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app.py

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+# Load model directly
+from transformers import AutoFeatureExtractor, AutoModelForImageClassification
+import torch
+import gradio as gr
+
+extractor = AutoFeatureExtractor.from_pretrained("ayoubkirouane/VIT_Beans_Leaf_Disease_Classifier")
+model = AutoModelForImageClassification.from_pretrained("ayoubkirouane/VIT_Beans_Leaf_Disease_Classifier")
+
+labels = ['angular_leaf_spot', 'bean_rust', 'healthy']
+
+def classify(im):
+  features = extractor(im, return_tensors='pt')
+  logits = model(features["pixel_values"])[-1]
+  probability = torch.nn.functional.softmax(logits, dim=-1)
+  probs = probability[0].detach().numpy()
+  confidences = {label: float(probs[i]) for i, label in enumerate(labels)} 
+  return confidences
+  
+interface = gr.Interface(
+    classify,
+    inputs='image',
+    outputs='label',
+    )
+
+interface.launch(share=True , debug=True)
+  

requirements.txt

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+ transformers[torch] 
+ Pillow