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import gradio as gr
from transformers import pipeline
custom_pipe = pipeline("text2text-generation", model="arifagustyawan/flan-t5-base-sentiment-product-review")
def genrate_sentiment(text, max_new_tokens, num_beams):
return custom_pipe(text, max_new_tokens=max_new_tokens, num_beams=int(num_beams))
with gr.Blocks() as demo:
gr.Markdown(
"""
# Product Review - Sentiment Analysis
Generate sentiment analysis and its reason based on product reviews using FLAN-T5 base model.
-----
""")
with gr.Row():
with gr.Accordion("Parameters!", open = False):
max_new_tokens = gr.Number(value=50, minimum=1, maximum=500, step=1, label="Max New Tokens")
num_beams = gr.Number(value=2, minimum=1, maximum=10, step=1, label="Num Beams")
with gr.Row():
text = gr.Textbox(lines=5, label="Product Review", value="Give sentiment and its reason: Kualitas produknya sangat rendah. Saya tidak merekomendasikan untuk pembelian.")
output = gr.Textbox(lines=5, label="Sentiment Analysis")
with gr.Row():
btn = gr.Button(value="Process")
btn.click(genrate_sentiment, inputs=[text, max_new_tokens, num_beams], outputs=[output])
if __name__ == "__main__":
demo.launch() |