Update app.py

app.py

@@ -1,63 +1,218 @@
 import gradio as gr
 import os
+import threading
+import subprocess
+import time
 from huggingface_hub import hf_hub_download
-
-# 1. Import your model logic directly, not via HTTP
 from handwriting_api import InputData, validate_input
 from hand import Hand
 
+# Create img directory if it doesn't exist
+os.makedirs("img", exist_ok=True)
 
-# 2. Initialize your model once
+# Initialize the handwriting model
 hand = Hand()
 
-# 3. Directly call the model logic in the Gradio function
-def generate_handwriting_interface(text: str, style: int) -> str:
-    lines = text.split('\n')
-    stroke_colors = ["#000000"] * len(lines)
-    stroke_widths = [2] * len(lines)
-    
-    data = InputData(
-        text=text,
-        style=style,
-        bias=0.75,
-        stroke_colors=stroke_colors,
-        stroke_widths=stroke_widths
-    )
-    
-    # Validate the input
+# Create a function to generate handwriting
+def generate_handwriting(
+    text, 
+    style, 
+    bias=0.75, 
+    color="#000000",
+    stroke_width=2,
+    multiline=True
+):
+    """Generate handwritten text using the model"""
+    try:
+        # Process the text
+        if multiline:
+            lines = text.split('\n')
+        else:
+            lines = [text]
+            
+        # Create arrays for parameters
+        stroke_colors = [color] * len(lines)
+        stroke_widths = [stroke_width] * len(lines)
+        biases = [bias] * len(lines)
+        styles = [style] * len(lines)
+        
+        # Input validation
+        for line_num, line in enumerate(lines):
+            if len(line) > 75:
+                return f"Error: Line {line_num+1} is too long (max 75 characters)"
+        
+        data = InputData(
+            text=text,
+            style=style,
+            bias=bias,
+            stroke_colors=stroke_colors,
+            stroke_widths=stroke_widths
+        )
+        
+        try:
+            validate_input(data)
+        except ValueError as e:
+            return f"Error: {str(e)}"
+        
+        # Generate the handwriting
+        hand.write(
+            filename='img/output.svg',
+            lines=lines,
+            biases=biases,
+            styles=styles,
+            stroke_colors=stroke_colors,
+            stroke_widths=stroke_widths
+        )
+        
+        # Read the generated SVG
+        with open("img/output.svg", "r") as f:
+            svg_content = f.read()
+            
+        return svg_content
+    except Exception as e:
+        return f"Error: {str(e)}"
 
-    # Generate the handwriting
-    lines = data.text.split('\n')
-    biases = [data.bias] * len(lines)
-    styles = [data.style] * len(lines)
+def export_to_png(svg_content):
+    """Convert SVG to PNG using CairoSVG"""
+    try:
+        import cairosvg
+        
+        if not svg_content or svg_content.startswith("Error:"):
+            return None
+            
+        # Save SVG to a temporary file
+        with open("img/temp.svg", "w") as f:
+            f.write(svg_content)
+            
+        # Convert SVG to PNG
+        cairosvg.svg2png(url="img/temp.svg", write_to="img/output.png", scale=2.0)
+        
+        return "img/output.png"
+    except Exception as e:
+        return None
 
-    hand.write(
-        filename='img/output.svg',
-        lines=lines,
-        biases=biases,
-        styles=styles,
-        stroke_colors=data.stroke_colors,
-        stroke_widths=data.stroke_widths
-    )
+def generate_lyrics_sample():
+    """Generate a sample using lyrics"""
+    from lyrics import all_star
+    return all_star.split("\n")[0:4]
+    
+def generate_handwriting_wrapper(
+    text, 
+    style, 
+    bias, 
+    color, 
+    stroke_width,
+    multiline=True
+):
+    svg = generate_handwriting(text, style, bias, color, stroke_width, multiline)
+    return svg
 
-    # Return the SVG contents
-    with open("img/output.svg", "r") as f:
-        return f.read()
+css = """
+.container {max-width: 900px; margin: auto;}
+.output-container {min-height: 300px;}
+.gr-box {border-radius: 8px; box-shadow: 0 4px 6px rgba(0,0,0,0.1);}
+.footer {text-align: center; margin-top: 20px; font-size: 0.8em; color: #666;}
+"""
 
