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model.py

@@ -1,19 +1,24 @@
 
-import torch
 import base64
-from PIL import Image, ImageDraw
 import io
+from PIL import Image, ImageDraw
+import json
 
 class VectorGraphicsModel:
-    def __init__(self):
-        self.device = "cuda" if torch.cuda.is_available() else "cpu"
+    def __init__(self, config=None):
+        self.config = config or {}
+        self.initialized = False
+        
+    def initialize(self):
+        """Initialize the model."""
+        self.initialized = True
         
-    def __call__(self, prompt, negative_prompt="", num_paths=96, guidance_scale=7.5, seed=42):
-        """Generate vector graphics from a text prompt."""
+    def generate(self, prompt, **kwargs):
+        """Generate vector graphics from a prompt."""
         # This is a placeholder implementation
         # In a real scenario, this would call the actual model
         
-        # Generate a simple SVG
+        # Create a simple SVG based on the prompt
         svg = f"""
         <svg xmlns="http://www.w3.org/2000/svg" width="512" height="512" viewBox="0 0 512 512">
             <rect width="512" height="512" fill="#f0f0f0"/>
@@ -34,13 +39,13 @@ class VectorGraphicsModel:
         draw.line((156, 156, 356, 356), fill="#2c3e50", width=5)
         draw.line((156, 356, 356, 156), fill="#2c3e50", width=5)
         
+        # Add text
+        # Note: This would require a font file, which we don't have in this example
+        # draw.text((256, 50), f'Generated from: "{prompt}"', fill="#333")
+        
         # Convert image to base64
         buffered = io.BytesIO()
         img.save(buffered, format="PNG")
         img_str = base64.b64encode(buffered.getvalue()).decode()
         
         return {"svg": svg, "image": img_str}
-
-def pipeline():
-    """Return the model pipeline."""
-    return VectorGraphicsModel()