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Update app.py
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app.py
CHANGED
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import streamlit as st
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import openai
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import logging
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import
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from typing import Optional
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# 配置日志
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logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
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def
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profile1: str,
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profile2: str,
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style: str = "friendly",
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api_key: Optional[str] = None
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) -> str:
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"""
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基于两个LinkedIn简介生成个性化咖啡聊天邀请邮件。
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参数:
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profile1 (str): 发件人的LinkedIn简介文本
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profile2 (str): 收件人的LinkedIn简介文本
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style (str): 邮件风格,可选项为 "formal", "friendly", "humorous"
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temperature (float): 生成文本的创造性程度 (0.0-1.0)
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api_key (str, optional):
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返回:
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str: 生成的个性化咖啡聊天邮件
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logging.warning(f"温度 {temperature} 超出范围,调整至 0.7")
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temperature = 0.7
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#
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if api_key:
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# 构建提示词
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prompt = f"""
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Format the response as a complete, ready-to-send email without explanations.
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"""
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#
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{"role": "user", "content": prompt}
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],
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temperature
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max_tokens
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#
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except ValueError as e:
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logging.error(f"输入错误: {str(e)}")
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return f"发生错误: {str(e)}"
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except
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logging.error(f"
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return f"
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except Exception as e:
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logging.error(f"意外错误: {str(e)}")
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return f"发生未预期的错误: {str(e)}"
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@@ -117,19 +147,50 @@ st.title("☕ 个性化咖啡聊天邮件生成器")
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st.markdown("""
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此应用可以帮助求职者生成个性化的咖啡聊天邀请邮件。
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只需输入您的简介和目标联系人的简介,系统将分析共同点并生成一封真诚的邀请邮件。
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""")
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# 侧边栏 - API
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with st.sidebar:
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st.header("设置")
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if not api_key:
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st.
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st.subheader("邮件风格")
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style = st.radio(
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submit_disabled = not api_key or not profile1 or not profile2
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if submit_disabled:
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if not api_key:
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reason = "请先输入
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else:
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reason = "请填写两个简介"
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st.warning(reason)
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if st.button("生成邮件", type="primary", disabled=submit_disabled):
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with st.spinner("正在分析简介并创建个性化邮件..."):
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email =
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profile1=profile1,
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profile2=profile2,
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style=style,
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api_key=api_key
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)
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-
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st.text_area("可复制的邮件内容", email, height=400)
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st.download_button(
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label="下载邮件",
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data=email,
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file_name="咖啡聊天邀请.txt",
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mime="text/plain"
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)
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# 使用提示
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st.markdown("---")
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- **尝试不同风格**以适应不同的行业和联系人
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- 生成后可以**微调邮件内容**,添加您认为合适的个人细节
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- 适合用于**金融、咨询和科技行业**的专业人士
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""")
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import streamlit as st
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import logging
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import requests
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import json
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from typing import Optional
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# 配置日志
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logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
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def generate_email_with_vveai(
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profile1: str,
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profile2: str,
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style: str = "friendly",
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api_key: Optional[str] = None
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) -> str:
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"""
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使用VVEAI API基于两个LinkedIn简介生成个性化咖啡聊天邀请邮件。
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参数:
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profile1 (str): 发件人的LinkedIn简介文本
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profile2 (str): 收件人的LinkedIn简介文本
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style (str): 邮件风格,可选项为 "formal", "friendly", "humorous"
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temperature (float): 生成文本的创造性程度 (0.0-1.0)
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api_key (str, optional): VVEAI API密钥
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返回:
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str: 生成的个性化咖啡聊天邮件
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logging.warning(f"温度 {temperature} 超出范围,调整至 0.7")
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temperature = 0.7
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# 验证API密钥
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if not api_key:
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raise ValueError("请提供VVEAI API密钥")
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# 构建提示词
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prompt = f"""
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Format the response as a complete, ready-to-send email without explanations.
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"""
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# 准备API请求
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system_message = "You are an expert email writer who helps professionals create personalized networking messages."
