From 24953f68dff690d30f09842c358172bb05d8191a Mon Sep 17 00:00:00 2001
From: superlishunqin <852326703@qq.com>
Date: Mon, 31 Mar 2025 03:06:26 +0800
Subject: [PATCH] fix_bug
---
.idea/vcs.xml | 6 +
app.py | 3 +-
routes/classify.py | 612 +++++++++++++++++++++++------------------
utils/model_service.py | 98 +++++--
4 files changed, 421 insertions(+), 298 deletions(-)
create mode 100644 .idea/vcs.xml
diff --git a/.idea/vcs.xml b/.idea/vcs.xml
new file mode 100644
index 0000000..35eb1dd
--- /dev/null
+++ b/.idea/vcs.xml
@@ -0,0 +1,6 @@
+
+
+
+
+
+
\ No newline at end of file
diff --git a/app.py b/app.py
index 460222f..82ce587 100644
--- a/app.py
+++ b/app.py
@@ -60,5 +60,4 @@ def internal_server_error(e):
return render_template('error.html', error='服务器内部错误'), 500
if __name__ == '__main__':
- app.run(debug=True, host='0.0.0.0', port=5009)
-
+ app.run(debug=True, host='0.0.0.0', port=50004)
\ No newline at end of file
diff --git a/routes/classify.py b/routes/classify.py
index 7ac0f98..09d38a6 100644
--- a/routes/classify.py
+++ b/routes/classify.py
@@ -8,7 +8,7 @@ import shutil
from flask import Blueprint, request, jsonify, current_app, send_file, g, session
from werkzeug.utils import secure_filename
import mysql.connector
-from utils.model_service import text_classifier
+from utils.model_service import text_classifier # Assuming text_classifier is initialized correctly elsewhere
from utils.db import get_db, close_db
import logging
@@ -59,7 +59,8 @@ def get_next_document_number(category):
if not result or result['max_num'] is None:
return 1
else:
- return result['max_num'] + 1
+ # Ensure it's an int before adding
+ return int(result['max_num']) + 1
except Exception as e:
logger.error(f"获取下一个文档编号时出错: {str(e)}")
return 1
@@ -80,12 +81,17 @@ def save_classified_document(user_id, original_filename, category, content, file
Returns:
tuple: (成功标志, 存储的文件名或错误信息)
"""
+ db = None # Initialize db to None
+ cursor = None # Initialize cursor to None
try:
# 获取下一个文档编号
next_num = get_next_document_number(category)
- # 安全处理文件名
- safe_original_name = secure_filename(original_filename)
+ # 安全处理文件名 - ensure it doesn't become empty
+ safe_original_name_base = os.path.splitext(secure_filename(original_filename))[0]
+ if not safe_original_name_base:
+ safe_original_name_base = "untitled" # Default if name becomes empty
+ safe_original_name = f"{safe_original_name_base}.txt" # Ensure .txt extension
# 生成新文件名 (类别-编号-原文件名)
formatted_num = f"{next_num:04d}" # 确保编号格式为4位数
@@ -117,16 +123,18 @@ def save_classified_document(user_id, original_filename, category, content, file
category_result = cursor.fetchone()
if not category_result:
- return False, "类别不存在"
+ logger.error(f"类别 '{category}' 在数据库中未找到。")
+ # Optionally create the category if needed, or return error
+ return False, f"类别 '{category}' 不存在"
category_id = category_result[0]
# 插入数据库记录
insert_query = """
- INSERT INTO documents
- (user_id, original_filename, stored_filename, file_path, file_size, category_id, status, classified_time)
- VALUES (%s, %s, %s, %s, %s, %s, %s, NOW())
- """
+ INSERT INTO documents
+ (user_id, original_filename, stored_filename, file_path, file_size, category_id, status, classified_time, upload_time)
+ VALUES (%s, %s, %s, %s, %s, %s, %s, NOW(), NOW())
+ """ # Added upload_time=NOW() assuming it's desired
cursor.execute(
insert_query,
