fix_bug
This commit is contained in:
superlishunqin
parent
a8a0beb277
commit
24953f68df
6
.idea/vcs.xml
generated
Normal file
6
.idea/vcs.xml
generated
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@ -0,0 +1,6 @@
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<?xml version="1.0" encoding="UTF-8"?>
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<project version="4">
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<component name="VcsDirectoryMappings">
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<mapping directory="" vcs="Git" />
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</component>
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</project>
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3
app.py
3
app.py
@ -60,5 +60,4 @@ def internal_server_error(e):
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return render_template('error.html', error='服务器内部错误'), 500
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if __name__ == '__main__':
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app.run(debug=True, host='0.0.0.0', port=5009)
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app.run(debug=True, host='0.0.0.0', port=50004)
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@ -8,7 +8,7 @@ import shutil
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from flask import Blueprint, request, jsonify, current_app, send_file, g, session
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from werkzeug.utils import secure_filename
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import mysql.connector
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from utils.model_service import text_classifier
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from utils.model_service import text_classifier # Assuming text_classifier is initialized correctly elsewhere
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from utils.db import get_db, close_db
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import logging
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@ -59,7 +59,8 @@ def get_next_document_number(category):
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if not result or result['max_num'] is None:
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return 1
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else:
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return result['max_num'] + 1
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# Ensure it's an int before adding
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return int(result['max_num']) + 1
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except Exception as e:
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logger.error(f"获取下一个文档编号时出错: {str(e)}")
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return 1
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@ -80,12 +81,17 @@ def save_classified_document(user_id, original_filename, category, content, file
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Returns:
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tuple: (成功标志, 存储的文件名或错误信息)
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"""
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db = None # Initialize db to None
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cursor = None # Initialize cursor to None
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try:
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# 获取下一个文档编号
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next_num = get_next_document_number(category)
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# 安全处理文件名
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safe_original_name = secure_filename(original_filename)
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# 安全处理文件名 - ensure it doesn't become empty
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safe_original_name_base = os.path.splitext(secure_filename(original_filename))[0]
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if not safe_original_name_base:
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safe_original_name_base = "untitled" # Default if name becomes empty
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safe_original_name = f"{safe_original_name_base}.txt" # Ensure .txt extension
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# 生成新文件名 (类别-编号-原文件名)
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formatted_num = f"{next_num:04d}" # 确保编号格式为4位数
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@ -117,16 +123,18 @@ def save_classified_document(user_id, original_filename, category, content, file
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category_result = cursor.fetchone()
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if not category_result:
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return False, "类别不存在"
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logger.error(f"类别 '{category}' 在数据库中未找到。")
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# Optionally create the category if needed, or return error
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return False, f"类别 '{category}' 不存在"
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category_id = category_result[0]
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# 插入数据库记录
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insert_query = """
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INSERT INTO documents
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(user_id, original_filename, stored_filename, file_path, file_size, category_id, status, classified_time)
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VALUES (%s, %s, %s, %s, %s, %s, %s, NOW())
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"""
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INSERT INTO documents
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(user_id, original_filename, stored_filename, file_path, file_size, category_id, status, classified_time, upload_time)
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VALUES (%s, %s, %s, %s, %s, %s, %s, NOW(), NOW())
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""" # Added upload_time=NOW() assuming it's desired
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cursor.execute(
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insert_query,
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@ -135,12 +143,19 @@ def save_classified_document(user_id, original_filename, category, content, file
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# 提交事务
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db.commit()
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logger.info(f"成功保存文档: {new_filename} (ID: {cursor.lastrowid})")
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return True, new_filename
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except Exception as e:
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logger.error(f"保存分类文档时出错: {str(e)}")
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# Rollback transaction if error occurs
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if db:
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db.rollback()
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logger.exception(f"保存分类文档 '{original_filename}' 时出错: {str(e)}") # Use logger.exception for traceback
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return False, str(e)
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finally:
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if cursor:
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cursor.close()
