app.py

import gradio as gr
import tensorflow as tf
import numpy as np

# 1. Load model TFLite (Pastikan nama file sesuai dengan yang Anda upload)
# Jika file model Anda bernama lain, ganti "model.tflite" di bawah ini
try:
    interpreter = tf.lite.Interpreter(model_path="tiny_sentiment_model_imdb.tflite")
    interpreter.allocate_tensors()
except Exception as e:
    print(f"Error loading model: {e}")

def predict_sentiment(text):
    # Logika inferensi (Sederhana sebagai contoh)
    # Catatan: Anda perlu menambahkan tokenizer di sini agar teks bisa dibaca model
    try:
        input_details = interpreter.get_input_details()
        output_details = interpreter.get_output_details()
        
        # Placeholder: Proses input text ke tensor di sini
        # interpreter.set_tensor(input_details[0]['index'], input_data)
        
        interpreter.invoke()
        output_data = interpreter.get_tensor(output_details[0]['index'])
        
        # Contoh logika output (sesuaikan dengan output model Anda)
        prediction = output_data[0][0] 
        return "Positive" if prediction > 0.5 else "Negative"
    except Exception as e:
        return f"Error saat prediksi: {str(e)}"

# 2. UI Gradio (Tanpa argumen 'allow_flagging' yang error)
demo = gr.Interface(
    fn=predict_sentiment,
    inputs=gr.Textbox(label="Masukkan Kalimat", placeholder="Ketik di sini..."),
    outputs=gr.Textbox(label="Hasil Analisis"),
    title="Sentimen Analisis TFLite",
    flagging_mode="never"  # Pengganti allow_flagging="never"
)

if __name__ == "__main__":
    demo.launch()