add application file

app.py

@@ -0,0 +1,45 @@
+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()