added sum text

app.py

@@ -27,6 +27,7 @@ class GradioApp:
             model_class = CustomUnet if model_name == 'Custom' else Unet
             self.models[model_name] = model_class(self.models[model_name], from_file=True, device='cpu')
             self.models[model_name].eval()
+            
         prediction = self.models[model_name].predict(img_file, option='mask')[0] * 1
         return img_file, [(prediction, 'person')]
     
@@ -43,10 +44,10 @@ class GradioApp:
             outputs=gr.AnnotatedImage(label='Model predictions'),
             examples=examples_list,
             cache_examples=False,
-            #title='Plants Diseases Classification',
-            #description=f'This model performs classification on images of leaves that are either healthy, \
-            #    have bean rust, or have an angular leaf spot. A vision transformer neural network architecture is used. \
-            #    The dataset can be downloaded from [Kaggle]({dataset_url}) and the source code is on [GitHub]({github_repo_url}).',
+            title='Person Segmentation',
+            description=f'This model performs segmentation on people in images. A Unet neural network architecture is used. \
+                The dataset can be found [here]() [Kaggle](https://github.com/VikramShenoy97/Human-Segmentation-Dataset) \
+                and the source code is on [GitHub](https://github.com/i4ata/UnetSegmentation).',
         )
         demo.launch()