Create app.py

app.py

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+import gradio as gr
+from PIL import Image
+from transformers import AutoModelForCausalLM
+from starvector.data.util import process_and_rasterize_svg
+import torch
+
+print("Loading model...")
+model_name = "starvector/starvector-8b-im2svg"
+starvector = AutoModelForCausalLM.from_pretrained(
+    model_name, 
+    torch_dtype=torch.float16, 
+    trust_remote_code=True
+)
+processor = starvector.model.processor
+tokenizer = starvector.model.svg_transformer.tokenizer
+
+# Move to GPU
+device = "cuda" if torch.cuda.is_available() else "cpu"
+starvector.to(device)
+starvector.eval()
+print(f"Model loaded on {device}!")
+
+def convert_image_to_svg(image_pil):
+    """Convert uploaded image to SVG"""
+    try:
+        # Process image
+        image = processor(image_pil, return_tensors="pt")['pixel_values'].to(device)
+        if image.shape[0] != 1:
+            image = image.unsqueeze(0)
+        
+        batch = {"image": image}
+        
+        # Generate SVG
+        raw_svg = starvector.generate_im2svg(batch, max_length=4000)[0]
+        svg, raster_image = process_and_rasterize_svg(raw_svg)
+        
+        # Save SVG to file
+        svg_path = "output.svg"
+        with open(svg_path, 'w') as f:
+            f.write(svg)
+        
+        return svg_path, raster_image
+    
+    except Exception as e:
+        return None, f"Error: {str(e)}"
+
+# Create Gradio interface
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    gr.Markdown("# 🎨 Image to SVG Converter")
+    gr.Markdown("Convert your images to SVG format using StarVector AI")
+    
+    with gr.Row():
+        with gr.Column():
+            input_image = gr.Image(type="pil", label="Upload Image")
+            convert_btn = gr.Button("Convert to SVG", variant="primary")
+        
+        with gr.Column():
+            output_file = gr.File(label="Download SVG")
+            output_preview = gr.Image(label="Preview")
+    
+    convert_btn.click(
+        fn=convert_image_to_svg,
+        inputs=input_image,
+        outputs=[output_file, output_preview]
+    )
+    
+    gr.Markdown("### Example: Upload a PNG/JPG image and get an SVG file!")
+
+demo.launch()