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