Update app.py

app.py

@@ -36,7 +36,7 @@ sparse_ic_pipeline = Pipeline.create(
         task=task,
         model_path="zoo:cv/classification/resnet_v1-50/pytorch/sparseml/imagenet/pruned95_quant-none",
         class_names = classes,
-        top_k = 2
+        top_k = 3
     
     )
 def run_pipeline(image):
@@ -47,7 +47,7 @@ def run_pipeline(image):
     sparse_result = dict(sparse_output)
     sparse_end = time.perf_counter()
     sparse_duration = (sparse_end - sparse_start) * 1000.0
-    dict_r = {sparse_result['labels'][0]:sparse_result['scores'][0]/100,sparse_result['labels'][1]:sparse_result['scores'][1]/100, }
+    dict_r = {sparse_result['labels'][0]:sparse_result['scores'][0]/100,sparse_result['labels'][1]:sparse_result['scores'][1]/100,sparse_result['labels'][2]:sparse_result['scores'][2]/100 }
     
     return dict_r, sparse_duration
 
@@ -66,7 +66,7 @@ with gr.Blocks() as demo:
             image = gr.Image(type="pil",shape=(224, 224))
             btn = gr.Button("Classify Image")
             sparse_answers = gr.Label(label="Sparse model answers",
-                                      num_top_classes=2
+                                      num_top_classes=3
                                      )
             sparse_duration = gr.Number(label="Sparse Latency (ms):")
             gr.Examples([["lion.jpeg"],[ "bird.jpeg"],[ "cheetah.jpeg"]],inputs=[image],)