Create app.py

app.py

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+import gradio as gr
+from transformers import AutoImageProcessor, SiglipForImageClassification
+from PIL import Image
+import torch
+
+model_name = "prithivMLmods/Dog-Breed-120"
+processor = AutoImageProcessor.from_pretrained(model_name)
+model = SiglipForImageClassification.from_pretrained(model_name)
+model.eval()
+
+def predict(image):
+    inputs = processor(images=image, return_tensors="pt")
+    with torch.no_grad():
+        outputs = model(**inputs)
+    logits = outputs.logits
+    probs = torch.nn.functional.softmax(logits, dim=1).squeeze()
+    top_idx = probs.argmax().item()
+    label = model.config.id2label[top_idx]
+    confidence = probs[top_idx].item()
+    return f"{label} ({round(confidence*100, 1)}%)"
+
+demo = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil"),
+    outputs="text",
+    title="Dog Breed Classifier 🐶",
+    description="Upload a dog photo to identify its breed using the prithivMLmods/Dog-Breed-120 model."
+)
+
+demo.launch()