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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} |