app.py

from flask import Flask, render_template, flash, request, url_for, redirect, session
import numpy as np
import re
import os
import tensorflow as tf
from tensorflow.keras.datasets import imdb
from tensorflow.keras.preprocessing import sequence
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing.text import Tokenizer
import pickle


IMAGE_FOLDER = os.path.join('static', 'img_pool')

app = Flask(__name__)

app.config['UPLOAD_FOLDER'] = IMAGE_FOLDER

with open('tokenizer.pickle', 'rb') as handle:
    tokenizer = pickle.load(handle)


def init():
    global model,graph
    graph = tf.Graph()
    

@app.route('/', methods=['GET', 'POST'])
def home():

    return render_template("index.html")

@app.route('/sentiment_prediction', methods = ['POST', "GET"])
def sent_anly_prediction():
    if request.method=='POST':
        text = request.form['text']

        
        tw = tokenizer.texts_to_sequences([text])
        tw = sequence.pad_sequences(tw,maxlen=200)

        
      
        
        with graph.as_default():
            # load the pre-trained Keras model
            model = load_model('sentiment_analysis.h5')

            probability = model.predict(tw)[0][0]
            prediction = int(model.predict(tw).round().item())
            
        if prediction == 0:
            sentiment = 'Negative'
            img_filename = os.path.join(app.config['UPLOAD_FOLDER'], 'sad.gif')
        else:
            sentiment = 'Positive'
            img_filename = os.path.join(app.config['UPLOAD_FOLDER'], 'happy.gif')
    return render_template('index.html', text=text, sentiment=sentiment, probability=probability, image=img_filename)


init()
app.run(debug=True)