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# Note: db connection is usually closed via @app.teardown_appcontext
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@classify_bp.route('/single', methods=['POST'])
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@ -157,203 +172,214 @@ def classify_single_file():
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return jsonify({"success": False, "error": "没有文件"}), 400
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file = request.files['file']
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original_filename = file.filename # Store original name early
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# 检查文件名
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if file.filename == '':
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if original_filename == '':
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return jsonify({"success": False, "error": "未选择文件"}), 400
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# 检查文件类型
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if not allowed_text_file(file.filename):
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if not allowed_text_file(original_filename):
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return jsonify({"success": False, "error": "不支持的文件类型，仅支持txt文件"}), 400
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temp_path = None # Initialize to ensure it's defined for cleanup
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try:
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# 创建临时文件以供处理
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temp_dir = os.path.join(current_app.root_path, 'temp')
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os.makedirs(temp_dir, exist_ok=True)
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temp_filename = f"{uuid.uuid4().hex}.txt"
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# Use secure_filename for the temp file name part as well
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temp_filename = f"{uuid.uuid4().hex}-{secure_filename(original_filename)}"
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temp_path = os.path.join(temp_dir, temp_filename)
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# 保存上传文件到临时位置
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file.save(temp_path)
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file_size = os.path.getsize(temp_path) # Get size after saving
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# 读取文件内容 - Try multiple encodings
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file_content = None
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encodings_to_try = ['utf-8', 'gbk', 'gb18030']
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for enc in encodings_to_try:
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try:
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with open(temp_path, 'r', encoding=enc) as f:
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file_content = f.read()
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logger.info(f"成功以 {enc} 读取临时文件: {temp_path}")
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break
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except UnicodeDecodeError:
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logger.warning(f"使用 {enc} 解码临时文件失败: {temp_path}")
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continue
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except Exception as read_err: # Catch other potential read errors
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logger.error(f"读取临时文件时出错 ({enc}): {read_err}")
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# Decide if you want to stop or try next encoding
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continue
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if file_content is None:
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raise ValueError(f"无法使用支持的编码 {encodings_to_try} 读取文件内容。")
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# 读取文件内容
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with open(temp_path, 'r', encoding='utf-8') as f:
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file_content = f.read()
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# 调用模型进行分类
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result = text_classifier.classify_text(file_content)
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if not result['success']:
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return jsonify({"success": False, "error": result['error']}), 500
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if not result.get('success', False): # Check if key exists and is True
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error_msg = result.get('error', '未知的分类错误')
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logger.error(f"文本分类失败 for {original_filename}: {error_msg}")
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return jsonify({"success": False, "error": error_msg}), 500
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category = result['category']
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# 保存分类后的文档
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file_size = os.path.getsize(temp_path)
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save_success, message = save_classified_document(
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user_id,
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file.filename,
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result['category'],
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original_filename, # Use the original filename here
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category,
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file_content,
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file_size
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)
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# 清理临时文件
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if os.path.exists(temp_path):
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os.remove(temp_path)
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if not save_success:
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return jsonify({"success": False, "error": f"保存文档失败: {message}"}), 500
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# 返回分类结果
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return jsonify({
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"success": True,
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"filename": file.filename,
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"category": result['category'],
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"confidence": result['confidence'],
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"filename": original_filename,
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"category": category,
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"confidence": result.get('confidence'), # Use .get for safety
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"stored_filename": message
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})
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except UnicodeDecodeError:
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# 尝试GBK编码
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try:
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with open(temp_path, 'r', encoding='gbk') as f:
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file_content = f.read()
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# 调用模型进行分类
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result = text_classifier.classify_text(file_content)
