Create app.py

app.py

@@ -0,0 +1,29 @@
+import datasets
+import gradio as gr
+from transformers import AutoFeatureExtractor, AutoModelForImageClassification
+
+dataset = load_dataset('beans') # This should be the same as the first line of Python code in this Colab notebook
+
+extractor = AutoFeatureExtractor.from_pretrained("saved_model_files")
+model = AutoModelForImageClassification.from_pretrained("saved_model_files")
+
+labels = dataset['train'].features['labels'].names
+
+def classify(im):
+  features = image_processor(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
+
+# Run the Gradio interface for the app
+interface = gr.Interface(
+    fn=classify,
+    inputs=["image"], 
+    outputs=["label"],
+    title="Leaf disaease classifier",
+    description="A pre-trained vit model for classifying leaf diseases"
+)
+
+interface.launch(debug=True)