Delete app.py with huggingface_hub

app.py

@@ -1,42 +0,0 @@
-import gradio as gr
-import torch
-import random
-import numpy as np
-from transformers import MaskFormerFeatureExtractor, MaskFormerForInstanceSegmentation
-
-device = torch.device("cpu")
-model = MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-tiny-ade").to(device)
-model.eval()
-preprocessor = MaskFormerFeatureExtractor.from_pretrained("facebook/maskformer-swin-tiny-ade")
-
-def visualize_instance_seg_mask(mask):
-    image = np.zeros((mask.shape[0], mask.shape[1], 3))
-    labels = np.unique(mask)
-    label2color = {label: (random.randint(0, 1), random.randint(0, 255), random.randint(0, 255)) for label in labels}
-    for i in range(image.shape[0]):
-      for j in range(image.shape[1]):
-        image[i, j, :] = label2color[mask[i, j]]
-    image = image / 255
-    return image
-
-def query_image(img):
-    target_size = (img.shape[0], img.shape[1])
-    inputs = preprocessor(images=img, return_tensors="pt")
-    with torch.no_grad():
-        outputs = model(**inputs)
-    outputs.class_queries_logits = outputs.class_queries_logits.cpu()
-    outputs.masks_queries_logits = outputs.masks_queries_logits.cpu()
-    results = preprocessor.post_process_segmentation(outputs=outputs, target_size=target_size)[0].cpu().detach()
-    results = torch.argmax(results, dim=0).numpy()
-    results = visualize_instance_seg_mask(results)
-    return results
-
-demo = gr.Interface(
-    query_image, 
-    inputs=[gr.Image()], 
-    outputs="image",
-    title="MaskFormer Demo",
-    examples=[["example_2.png"]]
-)
-
-demo.launch()