Mingu Kim
80 lines
2.1 KiB
Python
80 lines
2.1 KiB
Python
from dotenv import load_dotenv
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import os
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from ibm_watsonx_ai import APIClient
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from ibm_watsonx_ai import Credentials
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from ibm_watsonx_ai.foundation_models import ModelInference
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import gradio as gr
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import logging
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# .env 내용 가져오기
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load_dotenv()
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apikey = os.getenv("WATSONX_API_KEY")
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project_id = os.getenv("WATSONX_PROJECT_ID")
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watsonx_ai_url = os.getenv("WATSONX_URL")
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credentials = Credentials(
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url = f"{watsonx_ai_url}",
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api_key = f"{apikey}",
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)
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client = APIClient(credentials)
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model = ModelInference(
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model_id="ibm/granite-4-h-small",
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api_client=client,
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project_id=f"{project_id}",
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params = {
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"max_tokens": 1000
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}
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)
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def recommend(message, history):
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print("history :", history)
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system_prompt = """
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너는 여행 스캐줄러 AI
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한글로 답변해주고
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여행사의 여행 스케쥴 처럼 짜줘
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"""
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user_prompt=f"""
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다음 내용을 참고해서 계획 짜줘
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- 내용 : {message}
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"""
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messages = [
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# 시스템 프롬프트
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{"role" : "system", "content" : system_prompt},
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# {"role" : "user", "content" : user_prompt},
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]
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for item in history:
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content = item["content"][0]["text"]
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messages.append({"role" : item["role"], "content" : content})
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messages.append({"role" : "user", "content" : message})
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# generated_response = model.chat(messages=messages)
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# return generated_response['choices'][0]['message']['content']
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# chat_stream()
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generated_response = model.chat_stream(messages=messages)
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full_response = ""
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for chunk in generated_response:
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if chunk['choices'] :
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full_response += chunk["choices"][0]["delta"].get("content", "")
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yield full_response
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demo = gr.ChatInterface(
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fn=recommend,
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title="AI 여행 플래너",
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description="여행지역, 예산, 여행스타일, 여행 기간 등을 입력하면 AI가 맞춤형 여행일정을 추천해 드립니다."
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)
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demo.launch() |