Create app.py

app.py

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+from transformers import AutoFeatureExtractor, RegNetForImageClassification
+import torch
+import gradio as gr
+
+feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/regnet-y-040")
+model = RegNetForImageClassification.from_pretrained("facebook/regnet-y-040")
+
+def inference(image):
+  print("Type of image", type(image))
+  inputs = feature_extractor(image, return_tensors="pt")
+
+  with torch.no_grad():
+      logits = model(**inputs).logits
+
+  predicted_label = logits.argmax(-1).item()
+  return model.config.id2label[predicted_label]
+  
+title="RegNet-image-classification"
+description="This space uses RegNet Model with an image classification head on top (a linear layer on top of the pooled features). It predicts one of the 1000 ImageNet classes. Check [Docs](https://huggingface.co/docs/transformers/main/en/model_doc/regnet) for more details."
+
+examples=[['wolf.jpg'], ['ballon.jpg'], ['fountain.jpg']]
+iface = gr.Interface(inference, inputs=gr.inputs.Image(), outputs="text",title=title,description=description,examples=examples)
+iface.launch(enable_queue=True)