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from fastapi import FastAPI, Request
from fastapi.responses import JSONResponse
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel
from llmeval import LLM_as_Evaluator
app = FastAPI()
# CORS configuration
origins = ["*"] # Allow all origins; specify domains in production
app.add_middleware(
CORSMiddleware,
allow_origins=origins, # Allows all origins
allow_credentials=True,
allow_methods=["*"], # Allows all HTTP methods
allow_headers=["*"], # Allows all headers
)
le=LLM_as_Evaluator()
# Pydantic model for request body
class EvalInput(BaseModel):
promptversion: str
@app.post("/evaluate")
async def evaluation(request:EvalInput):
prompt_version = request.promptversion
#prompt_version_splitted=prompt_version.split(":")
#if prompt_version_splitted[0]=="paradigm_identifier":
#le.Paradigm_LLM_Evaluator(prompt_version)
#elif prompt_version_splitted[0]=="observational_biologist":
try:
le.LLM_Evaluator(prompt_version)
#elif prompt_version_splitted[0]=="ontology_generator":
# Example processing (replace with actual logic)
return JSONResponse(content={"evalsuccessfull":True},status_code=200)
except Exception as e:
return JSONResponse(content={"evalsuccessfull":False},status_code=200)
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