| from flask import Flask, request, jsonify
|
| import tensorflow as tf
|
| import pickle
|
| import numpy as np
|
|
|
| app = Flask(__name__)
|
| model = tf.keras.models.load_model('models/model.h5')
|
| with open('models/tokenizer.pickle','rb') as handle:
|
| tokenizer = pickle.load(handle)
|
|
|
| @app.route('/predict', methods = ['POST'])
|
| def predict():
|
| data = request.json
|
| text = data['text']
|
|
|
| sequences = tokenizer.texts_to_sequences([text])
|
| padded = tf.keras.preprocessing.sequence.pad_sequences(sequences, maxlen = 100)
|
|
|
| prediction = model.predic(padded)[0][0]
|
| sentiment = "Positive" if prediction > 0.5 else "Negative"
|
| confidence = float(prediction) if sentiment =='Positive' else float(1 - prediction)
|
|
|
| return jsonify({
|
| 'sentiment': sentiment,
|
| 'confidence' : confidence
|
| })
|
|
|
| if __name__=="__main__":
|
| app.run(debug = True) |
