Update app.py

app.py

@@ -1,24 +1,24 @@
 from transformers import AutoFeatureExtractor, ResNetForImageClassification
 import torch
-from datasets import load_dataset
+# from datasets import load_dataset
 
-dataset = load_dataset("huggingface/cats-image")
-image = dataset["test"]["image"][0]
+# dataset = load_dataset("huggingface/cats-image")
+# image = dataset["test"]["image"][0]
 
 feature_extractor = AutoFeatureExtractor.from_pretrained("microsoft/resnet-50")
 model = ResNetForImageClassification.from_pretrained("microsoft/resnet-50")
 
-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])
-
 import gradio as gr
 def segment(image):
-    pass  # Implement your image segmentation model here...
+    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])
+    
+    return model.config.id2label[predicted_label]
 
-gr.Interface(fn=segment, inputs="image", outputs="image").launch()
+gr.Interface(fn=segment, inputs="image", outputs="label").launch()