Create app.py

app.py

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+from transformers import AutoFeatureExtractor, AutoModelForObjectDetection
+import matplotlib.pyplot as plt
+import matplotlib.patches as patches
+from random import choice
+from PIL import Image
+import os
+from matplotlib import rcParams, font_manager
+
+extractor = AutoFeatureExtractor.from_pretrained("facebook/detr-resnet-50")
+
+model = AutoModelForObjectDetection.from_pretrained("facebook/detr-resnet-50")
+
+from transformers import pipeline
+
+pipe = pipeline('object-detection', model=model, feature_extractor=extractor)
+
+img_url = st.text_input('Image URL', 'https://images.unsplash.com/photo-1556911220-bff31c812dba?ixlib=rb-4.0.3&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=2468&q=80')
+
+output = pipe(img_url)
+
+fpath = os.path.join(r"Poppins-SemiBold.ttf")
+prop = font_manager.FontProperties(fname=fpath)
+
+img = Image.open('kitchen.jpg')
+plt.figure(dpi=2400)
+
+# Create figure and axes
+fig, ax = plt.subplots()
+
+# Display the image
+ax.imshow(img)
+
+colors = ["#ef4444", "#f97316", "#eab308", "#84cc16", "#06b6d4", "#6366f1"]
+
+# Create a Rectangle patch
+for prediction in output:
+    selected_color = choice(colors)
+    x, y, w, h = prediction['box']['xmin'], prediction['box']['ymin'], prediction['box']['xmax'] - prediction['box']['xmin'], prediction['box']['ymax'] - prediction['box']['ymin']
+    rect = patches.FancyBboxPatch((x, y), w, h, linewidth=1.25, edgecolor=selected_color, facecolor='none', boxstyle="round,pad=-0.0040,rounding_size=10",)
+    ax.add_patch(rect)
+    plt.text(x, y-25, f"{prediction['label']}: {round(prediction['score']*100, 1)}%", fontsize=100, color=selected_color, fontproperties=prop)
+
+plt.axis('off')
+
+plt.savefig('kitchen-bbox.jpg', dpi=1200, bbox_inches='tight')
+
+image = Image.open('kitchen-bbox.jpg')
+
+st.image(image, caption='DETR Image')
+
+plt.show()