add app

app.py

@@ -0,0 +1,53 @@
+import torch
+import gradio as gr
+import numpy as np
+from PIL import Image, ImageDraw, ImageFont
+from transformers import OwlViTProcessor, OwlViTForObjectDetection
+
+model = OwlViTForObjectDetection.from_pretrained("google/owlvit-base-patch32").eval()
+processor = OwlViTProcessor.from_pretrained("google/owlvit-base-patch32")
+
+
+def query_image(img, text_queries):
+    text_queries = text_queries.split(",")
+    inputs = processor(text=text_queries, images=img, return_tensors="pt")
+
+    with torch.no_grad():
+      outputs = model(**inputs)
+
+    target_sizes = torch.Tensor([[768, 768]])
+    results = processor.post_process(outputs=outputs, target_sizes=target_sizes)
+    boxes, scores, labels = results[0]["boxes"], results[0]["scores"], results[0]["labels"]
+
+    draw = ImageDraw.Draw(img)  
+    font = ImageFont.truetype("/System/Library/Fonts/Helvetica.ttc", size=22)
+
+    score_threshold = 0.1
+    for box, score, label in zip(boxes, scores, labels):
+        box = [int(i) for i in box.tolist()]
+
+        if score >= score_threshold:
+            draw.rectangle(box, outline="red", width=4)
+            text_loc =[box[0]+5, box[3]+10]
+            draw.text(text_loc, text_queries[label], fill="red", font=font, stroke_width=1)
+    
+    img = np.array(img)
+    return img
+
+
+description = 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 OWL-ViT 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. 
+"""
+demo = gr.Interface(
+    query_image, 
+    inputs=[gr.Image(shape=(768, 768), type="pil"), "text"], 
+    outputs="image",
+    title="Zero-Shot Object Detection with OWL-ViT",
+    description="You can use OWL-ViT to query images with text descriptions of any object",
+    examples=[["astronaut.png", "human face, rocket, flag, nasa badge"], ["coffee.png", "coffee mug, spoon, plate"]]
+)
+demo.launch(debug=True)