new app file

app.py

@@ -0,0 +1,31 @@
+
+import datasets
+from transformers import AutoFeatureExtractor, AutoModelForImageClassification
+import gradio as gr
+
+dataset = datasets.load_dataset("beans")
+
+extractor = AutoFeatureExtractor.from_pretrained("saved_model_files")
+model = AutoModelForImageClassification.from_pretrained("saved_model_files")
+
+labels = dataset['train'].features['labels'].names
+
+def classify(im):
+  features = feature_extractor(im, return_tensors='pt')
+  logits = model(**features).logits
+  logits = torch.nn.functional.softmax(logits, dim=-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
+
+ # following dummy till i figure out how to upload custom saved model
+
+ def classify1(im)
+ 	label = {'leaf spot' : 0.9, 'rust' : 0.1}
+ 	return label
+
+interface = interface = gr.Interface(classify1, inputs='image', outputs='label', title='Leaf Classification demo',
+                         description='Demo of fine-tuning a ViT for image classification based on the bean dataset classification') # FILL HERE
+
+interface.launch()