84 lines
2.4 KiB
Python
84 lines
2.4 KiB
Python
from flask import Flask, request, jsonify, render_template
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import os
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import base64
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from dotenv import load_dotenv
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from langchain_openai import ChatOpenAI
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from dashscope.audio.tts_v2 import SpeechSynthesizer, AudioFormat
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import dashscope
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# 加载环境变量
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ENV_PATH = "D:\\AI_Coding\\aicodes\\.env"
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load_dotenv(ENV_PATH)
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# 初始化DashScope API Key
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dashscope.api_key = os.getenv("DASHSCOPE_API_KEY")
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app = Flask(__name__)
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# 初始化DeepSeek LLM
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def get_llm():
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llm = ChatOpenAI(
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base_url=os.getenv("base_url1"),
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api_key=os.getenv("DEEPSEEK_API_KEY1"),
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model="deepseek-chat",
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temperature=0,
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)
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return llm
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# 系统提示词
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system_prompt = "你是一位专业的心血管健康顾问，专注于提供科学、准确的心血管疾病预防和健康管理建议。请基于医学知识回答用户的问题，保持专业、友好的态度。"
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@app.route('/')
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def home():
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return render_template('voice_index.html')
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@app.route('/ask_cardio', methods=['POST'])
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def ask_cardio():
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try:
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# 接收用户问题
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data = request.json
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user_question = data.get('question', '')
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if not user_question:
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return jsonify({'error': '请输入问题'}), 400
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# 初始化LLM
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llm = get_llm()
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# 构建完整的prompt
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full_prompt = f"{system_prompt}\n\n用户问题：{user_question}"
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# 获取LLM回答
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response = llm.invoke(full_prompt)
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text_answer = response.content
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# 使用CosyVoice合成语音
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model = "cosyvoice-v2"
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voice = "longxiaochun_v2"
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# 初始化SpeechSynthesizer
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synthesizer = SpeechSynthesizer(
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model=model,
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voice=voice,
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format=AudioFormat.MP3_22050HZ_MONO_256KBPS
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)
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# 同步调用合成语音
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audio_data = synthesizer.call(text_answer)
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# 将音频数据转换为Base64编码
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audio_base64 = base64.b64encode(audio_data).decode('utf-8')
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# 返回结果
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response_data = {
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'text_answer': text_answer,
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'audio_base64': audio_base64
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}
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return jsonify(response_data)
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except Exception as e:
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return jsonify({'error': str(e)}), 500
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if __name__ == '__main__':
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app.run(debug=True, host='0.0.0.0', port=5001) |