create app.py

app.py

@@ -0,0 +1,29 @@
+import datasets
+from transformers import AutoFeatureExtractor, AutoModelForImageClassification
+import numpy as np
+import gradio as gr
+
+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")
+
+# add to cuda?
+model.eval()
+model.to(device)
+
+labels = dataset['train'].features['labels'].names
+
+def classify(im):
+  features = extractor(im, return_tensors='pt')
+  features.to(device) # move to gpu as model, if available
+  with torch.no_grad():
+    logits = model(**features).logits
+  probability = torch.nn.functional.softmax(logits, dim=-1)
+  probs = probability[0].to('cpu').detach().numpy()
+  confidences = {label: float(probs[i]) for i, label in enumerate(labels)}
+  return confidences
+
+interface = gr.Interface(classify, gr.Image(shape=(200, 200)), 'text')
+#demo.launch()
+interface.launch(debug=False)