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import config.config as config
from app.src.src import pipeline_sentiment, pipeline_stats, pipeline_summarize
from fastapi import FastAPI
from pydantic import BaseModel
# from transformers import pipeline
import uvicorn
import pandas as pd
import os
# sentiment_model = pipeline(model=config.sentiment_model)
# sum_model = pipeline(model=config.sum_model, use_fast=True)
app = FastAPI()
class YouTubeUrl(BaseModel):
url_video: str
@app.get('/')
def read_root():
return {'message': 'FastAPI+HuggingFace app sentiment + summarize YouTube comments'}
@app.post('/comments')
def get_comments(url_video: YouTubeUrl):
data = pipeline_sentiment(url_video.url_video, config.API_KEY, sentiment_model)
data.to_csv(f"{config.DATA_FILE}", index=False)
return {'message': 'Success'}
@app.get('/stats')
def get_stats_sent():
if f"{config.NAME_DATA}" in os.listdir(f"{config.PATH_DATA}"):
data = pd.read_csv(f"{config.DATA_FILE}")
return pipeline_stats(data)
@app.get('/summarization')
def get_summarize():
if f"{config.NAME_DATA}" in os.listdir(f"{config.PATH_DATA}"):
data = pd.read_csv(f"{config.DATA_FILE}")
return pipeline_summarize(data['text_comment'], sum_model)
if __name__ == '__main__':
uvicorn.run(app, host='127.0.0.1', port=80)