Update app.py

app.py

@@ -7,14 +7,6 @@ import vtracer
 from skimage import feature, filters, morphology
 import cv2
 from rembg import remove
-import torch
-from transformers import AutoModelForImageSegmentation, AutoProcessor
-import requests
-from huggingface_hub import hf_hub_download
-
-# Load additional Hugging Face models
-segmentation_model = AutoModelForImageSegmentation.from_pretrained("facebook/dino-vitb16")
-segmentation_processor = AutoProcessor.from_pretrained("facebook/dino-vitb16")
 
 def preprocess_image(image, blur_radius, sharpen_radius, noise_reduction, detail_level, edge_method, color_quantization, enhance_with_ai, remove_bg):
     """Advanced preprocessing of the image before vectorization."""
@@ -67,28 +59,15 @@ def preprocess_image(image, blur_radius, sharpen_radius, noise_reduction, detail
 
     return image
 
-def vectorize_with_hf_model(image):
-    """Vectorizes the image using a Hugging Face model for segmentation or enhancement."""
-    inputs = segmentation_processor(images=image, return_tensors="pt")
-    outputs = segmentation_model(**inputs)
-    mask = outputs["masks"][0][0].cpu().detach().numpy()
-    mask = (mask > 0.5).astype(np.uint8) * 255
-    mask_image = Image.fromarray(mask)
-    return mask_image
-
 def convert_image(image, blur_radius, sharpen_radius, noise_reduction, detail_level, edge_method, color_quantization,
                   color_mode, hierarchical, mode, filter_speckle, color_precision, layer_difference,
                   corner_threshold, length_threshold, max_iterations, splice_threshold, path_precision,
-                  enhance_with_ai, remove_bg, model_choice):
+                  enhance_with_ai, remove_bg):
     """Convert an image to SVG using vtracer with customizable and advanced parameters."""
 
     # Preprocess the image with additional detail level settings
     image = preprocess_image(image, blur_radius, sharpen_radius, noise_reduction, detail_level, edge_method, color_quantization, enhance_with_ai, remove_bg)
 
-    # If a specific model is chosen, use it to process the image before vectorization
-    if model_choice == "Hugging Face Segmentation Model":
-        image = vectorize_with_hf_model(image)
-
     # Convert Gradio image to bytes for vtracer compatibility
     img_byte_array = io.BytesIO()
     image.save(img_byte_array, format='PNG')
@@ -163,8 +142,6 @@ with iface:
         splice_threshold_input = gr.Slider(minimum=1, maximum=100, value=45, step=1, label="Splice Threshold")
         path_precision_input = gr.Slider(minimum=1, maximum=100, value=8, step=1, label="Path Precision")
     
-    model_choice_input = gr.Radio(choices=["None", "Hugging Face Segmentation Model"], value="None", label="Choose Model")
-    
     convert_button = gr.Button("Convert Image to SVG")
     svg_output = gr.HTML(label="SVG Output")
     download_output = gr.File(label="Download SVG")
@@ -175,7 +152,7 @@ with iface:
             image_input, blur_radius_input, sharpen_radius_input, noise_reduction_input, detail_level_input, edge_method_input, color_quantization_input,
             color_mode_input, hierarchical_input, mode_input, filter_speckle_input, color_precision_input,
             layer_difference_input, corner_threshold_input, length_threshold_input, max_iterations_input,
-            splice_threshold_input, path_precision_input, enhance_with_ai_input, remove_bg_input, model_choice_input
+            splice_threshold_input, path_precision_input, enhance_with_ai_input, remove_bg_input
         ],
         outputs=[svg_output, download_output]
     )