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verify = requests.get(
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f"{ES_URL}/{index_name}/_mapping",
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auth=AUTH
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)
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print("\n验证结果：")
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print(verify.json())
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except requests.exceptions.HTTPError as e:
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print(f"请求失败: {e.response.text}")
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107
app.py
107
app.py
@@ -6,6 +6,7 @@ from PIL import Image
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import re
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import json
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from ESConnect import *
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from json_converter import json_to_string, string_to_json
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from openai import OpenAI
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# import config
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@@ -52,9 +53,9 @@ def ocr_and_extract_info(image_path):
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messages=[
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{'role': 'system', 'content': '你是一个能理解图片和文本的助手，请根据用户提供的信息进行回答。'},
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{'role': 'user', "content": [
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{"type": "text", "text": "请识别这张图片中的信息，只显示json不显示其它信息便于解析"
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"以JSON格式返回（id对应比赛名称或论文名称，name对应项目名称，students对应参赛学生，teacher对应指导老师,出现多个名字用列表存储）"
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"：{'id':'', 'name':'','students':'','teacher':''}"},
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{"type": "text", "text": "请识别这张图片中的信息，将你认为重要的数据转换为不包含嵌套的json，不要显示其它信息以便于解析"
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"直接输出json结果即可"
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"你可以自行决定使用哪些json字段"},
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{
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"type": "image_url",
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"image_url": {
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@@ -68,6 +69,12 @@ def ocr_and_extract_info(image_path):
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# 获取API返回的文本内容
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response_text = chat_completion.choices[0].message.content
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# 添加调试信息：输出模型返回的原始字符串
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print("=" * 50)
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print("模型返回的原始字符串:")
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print(response_text)
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print("=" * 50)
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def parse_respound(text):
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"""
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@@ -83,10 +90,10 @@ def ocr_and_extract_info(image_path):
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try:
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result=json.loads(text)
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if result:
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print("success")
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print("✓ 成功解析标准JSON格式")
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return result
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except json.JSONDecodeError:
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print("无法解析标准json")
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print("✗ 无法解析标准JSON格式")
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pass
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# 提取markdown代码块中的内容
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@@ -95,10 +102,10 @@ def ocr_and_extract_info(image_path):
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try:
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result=json.loads(code_block.group(1))
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if result:
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print("success")
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print("✓ 成功解析markdown代码块中的JSON")
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return result
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except json.JSONDecodeError:
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print("无法解析markdown")
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print("✗ 无法解析markdown代码块中的JSON")
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pass
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# 尝试替换单引号并解析
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@@ -106,14 +113,23 @@ def ocr_and_extract_info(image_path):
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fixed_json = text.replace("'", "\"")
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result=json.loads(fixed_json)
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if(result):
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print("success")
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print("✓ 成功解析替换单引号后的JSON")
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return result
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except json.JSONDecodeError:
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print("无法替换单引号")
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print("✗ 无法解析替换单引号后的JSON")
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pass
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# 解析API返回的文本
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result_data = parse_respound(response_text)
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# 添加调试信息：输出解析结果
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print("解析结果:")
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if result_data:
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print(f"✓ 解析成功: {result_data}")
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else:
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print("✗ 解析失败，返回None")
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print("=" * 50)
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return result_data
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"""
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@@ -154,10 +170,28 @@ def upload_image():
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# 调用大模型进行识别
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try:
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data = ocr_and_extract_info(image_path) # 替换为真实AI接口调用
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insert_data(data) # 存入ES
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return jsonify({"message": "成功录入", "data": data})
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print(f"开始处理图片: {image_path}")
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original_data = ocr_and_extract_info(image_path) # 获取原始JSON数据
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if original_data:
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# 使用json_converter将JSON数据转换为字符串
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data_string = json_to_string(original_data)
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print(f"转换后的数据字符串: {data_string}")
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# 构造新的数据结构，只包含data和image字段
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processed_data = {
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"data": data_string,
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"image": filename # 存储图片文件名
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}
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print(f"准备存储的数据: {processed_data}")
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insert_data(processed_data) # 存入ES
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print("✓ 数据成功存储到Elasticsearch")
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return jsonify({"message": "成功录入", "data": original_data, "processed": processed_data})
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else:
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print("✗ 无法识别图片内容")
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return jsonify({"error": "无法识别图片内容"}), 400
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except Exception as e:
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print(f"✗ 处理过程中发生错误: {str(e)}")
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return jsonify({"error": str(e)}), 500
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# 搜索路由
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@@ -173,8 +207,31 @@ def search():
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if not keyword:
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return jsonify([])
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results = search_by_any_field(keyword)
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print(results)
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return jsonify(results)
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# 处理搜索结果，将data字段转换回JSON格式
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processed_results = []
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for result in results:
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if '_source' in result and 'data' in result['_source']:
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try:
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# 将data字段的字符串转换回JSON
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original_data = string_to_json(result['_source']['data'])
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# 构造新的结果格式
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processed_result = {
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'_id': result.get('_id', ''),
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'_source': {
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'image': result['_source'].get('image', ''),
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**original_data # 展开原始数据字段
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}
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}
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processed_results.append(processed_result)
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except Exception as e:
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# 如果转换失败，保持原始格式
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processed_results.append(result)
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else:
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processed_results.append(result)
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print(processed_results)
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return jsonify(processed_results)
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# 结果页面路由
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@app.route('/results')
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@@ -197,7 +254,27 @@ def show_all():
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str: 渲染后的HTML页面，包含所有数据
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"""
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all_data = search_all()
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return render_template('all.html', data=all_data)
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# 将data字段从字符串转换回JSON格式以便显示
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processed_data = []
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for item in all_data:
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if 'data' in item and item['data']:
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try:
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# 将data字段的字符串转换回JSON
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original_data = string_to_json(item['data'])
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# 合并原始数据和其他字段
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display_item = {
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'_id': item['_id'],
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'image': item.get('image', ''),
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**original_data # 展开原始数据字段
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}
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processed_data.append(display_item)
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except Exception as e:
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# 如果转换失败，保持原始格式
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processed_data.append(item)
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else:
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processed_data.append(item)
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return render_template('all.html', data=processed_data)
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# 删除数据路由
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@app.route('/delete/<doc_id>', methods=['POST'])
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6
requirements.txt
Normal file
6
requirements.txt
Normal file
@@ -0,0 +1,6 @@
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flask==3.1.1
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pillow==11.1.0
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openai==1.88.0
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elasticsearch==7.17.0
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pandas==2.2.3
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requests
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Reference in New Issue
Block a user