Update app.py

app.py

@@ -1,85 +1,54 @@
-# app.py
 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
 
-from tensorflow.keras.preprocessing.sequence import pad_sequences
-from tensorflow.keras.preprocessing.text import tokenizer_from_json
-
-# Load the Keras model architecture from model.json
-def load_model_architecture(model_json_path='model.json'):
-    with open(model_json_path, 'r', encoding='utf-8') as json_file:
-        model_json = json_file.read()
-    model = tf.keras.models.model_from_json(model_json)
-    return model
+# Cargar el modelo
+model = tf.keras.models.load_model('polarisatie_model.h5')
 
-# Load the model weights from polarisatie_model.h5
-def load_model_weights(model, weights_path='polarisatie_model.h5'):
-    model.load_weights(weights_path)
-    return model
+# Cargar el tokenizador
+with open('tokenizer.json', 'r') as f:
+    tokenizer_json = json.load(f)
+tokenizer = tokenizer_from_json(json.dumps(tokenizer_json))
 
-# 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
+# Cargar max_length
+with open('max_length.txt', 'r') as f:
+    max_length = int(f.read().strip())
 
-# 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
-
-# Preprocessing function
-def preprocess(text, tokenizer, max_length):
-    # Tokenize the text
-    sequences = tokenizer.texts_to_sequences([text])
-    # Pad the sequences
-    padded = pad_sequences(sequences, maxlen=max_length, padding='post', truncating='post')
+def preprocess_text(text):
+    sequence = tokenizer.texts_to_sequences([text])
+    padded = pad_sequences(sequence, maxlen=max_length)
     return padded
 
-# Prediction function
-def predict_polarization(text, model, tokenizer, max_length):
-    if not text.strip():
-        return "Voer alstublieft een geldige zin in."
-    
-    # Preprocess the text
-    processed_text = preprocess(text, tokenizer, max_length)
-    
-    # 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"
-
-# Load all components
-model = load_model_architecture()
-model = load_model_weights(model)
-tokenizer = load_tokenizer()
-max_length = load_max_length()
-
-# 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=lambda text: predict_polarization(text, model, tokenizer, max_length),
-        inputs=input_text,
-        outputs=result
-    )
-
-# Launch the Gradio app
-if __name__ == "__main__":
-    demo.launch()
+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()