Create app.py

app.py

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+import torch
+from transformers import AutoModelForImageClassification, AutoFeatureExtractor
+import gradio as gr
+
+model_id = f'jonathanfernandes/vit-base-patch16-224-finetuned-flower'
+
+def classify_image(image):
+  model = AutoModelForImageClassification.from_pretrained(model_id)
+  feature_extractor = AutoFeatureExtractor.from_pretrained(model_id)
+  inp = feature_extractor(image, return_tensors='pt')
+  outp = model(**inp)
+  pred = torch.nn.functional.softmax(outp.logits, dim=-1)
+  preds = pred[0].cpu().detach().numpy()
+  confidence = {label: float(preds[i]) for i, label in enumerate(labels)}
+  return confidence
+
+interface = gr.Interface(fn=classify_image, 
+                         inputs='image', 
+                         examples=['flower-1.jpg', 'flower-2.jpeg'],
+                         outputs='label').launch(share=True)