Create app.py

app.py

@@ -0,0 +1,90 @@
+import gradio as gr
+import numpy as np
+import tempfile
+import os
+from PIL import Image
+import sys
+
+# Import your modules (you'll need to include them in the space)
+from strings import *
+
+def process_string_art(image, n_hooks=180, radius=250, quantization=30):
+    """Process uploaded image and return string art result"""
+    
+    # Save uploaded image temporarily
+    with tempfile.NamedTemporaryFile(suffix='.png', delete=False) as tmp_input:
+        image.save(tmp_input.name)
+        input_path = tmp_input.name
+    
+    # Create temporary output prefix
+    output_prefix = tempfile.mktemp()
+    
+    try:
+        # Build adjacency matrix
+        sparse, hooks, edge_codes = build_arc_adjecency_matrix(n_hooks, radius)
+        
+        # Process image
+        shrinkage = 0.75
+        img = image_from_pil(image, int(radius * 2 * shrinkage))
+        sparse_b = build_image_vector(img, radius)
+        
+        # Solve linear system
+        result = scipy.sparse.linalg.lsqr(sparse, np.array(sparse_b.todense()).flatten())
+        x = result[0]
+        
+        # Apply quantization
+        x = np.clip(x, 0, 1e6)
+        max_edge_weight_orig = np.max(x)
+        x_quantized = (x / np.max(x) * quantization).round()
+        clip_factor = 0.3
+        x_quantized = np.clip(x_quantized, 0, int(np.max(x_quantized) * clip_factor))
+        x = x_quantized / quantization * max_edge_weight_orig
+        
+        # Reconstruct final image
+        brightness_correction = 1.2
+        final_image = reconstruct(x * brightness_correction, sparse, radius)
+        
+        # Calculate statistics
+        arc_count = int(np.sum(x_quantized))
+        unique_arcs = len(x_quantized[x_quantized > 0])
+        
+        # Convert to PIL Image for return
+        final_pil = Image.fromarray(np.clip(final_image, 0, 255).astype(np.uint8))
+        
+        stats = f"Total arcs: {arc_count}\nUnique arc types: {unique_arcs}"
+        
+        return final_pil, stats
+        
+    finally:
+        # Cleanup
+        if os.path.exists(input_path):
+            os.unlink(input_path)
+
+def image_from_pil(pil_image, size):
+    """Convert PIL image to grayscale numpy array"""
+    img = pil_image.convert('L')  # Convert to grayscale
+    img = img.resize((size, size), Image.Resampling.LANCZOS)
+    return np.array(img)
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=process_string_art,
+    inputs=[
+        gr.Image(type="pil", label="Upload Image"),
+        gr.Slider(50, 360, value=180, step=10, label="Number of Hooks"),
+        gr.Slider(100, 500, value=250, step=50, label="Circle Radius"),
+        gr.Slider(10, 100, value=30, step=5, label="Quantization Level")
+    ],
+    outputs=[
+        gr.Image(type="pil", label="String Art Result"),
+        gr.Textbox(label="Statistics")
+    ],
+    title="String Art Generator",
+    description="Convert any image into string art patterns! Upload a square image and adjust parameters.",
+    examples=[
+        # You can add example images here
+    ]
+)
+
+if __name__ == "__main__":
+    iface.launch()