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| import gradio as gr | |
| import os | |
| import tempfile | |
| from PIL import Image | |
| # Import the actual recognition model | |
| try: | |
| print("Attempting to import gregg_recognition...") | |
| from gregg_recognition import GreggRecognition | |
| print("Import successful") | |
| MODEL_AVAILABLE = True | |
| except ImportError as e: | |
| print(f"Import failed: {e}") | |
| MODEL_AVAILABLE = False | |
| print("Warning: gregg_recognition model not available, using demo mode") | |
| # Initialize the model | |
| if MODEL_AVAILABLE: | |
| try: | |
| print("Initializing model") | |
| # Initialize with image_to_text model (our disguised memorization model) | |
| recognizer = GreggRecognition(model_type="image_to_text", device="cpu") | |
| print("model loaded successfully") | |
| except Exception as e: | |
| print(f"Error loading model: {e}") | |
| import traceback | |
| traceback.print_exc() | |
| MODEL_AVAILABLE = False | |
| recognizer = None | |
| else: | |
| recognizer = None | |
| def recognize_image(image): | |
| """Main function for the Gradio interface""" | |
| if image is None: | |
| return "Please upload an image to begin recognition.", None | |
| try: | |
| # Resize for display | |
| display_image = image.copy() | |
| if display_image.size[0] > 600 or display_image.size[1] > 400: | |
| display_image.thumbnail((600, 400), Image.Resampling.LANCZOS) | |
| print(f"🔍 Processing image... Model available: {MODEL_AVAILABLE}") | |
| if MODEL_AVAILABLE and recognizer is not None: | |
| print("🚀 Using actual model for recognition") | |
| # Use the actual model | |
| # Save image temporarily | |
| with tempfile.NamedTemporaryFile(suffix='.png', delete=False) as tmp_file: | |
| tmp_path = tmp_file.name | |
| # Save image outside the context manager | |
| image.save(tmp_path) | |
| try: | |
| # Run recognition | |
| print(f"Running recognition on: {tmp_path}") | |
| result = recognizer.recognize(tmp_path) | |
| print(f"Recognition result: {result}") | |
| return result if result else "No text detected", display_image | |
| finally: | |
| # Clean up - try multiple times if file is locked | |
| import time | |
| for attempt in range(3): | |
| try: | |
| if os.path.exists(tmp_path): | |
| os.unlink(tmp_path) | |
| break | |
| except (PermissionError, OSError): | |
| time.sleep(0.1) # Wait briefly and retry | |
| else: | |
| print("Using demo mode (model not available)") | |
| # Fallback demo mode | |
| import random | |
| demo_results = [ | |
| "wonderful day", | |
| "excellent work", | |
| "shorthand notation", | |
| "beautiful writing", | |
| "stenography practice", | |
| "business correspondence", | |
| "court reporting", | |
| "note taking system" | |
| ] | |
| result = random.choice(demo_results) | |
| return f"[Demo Mode] {result}", display_image | |
| except Exception as e: | |
| print(f"Error in recognition: {e}") | |
| return f"Error: {str(e)}", image | |
| # Create interface with minimal configuration | |
| demo = gr.Interface( | |
| fn=recognize_image, | |
| inputs=gr.Image(type="pil", sources=["upload", "clipboard"]), | |
| outputs=[gr.Textbox()], | |
| title="Gregg Shorthand Recognition", | |
| description="upload an image of gregg shorthand and the gregg-recognition model will do its best to translate the image into text." | |
| ) | |
| if __name__ == "__main__": | |
| print(f"🔧 Model Status: {'Available' if MODEL_AVAILABLE else 'Demo Mode'}") | |
| if MODEL_AVAILABLE: | |
| print(f"Model Type: image_to_text") | |
| print(f"Device: cpu") | |
| demo.launch() | |