Create app.py

app.py

@@ -0,0 +1,49 @@
+import torch
+from transformers import AutoProcessor, AutoModelForZeroShotObjectDetection
+from PIL import Image, ImageDraw
+import gradio as gr
+
+checkpoint = "google/owlvit-base-patch32"
+
+model = AutoModelForZeroShotObjectDetection.from_pretrained(checkpoint)
+processor = AutoProcessor.from_pretrained(checkpoint)
+
+def detect_objects(image, text_queries):
+
+    if isinstance(image, str):
+        image = Image.open(image)
+
+    inputs = processor(images=image, text=text_queries, return_tensors="pt")
+
+    with torch.no_grad():
+        outputs = model(**inputs)
+        target_sizes = torch.tensor([image.size[::-1]])
+        results = processor.post_process_object_detection(outputs, threshold=0.1, target_sizes=target_sizes)[0]
+
+    draw = ImageDraw.Draw(image)
+    
+    scores = results["scores"].tolist()
+    labels = results["labels"].tolist()
+    boxes = results["boxes"].tolist()
+
+    for box, score, label in zip(boxes, scores, labels):
+        xmin, ymin, xmax, ymax = box
+        draw.rectangle((xmin, ymin, xmax, ymax), outline="red", width=1)
+        draw.text((xmin, ymin), f"{text_queries[label]}: {round(score, 2)}", fill="black")
+
+    return image
+
+inputs = [
+    gr.Image(type="pil", label="Input Image"),
+    gr.Textbox(label="Text Queries (comma-separated)")
+]
+
+output = gr.Image(type="pil", label="Output Image")
+
+gr.Interface(
+    fn=detect_objects,
+    inputs=inputs,
+    outputs=output,
+    title="Zero-Shot Object Detection",
+    description="Detect objects in an image using zero-shot object detection.",
+).launch()