Create app.py

app.py

@@ -0,0 +1,32 @@
+import gradio as gr
+from transformers import pipeline
+from PIL import Image
+import requests
+
+from transformers import pipeline
+
+checkpoint = "openai/clip-vit-large-patch14"
+detector = pipeline(model=checkpoint, task="zero-shot-image-classification")
+# Function to predict dog category
+def predict_dog_category(image):
+    # List of dog categories
+    dog_category = [
+        'Siberian Husky', 'Boxer',  # Working Dogs
+        'Border Collie', 'Australian Shepherd',  # Herding Dogs
+        'Chihuahua', 'Pomeranian',  # Toy Dogs
+        'Labrador Retriever', 'Golden Retriever',  # Sporting Dogs
+        'Yorkshire Terrier', 'Bull Terrier',  # Terriers
+        'Bulldog', 'Poodle'  # Non-Sporting Dogs
+    ]
+    
+    # Use CLIP model to predict dog category
+    predictions = detector(image, candidate_labels=dog_category)
+    return {predictions[i]['label']: float(predictions[i]['score']) for i in range(len(predictions))}
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=predict_dog_category,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.Label(num_top_classes=12)
+)
+iface.launch(share=True)