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# app.py
import gradio as gr
import tensorflow as tf
import json
import numpy as np
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.preprocessing.text import tokenizer_from_json
# Load the Keras model
def load_model():
model = tf.keras.models.load_model('polarisatie_model.h5')
return model
model = load_model()
# Load the tokenizer from tokenizer.json
def load_tokenizer(tokenizer_path='tokenizer.json'):
with open(tokenizer_path, 'r', encoding='utf-8') as f:
tokenizer_json = f.read()
tokenizer = tokenizer_from_json(tokenizer_json)
return tokenizer
tokenizer = load_tokenizer()
# Load max_length from max_length.txt
def load_max_length(max_length_path='max_length.txt'):
with open(max_length_path, 'r') as f:
max_length = int(f.read().strip())
return max_length
max_length = load_max_length()
# Preprocessing function
def preprocess(text):
# Tokenize the text
sequences = tokenizer.texts_to_sequences([text])
# Pad the sequences
padded = pad_sequences(sequences, maxlen=max_length, padding='post', truncating='post')
return padded
# Prediction function
def predict_polarization(text):
if not text.strip():
return "Voer alstublieft een geldige zin in."
# Preprocess the text
processed_text = preprocess(text)
# Make prediction
prediction = model.predict(processed_text)
# Assume the model outputs a probability
is_polarizing = bool(prediction[0] > 0.5)
return "Ja" if is_polarizing else "Nee"
# Define the Gradio interface using Blocks
with gr.Blocks() as demo:
gr.Markdown("# Polarisatie Thermometer")
gr.Markdown("Voer een zin in om te beoordelen of deze polariserend is.")
with gr.Row():
input_text = gr.Textbox(label="Zin", placeholder="Schrijf hier een zin...", lines=2)
evaluate_button = gr.Button("Evalueren")
result = gr.Textbox(label="Is polariserend?", interactive=False)
evaluate_button.click(fn=predict_polarization, inputs=input_text, outputs=result)
# Launch the Gradio app
if __name__ == "__main__":
demo.launch()
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