create app.py

app.py

@@ -0,0 +1,51 @@
+# app.py for Gradio
+import gradio as gr
+import torch
+from transformers import T5Tokenizer, T5ForConditionalGeneration
+
+@gradio_cached_function
+def load_model():
+    model = T5ForConditionalGeneration.from_pretrained(
+        "cahya/t5-base-indonesian-summarization",
+        load_in_8bit=True,
+        device_map="auto"
+    )
+    tokenizer = T5Tokenizer.from_pretrained("cahya/t5-base-indonesian-summarization")
+    return model, tokenizer
+
+def summarize_text(text):
+    if not text.strip():
+        return "Please enter text to summarize."
+    
+    model, tokenizer = load_model()
+    
+    # Add T5 prefix
+    input_text = f"summarize: {text}"
+    inputs = tokenizer(input_text, return_tensors="pt", max_length=1024, truncation=True)
+    
+    with torch.no_grad():
+        outputs = model.generate(
+            **inputs,
+            max_length=512,
+            num_beams=4,
+            length_penalty=2.0,
+            early_stopping=True,
+            no_repeat_ngram_size=2
+        )
+    
+    summary = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return summary
+
+# Create Gradio interface
+interface = gr.Interface(
+    fn=summarize_text,
+    inputs=gr.Textbox(lines=10, placeholder="Enter Indonesian text here...", label="Input Text"),
+    outputs=gr.Textbox(label="Generated Summary"),
+    title="Indonesian Text Summarization",
+    description="Enter Indonesian text to generate a summary using T5 model",
+    examples=[
+        ["Your example Indonesian text here..."]
+    ]
+)
+
+interface.launch()