Delete app.py

app.py

@@ -1,40 +0,0 @@
-import gradio as gr
-import tensorflow as tf
-from transformers import AutoTokenizer
-import numpy as np
-
-# Load model dan tokenizer dari Hugging Face Hub
-model = tf.keras.models.load_model("jeanetrixsiee/bert-sentimen-model")
-tokenizer = AutoTokenizer.from_pretrained("jeanetrixsiee/bert-sentimen-model")
-
-# Label kelas
-labels = ['Negative', 'Neutral', 'Positive', 'Very Negative', 'Very Positive']
-
-def predict_sentiment(text):
-    # Tokenisasi
-    inputs = tokenizer(text, return_tensors="tf", padding=True, truncation=True, max_length=256)
-    
-    # Prediksi
-    outputs = model(inputs)  # TensorFlow model output
-    
-    # Kalau model output pakai logits, gunakan softmax
-    if hasattr(outputs, "logits"):
-        probs = tf.nn.softmax(outputs.logits, axis=1)
-    else:
-        probs = tf.nn.softmax(outputs, axis=1)
-    
-    # Konversi ke dictionary
-    return {labels[i]: float(probs[0][i]) for i in range(len(labels))}
-
-# Gradio UI
-demo = gr.Interface(
-    fn=predict_sentiment,
-    inputs=gr.Textbox(lines=3, placeholder="Tulis komentar di sini..."),
-    outputs=gr.Label(num_top_classes=3),
-    title="Demo Sentimen BERT Bahasa Inggris",
-    description="Prediksi sentimen komentar menggunakan BERT base (TensorFlow). Kategori: Very Negative, Negative, Neutral, Positive, Very Positive",
-)
-
-# Jalankan
-if __name__ == "__main__":
-    demo.launch()