-# 4. Set up the Gradio UI
-with gr.Blocks() as demo:
-    gr.Markdown("# Handwriting Synthesis")
+with gr.Blocks(css=css) as demo:
+    gr.Markdown("# 🖋️ Handwriting Synthesis")
+    gr.Markdown("Generate realistic handwritten text using neural networks. Based on Alex Graves' [paper](https://arxiv.org/abs/1308.0850).")
+    
     with gr.Row():
-        text_input = gr.Textbox(label="Input Text", lines=3)
-        style_select = gr.Slider(minimum=0, maximum=12, step=1, label="Style", value=0)
+        with gr.Column(scale=2):
+            text_input = gr.Textbox(
+                label="Text Input",
+                placeholder="Enter text to convert to handwriting...",
+                lines=5,
+                max_lines=10,
+            )
+            
+            with gr.Row():
+                with gr.Column(scale=1):
+                    style_select = gr.Slider(
+                        minimum=0, 
+                        maximum=12, 
+                        step=1, 
+                        value=9, 
+                        label="Handwriting Style"
+                    )
+                with gr.Column(scale=1):
+                    bias_slider = gr.Slider(
+                        minimum=0.5, 
+                        maximum=1.0, 
+                        step=0.05, 
+                        value=0.75, 
+                        label="Neatness (Higher = Neater)"
+                    )
+            
+            with gr.Row():
+                with gr.Column(scale=1):
+                    color_picker = gr.ColorPicker(
+                        label="Ink Color", 
+                        value="#000000"
+                    )
+                with gr.Column(scale=1):
+                    stroke_width = gr.Slider(
+                        minimum=1, 
+                        maximum=4, 
+                        step=0.5, 
+                        value=2, 
+                        label="Stroke Width"
+                    )
+            
+            with gr.Row():
+                generate_btn = gr.Button("Generate Handwriting", variant="primary")
+                clear_btn = gr.Button("Clear")
+            
+            with gr.Accordion("Examples", open=False):
+                sample_btn = gr.Button("Insert Sample Text")
+            
+        with gr.Column(scale=3):
+            output_svg = gr.HTML(label="Generated Handwriting", elem_classes=["output-container"])
+            
+            with gr.Row():
+                download_btn = gr.Button("Download SVG")
     
-    output_svg = gr.HTML(label="Generated Handwriting")
-    generate_button = gr.Button("Generate")
-
-    generate_button.click(
-        fn=generate_handwriting_interface,
-        inputs=[text_input, style_select],
-        outputs=output_svg
+    gr.Markdown("""
+    ### Tips:
+    - Try different styles (0-12) to get various handwriting appearances
+    - Adjust the neatness slider to make writing more or less tidy
+    - Each line should be 75 characters or less
+    - The model works best for English text
+    """)
+    
+    gr.Markdown("""
+    <div class="footer">
+    Created with Gradio • Model based on Alex Graves' <a href="https://arxiv.org/abs/1308.0850">Handwriting Synthesis paper</a>
+    </div>
+    """)
+    
+    # Define interactions
+    generate_btn.click(
+        fn=generate_handwriting_wrapper,
+        inputs=[text_input, style_select, bias_slider, color_picker, stroke_width],
+        outputs=[output_svg]
+    )
+    
+    clear_btn.click(
+        fn=lambda: ("", 9, 0.75, "#000000", 2),
+        inputs=None,
+        outputs=[text_input, style_select, bias_slider, color_picker, stroke_width]
+    )
+    
+    sample_btn.click(
+        fn=lambda: ("\n".join(generate_lyrics_sample())),
+        inputs=None,
+        outputs=[text_input]
+    )
+    
+    download_btn.click(
+        fn=lambda x: x,
+        inputs=[output_svg],
+        outputs=[gr.File(label="Download SVG", file_count="single", file_types=[".svg"])]
     )
 
-demo.launch(server_name="0.0.0.0", server_port=7860)
+if __name__ == "__main__":
+    # Set port based on environment variable or default to 7860
+    port = int(os.environ.get("PORT", 7860))
+    demo.launch(server_name="0.0.0.0", server_port=port)