from transformers import pipeline
import gradio as gr

# Load your custom FLAN-T5 sentiment analysis pipeline
custom_pipe = pipeline("text-generation", model="arifagustyawan/flan-t5-base-sentiment-product-review")

# Define the header and subheader
header = "Sentiment Analysis"
subheader = "Generate sentiment analysis and its reason based on product reviews using your FLAN-T5 base model."

# Default text prompt
default_text_prompt = "Produk ini sangat bagus. Saya sangat puas dengan kualitasnya."

# Example texts in Bahasa Indonesia
example_texts = [
    "Give sentiment and its reason: Saya kecewa dengan layanan pelanggan mereka. Tidak responsif dan tidak membantu.",
    "Give sentiment and its reason: Pengalaman belanja online kali ini luar biasa. Barang sampai tepat waktu dan sesuai ekspektasi.",
    "Give sentiment and its reason: Kualitas produknya sangat rendah. Saya tidak merekomendasikan untuk pembelian.",
]

# Create Gradio interface with input parameters
iface = gr.Interface(
    fn=custom_pipe,
    inputs=[
        gr.Textbox(prompt="Give sentiment and its reason: ", default=default_text_prompt, lines=5),
        gr.Number("Max New Tokens", default=50, min=1, max=500, step=1),
        gr.Number("Num Beams", default=1, min=1, max=10, step=1),
        gr.Checkbox("Gunakan Temperature", default=False),
        gr.Number("Temperature", default=1.0, min=0.1, max=5.0, step=0.1),
    ],
    outputs=gr.Textbox(),
    live=True,
    title=header,
    description=subheader
)

# Set example texts for users to try
iface.set_examples([
    [example_texts[0]],
    [example_texts[1]],
    [example_texts[2]],
])

# Launch the Gradio interface
iface.launch()