update app.py to remove @gradio_cached function or whatever it's called idc anymore

app.py

@@ -1,23 +1,38 @@
-# app.py for Gradio
+# app.py for Gradio with PEFT
 import gradio as gr
 import torch
 from transformers import T5Tokenizer, T5ForConditionalGeneration
+from peft import PeftModel, PeftConfig
 
-@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")
+# Load model once at startup
+model = None
+tokenizer = None
+
+def load_model_once():
+    global model, tokenizer
+    if model is None:
+        # Load base model
+        base_model_name = "cahya/t5-base-indonesian-summarization"
+        tokenizer = T5Tokenizer.from_pretrained(base_model_name)
+        base_model = T5ForConditionalGeneration.from_pretrained(
+            base_model_name,
+            load_in_8bit=True,  # Quantize for CPU efficiency
+            device_map="auto"
+        )
+        
+        # Load your trained PEFT adapters
+        # Replace with your actual PEFT adapter path
+        model = PeftModel.from_pretrained(
+            base_model, 
+            "./path-to-your-adapter"  # Upload your wandb artifact files here
+        )
     return model, tokenizer
 
 def summarize_text(text):
     if not text.strip():
         return "Please enter text to summarize."
     
-    model, tokenizer = load_model()
+    model, tokenizer = load_model_once()
     
     # Add T5 prefix
     input_text = f"summarize: {text}"
@@ -42,10 +57,10 @@ interface = gr.Interface(
     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",
+    description="Enter Indonesian text to generate a summary using T5 model with PEFT adapters",
     examples=[
         ["Your example Indonesian text here..."]
     ]
 )
 
-interface.launch()
+interface.launch()