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app.py

@@ -1,29 +1,40 @@
 
 import gradio as gr
 from transformers import T5ForConditionalGeneration, T5Tokenizer
+import os
 
-# Load model
-print("Loading model...")
-model = T5ForConditionalGeneration.from_pretrained("younsmirzai/stackadvisor-extractor")
-tokenizer = T5Tokenizer.from_pretrained("younsmirzai/stackadvisor-extractor")
-print("✅ Model loaded!")
+# Global variables
+model = None
+tokenizer = None
+
+def load_model():
+    global model, tokenizer
+    if model is None:
+        print("Loading model...")
+        tokenizer = T5Tokenizer.from_pretrained("younsmirzai/stackadvisor-extractor")
+        model = T5ForConditionalGeneration.from_pretrained("younsmirzai/stackadvisor-extractor")
+        print("✅ Model loaded!")
+    return model, tokenizer
 
 def extract_order(ocr_text):
-    if not ocr_text.strip():
+    if not ocr_text or not ocr_text.strip():
         return "Error: Empty input"
     
-    inputs = tokenizer(ocr_text, return_tensors="pt", max_length=256, truncation=True)
-    outputs = model.generate(**inputs, max_length=128)
-    result = tokenizer.decode(outputs[0], skip_special_tokens=True)
-    return result
+    try:
+        m, t = load_model()
+        inputs = t(ocr_text, return_tensors="pt", max_length=256, truncation=True)
+        outputs = m.generate(**inputs, max_length=128)
+        result = t.decode(outputs[0], skip_special_tokens=True)
+        return result
+    except Exception as e:
+        return f"Error: {str(e)}"
 
-# Create Gradio interface
+# Simple interface
 demo = gr.Interface(
     fn=extract_order,
-    inputs=gr.Textbox(label="OCR Text", lines=5, placeholder="Paste OCR text here..."),
-    outputs=gr.Textbox(label="Extracted Order"),
-    title="StackAdvisor Order Extractor",
-    description="Extract delivery order info from OCR text"
+    inputs=gr.Textbox(label="OCR Text", lines=3),
+    outputs=gr.Textbox(label="Result"),
+    title="StackAdvisor",
 )
 
 demo.launch()