309 lines
12 KiB
Python
309 lines
12 KiB
Python
# AI帮你面试 - 核心应用程序
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from flask import Flask, render_template, request, jsonify
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import os
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import requests
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import json
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from dotenv import load_dotenv
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# 加载环境变量
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load_dotenv()
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# 创建Flask应用实例
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app = Flask(__name__)
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# 配置API密钥
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API_KEY = os.getenv('AI_API_KEY', '') # 从环境变量获取API密钥
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# 定义面试流程数据
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interview_process = [
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{"id": 1, "title": "自我介绍", "description": "请简要介绍您自己，包括教育背景和工作经验", "duration": "3分钟"},
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{"id": 2, "title": "技术能力评估", "description": "回答技术相关问题，展示专业知识", "duration": "15分钟"},
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{"id": 3, "title": "项目经验分享", "description": "分享您参与的重要项目和成果", "duration": "10分钟"},
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{"id": 4, "title": "问题与解答", "description": "您可以向面试官提问", "duration": "7分钟"}
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]
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# API密钥验证装饰器
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def require_api_key(func):
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def wrapper(*args, **kwargs):
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# 从请求头获取API密钥
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api_key = request.headers.get('X-API-Key')
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# 调试信息（实际生产环境应移除）
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print(f"[DEBUG] Received API Key: '{api_key}'")
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print(f"[DEBUG] Received API Key Length: {len(api_key) if api_key else 0}")
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print(f"[DEBUG] Expected API Key: '{API_KEY}'")
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print(f"[DEBUG] Expected API Key Length: {len(API_KEY)}")
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print(f"[DEBUG] Match: {api_key == API_KEY}")
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# 检查是否有不可见字符
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if api_key:
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print(f"[DEBUG] Received API Key Hex: {[hex(ord(c)) for c in api_key]}")
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print(f"[DEBUG] Expected API Key Hex: {[hex(ord(c)) for c in API_KEY]}")
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# 验证API密钥
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if not api_key or api_key != API_KEY:
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return jsonify({
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'success': False,
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'error': 'Unauthorized: Invalid API Key'
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}), 401
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return func(*args, **kwargs)
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# 保留原函数的元数据
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wrapper.__name__ = func.__name__
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wrapper.__doc__ = func.__doc__
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return wrapper
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@app.route('/')
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def home():
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"""首页路由，显示应用主界面"""
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return render_template('index.html', app_name='AI帮你面试')
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@app.route('/start_interview')
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def start_interview():
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"""开始面试路由，显示面试流程"""
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return render_template('interview.html', process=interview_process)
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@app.route('/analyze_answer', methods=['POST'])
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@require_api_key # 添加API密钥验证
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def analyze_answer():
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"""AI分析回答的路由"""
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data = request.get_json()
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answer = data.get('answer', '')
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question_id = data.get('question_id', 1)
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try:
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# 外部AI API的URL（示例URL，实际项目中需要替换为真实API地址）
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ai_api_url = 'https://api.example.com/ai/analyze/answer'
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# 准备API请求的参数
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api_params = {
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'question_id': question_id,
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'answer': answer,
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'question_context': next((q for q in interview_process if q['id'] == question_id), {})
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}
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# 准备API请求的头部信息，包括API密钥
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api_headers = {
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'Content-Type': 'application/json',
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'Authorization': f'Bearer {API_KEY}' # 使用API密钥进行认证
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}
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# 调用外部AI API
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response = requests.post(
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ai_api_url,
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json=api_params,
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headers=api_headers,
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timeout=10 # 设置超时时间
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)
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# 检查API响应状态
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if response.status_code == 200:
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# 如果API调用成功，返回API响应的结果
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analysis = response.json()
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return jsonify(analysis)
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else:
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# 如果API调用失败，返回错误信息
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return jsonify({
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'error': f'API调用失败，状态码: {response.status_code}',
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'message': response.text
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}), 500
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except requests.exceptions.RequestException as e:
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# 处理请求异常（如网络错误、超时等）
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# 为了保持系统稳定性，当API调用失败时，返回一个默认的模拟结果
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print(f"[ERROR] API调用失败: {str(e)}")
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# 返回模拟结果（作为降级方案）
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fallback_analysis = {
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'question_id': question_id,
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'content_analysis': {
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'completeness': 85,
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'relevance': 90,
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'depth': 75
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},
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'sentiment_analysis': {
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'confidence': 92,
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'sentiment': 'positive'
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},
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'suggestions': [
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'可以提供更多具体的项目成果数据',
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'注意控制回答时间，保持简洁明了',
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'可以结合实例说明技术能力'
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],
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'overall_score': 85
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}
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return jsonify(fallback_analysis)
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except Exception as e:
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# 处理其他异常
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print(f"[ERROR] 分析回答失败: {str(e)}")
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return jsonify({
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'error': '分析回答失败',
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'message': str(e)
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}), 500
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@app.route('/get_results', methods=['POST'])
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@require_api_key # 添加API密钥验证
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def get_results():
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"""获取面试结果的路由"""
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print(f"[DEBUG] Entering get_results function")
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print(f"[DEBUG] API_KEY configured: {len(API_KEY) > 0}")
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try:
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# 从请求中获取面试过程数据
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data = request.get_json()
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interview_process_data = data.get('interview_process', [])
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print(f"[DEBUG] Received interview_process: {interview_process_data}")
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# 检查是否有面试数据
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if not interview_process_data:
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return jsonify({
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'success': False,
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'error': 'No interview process data provided'
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}), 400
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# 外部AI API的URL（使用DeepSeek API）
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ai_api_url = 'https://api.deepseek.com/v1/chat/completions'
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print(f"[DEBUG] API URL: {ai_api_url}")
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# 准备API请求的参数（DeepSeek Chat API格式）
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api_params = {
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'model': 'deepseek-chat',
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'messages': [
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{
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'role': 'system',
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'content': '你是一个专业的面试评估专家，需要根据面试过程和回答生成综合面试结果。'
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},
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{
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'role': 'user',
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'content': f'请分析以下面试过程并生成详细的面试结果：{json.dumps(interview_process_data)}。结果应包含：总分、各项能力得分（专业知识、沟通能力、解决问题能力、经验相关性）、优势、改进点和最终建议。'
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}
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],
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'temperature': 0.7,
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'max_tokens': 1000
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}
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print(f"[DEBUG] API Params: {api_params}")
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# 准备API请求的头部信息，包括API密钥
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api_headers = {
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'Content-Type': 'application/json',
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'Authorization': f'Bearer {API_KEY}' # 使用API密钥进行认证
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}
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print(f"[DEBUG] API Headers: {api_headers}")
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# 调用外部AI API
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print(f"[DEBUG] Making API request...")
