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Update title in app.py
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app.py
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@@ -76,7 +76,7 @@ def predict(img, model_name='EffNetB2') -> Tuple[Dict, float]:
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### 4. Gradio app ###
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# Create title, description and article strings
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title = "FoodVision Small
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description = "A computer vision app to classify images of food as hamburger, waffles or risotto. Utilizes either an EfficientNetB2 (EffNetB2) or a Vision Transformer (ViT) feature extractor model. (**Predictions for the example images default to EffNetB2, so when using ViT with either of the provided examples, make sure to click Submit to get the prediction from the ViT model**)"
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article = "The pretrained ViT and EffNetB2 models were further trained for this task [here](https://github.com/WeyinmiA/pytorch-deep-learning/blob/main/food_vision_small_model_deployment.ipynb), on a subset of the [Food-101 dataset](https://data.vision.ee.ethz.ch/cvl/datasets_extra/food-101/) (20% of the data and 3 out of 101 classes). This project was inspired by [09. PyTorch Model Deployment](https://www.learnpytorch.io/09_pytorch_model_deployment/)."
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### 4. Gradio app ###
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# Create title, description and article strings
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title = "FoodVision Small 🧇🍔🍛"
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description = "A computer vision app to classify images of food as hamburger, waffles or risotto. Utilizes either an EfficientNetB2 (EffNetB2) or a Vision Transformer (ViT) feature extractor model. (**Predictions for the example images default to EffNetB2, so when using ViT with either of the provided examples, make sure to click Submit to get the prediction from the ViT model**)"
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article = "The pretrained ViT and EffNetB2 models were further trained for this task [here](https://github.com/WeyinmiA/pytorch-deep-learning/blob/main/food_vision_small_model_deployment.ipynb), on a subset of the [Food-101 dataset](https://data.vision.ee.ethz.ch/cvl/datasets_extra/food-101/) (20% of the data and 3 out of 101 classes). This project was inspired by [09. PyTorch Model Deployment](https://www.learnpytorch.io/09_pytorch_model_deployment/)."
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