Update handler to return PIL Images for Inference API compatibility

config.json

@@ -3,7 +3,7 @@
   "model_type": "svgdreamer",
   "task": "text-to-svg",
   "framework": "pytorch",
-  "pipeline_tag": "text-generation",
+  "pipeline_tag": "text-to-image",
   "library_name": "diffusers",
   "inference": {
     "parameters": {

handler.py

@@ -4,6 +4,9 @@ import json
 import torch
 import numpy as np
 from typing import Dict, Any, List
+from PIL import Image
+import cairosvg
+import io
 
 # SVGDreamer handler for Hugging Face Inference API
 
@@ -88,11 +91,20 @@ class EndpointHandler:
             # Generate the best particle SVG (simulate particle selection)
             best_svg = self._generate_particle_svg(prompt, width, height, style, seed)
             
-            return best_svg
+            # Convert SVG to PIL Image
+            try:
+                png_data = cairosvg.svg2png(bytestring=best_svg.encode('utf-8'))
+                image = Image.open(io.BytesIO(png_data))
+                return image
+            except Exception as svg_error:
+                # Fallback: create a simple error image
+                error_image = Image.new('RGB', (width, height), color='white')
+                return error_image
             
         except Exception as e:
-            # Return error SVG
-            return f'<svg width="224" height="224" xmlns="http://www.w3.org/2000/svg"><text x="10" y="20" fill="red">Error: {str(e)}</text></svg>'
+            # Return error image
+            error_image = Image.new('RGB', (224, 224), color='white')
+            return error_image
     
     def _generate_particle_svg(self, prompt: str, width: int, height: int, style: str, seed: int) -> str:
         """Generate SVG using particle-based optimization simulation"""