Update app.py

app.py

@@ -2,22 +2,27 @@ 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")
+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)
 
-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,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) )