Simplify app.py to fix API error - use gr.Interface instead of gr.Blocks

README.md

@@ -4,7 +4,7 @@ emoji: 🖋️
 colorFrom: blue
 colorTo: purple
 sdk: gradio
-sdk_version: 4.44.0
+sdk_version: 4.36.0
 app_file: app.py
 pinned: false
 license: mit

app.py

@@ -1,251 +0,0 @@
-import gradio as gr
-import torch
-from PIL import Image
-import numpy as np
-import random
-
-# Simplified demo for Hugging Face Spaces
-class GreggRecognitionDemo:
-    def __init__(self):
-        print("🚀 Initializing Gregg Shorthand Recognition Demo")
-        # For the Space demo, we'll use simulated recognition
-        # In a real deployment, you'd load your actual model here
-        
-    def recognize_shorthand(self, image, confidence_threshold=0.5):
-        """Simulate shorthand recognition for demo purposes"""
-        if image is None:
-            return "Please upload an image", 0.0, None
-        
-        try:
-            # Resize for display
-            display_image = image.copy()
-            if display_image.size[0] > 800 or display_image.size[1] > 600:
-                display_image.thumbnail((800, 600), Image.Resampling.LANCZOS)
-            
-            # Demo recognition results
-            demo_results = [
-                ("voluptuous", 0.92),
-                ("beautiful writing", 0.88),
-                ("wonderful day", 0.85),
-                ("excellent work", 0.90),
-                ("shorthand notation", 0.87),
-                ("recognition successful", 0.91),
-                ("artificial intelligence", 0.89),
-                ("machine learning model", 0.86),
-                ("stenography practice", 0.84),
-                ("historical document", 0.83),
-                ("business correspondence", 0.81),
-                ("court reporting", 0.89),
-                ("note taking system", 0.86),
-                ("administrative record", 0.82)
-            ]
-            
-            # Simulate processing based on image characteristics
-            # This is just for demo - replace with actual model inference
-            result, confidence = random.choice(demo_results)
-            
-            # Adjust confidence based on threshold
-            if confidence < confidence_threshold:
-                return f"Low confidence: {result}", confidence, display_image
-            
-            return result, confidence, display_image
-            
-        except Exception as e:
-            return f"Error: {str(e)}", 0.0, image
-
-# Initialize demo
-demo_model = GreggRecognitionDemo()
-
-def process_image(image, confidence_threshold):
-    """Process uploaded image"""
-    text, confidence, processed_img = demo_model.recognize_shorthand(image, confidence_threshold)
-    
-    if confidence > 0:
-        result_text = f"**📝 Recognized Text:**\n\n{text}\n\n**🎯 Confidence:** {confidence:.1%}"
-    else:
-        result_text = text
-    
-    return result_text, processed_img
-
-# Create the Gradio interface
-with gr.Blocks(
-    title="🖋️ Gregg Shorthand Recognition",
-    theme=gr.themes.Soft(),
-) as demo:
-    
-    gr.HTML("""
-    <div style="text-align: center; margin-bottom: 2rem;">
-        <h1>🖋️ Gregg Shorthand Recognition</h1>
-        <p style="font-size: 1.1em;">Upload an image of Gregg shorthand notation to convert it to readable text!</p>
-        <p><em>Specialized AI model for historical stenography recognition</em></p>
-    </div>
-    """)
-    
-    with gr.Row():
-        with gr.Column(scale=1):
-            gr.HTML("<h3>📤 Upload Image</h3>")
-            
-            image_input = gr.Image(
-                label="Shorthand Image",
-                type="pil",
-                height=350
-            )
-            
-            confidence_slider = gr.Slider(
-                minimum=0.0,
-                maximum=1.0,
-                value=0.5,
-                step=0.05,
-                label="Confidence Threshold",
-                info="Minimum confidence for text recognition"
-            )
-            
-            with gr.Row():
-                clear_btn = gr.Button("🗑️ Clear", variant="secondary")
-                process_btn = gr.Button("🔍 Recognize Text", variant="primary")
-        
-        with gr.Column(scale=1):
-            gr.HTML("<h3>📋 Recognition Results</h3>")
-            
-            result_output = gr.Markdown(
-                value="*Upload an image to see recognition results here...*"
-            )
-            
-            processed_image = gr.Image(
-                label="Processed Image",
-                type="pil",
-                height=350
-            )
-    
-    # Information panels
-    with gr.Accordion("ℹ️ About Gregg Shorthand", open=False):
-        gr.Markdown("""
-        ### What is Gregg Shorthand?
-        
-        Gregg shorthand is a phonetic writing system invented by **John Robert Gregg** in 1888. 
-        It was the most popular shorthand system in the English-speaking world for over a century.
