Upload app.py

app.py

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+import datasets
+import gradio as gr
+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 = extractor(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
+
+
+interface = gr.Interface(classify, inputs='image', outputs='label', title='Bean plant disease classifier',description='Detect diseases in beans using images of leaves')
+
+interface.launch()