app.py

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":
        le.Observation_LLM_Evaluator(prompt_version)
            
    # Example processing (replace with actual logic)
    return JSONResponse(content={"evalsuccessful":True},status_code=200)