-        
-        **Key Features:**
-        - **Phonetic**: Based on sounds rather than spelling
-        - **Cursive**: Written in flowing, connected strokes
-        - **Efficient**: Much faster than longhand writing
-        - **Geometric**: Uses circles, curves, and straight lines
-        
-        **Historical Uses:**
-        - Court reporting and legal documentation
-        - Business correspondence and meeting minutes
-        - Journalism and news reporting
-        - Personal note-taking and diary writing
-        - Administrative and government records
-        
-        **Why Digitize?**
-        - Preserve historical documents
-        - Make archives searchable
-        - Support stenography education
-        - Research historical communications
-        """)
-    
-    with gr.Accordion("🎯 How to Get Best Results", open=False):
-        gr.Markdown("""
-        ### Image Guidelines:
-        
-        **✅ Best Practices:**
-        - Use **high-resolution** images (300+ DPI)
-        - Ensure **good contrast** between ink and paper
-        - Crop images to focus on **shorthand text only**
-        - Keep text **right-side up** and **straight**
-        - Use **well-lit** photos without shadows
-        
-        **📱 Phone Camera Tips:**
-        - Hold steady and focus clearly
-        - Use good lighting (natural light works best)
-        - Avoid glare and reflections
-        - Fill the frame with the shorthand text
-        - Take multiple shots if needed
-        
-        **📄 Document Scanning:**
-        - Scan at 300 DPI or higher
-        - Use grayscale or color mode
-        - Ensure flat documents without curves
-        - Clean dust and marks if possible
-        
-        **⚙️ Confidence Threshold:**
-        - **Low (0.3-0.5)**: Shows more results, including uncertain ones
-        - **Medium (0.5-0.7)**: Balanced accuracy and coverage
-        - **High (0.7-1.0)**: Only high-confidence results
-        """)
-    
-    with gr.Accordion("🔧 Technical Information", open=False):
-        gr.Markdown("""
-        ### Model Architecture:
-        
-        This recognition system uses:
-        - **Convolutional Neural Networks (CNN)** for visual feature extraction
-        - **Long Short-Term Memory (LSTM)** networks for sequence modeling
-        - **Advanced pattern recognition** algorithms
-        - **Custom preprocessing** optimized for shorthand notation
-        
-        ### Model Specifications:
-        - **Input Size**: 256×256 pixels
-        - **Framework**: PyTorch
-        - **Training Data**: Specialized Gregg shorthand dataset
-        - **Preprocessing**: Grayscale conversion, normalization, noise reduction
-        
-        ### Performance Notes:
-        - Optimized specifically for Gregg shorthand notation
-        - Performance varies with image quality and clarity
-        - Best results with clear, high-contrast historical documents
-        - Continuous improvements through user feedback
-        
-        ### Integration Options:
-        
-        **Python Package:**
-        ```bash
-        pip install gregg-recognition
-        ```
-        
-        **Hugging Face Transformers:**
-        ```python
-        from transformers import pipeline
-        pipe = pipeline("image-to-text", model="a0a7/gregg-recognition")
-        ```
-        
-        **Command Line:**
-        ```bash
-        gregg-recognize image.jpg --verbose
-        ```
-        """)
-    
-    # Event handlers
-    process_btn.click(
-        fn=process_image,
-        inputs=[image_input, confidence_slider],
-        outputs=[result_output, processed_image]
-    )
-    
-    clear_btn.click(
-        fn=lambda: (None, "*Upload an image to see recognition results here...*", None),
-        outputs=[image_input, result_output, processed_image]
-    )
-    
-    image_input.change(
-        fn=process_image,
-        inputs=[image_input, confidence_slider],
-        outputs=[result_output, processed_image]
-    )
-    
-    # Footer
-    gr.HTML("""
-    <div style="text-align: center; margin-top: 2rem; padding: 1rem; border-top: 1px solid #ddd;">
-        <p>🔗 <strong>Links:</strong> 
-        <a href="https://huggingface.co/a0a7/gregg-recognition" target="_blank">Model</a> | 
-        <a href="https://github.com/a0a7/GreggRecognition" target="_blank">Source Code</a> | 
-        <a href="https://en.wikipedia.org/wiki/Gregg_shorthand" target="_blank">About Gregg Shorthand</a>
-        </p>
-        <p><em>Built with ❤️ for preserving stenographic heritage</em></p>
-    </div>
-    """)
-
-# Launch the demo
-if __name__ == "__main__":
-    demo.launch()

app_simple.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()

requirements.txt

@@ -1,7 +1,2 @@
-gradio==4.44.0
-torch>=1.9.0
-torchvision>=0.10.0
+gradio==4.36.0
 Pillow>=8.0.0
-numpy>=1.21.0
-huggingface_hub>=0.16.0
-transformers>=4.21.0