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")
+labels = dataset["train"].features["labels"].names
+
+extractor = AutoFeatureExtractor.from_pretrained("RKoops/BeanLeaf")
+model = AutoModelForImageClassification.from_pretrained("RKoops/BeanLeaf")
+
+
+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()