Update app.py

app.py

@@ -1,77 +1,77 @@
 import streamlit as st
 # %%
-# Datasets installation
-#! pip install datasets transformers
-# To install from source instead of the last release, comment the command above and uncomment the following one.
-# ! pip install git+https://github.com/huggingface/datasets.git
+Datasets installation
+! pip install datasets transformers
+To install from source instead of the last release, comment the command above and uncomment the following one.
+! pip install git+https://github.com/huggingface/datasets.git
 
-# %% [markdown]
-# # Quickstart
+%% [markdown]
+# Quickstart
 
-# %% [markdown]
-# This quickstart is intended for developers who are ready to dive into the code and see an example of how to integrate 🤗 Datasets into their model training workflow. If you're a beginner, we recommend starting with our [tutorials](https://huggingface.co/docs/datasets/main/en/./tutorial), where you'll get a more thorough introduction.
-# 
-# Each dataset is unique, and depending on the task, some datasets may require additional steps to prepare it for training. But you can always use 🤗 Datasets tools to load and process a dataset. The fastest and easiest way to get started is by loading an existing dataset from the [Hugging Face Hub](https://huggingface.co/datasets). There are thousands of datasets to choose from, spanning many tasks. Choose the type of dataset you want to work with, and let's get started!
-# 
-# <div class="mt-4">
-#    <div class="w-full flex flex-col space-y-4 md:space-y-0 md:grid md:grid-cols-3 md:gap-y-4 md:gap-x-5">
-#       <a class="!no-underline border dark:border-gray-700 p-5 rounded-lg shadow hover:shadow-lg" href="#audio"
-#          ><div class="w-full text-center bg-gradient-to-r from-violet-300 via-sky-400 to-green-500 rounded-lg py-1.5 font-semibold mb-5 text-white text-lg leading-relaxed">Audio</div>
-#          <p class="text-gray-700">Resample an audio dataset and get it ready for a model to classify what type of banking issue a speaker is calling about.</p>
-#       </a>
-#       <a class="!no-underline border dark:border-gray-700 p-5 rounded-lg shadow hover:shadow-lg" href="#vision"
-#          ><div class="w-full text-center bg-gradient-to-r from-pink-400 via-purple-400 to-blue-500 rounded-lg py-1.5 font-semibold mb-5 text-white text-lg leading-relaxed">Vision</div>
-#          <p class="text-gray-700">Apply data augmentation to an image dataset and get it ready for a model to diagnose disease in bean plants.</p>
-#       </a>
-#       <a class="!no-underline border dark:border-gray-700 p-5 rounded-lg shadow hover:shadow-lg" href="#nlp"
-#          ><div class="w-full text-center bg-gradient-to-r from-orange-300 via-red-400 to-violet-500 rounded-lg py-1.5 font-semibold mb-5 text-white text-lg leading-relaxed">NLP</div>
-#          <p class="text-gray-700">Tokenize a dataset and get it ready for a model to determine whether a pair of sentences have the same meaning.</p>
-#       </a>
-#    </div>
-# </div>
-# 
-# <Tip>
-# 
-# Check out [Chapter 5](https://huggingface.co/course/chapter5/1?fw=pt) of the Hugging Face course to learn more about other important topics such as loading remote or local datasets, tools for cleaning up a dataset, and creating your own dataset.
-# 
-# </Tip>
-# 
-# Start by installing 🤗 Datasets:
-# 
-# ```bash
-# pip install datasets
-# ```
-# 
-# 🤗 Datasets also support audio and image data formats:
-# 
-# * To work with audio datasets, install the [Audio](https://huggingface.co/docs/datasets/main/en/package_reference/main_classes#datasets.Audio) feature:
-# 
-#    ```bash
-#    pip install datasets[audio]
-#    ```
-# 
-# * To work with image datasets, install the [Image](https://huggingface.co/docs/datasets/main/en/package_reference/main_classes#datasets.Image) feature:
-# 
-#    ```bash
-#    pip install datasets[vision]
-#    ```
-# 
-# Besides 🤗 Datasets, make sure your preferred machine learning framework is installed:
-# 
-# ```bash
-# pip install torch
-# ```
-# ```bash
-# pip install tensorflow
-# ```
+%% [markdown]
+This quickstart is intended for developers who are ready to dive into the code and see an example of how to integrate 🤗 Datasets into their model training workflow. If you're a beginner, we recommend starting with our [tutorials](https://huggingface.co/docs/datasets/main/en/./tutorial), where you'll get a more thorough introduction.
 
