|
|
from fastapi import FastAPI
|
|
|
from pydantic import BaseModel
|
|
|
from langchain_openai import AzureChatOpenAI
|
|
|
import os
|
|
|
|
|
|
app = FastAPI(title="Translation API")
|
|
|
os.environ['AZURE_OPENAI_API_KEY'] = os.getenv("AZURE_OPENAI_API_KEY")
|
|
|
os.environ['OPENAI_API_VERSION'] = os.getenv("OPENAI_API_VERSION")
|
|
|
os.environ['AZURE_OPENAI_ENDPOINT'] = os.getenv("AZURE_OPENAI_ENDPOINT")
|
|
|
|
|
|
llm = AzureChatOpenAI(
|
|
|
azure_deployment="gpt-4o",
|
|
|
temperature=0,
|
|
|
max_tokens=None,
|
|
|
timeout=None,
|
|
|
max_retries=2,
|
|
|
)
|
|
|
|
|
|
class TranslateRequest(BaseModel):
|
|
|
text: str
|
|
|
|
|
|
class TranslateResponse(BaseModel):
|
|
|
translated_text: str
|
|
|
|
|
|
|
|
|
@app.post("/chat", response_model=TranslateResponse)
|
|
|
def translate_text(request: TranslateRequest):
|
|
|
messages = [
|
|
|
(
|
|
|
"system",
|
|
|
"You are a helpful assistant that answers questions about the user's query.",
|
|
|
),
|
|
|
("human", request.text),
|
|
|
]
|
|
|
|
|
|
ai_msg = llm.invoke(messages)
|
|
|
|
|
|
return TranslateResponse(
|
|
|
translated_text=ai_msg.content
|
|
|
)
|
|
|
|
