Update app.py

app.py

@@ -174,13 +174,6 @@ def process_input(image_path):
     print(probs)
     print(idxs)
     
-    # Swap class to index mapping with index to class mapping and then map the classes to flower category labels using the json file
-    #idx_to_class = {v: k for k, v in pretrained_model.class_to_idx.items()}
-    #with open('cat_to_name.json','r') as f:
-    #    cat_to_name = json.load(f)
-    #names = list(map(lambda x: cat_to_name[f"{idx_to_class[x]}"],idxs))
-    #return names, probs
-    #return { idxs[i].item() : float(probs[i].item()) for i in range(len(idxs))}
     return { idxs : float(probs) }
     
     #append to list to be returned at end
@@ -198,4 +191,12 @@ enable_queue = True
 iface = gr.Interface(fn=process_input, inputs=gr.inputs.Image(type='filepath'), outputs=gr.outputs.Label(num_top_classes=3), examples = examples,
                      title=title, description=description,article=article,interpretation=interpretation, enable_queue=enable_queue
                     )
-iface.launch()
+iface.launch()
+
+    # Swap class to index mapping with index to class mapping and then map the classes to flower category labels using the json file
+    #idx_to_class = {v: k for k, v in pretrained_model.class_to_idx.items()}
+    #with open('cat_to_name.json','r') as f:
+    #    cat_to_name = json.load(f)
+    #names = list(map(lambda x: cat_to_name[f"{idx_to_class[x]}"],idxs))
+    #return names, probs
+        #return { idxs[i].item() : float(probs[i].item()) for i in range(len(idxs))}