Create app.py

app.py

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+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()