Update app.py

app.py

@@ -2,27 +2,22 @@ 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)
+feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/regnet-y-040")
+model = RegNetForImageClassification.from_pretrained("facebook/regnet-y-040")
 
-except Exception as e:
-  print("Oops got an error: Create an issue/PR at github.com/satpalsr/space-repo")
-  print( "Error: %s" % str(e) )
+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)