commit files to HF hub

app.py

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+
+import gradio as gr
+import torch
+import numpy as np
+from PIL import Image
+from torchvision import transforms as T
+import joblib
+
+# Load models
+dinov2_vits14 = torch.load('dinov2_vits14.pth', map_location=torch.device('cpu'))
+clf = joblib.load('svm_model.joblib')
+
+# Transform for input image
+transform_image = T.Compose([T.ToTensor(), T.Resize(244), T.CenterCrop(224), T.Normalize([0.5], [0.5])])
+
+def predict(image):
+    image = Image.fromarray(image)
+    transformed_img = transform_image(image)[:3].unsqueeze(0)
+    with torch.no_grad():
+        embedding = dinov2_vits14(transformed_img)
+        prediction = clf.predict(np.array(embedding[0].cpu()).reshape(1, -1))
+    return prediction[0]
+
+iface = gr.Interface(fn=predict, inputs="image", outputs="text")
+iface.launch()
+    

requirements.txt

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+
+gradio
+torch
+torchvision
+joblib
+Pillow
+numpy
+