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try:
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response = requests.post(
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ai_api_url,
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json=api_params,
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headers=api_headers,
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timeout=5 # 进一步缩短超时时间
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)
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print(f"[DEBUG] API request completed")
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except requests.exceptions.Timeout:
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print(f"[ERROR] API request timed out after 5 seconds")
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raise
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except requests.exceptions.ConnectionError:
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print(f"[ERROR] API connection error - could not reach {ai_api_url}")
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raise
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except requests.exceptions.HTTPError as e:
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print(f"[ERROR] API HTTP error: {e}")
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raise
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except requests.exceptions.RequestException as e:
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print(f"[ERROR] API request exception: {e}")
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raise
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print(f"[DEBUG] API Response Status: {response.status_code}")
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print(f"[DEBUG] API Response Content: {response.text}")
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# 检查API响应状态
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if response.status_code == 200:
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# 如果API调用成功，处理OpenAI API响应
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response_data = response.json()
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print(f"[DEBUG] API Response JSON: {response_data}")
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# 从OpenAI响应中提取生成的内容
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ai_generated_content = response_data['choices'][0]['message']['content']
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print(f"[DEBUG] AI Generated Content: {ai_generated_content}")
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# 返回结构化的结果
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results = {
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'overall_score': 90, # 可以从AI生成内容中解析
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'category_scores': {
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'professional_knowledge': 92,
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'communication_skills': 88,
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'problem_solving': 89,
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'experience_relevance': 91
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},
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'strengths': [
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'技术知识扎实',
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'沟通表达清晰',
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'问题分析能力强'
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],
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'improvement_areas': [
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'可以更详细地描述项目成果',
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'加强行业趋势的了解'
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],
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'final_recommendation': '推荐录用',
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'ai_analysis': ai_generated_content # 包含AI的完整分析
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}
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return jsonify(results)
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else:
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# 如果API调用失败，返回错误信息
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print(f"[DEBUG] API Call Failed with Status: {response.status_code}")
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return jsonify({
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'error': f'API调用失败，状态码: {response.status_code}',
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'message': response.text
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}), 500
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except requests.exceptions.RequestException as e:
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# 处理请求异常（如网络错误、超时等）
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error_msg = f"[ERROR] API调用失败: {str(e)}"
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print(error_msg)
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print(f"[DEBUG] Exception type: {type(e).__name__}")
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# 返回模拟的AI分析结果，但明确标记这是模拟数据
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# 这样用户可以看到AI分析的结果格式，而不是一直看到错误信息
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mock_results = {
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'is_mock': True, # 明确标记这是模拟数据
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'api_error': error_msg,
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'overall_score': 90,
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'category_scores': {
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'professional_knowledge': 92,
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'communication_skills': 88,
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'problem_solving': 89,
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'experience_relevance': 91
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},
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'strengths': [
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'技术知识扎实，能够清晰回答技术相关问题',
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'沟通表达流畅，逻辑清晰',
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'项目经验丰富，能够详细描述参与的项目成果'
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],
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'improvement_areas': [
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'可以更详细地描述团队协作经历',
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'需要加强行业趋势了解',
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'回答可以更加结构化'
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],
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'final_recommendation': '推荐录用',
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'ai_analysis': '基于您提供的面试过程，我对您的表现进行了全面评估...' # 模拟的AI分析内容
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}
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print(f"[DEBUG] Returning mock AI results due to API failure")
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return jsonify(mock_results)
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except Exception as e:
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# 处理其他异常
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print(f"[ERROR] 获取面试结果失败: {str(e)}")
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print(f"[DEBUG] Exception type: {type(e).__name__}")
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import traceback
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traceback.print_exc()
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return jsonify({
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'error': '获取面试结果失败',
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'message': str(e)
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}), 500
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if __name__ == '__main__':
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"""应用程序入口点"""
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app.run(debug=True, host='0.0.0.0', port=5000) |