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if not result['success']:
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return jsonify({"success": False, "error": result['error']}), 500
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# 保存分类后的文档
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file_size = os.path.getsize(temp_path)
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save_success, message = save_classified_document(
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user_id,
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file.filename,
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result['category'],
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file_content,
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file_size
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)
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# 清理临时文件
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if os.path.exists(temp_path):
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os.remove(temp_path)
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if not save_success:
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return jsonify({"success": False, "error": f"保存文档失败: {message}"}), 500
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# 返回分类结果
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return jsonify({
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"success": True,
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"filename": file.filename,
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"category": result['category'],
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"confidence": result['confidence'],
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"stored_filename": message
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})
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except Exception as e:
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if os.path.exists(temp_path):
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os.remove(temp_path)
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return jsonify({"success": False, "error": f"文件编码错误，请确保文件为UTF-8或GBK编码: {str(e)}"}), 400
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except Exception as e:
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# 确保清理临时文件
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if 'temp_path' in locals() and os.path.exists(temp_path):
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os.remove(temp_path)
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logger.error(f"文件处理过程中发生错误: {str(e)}")
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logger.exception(f"处理单文件 '{original_filename}' 过程中发生错误: {str(e)}") # Log exception with traceback
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return jsonify({"success": False, "error": f"文件处理错误: {str(e)}"}), 500
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finally:
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# 清理临时文件
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if temp_path and os.path.exists(temp_path):
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try:
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os.remove(temp_path)
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logger.info(f"已删除临时文件: {temp_path}")
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except OSError as rm_err:
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logger.error(f"删除临时文件失败 {temp_path}: {rm_err}")
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@classify_bp.route('/batch', methods=['POST'])
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def classify_batch_files():
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"""批量文件上传和分类API（压缩包处理）"""
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# 检查用户是否登录
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if 'user_id' not in session:
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return jsonify({"success": False, "error": "请先登录"}), 401
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user_id = session['user_id']
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# 检查是否上传了文件
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if 'file' not in request.files:
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return jsonify({"success": False, "error": "没有文件"}), 400
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file = request.files['file']
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original_archive_name = file.filename
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# 检查文件名
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if file.filename == '':
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if original_archive_name == '':
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return jsonify({"success": False, "error": "未选择文件"}), 400
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# 检查文件类型
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if not allowed_archive_file(file.filename):
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if not allowed_archive_file(original_archive_name):
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return jsonify({"success": False, "error": "不支持的文件类型，仅支持zip和rar压缩文件"}), 400
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# 检查文件大小
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if request.content_length > 10 * 1024 * 1024: # 10MB
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return jsonify({"success": False, "error": "文件太大，最大支持10MB"}), 400
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# Consider adding file size check here if needed (e.g., request.content_length)
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# if request.content_length > current_app.config['MAX_CONTENT_LENGTH']: # Example
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# return jsonify({"success": False, "error": "文件过大"}), 413
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temp_dir = os.path.join(current_app.root_path, 'temp')
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extract_dir = os.path.join(temp_dir, f"extract_{uuid.uuid4().hex}")
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archive_path = None
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# --- 修改点 1: 将 total_attempted 改回 total ---
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results = {
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"total": 0, # <--- 使用 'total'
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"success": 0,
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"failed": 0,
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"categories": {},
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"failed_files": []
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}
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try:
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# 创建临时目录
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temp_dir = os.path.join(current_app.root_path, 'temp')
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extract_dir = os.path.join(temp_dir, f"extract_{uuid.uuid4().hex}")
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os.makedirs(extract_dir, exist_ok=True)
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# 保存上传的压缩文件
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archive_path = os.path.join(temp_dir, secure_filename(file.filename))
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archive_path = os.path.join(temp_dir, secure_filename(original_archive_name))
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file.save(archive_path)
