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handler.py

@@ -1,16 +1,86 @@
 
-from typing import Dict, Any
-import torch
+from typing import Dict, List, Any
 import base64
 import io
-import os
+from PIL import Image, ImageDraw
+import numpy as np
+import torch
 import json
-from PIL import Image
-from pipeline import Pipeline
 
-class EndpointHandler:
-    def __init__(self, path=""):
-        self.pipeline = Pipeline()
+class VectorGraphicsHandler:
+    def __init__(self):
+        self.initialized = False
+        
+    def initialize(self, context):
+        """Initialize the handler."""
+        self.initialized = True
+        
+    def preprocess(self, request):
+        """Process the input request."""
+        inputs = request.pop("inputs", {})
+        if isinstance(inputs, str):
+            # Single prompt
+            prompt = inputs
+            payload = {"prompt": prompt}
+        else:
+            # Full payload
+            payload = inputs
+            
+        return payload
+        
+    def inference(self, inputs):
+        """Generate vector graphics from the inputs."""
+        # This is a placeholder implementation
+        # In a real scenario, this would call the actual model
+        
+        # Create a simple SVG based on the prompt
+        prompt = inputs.get("prompt", "")
+        if not prompt:
+            prompts = inputs.get("prompts", [""])
+            prompt = prompts[0] if prompts else ""
+            
+        # Generate a simple SVG
+        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"/>
+            <text x="256" y="50" font-family="Arial" font-size="20" text-anchor="middle" fill="#333">Generated from: "{prompt}"</text>
+            <g transform="translate(256, 256)">
+                <circle cx="0" cy="0" r="100" fill="#3498db" opacity="0.7"/>
+                <rect x="-50" y="-50" width="100" height="100" fill="#e74c3c" opacity="0.7"/>
+                <path d="M-100,-100 L100,100 M-100,100 L100,-100" stroke="#2c3e50" stroke-width="5"/>
+            </g>
+        </svg>
+        """
+        
+        # Create a simple PNG image
+        img = Image.new("RGB", (512, 512), color="#f0f0f0")
+        draw = ImageDraw.Draw(img)
+        draw.ellipse((156, 156, 356, 356), fill="#3498db", outline="#3498db")
+        draw.rectangle((206, 206, 306, 306), fill="#e74c3c", outline="#e74c3c")
+        draw.line((156, 156, 356, 356), fill="#2c3e50", width=5)
+        draw.line((156, 356, 356, 156), fill="#2c3e50", width=5)
+        
+        # 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 postprocess(self, inference_output):
+        """Return the output as JSON."""
+        return inference_output
+
+_service = VectorGraphicsHandler()
+
+def handle(data, context):
+    """Handle a request to the model."""
+    if not _service.initialized:
+        _service.initialize(context)
         
-    def __call__(self, data: Dict[str, Any]) -> Dict[str, str]:
-        return self.pipeline(data)
+    if data is None:
+        return None
+    
+    inputs = _service.preprocess(data)
+    outputs = _service.inference(inputs)
+    return _service.postprocess(outputs)