Add app.py and requirements.txt

app.py

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+import datasets
+import gradio as gr
+from transformers import AutoFeatureExtractor, AutoModelForImageClassification
+import torch
+
+dataset = datasets.load_dataset('beans')
+
+extractor = AutoFeatureExtractor.from_pretrained("rahult/bean_classification")
+model = AutoModelForImageClassification.from_pretrained("rahult/bean_classification")
+model.eval()
+
+labels = dataset['train'].features['labels'].names
+
+def classify(im):
+  features = 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
+
+
+interface = gr.Interface(fn=classify, inputs="image", outputs="label", allow_flagging='manual')
+interface.launch()

requirements.txt

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+datasets
+transformers
+gradio
+torch