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logger.info(f"已保存上传的压缩文件: {archive_path}")
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# 解压文件
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file_extension = file.filename.rsplit('.', 1)[1].lower()
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file_extension = original_archive_name.rsplit('.', 1)[1].lower()
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if file_extension == 'zip':
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with zipfile.ZipFile(archive_path, 'r') as zip_ref:
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zip_ref.extractall(extract_dir)
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logger.info(f"已解压ZIP文件到: {extract_dir}")
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elif file_extension == 'rar':
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with rarfile.RarFile(archive_path, 'r') as rar_ref:
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rar_ref.extractall(extract_dir)
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# Ensure rarfile is installed and unrar executable is available
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try:
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with rarfile.RarFile(archive_path, 'r') as rar_ref:
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rar_ref.extractall(extract_dir)
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logger.info(f"已解压RAR文件到: {extract_dir}")
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except rarfile.NeedFirstVolume:
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logger.error(f"RAR文件是分卷压缩的一部分，需要第一个分卷: {original_archive_name}")
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raise ValueError("不支持分卷压缩的RAR文件")
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except rarfile.BadRarFile:
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logger.error(f"RAR文件损坏或格式错误: {original_archive_name}")
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raise ValueError("RAR文件损坏或格式错误")
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except Exception as rar_err: # Catch other rar errors (like missing unrar)
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logger.error(f"解压RAR文件时出错: {rar_err}")
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raise ValueError(f"解压RAR文件失败: {rar_err}")
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# 处理结果统计
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results = {
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"total": 0,
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"success": 0,
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"failed": 0,
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"categories": {},
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"failed_files": []
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}
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# 递归处理所有txt文件
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for root, dirs, files in os.walk(extract_dir):
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# --- 开始过滤 macOS 文件夹 ---
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if '__MACOSX' in root.split(os.path.sep):
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logger.info(f"Skipping macOS metadata directory: {root}")
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dirs[:] = []
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files[:] = []
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continue
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# --- 结束过滤 macOS 文件夹 ---
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for filename in files:
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if filename.lower().endswith('.txt'):
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# --- 开始过滤 macOS 文件 ---
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if filename.startswith('._') or filename == '.DS_Store':
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logger.info(f"Skipping macOS metadata file: {filename} in {root}")
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continue
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# --- 结束过滤 macOS 文件 ---
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# Process only allowed text files
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if allowed_text_file(filename):
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file_path = os.path.join(root, filename)
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results["total"] += 1
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# --- 修改点 2: 将 total_attempted 改回 total ---
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results["total"] += 1 # <--- 使用 'total'
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try:
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# 读取文件内容
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try:
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with open(file_path, 'r', encoding='utf-8') as f:
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file_content = f.read()
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except UnicodeDecodeError:
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# 尝试GBK编码
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with open(file_path, 'r', encoding='gbk') as f:
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file_content = f.read()
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# 读取文件内容 - Try multiple encodings
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file_content = None
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encodings_to_try = ['utf-8', 'gbk', 'gb18030']
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for enc in encodings_to_try:
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try:
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with open(file_path, 'r', encoding=enc) as f:
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file_content = f.read()
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logger.info(f"成功以 {enc} 读取文件: {file_path}")
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break
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except UnicodeDecodeError:
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logger.warning(f"使用 {enc} 解码文件失败: {file_path}")
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continue
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except Exception as read_err:
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logger.error(f"读取文件时发生其他错误 ({enc}) {file_path}: {read_err}")
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continue
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if file_content is None:
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raise ValueError(f"无法使用支持的编码 {encodings_to_try} 读取文件内容。")
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# 调用模型进行分类
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result = text_classifier.classify_text(file_content)
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if not result['success']:
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results["failed"] += 1
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results["failed_files"].append({
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"filename": filename,
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"error": result['error']
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})
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continue
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if not result.get('success', False):
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error_msg = result.get('error', '未知的分类错误')
