Add application file

app.py

@@ -0,0 +1,46 @@
+#Falah with Gradio
+import gradio as gr
+from transformers import pipeline
+from PIL import Image, ImageDraw
+
+checkpoint = "google/owlvit-base-patch32"
+detector = pipeline(model=checkpoint, task="zero-shot-object-detection")
+
+def detect_and_visualize_objects(image):
+    # Convert the image to RGB format
+    image = image.convert("RGB")
+
+    # Process the image using the object detection model
+    predictions = detector(
+        image,
+        candidate_labels=["human face", "rocket", "nasa badge", "star-spangled banner"],
+    )
+
+    # Draw bounding boxes and labels on the image
+    draw = ImageDraw.Draw(image)
+    for prediction in predictions:
+        box = prediction["box"]
+        label = prediction["label"]
+        score = prediction["score"]
+
+        xmin, ymin, xmax, ymax = box.values()
+        draw.rectangle((xmin, ymin, xmax, ymax), outline="red", width=1)
+        draw.text((xmin, ymin), f"{label}: {round(score, 2)}", fill="white")
+
+    # Return the annotated image
+    return image
+
+# Define the Gradio interface
+image_input = gr.inputs.Image(type="pil")
+image_output = gr.outputs.Image(type="pil")
+
+iface = gr.Interface(
+    fn=detect_and_visualize_objects,
+    inputs=image_input,
+    outputs=image_output,
+    title="Object Detection",
+    description="Detect objects in an image using a pre-trained model and visualize the results.",
+)
+
+# Launch the Gradio interface
+iface.launch(debug=True)