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

# Cargar el modelo
model = tf.keras.models.load_model('polarisatie_model.h5')

# Cargar el tokenizador
with open('tokenizer.json', 'r') as f:
    tokenizer_json = json.load(f)
tokenizer = tokenizer_from_json(json.dumps(tokenizer_json))

# Cargar max_length
with open('max_length.txt', 'r') as f:
    max_length = int(f.read().strip())

def preprocess_text(text):
    sequence = tokenizer.texts_to_sequences([text])
    padded = pad_sequences(sequence, maxlen=max_length)
    return padded

def predict_polarization(text):
    preprocessed_text = preprocess_text(text)
    prediction = model.predict(preprocessed_text)
    probability = float(prediction[0][1])
    is_polarizing = bool(probability > 0.5)
    response = "Polariserend" if is_polarizing else "Niet polariserend"
    
    return {
        "Is Polarizing": is_polarizing,
        "Probability": f"{probability:.2%}",
        "Response": response
    }

# Crear la interfaz Gradio
iface = gr.Interface(
    fn=predict_polarization,
    inputs=gr.Textbox(lines=2, placeholder="Voer hier je Nederlandse tekst in..."),
    outputs=gr.JSON(),
    title="Dutch Text Polarization Detector",
    description="Voer een Nederlandse tekst in om te bepalen of deze polariserend is.",
    examples=[
        ["Dit is een neutrale zin."],
        ["Alle politici zijn leugenaars en dieven!"],
        ["Het weer is vandaag erg mooi."],
        ["Die groep mensen is de oorzaak van al onze problemen."]
    ]
)

# Lanzar la app
iface.launch()