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)