Application

app.py

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+import torch
+from datasets import load_dataset
+from transformers import AutoFeatureExtractor, AutoModelForImageClassification
+
+# This should be the same as the first line of Python code in this Colab notebook
+dataset = 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')
+  inp = model(**features)
+  logits = torch.nn.functional.softmax(inp.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
+
+import gradio as gr
+
+interface = gr.Interface(fn=classify, inputs=gr.Image(shape=(224, 224)), outputs="text")
+
+interface.launch(debug=True)

requirements.txt

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+torch
+transformers