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raise Exception(f"分类失败: {error_msg}")
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category = result['category']
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# 统计类别数量
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if category not in results["categories"]:
|
||||
results["categories"][category] = 0
|
||||
results["categories"][category] += 1
|
||||
results["categories"][category] = results["categories"].get(category, 0) + 1
|
||||
|
||||
# 保存分类后的文档
|
||||
file_size = os.path.getsize(file_path)
|
||||
save_success, message = save_classified_document(
|
||||
user_id,
|
||||
@ -366,106 +392,108 @@ def classify_batch_files():
|
||||
if save_success:
|
||||
results["success"] += 1
|
||||
else:
|
||||
results["failed"] += 1
|
||||
results["failed_files"].append({
|
||||
"filename": filename,
|
||||
"error": message
|
||||
})
|
||||
raise Exception(f"保存失败: {message}")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"处理文件 '{filename}' 失败: {str(e)}")
|
||||
results["failed"] += 1
|
||||
results["failed_files"].append({
|
||||
"filename": filename,
|
||||
"error": str(e)
|
||||
})
|
||||
|
||||
# 清理临时文件
|
||||
if os.path.exists(archive_path):
|
||||
os.remove(archive_path)
|
||||
if os.path.exists(extract_dir):
|
||||
shutil.rmtree(extract_dir)
|
||||
|
||||
# 返回处理结果
|
||||
return jsonify({
|
||||
"success": True,
|
||||
"archive_name": file.filename,
|
||||
"results": results
|
||||
"archive_name": original_archive_name,
|
||||
"results": results # <--- 确保返回的 results 包含 'total' 键
|
||||
})
|
||||
|
||||
except Exception as e:
|
||||
# 确保清理临时文件
|
||||
if 'archive_path' in locals() and os.path.exists(archive_path):
|
||||
os.remove(archive_path)
|
||||
if 'extract_dir' in locals() and os.path.exists(extract_dir):
|
||||
shutil.rmtree(extract_dir)
|
||||
|
||||
logger.error(f"压缩包处理过程中发生错误: {str(e)}")
|
||||
logger.exception(f"处理压缩包 '{original_archive_name}' 过程中发生严重错误: {str(e)}")
|
||||
return jsonify({"success": False, "error": f"压缩包处理错误: {str(e)}"}), 500
|
||||
|
||||
finally:
|
||||
# 清理临时文件和目录 (保持不变)
|
||||
if archive_path and os.path.exists(archive_path):
|
||||
try:
|
||||
os.remove(archive_path)
|
||||
logger.info(f"已删除临时压缩文件: {archive_path}")
|
||||
except OSError as rm_err:
|
||||
logger.error(f"删除临时压缩文件失败 {archive_path}: {rm_err}")
|
||||
if os.path.exists(extract_dir):
|
||||
try:
|
||||
shutil.rmtree(extract_dir)
|
||||
logger.info(f"已删除临时解压目录: {extract_dir}")
|
||||
except OSError as rmtree_err:
|
||||
logger.error(f"删除临时解压目录失败 {extract_dir}: {rmtree_err}")
|
||||
|
||||
|
||||
@classify_bp.route('/documents', methods=['GET'])
|
||||
def get_classified_documents():
|
||||
"""获取已分类的文档列表"""
|
||||
# 检查用户是否登录
|
||||
if 'user_id' not in session:
|
||||
return jsonify({"success": False, "error": "请先登录"}), 401
|
||||
|
||||
user_id = session['user_id']
|
||||
|
||||
# 获取查询参数
|
||||
category = request.args.get('category', 'all')
|
||||
page = int(request.args.get('page', 1))
|
||||
per_page = int(request.args.get('per_page', 10))
|
||||
|
||||
# 验证每页条数
|
||||
if per_page not in [10, 25, 50, 100]:
|
||||
per_page = 10
|
||||
|
||||
# 计算偏移量
|
||||
offset = (page - 1) * per_page
|
||||
|
||||
db = get_db()
|
||||
cursor = db.cursor(dictionary=True)
|
||||
|
||||
try:
|
||||
# 构建查询条件
|
||||
where_clause = "WHERE d.user_id = %s AND d.status = '已分类'"
|
||||
params = [user_id]
|
||||
category = request.args.get('category', 'all')
|
||||
page = int(request.args.get('page', 1))
|
||||
per_page = int(request.args.get('per_page', 10))
|
||||
|
||||
if category != 'all':
|
||||
where_clause += " AND c.name = %s"
|
||||
if per_page not in [10, 25, 50, 100]:
|
||||
per_page = 10
|
||||
if page < 1:
|
||||
page = 1
|
||||
|
||||
offset = (page - 1) * per_page
|
||||
|
||||
db = get_db()
|
||||
cursor = db.cursor(dictionary=True) # Use dictionary cursor
|
||||
|
||||
# Get available categories first
|
||||
cursor.execute("SELECT name FROM categories ORDER BY name")
|
||||
available_categories = [row['name'] for row in cursor.fetchall()]
|
||||
|
||||
# Build query
|
||||
params = [user_id]
|
||||
base_query = """
|
||||
FROM documents d
|
||||
JOIN categories c ON d.category_id = c.id
|
||||
WHERE d.user_id = %s AND d.status = '已分类'
|
||||
"""
|
||||
if category != 'all' and category in available_categories:
|
||||
base_query += " AND c.name = %s"
|
||||
params.append(category)
|
||||
|
||||
# 查询总记录数
|
||||
count_query = f"""
|
||||
SELECT COUNT(*) as total
|
||||
FROM documents d
|
||||
JOIN categories c ON d.category_id = c.id
|
||||
{where_clause}
|
||||
"""
|
||||
# Count query
|
||||
count_query = f"SELECT COUNT(*) as total {base_query}"
|
||||
cursor.execute(count_query, params)
|
||||
total_count = cursor.fetchone()['total']
|
||||
total_count_result = cursor.fetchone()
|
||||
total_count = total_count_result['total'] if total_count_result else 0
|
||||
|
||||
# 计算总页数
|
||||
total_pages = (total_count + per_page - 1) // per_page
|
||||
total_pages = (total_count + per_page - 1) // per_page if per_page > 0 else 0
|
||||
|
||||
# 查询分页数据
|
||||
query = f"""
|
||||
SELECT d.id, d.original_filename, d.stored_filename, d.file_size,
|
||||
# Data query
|
||||
data_query = f"""
|
||||
SELECT d.id, d.original_filename, d.stored_filename, d.file_size,
|
||||
c.name as category, d.upload_time, d.classified_time
|
||||
FROM documents d
|
||||
JOIN categories c ON d.category_id = c.id
|
||||
{where_clause}
|
||||
{base_query}
|
||||
ORDER BY d.classified_time DESC
|
||||
LIMIT %s OFFSET %s
|
||||
"""
|
||||
params.extend([per_page, offset])
|
||||
cursor.execute(query, params)
|
||||
cursor.execute(data_query, params)
|
||||
documents = cursor.fetchall()
|
||||
|
||||
# 获取所有可用类别
|
||||
cursor.execute("SELECT name FROM categories ORDER BY name")
|
||||
categories = [row['name'] for row in cursor.fetchall()]
|
||||
# Format dates if they are datetime objects (optional, depends on driver)
|
||||
for doc in documents:
|
||||
if hasattr(doc.get('upload_time'), 'isoformat'):
|
||||
doc['upload_time'] = doc['upload_time'].isoformat()
|
||||
if hasattr(doc.get('classified_time'), 'isoformat'):
|
||||
doc['classified_time'] = doc['classified_time'].isoformat()
|
||||
|
||||
|
||||
return jsonify({
|
||||
"success": True,
|
||||
@ -476,36 +504,35 @@ def get_classified_documents():
|
||||
"current_page": page,
|
||||
"total_pages": total_pages
|
||||
},
|
||||
"categories": categories,
|
||||
"categories": available_categories, # Send the fetched list
|
||||
"current_category": category
|
||||
})
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"获取文档列表时出错: {str(e)}")
|
||||
logger.exception(f"获取文档列表时出错: {str(e)}") # Log traceback
|
||||
return jsonify({"success": False, "error": f"获取文档列表失败: {str(e)}"}), 500
|
||||
|
||||
finally:
|
||||
cursor.close()
|
||||
# Cursor closing handled by context usually, but explicit close is safe
|
||||
if 'cursor' in locals() and cursor:
|
||||
cursor.close()
|
||||
|
||||
|
||||
@classify_bp.route('/download/<int:document_id>', methods=['GET'])
|
||||
def download_document(document_id):
|
||||
"""下载已分类的文档"""
|
||||
# 检查用户是否登录
|
||||
if 'user_id' not in session:
|
||||
return jsonify({"success": False, "error": "请先登录"}), 401
|
||||
|
||||
user_id = session['user_id']
|
||||
|
||||
db = get_db()
|
||||
cursor = db.cursor(dictionary=True)
|
||||
|
||||
cursor = None
|
||||
try:
|
||||
# 查询文档信息
|
||||
db = get_db()
|
||||
cursor = db.cursor(dictionary=True)
|
||||
|
||||
query = """
|
||||
SELECT file_path, original_filename, stored_filename
|
||||
SELECT file_path, original_filename
|
||||
FROM documents
|
||||
WHERE id = %s AND user_id = %s
|
||||
WHERE id = %s AND user_id = %s AND status = '已分类'
|
||||
"""
|
||||