@@ -135,12 +143,19 @@ def save_classified_document(user_id, original_filename, category, content, file
# 提交事务
db.commit()
-
+ logger.info(f"成功保存文档: {new_filename} (ID: {cursor.lastrowid})")
return True, new_filename
except Exception as e:
- logger.error(f"保存分类文档时出错: {str(e)}")
+ # Rollback transaction if error occurs
+ if db:
+ db.rollback()
+ logger.exception(f"保存分类文档 '{original_filename}' 时出错: {str(e)}") # Use logger.exception for traceback
return False, str(e)
+ finally:
+ if cursor:
+ cursor.close()
+ # Note: db connection is usually closed via @app.teardown_appcontext
@classify_bp.route('/single', methods=['POST'])
@@ -157,203 +172,214 @@ def classify_single_file():
return jsonify({"success": False, "error": "没有文件"}), 400
file = request.files['file']
+ original_filename = file.filename # Store original name early
# 检查文件名
- if file.filename == '':
+ if original_filename == '':
return jsonify({"success": False, "error": "未选择文件"}), 400
# 检查文件类型
- if not allowed_text_file(file.filename):
+ if not allowed_text_file(original_filename):
return jsonify({"success": False, "error": "不支持的文件类型，仅支持txt文件"}), 400
+ temp_path = None # Initialize to ensure it's defined for cleanup
try:
# 创建临时文件以供处理
temp_dir = os.path.join(current_app.root_path, 'temp')
os.makedirs(temp_dir, exist_ok=True)
- temp_filename = f"{uuid.uuid4().hex}.txt"
+ # Use secure_filename for the temp file name part as well
+ temp_filename = f"{uuid.uuid4().hex}-{secure_filename(original_filename)}"
temp_path = os.path.join(temp_dir, temp_filename)
# 保存上传文件到临时位置
file.save(temp_path)
+ file_size = os.path.getsize(temp_path) # Get size after saving
+
+ # 读取文件内容 - Try multiple encodings
+ file_content = None
+ encodings_to_try = ['utf-8', 'gbk', 'gb18030']
+ for enc in encodings_to_try:
+ try:
+ with open(temp_path, 'r', encoding=enc) as f:
+ file_content = f.read()
+ logger.info(f"成功以 {enc} 读取临时文件: {temp_path}")
+ break
+ except UnicodeDecodeError:
+ logger.warning(f"使用 {enc} 解码临时文件失败: {temp_path}")
+ continue
+ except Exception as read_err: # Catch other potential read errors
+ logger.error(f"读取临时文件时出错 ({enc}): {read_err}")
+ # Decide if you want to stop or try next encoding
+ continue
+
+ if file_content is None:
+ raise ValueError(f"无法使用支持的编码 {encodings_to_try} 读取文件内容。")
- # 读取文件内容
- with open(temp_path, 'r', encoding='utf-8') as f:
- file_content = f.read()
# 调用模型进行分类
result = text_classifier.classify_text(file_content)
- if not result['success']:
- return jsonify({"success": False, "error": result['error']}), 500
+ if not result.get('success', False): # Check if key exists and is True
+ error_msg = result.get('error', '未知的分类错误')
+ logger.error(f"文本分类失败 for {original_filename}: {error_msg}")
+ return jsonify({"success": False, "error": error_msg}), 500
+
+ category = result['category']
# 保存分类后的文档
- file_size = os.path.getsize(temp_path)
save_success, message = save_classified_document(
user_id,
- file.filename,
- result['category'],
+ original_filename, # Use the original filename here
+ category,
file_content,
file_size
)
- # 清理临时文件
- if os.path.exists(temp_path):
- os.remove(temp_path)
-
if not save_success:
return jsonify({"success": False, "error": f"保存文档失败: {message}"}), 500
# 返回分类结果
return jsonify({
"success": True,
- "filename": file.filename,
- "category": result['category'],
- "confidence": result['confidence'],
+ "filename": original_filename,
+ "category": category,
