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app.py

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+
+import gradio as gr
+from transformers import T5ForConditionalGeneration, T5Tokenizer
+
+# Load model
+print("Loading model...")
+model = T5ForConditionalGeneration.from_pretrained("younsmirzai/stackadvisor-extractor")
+tokenizer = T5Tokenizer.from_pretrained("younsmirzai/stackadvisor-extractor")
+print("✅ Model loaded!")
+
+def extract_order(ocr_text):
+    if 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
+
+# Create Gradio 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"
+)
+
+demo.launch()