Create app.py

app.py

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+import torch
+import cv2
+import gradio as gr
+import numpy as np
+from transformers import OwlViTProcessor, OwlViTForObjectDetection
+
+
+# Use GPU if available
+if torch.cuda.is_available():
+    device = torch.device("cuda")
+else:
+    device = torch.device("cpu")
+
+model = OwlViTForObjectDetection.from_pretrained("nielsr/owlv2-base-patch16-ensemble").to(device)
+model.eval()
+processor = OwlViTProcessor.from_pretrained("nielsr/owlv2-base-patch16-ensemble")
+
+
+def query_image(img, text_queries, score_threshold):
+    text_queries = text_queries
+    text_queries = text_queries.split(",")
+
+    target_sizes = torch.Tensor([img.shape[:2]])
+    inputs = processor(text=text_queries, images=img, return_tensors="pt").to(device)
+
+    with torch.no_grad():
+        outputs = model(**inputs)
+    
+    outputs.logits = outputs.logits.cpu()
+    outputs.pred_boxes = outputs.pred_boxes.cpu() 
+    results = processor.post_process_object_detection(outputs=outputs, target_sizes=target_sizes)
+    boxes, scores, labels = results[0]["boxes"], results[0]["scores"], results[0]["labels"]
+
+    font = cv2.FONT_HERSHEY_SIMPLEX
+
+    for box, score, label in zip(boxes, scores, labels):
+        box = [int(i) for i in box.tolist()]
+
+        if score >= score_threshold:
+            img = cv2.rectangle(img, box[:2], box[2:], (255,0,0), 5)
+            if box[3] + 25 > 768:
+                y = box[3] - 10
+            else:
+                y = box[3] + 25
+                
+            img = cv2.putText(
+                img, text_queries[label], (box[0], y), font, 1, (255,0,0), 2, cv2.LINE_AA
+            )
+    return img
+
+
+description = """
+Gradio demo for <a href="https://huggingface.co/docs/transformers/main/en/model_doc/owlvit">OWL-ViT</a>, 
+introduced in <a href="https://arxiv.org/abs/2205.06230">Simple Open-Vocabulary Object Detection
+with Vision Transformers</a>. 
+\n\nYou can use OWLv2 to query images with text descriptions of any object. 
+To use it, simply upload an image and enter comma separated text descriptions of objects you want to query the image for. You
+can also use the score threshold slider to set a threshold to filter out low probability predictions. 
+\n\nOWL-ViT is trained on text templates,
+hence you can get better predictions by querying the image with text templates used in training the original model: *"photo of a star-spangled banner"*, 
+*"image of a shoe"*. Refer to the <a href="https://arxiv.org/abs/2103.00020">CLIP</a> paper to see the full list of text templates used to augment the training data.
+\n\n<a href="https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/zeroshot_object_detection_with_owlvit.ipynb">Colab demo</a>
+"""
+demo = gr.Interface(
+    query_image, 
+    inputs=[gr.Image(), "text", gr.Slider(0, 1, value=0.1)], 
+    outputs="image",
+    title="Zero-Shot Object Detection with OWLv2",
+    description=description,
+    examples=[
+        ["assets/astronaut.png", "human face, rocket, star-spangled banner, nasa badge", 0.11], 
+        ["assets/coffee.png", "coffee mug, spoon, plate", 0.1],
+        ["assets/butterflies.jpeg", "orange butterfly", 0.3],
+    ],
+)
+demo.launch()