+ "confidence": result.get('confidence'), # Use .get for safety
"stored_filename": message
})
- except UnicodeDecodeError:
- # 尝试GBK编码
- try:
- with open(temp_path, 'r', encoding='gbk') as f:
- file_content = f.read()
-
- # 调用模型进行分类
- result = text_classifier.classify_text(file_content)
-
- if not result['success']:
- return jsonify({"success": False, "error": result['error']}), 500
-
- # 保存分类后的文档
- file_size = os.path.getsize(temp_path)
- save_success, message = save_classified_document(
- user_id,
- file.filename,
- result['category'],
- file_content,
- file_size
- )
-
- # 清理临时文件
- if os.path.exists(temp_path):
- os.remove(temp_path)
-
- if not save_success:
- return jsonify({"success": False, "error": f"保存文档失败: {message}"}), 500
-
- # 返回分类结果
- return jsonify({
- "success": True,
- "filename": file.filename,
- "category": result['category'],
- "confidence": result['confidence'],
- "stored_filename": message
- })
- except Exception as e:
- if os.path.exists(temp_path):
- os.remove(temp_path)
- 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
-
except Exception as e:
- # 确保清理临时文件
- if 'temp_path' in locals() and os.path.exists(temp_path):
- os.remove(temp_path)
- logger.error(f"文件处理过程中发生错误: {str(e)}")
+ logger.exception(f"处理单文件 '{original_filename}' 过程中发生错误: {str(e)}") # Log exception with traceback
return jsonify({"success": False, "error": f"文件处理错误: {str(e)}"}), 500
+ finally:
+ # 清理临时文件
+ if temp_path and os.path.exists(temp_path):
+ try:
+ os.remove(temp_path)
+ logger.info(f"已删除临时文件: {temp_path}")
+ except OSError as rm_err:
+ logger.error(f"删除临时文件失败 {temp_path}: {rm_err}")
@classify_bp.route('/batch', methods=['POST'])
def classify_batch_files():
"""批量文件上传和分类API（压缩包处理）"""
- # 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']
- # 检查是否上传了文件
if 'file' not in request.files:
return jsonify({"success": False, "error": "没有文件"}), 400
file = request.files['file']
+ original_archive_name = file.filename
- # 检查文件名
- if file.filename == '':
+ if original_archive_name == '':
return jsonify({"success": False, "error": "未选择文件"}), 400
- # 检查文件类型
- if not allowed_archive_file(file.filename):
+ if not allowed_archive_file(original_archive_name):
return jsonify({"success": False, "error": "不支持的文件类型，仅支持zip和rar压缩文件"}), 400
- # 检查文件大小
- if request.content_length > 10 * 1024 * 1024: # 10MB
- return jsonify({"success": False, "error": "文件太大，最大支持10MB"}), 400
+ # Consider adding file size check here if needed (e.g., request.content_length)
+ # if request.content_length > current_app.config['MAX_CONTENT_LENGTH']: # Example
+ # return jsonify({"success": False, "error": "文件过大"}), 413
+
+ temp_dir = os.path.join(current_app.root_path, 'temp')
+ extract_dir = os.path.join(temp_dir, f"extract_{uuid.uuid4().hex}")
+ archive_path = None
+ # --- 修改点 1: 将 total_attempted 改回 total ---
+ results = {
+ "total": 0, # <--- 使用 'total'
+ "success": 0,
+ "failed": 0,
+ "categories": {},
+ "failed_files": []
+ }
try:
- # 创建临时目录
- temp_dir = os.path.join(current_app.root_path, 'temp')
- extract_dir = os.path.join(temp_dir, f"extract_{uuid.uuid4().hex}")
os.makedirs(extract_dir, exist_ok=True)
-
- # 保存上传的压缩文件
- archive_path = os.path.join(temp_dir, secure_filename(file.filename))
+ archive_path = os.path.join(temp_dir, secure_filename(original_archive_name))
file.save(archive_path)
+ logger.info(f"已保存上传的压缩文件: {archive_path}")
