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app.py
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app.py
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from flask import Flask, render_template, request
from sklearn.externals import joblib
import pandas as pd
import numpy as np
app=Flask(__name__)
'''
@app.route('/test')
def test():
return "Flask is being used for development"
'''
#load model_prediction
#We can load model here such that it will not be loaded again if the page refreshes, iproves the performance
from tensorflow.keras.models import load_model
import string
import pandas as pd
import numpy as np
model = load_model('30_real_user_weight.h5')
from sklearn.preprocessing import LabelEncoder
encoder = LabelEncoder()
encoder.classes_ = np.load('classes.npy',allow_pickle=True)
def create_vocab_set():
alphabet = (list(string.ascii_lowercase) + list(string.digits) +
list(string.punctuation) + ['\n'] + [' '] )
vocab_size = len(alphabet)
check = set(alphabet)
vocab = {}
reverse_vocab = {}
for ix, t in enumerate(alphabet):
vocab[t] = ix
reverse_vocab[ix] = t
return vocab, reverse_vocab, vocab_size, check
maxlen = 140
vocab, reverse_vocab, vocab_size, check =create_vocab_set()
def encode_data(x, maxlen, vocab, vocab_size, check):
input_data = np.zeros((len(x), maxlen, vocab_size))
for dix, sent in enumerate(x):
counter = 0
sent_array = np.zeros((maxlen, vocab_size))
chars = list(sent.lower())
for c in chars:
if counter >= maxlen:
pass
else:
char_array = np.zeros(vocab_size, dtype=np.int)
if c in check:
ix = vocab[c]
char_array[ix] = 1
sent_array[counter, :] = char_array
counter += 1
input_data[dix, :, :] = sent_array
return input_data
@app.route('/')
def home():
return render_template('home.html')
@app.route("/predict",methods=['GET','POST'])
def predict():
if request.method == 'POST':
try:
text=(request.form['text'])
type_here=[]
type_here.append(text)
typr_here=pd.DataFrame(type_here)
typr_here = encode_data(type_here, maxlen, vocab, vocab_size, check)
y_pred = model.predict(typr_here)
y_pred=pd.DataFrame(y_pred)
y_pred=y_pred.eq(y_pred.where(y_pred != 0).max(1), axis=0).astype(int)
y_pred=y_pred.iloc[:,:].values
result=[]
for i in range(0,len(y_pred)):
for j in range(0,len(y_pred[0])):
if(y_pred[i][j]==1):
result.append(j)
author=encoder.inverse_transform(result)
except valueError:
return "please Check if the values are entered correctly"
return render_template('predict.html', prediction=author[0])
if __name__=="__main__":
app.run()