Update app.py

app.py

@@ -1,3 +1,32 @@
 import gradio as gr
+from transformers import CLIPProcessor, CLIPModel
+from PIL import Image
+import torch
 
-gr.load("models/DGurgurov/clip-vit-base-patch32-oxford-pets").launch()
+# Load the model and processor
+model_id = "DGurgurov/clip-vit-base-patch32-oxford-pets"
+model = CLIPModel.from_pretrained(model_id)
+processor = CLIPProcessor.from_pretrained(model_id)
+
+# Define the inference function
+def predict(image):
+    inputs = processor(images=image, return_tensors="pt")
+    outputs = model.get_image_features(**inputs)
+    logits_per_image = outputs.logits_per_image
+    probs = torch.nn.functional.softmax(logits_per_image, dim=1)
+    return {f"Class {i}": prob.item() for i, prob in enumerate(probs[0])}
+
+# Define Gradio interface
+image = gr.inputs.Image(type="pil")
+label = gr.outputs.Label(num_top_classes=5)
+
+interface = gr.Interface(
+    fn=predict,
+    inputs=image,
+    outputs=label,
+    title="CLIP Model - Oxford Pets",
+    description="Upload an image and get the top 5 class predictions."
+)
+
+# Launch the Gradio app
+interface.launch()