# 解压文件
- file_extension = file.filename.rsplit('.', 1)[1].lower()
+ file_extension = original_archive_name.rsplit('.', 1)[1].lower()
if file_extension == 'zip':
with zipfile.ZipFile(archive_path, 'r') as zip_ref:
zip_ref.extractall(extract_dir)
+ logger.info(f"已解压ZIP文件到: {extract_dir}")
elif file_extension == 'rar':
- with rarfile.RarFile(archive_path, 'r') as rar_ref:
- rar_ref.extractall(extract_dir)
+ # Ensure rarfile is installed and unrar executable is available
+ try:
+ with rarfile.RarFile(archive_path, 'r') as rar_ref:
+ rar_ref.extractall(extract_dir)
+ logger.info(f"已解压RAR文件到: {extract_dir}")
+ except rarfile.NeedFirstVolume:
+ 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}")
+ raise ValueError("不支持分卷压缩的RAR文件")
+ except rarfile.BadRarFile:
+ logger.error(f"RAR文件损坏或格式错误: {original_archive_name}")
+ raise ValueError("RAR文件损坏或格式错误")
+ except Exception as rar_err: # Catch other rar errors (like missing unrar)
+ logger.error(f"解压RAR文件时出错: {rar_err}")
+ raise ValueError(f"解压RAR文件失败: {rar_err}")
- # 处理结果统计
- results = {
- "total": 0,
- "success": 0,
- "failed": 0,
- "categories": {},
- "failed_files": []
- }
# 递归处理所有txt文件
for root, dirs, files in os.walk(extract_dir):
+ # --- 开始过滤 macOS 文件夹 ---
+ if '__MACOSX' in root.split(os.path.sep):
+ logger.info(f"Skipping macOS metadata directory: {root}")
+ dirs[:] = []
+ files[:] = []
+ continue
+ # --- 结束过滤 macOS 文件夹 ---
+
for filename in files:
- if filename.lower().endswith('.txt'):
+ # --- 开始过滤 macOS 文件 ---
+ if filename.startswith('._') or filename == '.DS_Store':
+ logger.info(f"Skipping macOS metadata file: {filename} in {root}")
+ continue
+ # --- 结束过滤 macOS 文件 ---
+
+ # Process only allowed text files
+ if allowed_text_file(filename):
file_path = os.path.join(root, filename)
- results["total"] += 1
+ # --- 修改点 2: 将 total_attempted 改回 total ---
+ results["total"] += 1 # <--- 使用 'total'
try:
- # 读取文件内容
- try:
- with open(file_path, 'r', encoding='utf-8') as f:
- file_content = f.read()
- except UnicodeDecodeError:
- # 尝试GBK编码
- with open(file_path, 'r', encoding='gbk') as f:
- file_content = f.read()
+ # 读取文件内容 - Try multiple encodings
+ file_content = None
+ encodings_to_try = ['utf-8', 'gbk', 'gb18030']
+ for enc in encodings_to_try:
+ try:
+ with open(file_path, 'r', encoding=enc) as f:
+ file_content = f.read()
+ logger.info(f"成功以 {enc} 读取文件: {file_path}")
+ break
+ except UnicodeDecodeError:
+ logger.warning(f"使用 {enc} 解码文件失败: {file_path}")
+ continue
+ except Exception as read_err:
+ logger.error(f"读取文件时发生其他错误 ({enc}) {file_path}: {read_err}")
+ continue
+
+ if file_content is None:
+ raise ValueError(f"无法使用支持的编码 {encodings_to_try} 读取文件内容。")
# 调用模型进行分类
result = text_classifier.classify_text(file_content)
- if not result['success']:
- results["failed"] += 1
- results["failed_files"].append({
- "filename": filename,
- "error": result['error']
- })
- continue
+ if not result.get('success', False):
+ error_msg = result.get('error', '未知的分类错误')
+ raise Exception(f"分类失败: {error_msg}")
category = result['category']
- # 统计类别数量
- 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/', 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()
diff --git a/utils/model_service.py b/utils/model_service.py
index 7f8937a..9b88659 100644
--- a/utils/model_service.py
+++ b/utils/model_service.py
@@ -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()
-