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from config import 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)
headers = {"Authorization": f"Bearer {os.environ.get('API_TOKEN')}"}
SENT_API_URL = f"https://api-inference.huggingface.co/models/{config.sentiment_model}"
SUM_API_URL = f"https://api-inference.huggingface.co/models/{config.sum_model}"
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, os.environ.get("API_KEY"), headers, SENT_API_URL)
data.to_csv(f"{config.DATA_FILE}", index=False)
return data # {'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'], headers, SUM_API_URL)
#if __name__ == '__main__':
# uvicorn.run(app, host='127.0.0.1', port=80) |