add process button

app.py

@@ -1,49 +1,27 @@
-from transformers import pipeline
 import gradio as gr
+from transformers import pipeline
 
-# 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 = "Give sentiment and its reason: 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=lambda text, max_new_tokens, num_beams, use_temperature, temperature:
-        custom_pipe(text, max_length=int(max_new_tokens) if max_new_tokens else 100, num_beams=int(num_beams) if num_beams else 2,
-                    temperature=float(temperature) if use_temperature else 1.0),
-    inputs=[
-        gr.Textbox(lines=5),
-        gr.Number(value=50, minimum=1, maximum=500, step=1, label="Max New Tokens"),
-        gr.Number(value=1, minimum=2, maximum=10, step=1, label="Num Beams"),
-        gr.Checkbox(value=False, label="Gunakan Temperature"),
-        gr.Number(value=1.0, minimum=0.1, maximum=5.0, step=0.1, label="Temperature"),
-        gr.Button("Proses", click=lambda: None),
-    ],
-    outputs=gr.Textbox(),
-    live=True,
-    title=header,
-    description=subheader,
-    examples=[[text] for text in example_texts]
-)
+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.
+    -----
+    """)
 
-# # Set example texts for users to try
-# iface.set_examples([
-#     [example_texts[0]],
-#     [example_texts[1]],
-#     [example_texts[2]],
-# ])
+    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(custom_pipe, inputs=[text, max_new_tokens, num_beams], outputs=[output])
 
-# Launch the Gradio interface
-iface.launch()
+if __name__ == "__main__":
+    demo.launch()