Create app.py

app.py

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+import comet_ml
+comet_ml.init(anonymous=True, project_name="3: OWL-ViT + SAM")
+exp = comet_ml.Experiment()
+# To display the image
+from PIL import Image
+logged_artifact = exp.get_artifact("L3-data", "anmorgan24")
+# Display the images
+raw_image = Image.open("L3_data/dogs.jpg")
+raw_image
+
+from transformers import pipeline
+OWL_checkpoint = "./models/google/owlvit-base-patch32"
+
+# Load the model
+detector = pipeline(
+    model= OWL_checkpoint,
+    task="zero-shot-object-detection"
+)
+
+# What you want to identify in the image
+text_prompt = "dog"
+
+output = detector(
+    raw_image,
+    candidate_labels = [text_prompt]
+)
+
+# Print the output to identify the bounding boxes detected
+output
+
+from utils import preprocess_outputs
+
+input_scores, input_labels, input_boxes = preprocess_outputs(output)
+
+from utils import show_boxes_and_labels_on_image
+
+# Show the image with the bounding boxes
+show_boxes_and_labels_on_image(
+    raw_image,
+    input_boxes[0],
+    input_labels,
+    input_scores
+)
+
+#Model distillation
+