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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+examples/football65.png filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,41 @@
+import os
+
+import gradio as gr
+import timm
+from huggingface_hub import login
+from torch import no_grad, softmax, topk
+
+MODEL_NAME = os.getenv("MODEL_NAME")
+HF_TOKEN = os.getenv("HF_TOKEN")
+
+login(token=HF_TOKEN)
+
+model = timm.create_model(f"hf_hub:{MODEL_NAME}", pretrained=True)
+model.eval()
+
+data_cfg = timm.data.resolve_data_config(model.pretrained_cfg)
+transform = timm.data.create_transform(**data_cfg)
+
+
+def classify_image(input):
+    inp = transform(input)
+    with no_grad():
+        output = model(inp.unsqueeze(0))
+    probabilities = softmax(output[0], dim=0)
+    values, indices = topk(probabilities, 3)
+
+    return {
+        model.pretrained_cfg["label_names"][str(id.item())].title(): prob
+        for id, prob in zip(indices, values)
+    }
+
+
+demo = gr.Interface(
+    fn=classify_image,
+    inputs=gr.Image(type="pil", sources=["upload", "clipboard"]),
+    outputs=gr.Label(num_top_classes=3),
+    allow_flagging="never",
+    examples="examples",
+)
+demo.queue()
+demo.launch(debug=True)