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# VVEAI API端点
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url = "https://api.vveai.com/v1/chat/completions"
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# 构建请求
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headers = {
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"Content-Type": "application/json",
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"Authorization": f"Bearer {api_key}"
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}
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payload = {
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"model": "gpt-4", # 可能需要根据VVEAI支持的模型调整
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"messages": [
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{"role": "system", "content": system_message},
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{"role": "user", "content": prompt}
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],
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"temperature": temperature,
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"max_tokens": 1000
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}
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# 发送请求
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response = requests.post(url, headers=headers, data=json.dumps(payload), timeout=60)
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# 添加详细日志便于调试
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logging.info(f"API响应状态码: {response.status_code}")
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logging.info(f"API响应头: {response.headers}")
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# 处理响应
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if response.status_code == 200:
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result = response.json()
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# 记录完整响应便于调试
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logging.info(f"API响应内容: {result}")
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try:
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email_text = result["choices"][0]["message"]["content"].strip()
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return email_text
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except KeyError as e:
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logging.error(f"响应格式错误: {e}. 完整响应: {result}")
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return f"API响应格式错误: {e}。请联系VVEAI支持获取帮助。"
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else:
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error_message = f"API请求失败: {response.status_code} - {response.text}"
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logging.error(error_message)
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return f"API错误: {response.status_code}。请检查您的API密钥是否正确。详细信息: {response.text}"
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except ValueError as e:
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logging.error(f"输入错误: {str(e)}")
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return f"发生错误: {str(e)}"
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except requests.RequestException as e:
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logging.error(f"请求错误: {str(e)}")
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return f"网络请求错误: {str(e)}"
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except Exception as e:
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logging.error(f"意外错误: {str(e)}")
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return f"发生未预期的错误: {str(e)}"
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st.markdown("""
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此应用可以帮助求职者生成个性化的咖啡聊天邀请邮件。
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只需输入您的简介和目标联系人的简介,系统将分析共同点并生成一封真诚的邀请邮件。
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**注意:此应用使用 VVEAI API 服务,请确保输入正确的 API 密钥。**
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""")
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# 侧边栏 - API设置
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with st.sidebar:
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st.header("API设置")
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api_key = st.text_input("VVEAI API密钥", type="password", key="api_key",
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help="输入以 sk- 开头的 VVEAI API 密钥")
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# 添加API状态检测按钮
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if st.button("测试API连接"):
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if not api_key:
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st.error("请先输入API密钥")
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else:
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try:
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# 简单的API测试请求
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test_url = "https://api.vveai.com/v1/chat/completions"
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test_headers = {
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"Content-Type": "application/json",
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"Authorization": f"Bearer {api_key}"
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}
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test_payload = {
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"model": "gpt-4",
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"messages": [
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{"role": "user", "content": "Hello"}
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],
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"max_tokens": 5
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}
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test_response = requests.post(
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test_url,
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headers=test_headers,
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data=json.dumps(test_payload),
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timeout=10
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)
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if test_response.status_code == 200:
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st.success("API连接成功!")
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else:
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st.error(f"API连接失败: {test_response.status_code} - {test_response.text}")
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except Exception as e:
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st.error(f"测试API时出错: {str(e)}")
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st.subheader("邮件风格")
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style = st.radio(
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submit_disabled = not api_key or not profile1 or not profile2
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if submit_disabled:
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if not api_key:
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reason = "请先输入VVEAI API密钥"
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else:
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reason = "请填写两个简介"
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st.warning(reason)
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if st.button("生成邮件", type="primary", disabled=submit_disabled):
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with st.spinner("正在分析简介并创建个性化邮件..."):
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email = generate_email_with_vveai(
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profile1=profile1,
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profile2=profile2,
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style=style,
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api_key=api_key
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)
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# 检查是否发生错误
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if email.startswith("发生错误") or email.startswith("API错误") or email.startswith("网络请求错误"):
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st.error(email)
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else:
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st.success("邮件已生成!")
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# 显示结果
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st.header("您的个性化咖啡聊天邮件")
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st.text_area("可复制的邮件内容", email, height=400)
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st.download_button(
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label="下载邮件",
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data=email,
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file_name="咖啡聊天邀请.txt",
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mime="text/plain"
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)
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# 调试信息(可选,帮助解决API问题)
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if st.checkbox("显示调试信息"):
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st.subheader("API信息")
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st.markdown("""
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- API端点: `https://api.vveai.com/v1/chat/completions`
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- 请求方法: POST
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- 内容类型: application/json
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- 授权方式: Bearer Token
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如果遇到持续的API问题,请联系VVEAI支持团队并提供以上信息。
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""")
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# 使用提示
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st.markdown("---")
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- **尝试不同风格**以适应不同的行业和联系人
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- 生成后可以**微调邮件内容**,添加您认为合适的个人细节
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- 适合用于**金融、咨询和科技行业**的专业人士
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- 如果API连接测试失败,请确认您的**VVEAI API密钥**是否正确
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""")
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