create app.py

app.py

@@ -0,0 +1,31 @@
+import gradio as gr
+import datasets
+import transformers
+import torch
+
+from transformers import AutoFeatureExtractor, AutoModelForImageClassification
+
+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')
+  with torch.no_grad():
+    logits = model(**features).logits
+  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
+
+
+gr.Interface(fn = classify,
+             inputs = "image",
+             outputs = "label",
+             examples='/examples',
+             title='Leaf classification on beans dataset',
+             description='Fine-tuning a ViT for bean plant health classification'
+                         ).launch(debug=True)