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)