update

app.py

@@ -0,0 +1,77 @@
+import gradio as gr
+import random
+from PIL import Image
+
+class GreggRecognitionDemo:
+    def __init__(self):
+        print("🚀 Initializing Gregg Shorthand Recognition Demo")
+        
+    def recognize_shorthand(self, image):
+        """Simulate shorthand recognition for demo purposes"""
+        if image is None:
+            return "Please upload an image to begin recognition.", None
+        
+        try:
+            # Demo recognition results
+            demo_results = [
+                "wonderful day",
+                "excellent work", 
+                "shorthand notation",
+                "beautiful writing",
+                "stenography practice",
+                "business correspondence",
+                "court reporting",
+                "note taking system"
+            ]
+            
+            # Simulate processing
+            result = random.choice(demo_results)
+            confidence = random.uniform(0.75, 0.95)
+            
+            # 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)
+            
+            formatted_result = f"**Recognized Text:** {result}\n**Confidence:** {confidence:.1%}"
+            
+            return formatted_result, display_image
+            
+        except Exception as e:
+            return f"Error processing image: {str(e)}", image
+
+# Initialize demo
+demo_model = GreggRecognitionDemo()
+
+def process_image(image):
+    """Process uploaded image"""
+    return demo_model.recognize_shorthand(image)
+
+# Create simple interface
+demo = gr.Interface(
+    fn=process_image,
+    inputs=gr.Image(type="pil", label="Upload Gregg Shorthand Image"),
+    outputs=[
+        gr.Textbox(label="Recognition Result", lines=3),
+        gr.Image(label="Processed Image")
+    ],
+    title="🖋️ Gregg Shorthand Recognition",
+    description="Upload an image of Gregg shorthand notation to convert it to readable text!",
+    article="""
+    ### About This Demo
+    This is a demonstration of Gregg shorthand recognition using AI. 
+    Gregg shorthand was a popular stenographic writing system used for over a century.
+    
+    **Tips for best results:**
+    - Use clear, high-contrast images
+    - Ensure good lighting
+    - Crop to focus on the shorthand text
+    
+    For more information, visit the [GitHub repository](https://github.com/a0a7/GreggRecognition).
+    """,
+    examples=None,
+    cache_examples=False
+)
+
+if __name__ == "__main__":
+    demo.launch()

app_simple.py

@@ -1,77 +0,0 @@
-import gradio as gr
-import random
-from PIL import Image
-
-class GreggRecognitionDemo:
-    def __init__(self):
-        print("🚀 Initializing Gregg Shorthand Recognition Demo")
-        
-    def recognize_shorthand(self, image):
-        """Simulate shorthand recognition for demo purposes"""
-        if image is None:
-            return "Please upload an image to begin recognition.", None
-        
-        try:
-            # Demo recognition results
-            demo_results = [
-                "wonderful day",
-                "excellent work", 
-                "shorthand notation",
-                "beautiful writing",
-                "stenography practice",
-                "business correspondence",
-                "court reporting",
-                "note taking system"
-            ]
-            
-            # Simulate processing
-            result = random.choice(demo_results)
-            confidence = random.uniform(0.75, 0.95)
-            
-            # 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)
-            
-            formatted_result = f"**Recognized Text:** {result}\n**Confidence:** {confidence:.1%}"
-            
-            return formatted_result, display_image
-            
-        except Exception as e:
-            return f"Error processing image: {str(e)}", image
-
-# Initialize demo
-demo_model = GreggRecognitionDemo()
-
-def process_image(image):
-    """Process uploaded image"""
-    return demo_model.recognize_shorthand(image)
-
-# Create simple interface
-demo = gr.Interface(
-    fn=process_image,
-    inputs=gr.Image(type="pil", label="Upload Gregg Shorthand Image"),
-    outputs=[
-        gr.Textbox(label="Recognition Result", lines=3),
-        gr.Image(label="Processed Image")
-    ],
-    title="🖋️ Gregg Shorthand Recognition",
-    description="Upload an image of Gregg shorthand notation to convert it to readable text!",
-    article="""
-    ### About This Demo
-    This is a demonstration of Gregg shorthand recognition using AI. 
-    Gregg shorthand was a popular stenographic writing system used for over a century.
-    
-    **Tips for best results:**
-    - Use clear, high-contrast images
-    - Ensure good lighting
-    - Crop to focus on the shorthand text
-    
-    For more information, visit the [GitHub repository](https://github.com/a0a7/GreggRecognition).
-    """,
-    examples=None,
-    cache_examples=False
-)
-
-if __name__ == "__main__":
-    demo.launch()