fix space

README.md

@@ -24,20 +24,9 @@ models:
 
 This is an interactive demo for recognizing Gregg shorthand notation from images.
 
-## Features
-
-- Upload images containing Gregg shorthand
-- Real-time text recognition
-- Confidence scoring
-- Support for various image formats
-- Historical document processing
-
 ## How to Use
 
-1. Upload an image containing Gregg shorthand notation
-2. Adjust the confidence threshold if needed
-3. Click "Recognize" to process the image
-4. View the recognized text and confidence score
+Upload an image containing Gregg shorthand notation and submit
 
 ## Model Information
 
@@ -48,17 +37,6 @@ This demo uses the Gregg Recognition model trained specifically for Gregg shorth
 - Advanced pattern recognition techniques
 - Specialized preprocessing for shorthand symbols
 
-## About Gregg Shorthand
-
-Gregg shorthand is a system of stenography invented by John Robert Gregg in 1888. It was widely used for:
-
-- Court reporting
-- Business correspondence
-- Note-taking
-- Administrative documentation
-
-This model helps digitize historical shorthand documents and assists in stenography education.
-
 ## Technical Details
 
 - **Model Type**: Image-to-Text Recognition

app.py

@@ -5,20 +5,26 @@ 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:
+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")
+        print("model loaded successfully")
     except Exception as e:
-        print(f"❌ Error loading model: {e}")
+        print(f"Error loading model: {e}")
+        import traceback
+        traceback.print_exc()
         MODEL_AVAILABLE = False
         recognizer = None
 else:
@@ -35,20 +41,36 @@ def recognize_image(image):
         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:
-                image.save(tmp_file.name)
+                tmp_path = tmp_file.name
                 
+            # Save image outside the context manager
+            image.save(tmp_path)
+            
+            try:
                 # Run recognition
-                result = recognizer.recognize(tmp_file.name)
-                
-                # Clean up
-                os.unlink(tmp_file.name)
-                
+                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 = [
@@ -65,20 +87,21 @@ def recognize_image(image):
             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(), gr.Image()],
+    outputs=[gr.Textbox()],
     title="Gregg Shorthand Recognition",
-    description="Upload an image of Gregg shorthand notation to convert it to readable text using our specialized AI model!"
+    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")
+        print(f"Model Type: image_to_text")
+        print(f"Device: cpu")
     demo.launch()