from flask import Flask, jsonify, render_template, request, make_response
import requests
import transformers
from huggingface_hub import cached_download
import torch
from torch import nn
import re
import numpy as np
import pandas as pd
from collections import OrderedDict
# import requests
# from bs4 import BeautifulSoup

app = Flask(__name__)


headers = {"Authorization": f"Bearer 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"}
API_URL = "https://api-inference.huggingface.co/models/nlptown/bert-base-multilingual-uncased-sentiment"
def query(payload):
    response = requests.post(API_URL, headers=headers, json=payload)
    return response.json()

@app.route('/', methods=['GET'])
def get():
    data = query({"inputs": "The movie is good"})
    return data

@app.route('/', methods=['POST'])
def predict():
    message = "This is good movies" #request.form['message']
    # choice of the model
    results = get_prediction(message, dictOfModels['BERT']) # get_prediction(message, dictOfModels['request.form.get("model_choice")'])
    print(f'User selected model : {request.form.get("model_choice")}')
    my_prediction = f'The feeling of this text is {results[0]["label"]} with probability of {results[0]["score"]*100}%.'
    return render_template('result.html', text = f'{message}', prediction = my_prediction)



# @app.route('/')
# def home():
#     print(1)
#     return {'key':"Hello HuggingFace! Successfully deployed. "}
#     # model = load_checkpoint('checkpoint.pth')
#     # print(2)
#     # res = sample(model, obj.maxlen, 'ap')
#     # print(3)
#     # return {'key':res}