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app.py

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+from transformers import AutoFeatureExtractor, ResNetForImageClassification
+import torch
+import gradio as gr
+
+# load model
+feature_extractor = AutoFeatureExtractor.from_pretrained("microsoft/resnet-50")
+model = ResNetForImageClassification.from_pretrained("microsoft/resnet-50")
+
+def predict(image):
+    
+    inputs = feature_extractor(image, return_tensors="pt")
+    with torch.no_grad():
+        logits = model(**inputs).logits
+
+    # model predicts one of the 1000 ImageNet classes
+    predicted_label = logits.argmax(-1).item()
+    print(model.config.id2label[predicted_label])
+
+# setup Gradio interface
+title = "Image classifier"
+description = "Image classification with pretrained resnet50 model"
+#examples = ['elephant.jpg']
+interpretation='default'
+enable_queue=True
+
+gr.Interface(
+    fn=predict,
+    inputs=gr.inputs.Image(),
+    outputs=gr.outputs.Label(num_top_classes=1),
+    title=title,
+    description=description,
+    #examples=examples,
+    interpretation=interpretation,
+    enable_queue=enable_queue
+).launch()