app.py

from transformers import AutoFeatureExtractor, RegNetForImageClassification
import torch
import gradio as gr

try:
    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.inpu, outputs="text",title=title,description=description,examples=examples)
    iface.launch(enable_queue=True,cache_examples=True)

except Exception as e:
  print("Oops got an error: Create an issue/PR at github.com/satpalsr/space-repo")
  print( "Error: %s" % str(e) )