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#uvicorn main:app --reload
import os
os.environ['TRANSFORMERS_CACHE'] = 'src/cache/hub'
from fastapi import FastAPI, status
from fastapi.responses import Response, JSONResponse
from pydantic import BaseModel
from typing import List
import os
import json
import time
from src.myNLI import FactChecker
from src.crawler import MyCrawler
#request body
class Claim(BaseModel):
claim: str
class ScrapeBase(BaseModel):
id: int
name: str
scraping_url: str
class ScrapeList(BaseModel):
data: List[ScrapeBase]
app = FastAPI()
# load model
t_0 = time.time()
fact_checker = FactChecker()
t_load = time.time() - t_0
print("time load model: {}".format(t_load))
crawler = MyCrawler()
label_code = {
"REFUTED": 0,
"SUPPORTED": 1,
"NEI": 2
}
@app.get("/")
async def root():
return {"msg": "This is for interacting with Fact-checking AI Model"}
@app.post("/ai-fact-check")
async def get_claim(req: Claim):
claim = req.claim
result = fact_checker.predict(claim)
print(result)
if not result:
return Response(status_code=status.HTTP_204_NO_CONTENT)
return { "claim": claim,
"final_label": label_code[result["label"]],
"evidence": result["evidence"],
"provider": result["provider"],
"url": result["url"]
}
@app.post("/scraping-check")
async def get_claim(req: ScrapeList):
response = []
for ele in req.data:
response.append({
"id": ele.id,
"name": ele.name,
"scraping_url": ele.scraping_url,
"status": crawler.scraping(ele.scraping_url)
})
return JSONResponse({
"list": response
}) |