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| from fastapi import FastAPI, HTTPException,APIRouter,Request | |
| from pydantic import BaseModel | |
| from fastapi.responses import HTMLResponse | |
| from typing import List | |
| from fastapi.staticfiles import StaticFiles | |
| from starlette.responses import FileResponse | |
| from fastapi.middleware.cors import CORSMiddleware | |
| import os | |
| app = FastAPI() | |
| origins = ["*"] | |
| app.add_middleware( | |
| CORSMiddleware, | |
| allow_origins=origins, | |
| ) | |
| os.environ['SENTENCE_TRANSFORMERS_HOME'] = './.cache' ## For Docker | |
| app.mount("/files/", StaticFiles(directory='../code'), name="index") | |
| class TextInput(BaseModel): | |
| InputText: str # python casing?????? | |
| def emotion_detection(str1: str) -> str: | |
| from transformers import pipeline | |
| pipe = pipeline(model="distilbert-base-uncased-finetuned-sst-2-english") | |
| expected=(pipe(str1)) | |
| return expected[0].get('label') | |
| async def read_index(): | |
| return FileResponse('app/index.html') | |
| async def detect_emotion(input_data: TextInput): | |
| text1 = input_data.InputText | |
| emotion= emotion_detection(text1) | |
| response={"Text Entered":text1,"emotion":emotion} | |
| return response | |
| async def testing_emotion(input_data): | |
| emotion= emotion_detection(input_data) | |
| response={"Text Entered":input_data,"emotion":emotion} | |
| return response |