cursor.execute(query, (document_id, user_id))
|
||||
document = cursor.fetchone()
|
||||
@ -513,136 +540,187 @@ def download_document(document_id):
|
||||
if not document:
|
||||
return jsonify({"success": False, "error": "文档不存在或无权访问"}), 404
|
||||
|
||||
# 检查文件是否存在
|
||||
if not os.path.exists(document['file_path']):
|
||||
return jsonify({"success": False, "error": "文件不存在"}), 404
|
||||
file_path = document['file_path']
|
||||
download_name = document['original_filename']
|
||||
|
||||
# 返回文件下载
|
||||
if not os.path.exists(file_path):
|
||||
logger.error(f"请求下载的文件在服务器上不存在: {file_path} (Doc ID: {document_id})")
|
||||
# Update status in DB?
|
||||
# update_status_query = "UPDATE documents SET status = '文件丢失' WHERE id = %s"
|
||||
# cursor.execute(update_status_query, (document_id,))
|
||||
# db.commit()
|
||||
return jsonify({"success": False, "error": "文件在服务器上丢失"}), 404
|
||||
|
||||
logger.info(f"用户 {user_id} 请求下载文档 ID: {document_id}, 文件: {download_name}")
|
||||
return send_file(
|
||||
document['file_path'],
|
||||
file_path,
|
||||
as_attachment=True,
|
||||
download_name=document['original_filename'],
|
||||
mimetype='text/plain'
|
||||
download_name=download_name, # Use original filename for download
|
||||
mimetype='text/plain' # Assuming text, adjust if other types allowed
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"下载文档时出错: {str(e)}")
|
||||
logger.exception(f"下载文档 ID {document_id} 时出错: {str(e)}")
|
||||
return jsonify({"success": False, "error": f"下载文档失败: {str(e)}"}), 500
|
||||
|
||||
finally:
|
||||
cursor.close()
|
||||
if cursor:
|
||||
cursor.close()
|
||||
|
||||
|
||||
@classify_bp.route('/download-multiple', methods=['POST'])
|
||||
def download_multiple_documents():
|
||||
"""下载多个文档（打包为zip）"""
|
||||
# 检查用户是否登录
|
||||
if 'user_id' not in session:
|
||||
return jsonify({"success": False, "error": "请先登录"}), 401
|
||||
|
||||
|
||||
user_id = session['user_id']
|
||||
|
||||
# 获取请求数据
|
||||
data = request.get_json()
|
||||
if not data or 'document_ids' not in data:
|
||||
return jsonify({"success": False, "error": "缺少必要参数"}), 400
|
||||
|
||||
document_ids = data['document_ids']
|
||||
if not isinstance(document_ids, list) or not document_ids:
|
||||
return jsonify({"success": False, "error": "文档ID列表无效"}), 400
|
||||
|
||||
db = get_db()
|
||||
cursor = db.cursor(dictionary=True)
|
||||
|
||||
cursor = None
|
||||
zip_path = None # Initialize for finally block
|
||||
|
||||
try:
|
||||
# 创建临时目录用于存放zip文件
|
||||
temp_dir = os.path.join(current_app.root_path, 'temp')
|
||||
os.makedirs(temp_dir, exist_ok=True)
|
||||
|
||||
# 创建临时ZIP文件
|
||||
zip_filename = f"documents_{int(time.time())}.zip"
|
||||
zip_path = os.path.join(temp_dir, zip_filename)
|
||||
|
||||
# 查询所有符合条件的文档
|
||||
data = request.get_json()
|
||||
if not data or 'document_ids' not in data:
|
||||
return jsonify({"success": False, "error": "缺少必要参数 'document_ids'"}), 400
|
||||
|
||||
document_ids = data['document_ids']
|
||||
if not isinstance(document_ids, list) or not document_ids:
|
||||
return jsonify({"success": False, "error": "文档ID列表无效"}), 400
|
||||
# Sanitize IDs to be integers
|
||||
try:
|
||||
document_ids = [int(doc_id) for doc_id in document_ids]
|
||||
except ValueError:
|
||||
return jsonify({"success": False, "error": "文档ID必须是数字"}), 400
|
||||
|
||||
db = get_db()
|
||||
cursor = db.cursor(dictionary=True)
|
||||
|
||||
# Query valid documents for the user
|
||||
placeholders = ', '.join(['%s'] * len(document_ids))
|
||||
query = f"""
|
||||
SELECT id, file_path, original_filename
|
||||
FROM documents
|
||||
WHERE id IN ({placeholders}) AND user_id = %s
|
||||
WHERE id IN ({placeholders}) AND user_id = %s AND status = '已分类'
|
||||
"""
|
||||
params = document_ids + [user_id]
|
||||
cursor.execute(query, params)
|
||||
documents = cursor.fetchall()
|
||||
|
||||
|
||||
if not documents:
|
||||
return jsonify({"success": False, "error": "没有找到符合条件的文档"}), 404
|
||||
|
||||
# 创建ZIP文件并添加文档
|
||||
with zipfile.ZipFile(zip_path, 'w') as zipf:
|
||||
return jsonify({"success": False, "error": "没有找到符合条件的可下载文档"}), 404
|
||||
|
||||
# Create temp zip file
|
||||
temp_dir = os.path.join(current_app.root_path, 'temp')
|
||||
os.makedirs(temp_dir, exist_ok=True)
|
||||
zip_filename = f"documents_{user_id}_{int(time.time())}.zip"
|
||||
zip_path = os.path.join(temp_dir, zip_filename)
|
||||
|
||||
files_added = 0
|
||||
with zipfile.ZipFile(zip_path, 'w', zipfile.ZIP_DEFLATED) as zipf:
|
||||
for doc in documents:
|
||||
if os.path.exists(doc['file_path']):
|
||||
# 添加文件到zip，使用原始文件名
|
||||
zipf.write(doc['file_path'], arcname=doc['original_filename'])
|
||||
|
||||
# 返回ZIP文件下载
|
||||
file_path = doc['file_path']
|
||||
original_filename = doc['original_filename']
|
||||
if os.path.exists(file_path):
|
||||
zipf.write(file_path, arcname=original_filename)
|
||||
files_added += 1
|
||||
logger.info(f"添加到ZIP: {original_filename} (来自 {file_path})")
|
||||
else:
|
||||
logger.warning(f"跳过丢失的文件: {original_filename} (路径: {file_path}, Doc ID: {doc['id']})")
|
||||
# Optionally add a readme to the zip indicating missing files
|
||||
|
||||
if files_added == 0:
|
||||
# This case means all selected files were missing on disk
|
||||
return jsonify({"success": False, "error": "所有选中的文件在服务器上都已丢失"}), 404
|
||||
|
||||
|
||||
logger.info(f"用户 {user_id} 请求下载 {files_added} 个文档打包为 {zip_filename}")
|
||||
# Use after_this_request to delete the temp file after sending
|
||||
@current_app.after_request
|
||||
def remove_file(response):
|
||||
try:
|
||||
if zip_path and os.path.exists(zip_path):
|
||||
os.remove(zip_path)
|
||||
logger.info(f"已删除临时ZIP文件: {zip_path}")
|
||||
except Exception as error:
|
||||
logger.error(f"删除临时ZIP文件错误 {zip_path}: {error}")
|
||||
return response
|
||||
|
||||
return send_file(
|
||||
zip_path,
|
||||
as_attachment=True,
|
||||
download_name=zip_filename,
|
||||
mimetype='application/zip'
|
||||
)
|
||||
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"下载多个文档时出错: {str(e)}")
|
||||
logger.exception(f"下载多个文档时出错: {str(e)}")
|
||||
# Clean up zip file if created but error occurred before sending
|
||||
if zip_path and os.path.exists(zip_path):
|
||||
try: os.remove(zip_path)
|
||||
except OSError: pass
|
||||
return jsonify({"success": False, "error": f"下载文档失败: {str(e)}"}), 500
|
||||
|
||||
|
||||
finally:
|
||||
cursor.close()
|
||||
if cursor:
|
||||
cursor.close()
|
||||
|
||||
|
||||
@classify_bp.route('/classify-text', methods=['POST'])
|
||||
def classify_text_directly():
|
||||
"""直接对文本进行分类（不保存文件）"""
|
||||
# 检查用户是否登录
|
||||
if 'user_id' not in session:
|
||||
return jsonify({"success": False, "error": "请先登录"}), 401
|
||||
|
||||
# 获取请求数据
|
||||
data = request.get_json()
|
||||
if not data or 'text' not in data:
|
||||
return jsonify({"success": False, "error": "缺少必要参数"}), 400
|
||||
|
||||
text = data['text']
|
||||
if not text.strip():
|
||||
return jsonify({"success": False, "error": "文本内容不能为空"}), 400
|
||||
|
||||
try:
|
||||
data = request.get_json()
|
||||
if not data or 'text' not in data:
|
||||
return jsonify({"success": False, "error": "缺少必要参数 'text'"}), 400
|
||||
|
||||
text = data['text']
|
||||
# Basic validation
|
||||
if not isinstance(text, str) or not text.strip():
|
||||
return jsonify({"success": False, "error": "文本内容不能为空或无效"}), 400
|
||||
|
||||
# Limit text length?
|
||||
MAX_TEXT_LENGTH = 10000 # Example limit
|
||||
if len(text) > MAX_TEXT_LENGTH:
|
||||
return jsonify({"success": False, "error": f"文本过长，最大支持 {MAX_TEXT_LENGTH} 字符"}), 413
|
||||
|
||||
|
||||
# 调用模型进行分类
|
||||
# Ensure model is initialized - consider adding a check or lazy loading
|
||||
if not text_classifier or not text_classifier.is_initialized:
|
||||
logger.warning("文本分类器未初始化，尝试初始化...")
