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from flask import Flask, render_template, request
import numpy as np
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.preprocessing.text import tokenizer_from_json
import json
app = Flask(__name__)
model = load_model('imdb-002.keras')
with open('D:\Python_vscode\jupyter_notebook\imdb.json') as file:
imdb = file.read()
token = tokenizer_from_json(imdb)
def preprocess_text(text, max_length=20):
sequ = token.texts_to_sequences([text])
pad = pad_sequences(sequ, maxlen = max_length, padding = 'post', truncating = 'post')
return pad
@app.route('/')
def home():
return render_template('index.html')
@app.route('/predict', methods =['POST'])
def predict():
review = request.form['review']
processed_review = preprocess_text(review)
prediction = model.predict(processed_review)[0][0]
if prediction >= 0.5:
sentiment = "Positive"
else:
"Negative"
return render_template('index.html', review=review, sentiment=sentiment, probability=round(prediction,2))
if __name__ == '__main__':
app.run(debug=True) |