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import streamlit as st
from fastapi import FastAPI
from transformers import pipeline
classifier = pipeline("text-classification", model="hun3359/klue-bert-base-sentiment")
# text = st.text_area('enter some text!')
# classifier = pipeline("text-classification", model="hun3359/klue-bert-base-sentiment")
# preds = classifier(text, top_k=None)
# sorted_preds = sorted(preds, key=lambda x: x['score'], reverse=True)
# for item in sorted_preds:
# item['score'] = round(item['score'], 5)
# if text:
# st.json(sorted_preds)
app = FastAPI()
@app.get("/")
async def root():
return {"messsage" : "Successfully Initiated"}
# 유저로부터 text를 받아서 감정 분석 결과를 반환해주는 API
@app.get("/sentiment/")
async def sentiment(text: str = None):
preds = classifier(text, top_k=None)
sorted_preds = sorted(preds, key=lambda x: x['score'], reverse=True)
for item in sorted_preds:
item['score'] = round(item['score'], 5)
return sorted_preds
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