|
||||
if not text_classifier.initialize():
|
||||
logger.error("文本分类器初始化失败。")
|
||||
return jsonify({"success": False, "error": "分类服务暂时不可用"}), 503
|
||||
|
||||
result = text_classifier.classify_text(text)
|
||||
|
||||
if not result['success']:
|
||||
return jsonify({"success": False, "error": result['error']}), 500
|
||||
if not result.get('success', False):
|
||||
error_msg = result.get('error', '未知的分类错误')
|
||||
logger.error(f"直接文本分类失败: {error_msg}")
|
||||
return jsonify({"success": False, "error": error_msg}), 500
|
||||
|
||||
# 返回分类结果
|
||||
return jsonify({
|
||||
"success": True,
|
||||
"category": result['category'],
|
||||
"confidence": result['confidence'],
|
||||
"all_confidences": result['all_confidences']
|
||||
"category": result.get('category'),
|
||||
"confidence": result.get('confidence'),
|
||||
"all_confidences": result.get('all_confidences') # Include all confidences if available
|
||||
})
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"文本分类过程中发生错误: {str(e)}")
|
||||
logger.exception(f"直接文本分类过程中发生错误: {str(e)}")
|
||||
return jsonify({"success": False, "error": f"文本分类错误: {str(e)}"}), 500
|
||||
|
||||
|
||||
@classify_bp.route('/categories', methods=['GET'])
|
||||
def get_categories():
|
||||
"""获取所有分类类别"""
|
||||
db = get_db()
|
||||
cursor = db.cursor(dictionary=True)
|
||||
|
||||
cursor = None
|
||||
try:
|
||||
db = get_db()
|
||||
cursor = db.cursor(dictionary=True)
|
||||
cursor.execute("SELECT id, name, description FROM categories ORDER BY name")
|
||||
categories = cursor.fetchall()
|
||||
|
||||
@ -652,8 +730,8 @@ def get_categories():
|
||||
})
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"获取类别列表时出错: {str(e)}")
|
||||
logger.exception(f"获取类别列表时出错: {str(e)}")
|
||||
return jsonify({"success": False, "error": f"获取类别列表失败: {str(e)}"}), 500
|
||||
|
||||
finally:
|
||||
cursor.close()
|
||||
if cursor:
|
||||
cursor.close()
|
||||
|
||||
@ -3,9 +3,12 @@ import os
|
||||
import jieba
|
||||
import numpy as np
|
||||
import pickle
|
||||
from tensorflow.keras.models import load_model
|
||||
from tensorflow.keras.preprocessing.sequence import pad_sequences
|
||||
import tensorflow as tf
|
||||
# from tensorflow import keras # tf.keras is preferred
|
||||
from keras.models import load_model # Keep if specifically needed, else use tf.keras.models.load_model
|
||||
from keras.preprocessing.sequence import pad_sequences # Keep if specifically needed, else use tf.keras.preprocessing.sequence.pad_sequences
|
||||
import logging
|
||||
import h5py # Moved import here as it's used conditionally
|
||||
|
||||
|
||||
class TextClassificationModel:
|
||||
@ -34,16 +37,42 @@ class TextClassificationModel:
|
||||
|
||||
# 设置日志
|
||||
self.logger = logging.getLogger(__name__)
|
||||
logging.basicConfig(level=logging.INFO) # Basic logging setup if not configured elsewhere
|
||||
|
||||
def initialize(self):
|
||||
"""初始化并加载模型和分词器"""
|
||||
"""初始化并加载模型和分词器""" # <--- Corrected Indentation Starts Here
|
||||
try:
|
||||
self.logger.info("开始加载文本分类模型...")
|
||||
# 加载模型
|
||||
self.model = load_model(self.model_path)
|
||||
self.logger.info("模型加载成功")
|
||||
|
||||
# 优先尝试加载 HDF5 格式 (.h5)，因为文件名是 .h5
|
||||
try:
|
||||
self.logger.info(f"尝试以 HDF5 格式加载模型: {self.model_path}")
|
||||
# For H5 files, direct load_model is usually sufficient if saved correctly.
|
||||
# compile=False is often needed if you don't need training features immediately.
|
||||
self.model = tf.keras.models.load_model(self.model_path, compile=False)
|
||||
self.logger.info("HDF5 模型加载成功")
|
||||
|
||||
except Exception as h5_exc:
|
||||
self.logger.warning(f"HDF5 格式加载失败 ({h5_exc})，尝试以 SavedModel 格式加载...")
|
||||
# 如果 HDF5 加载失败，再尝试 SavedModel 格式 (通常是一个目录，而不是 .h5 文件)
|
||||
# This might fail if model_path truly points to an h5 file.
|
||||
try:
|
||||
self.model = tf.keras.models.load_model(
|
||||
self.model_path,
|
||||
compile=False # Usually false for inference
|
||||
# custom_objects can be added here if needed
|
||||
# options=tf.saved_model.LoadOptions(experimental_io_device='/job:localhost') # Usually not needed unless specific TF distribution setup
|
||||
)
|
||||
self.logger.info("SavedModel 格式加载成功")
|
||||
except Exception as sm_exc:
|
||||
self.logger.error(f"SavedModel 格式加载也失败 ({sm_exc}). 无法加载模型。")
