Spaces:
Sleeping
Sleeping
| import gradio as gr | |
| from transformers import T5ForConditionalGeneration, T5Tokenizer | |
| import os | |
| # 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 or not ocr_text.strip(): | |
| return "Error: Empty input" | |
| 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)}" | |
| # Simple interface | |
| demo = gr.Interface( | |
| fn=extract_order, | |
| inputs=gr.Textbox(label="OCR Text", lines=3), | |
| outputs=gr.Textbox(label="Result"), | |
| title="StackAdvisor", | |
| ) | |
| demo.launch() | |