Merge branch 'main' of https://huggingface.co/spaces/a0a7/gregg-recognition

app.py

@@ -2,54 +2,43 @@ import gradio as gr
 import random
 from PIL import Image
 
-class GreggRecognitionDemo:
-    def __init__(self):
-        print("🚀 Initializing Gregg Shorthand Recognition Demo")
+# Create simple interface
+def recognize_image(image):
+    """Main function for the Gradio interface"""
+    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"
+        ]
         
-    def recognize_shorthand(self, image):
-        """Simulate shorthand recognition for demo purposes"""
-        if image is None:
-            return "Please upload an image to begin recognition.", None
+        # Simulate processing
+        result = random.choice(demo_results)
+        confidence = random.uniform(0.75, 0.95)
         
-        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)
+        # 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
 
-# Create simple interface
 demo = gr.Interface(
-    fn=process_image,
+    fn=recognize_image,
     inputs=gr.Image(type="pil", label="Upload Gregg Shorthand Image"),
     outputs=[
         gr.Textbox(label="Recognition Result", lines=3),