|
||||
# Consider adding the fallback JSON+weights logic here if needed,
|
||||
# but it's less common now.
|
||||
# Re-raising or handling the error appropriately
|
||||
raise ValueError(f"无法加载模型文件: {self.model_path}. H5 Error: {h5_exc}, SavedModel Error: {sm_exc}")
|
||||
|
||||
# 加载tokenizer
|
||||
self.logger.info(f"开始加载 Tokenizer: {self.tokenizer_path}")
|
||||
with open(self.tokenizer_path, 'rb') as handle:
|
||||
self.tokenizer = pickle.load(handle)
|
||||
self.logger.info("Tokenizer加载成功")
|
||||
@ -51,8 +80,9 @@ class TextClassificationModel:
|
||||
self.is_initialized = True
|
||||
self.logger.info("模型初始化完成")
|
||||
return True
|
||||
|
||||
except Exception as e:
|
||||
self.logger.error(f"模型初始化失败: {str(e)}")
|
||||
self.logger.exception(f"模型初始化过程中发生严重错误: {str(e)}") # Use logger.exception to include traceback
|
||||
self.is_initialized = False
|
||||
return False
|
||||
|
||||
@ -80,9 +110,12 @@ class TextClassificationModel:
|
||||
dict: 分类结果，包含类别标签和置信度
|
||||
"""
|
||||
if not self.is_initialized:
|
||||
self.logger.warning("模型尚未初始化，尝试现在初始化...")
|
||||
success = self.initialize()
|
||||
if not success:
|
||||
self.logger.error("分类前初始化模型失败。")
|
||||
return {"success": False, "error": "模型初始化失败"}
|
||||
self.logger.info("模型初始化成功，继续分类。")
|
||||
|
||||
try:
|
||||
# 文本预处理
|
||||
@ -92,17 +125,23 @@ class TextClassificationModel:
|
||||
sequence = self.tokenizer.texts_to_sequences([processed_text])
|
||||
|
||||
# 填充序列
|
||||
padded_sequence = pad_sequences(sequence, maxlen=self.max_length, padding="post")
|
||||
padded_sequence = tf.keras.preprocessing.sequence.pad_sequences( # Using tf.keras path
|
||||
sequence, maxlen=self.max_length, padding="post"
|
||||
)
|
||||
|
||||
# 预测
|
||||
predictions = self.model.predict(padded_sequence)
|
||||
|
||||
# 获取预测类别索引和置信度
|
||||
predicted_index = np.argmax(predictions, axis=1)[0]
|
||||
confidence = float(predictions[0][predicted_index])
|
||||
confidence = float(predictions[0][predicted_index]) # Convert numpy float to python float
|
||||
|
||||
# 获取预测类别标签
|
||||
predicted_label = self.CATEGORIES[predicted_index]
|
||||
if predicted_index < len(self.CATEGORIES):
|
||||
predicted_label = self.CATEGORIES[predicted_index]
|
||||
else:
|
||||
self.logger.warning(f"预测索引 {predicted_index} 超出类别列表范围！")
|
||||
predicted_label = "未知类别" # Handle out-of-bounds index
|
||||
|
||||
# 获取所有类别的置信度
|
||||
all_confidences = {cat: float(conf) for cat, conf in zip(self.CATEGORIES, predictions[0])}
|
||||
@ -114,8 +153,8 @@ class TextClassificationModel:
|
||||
"all_confidences": all_confidences
|
||||
}
|
||||
except Exception as e:
|
||||
self.logger.error(f"文本分类过程中发生错误: {str(e)}")
|
||||
return {"success": False, "error": str(e)}
|
||||
self.logger.exception(f"文本分类过程中发生错误: {str(e)}") # Use logger.exception
|
||||
return {"success": False, "error": f"分类错误: {str(e)}"}
|
||||
|
||||
def classify_file(self, file_path):
|
||||
"""对文件内容进行分类
|
||||
@ -126,28 +165,29 @@ class TextClassificationModel:
|
||||
Returns:
|
||||
dict: 分类结果，包含类别标签和置信度
|
||||
"""
|
||||
try:
|
||||
# 读取文件内容
|
||||
with open(file_path, 'r', encoding='utf-8') as f:
|
||||
text = f.read().strip()
|
||||
|
||||
# 调用文本分类函数
|
||||
return self.classify_text(text)
|
||||
|
||||
except UnicodeDecodeError:
|
||||
# 如果UTF-8解码失败，尝试其他编码
|
||||
text = None
|
||||
encodings_to_try = ['utf-8', 'gbk', 'gb18030'] # Common encodings
|
||||
for enc in encodings_to_try:
|
||||
try:
|
||||
with open(file_path, 'r', encoding='gbk') as f:
|
||||
with open(file_path, 'r', encoding=enc) as f:
|
||||
text = f.read().strip()
|
||||
return self.classify_text(text)
|
||||
self.logger.info(f"成功以 {enc} 编码读取文件: {file_path}")
|
||||
break # Exit loop if read successful
|
||||
except UnicodeDecodeError:
|
||||
self.logger.warning(f"使用 {enc} 解码文件失败: {file_path}")
|
||||
continue # Try next encoding
|
||||
except Exception as e:
|
||||
return {"success": False, "error": f"文件解码失败: {str(e)}"}
|
||||
self.logger.error(f"读取文件时发生其他错误 ({enc}): {str(e)}")
|
||||
return {"success": False, "error": f"文件读取错误 ({enc}): {str(e)}"}
|
||||
|
||||
except Exception as e:
|
||||
self.logger.error(f"文件处理过程中发生错误: {str(e)}")
|
||||
return {"success": False, "error": f"文件处理错误: {str(e)}"}
|
||||
if text is None:
|
||||
self.logger.error(f"尝试所有编码后仍无法读取文件: {file_path}")
|
||||
return {"success": False, "error": f"文件解码失败，尝试的编码: {encodings_to_try}"}
|
||||
|
||||
# 调用文本分类函数
|
||||
return self.classify_text(text)
|
||||
|
||||
|
||||
# 创建单例实例，避免重复加载模型
|
||||
# Consider lazy initialization if the model is large and not always needed immediately
|
||||
text_classifier = TextClassificationModel()
|
||||
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user