Create app.py

app.py

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+import gradio as gr
+from transformers import pipeline
+
+# Initialize the zero-shot-object-detection pipeline
+pipe = pipeline("zero-shot-object-detection", model="google/owlvit-base-patch16")
+
+# Define the function to use the pipeline
+def detect_objects(image, labels):
+    # Split the labels into a list
+    candidate_labels = [label.strip() for label in labels.split(",")]
+    result = pipe(image, candidate_labels=candidate_labels)
+    # Process the result to return the detected objects and their confidence scores
+    return result
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=detect_objects,  # function to process input
+    inputs=[
+        gr.inputs.Image(type="filepath", label="Upload Image"),  # input for image
+        gr.inputs.Textbox(lines=2, label="Candidate Labels (comma separated)"),  # input for candidate labels
+    ],
+    outputs="json",  # output as JSON for multiple object detection results
+    title="Zero-Shot Object Detection",  # Title of the interface
+    description="Upload an image and provide a list of labels (comma separated) for object detection.",  # Description
+)
+
+# Launch the interface
+iface.launch()