app.py

import numpy as np
import tensorflow as tf
import gradio as gr
from tensorflow import keras
from tensorflow.keras.preprocessing.sequence import pad_sequences
import json
from tensorflow.keras.preprocessing.text import tokenizer_from_json

model_cnn = keras.models.load_model('model_cnn.h5')

with open('tokenizer_cnn.json') as f:
    data = json.load(f)
    tokenizer = tokenizer_from_json(json.dumps(data))

words_per_review = 200
def predict_comment_score(comment):
    sequence = tokenizer.texts_to_sequences([comment])
    padded_sequence = pad_sequences(sequence, padding='post', maxlen=words_per_review)
    prediction = model_cnn.predict(padded_sequence)
    predictions = { }
    predictions["Negative"] = float(np.round(1 - prediction[0], 3))
    predictions["Positive"] = float(np.round(prediction[0], 3))
    return predictions

demo = gr.Blocks()

# Створення інтерфейсу Gradio
with demo:
    with gr.Tab("Predict comment Sentiment"):
      comment_input = gr.TextArea(label="Enter a comment")
      output = gr.Label(label="Comment sentiment")
      predict_button = gr.Button("Predict")
      predict_button.click(predict_comment_score, inputs=comment_input, outputs=output)

demo.launch(debug=True)