-# %% [markdown]
-# ## Audio
+Each dataset is unique, and depending on the task, some datasets may require additional steps to prepare it for training. But you can always use 🤗 Datasets tools to load and process a dataset. The fastest and easiest way to get started is by loading an existing dataset from the [Hugging Face Hub](https://huggingface.co/datasets). There are thousands of datasets to choose from, spanning many tasks. Choose the type of dataset you want to work with, and let's get started!
 
-# %% [markdown]
-# Audio datasets are loaded just like text datasets. However, an audio dataset is preprocessed a bit differently. Instead of a tokenizer, you'll need a [feature extractor](https://huggingface.co/docs/transformers/main_classes/feature_extractor#feature-extractor). An audio input may also require resampling its sampling rate to match the sampling rate of the pretrained model you're using. In this quickstart, you'll prepare the [MInDS-14](https://huggingface.co/datasets/PolyAI/minds14) dataset for a model train on and classify the banking issue a customer is having.
-# 
-# **1**. Load the MInDS-14 dataset by providing the [load_dataset()](https://huggingface.co/docs/datasets/main/en/package_reference/loading_methods#datasets.load_dataset) function with the dataset name, dataset configuration (not all datasets will have a configuration), and a dataset split:
+<div class="mt-4">
+   <div class="w-full flex flex-col space-y-4 md:space-y-0 md:grid md:grid-cols-3 md:gap-y-4 md:gap-x-5">
+      <a class="!no-underline border dark:border-gray-700 p-5 rounded-lg shadow hover:shadow-lg" href="#audio"
+         ><div class="w-full text-center bg-gradient-to-r from-violet-300 via-sky-400 to-green-500 rounded-lg py-1.5 font-semibold mb-5 text-white text-lg leading-relaxed">Audio</div>
+         <p class="text-gray-700">Resample an audio dataset and get it ready for a model to classify what type of banking issue a speaker is calling about.</p>
+      </a>
+      <a class="!no-underline border dark:border-gray-700 p-5 rounded-lg shadow hover:shadow-lg" href="#vision"
+         ><div class="w-full text-center bg-gradient-to-r from-pink-400 via-purple-400 to-blue-500 rounded-lg py-1.5 font-semibold mb-5 text-white text-lg leading-relaxed">Vision</div>
+         <p class="text-gray-700">Apply data augmentation to an image dataset and get it ready for a model to diagnose disease in bean plants.</p>
+      </a>
+      <a class="!no-underline border dark:border-gray-700 p-5 rounded-lg shadow hover:shadow-lg" href="#nlp"
+         ><div class="w-full text-center bg-gradient-to-r from-orange-300 via-red-400 to-violet-500 rounded-lg py-1.5 font-semibold mb-5 text-white text-lg leading-relaxed">NLP</div>
+         <p class="text-gray-700">Tokenize a dataset and get it ready for a model to determine whether a pair of sentences have the same meaning.</p>
+      </a>
+   </div>
+</div>
+
+<Tip>
+
+Check out [Chapter 5](https://huggingface.co/course/chapter5/1?fw=pt) of the Hugging Face course to learn more about other important topics such as loading remote or local datasets, tools for cleaning up a dataset, and creating your own dataset.
+
+</Tip>
+
+Start by installing 🤗 Datasets:
+
+```bash
+pip install datasets
+```
+
+🤗 Datasets also support audio and image data formats:
+
+* To work with audio datasets, install the [Audio](https://huggingface.co/docs/datasets/main/en/package_reference/main_classes#datasets.Audio) feature:
+
+   ```bash
+   pip install datasets[audio]
+   ```
+
+* To work with image datasets, install the [Image](https://huggingface.co/docs/datasets/main/en/package_reference/main_classes#datasets.Image) feature:
+
+   ```bash
+   pip install datasets[vision]
+   ```
+
+Besides 🤗 Datasets, make sure your preferred machine learning framework is installed:
+
+```bash
+pip install torch
+```
+```bash
+pip install tensorflow
+```
+
+%% [markdown]
+## Audio
+
+%% [markdown]
+Audio datasets are loaded just like text datasets. However, an audio dataset is preprocessed a bit differently. Instead of a tokenizer, you'll need a [feature extractor](https://huggingface.co/docs/transformers/main_classes/feature_extractor#feature-extractor). An audio input may also require resampling its sampling rate to match the sampling rate of the pretrained model you're using. In this quickstart, you'll prepare the [MInDS-14](https://huggingface.co/datasets/PolyAI/minds14) dataset for a model train on and classify the banking issue a customer is having.
+
+**1**. Load the MInDS-14 dataset by providing the [load_dataset()](https://huggingface.co/docs/datasets/main/en/package_reference/loading_methods#datasets.load_dataset) function with the dataset name, dataset configuration (not all datasets will have a configuration), and a dataset split:
 
 # %%
 from datasets import load_dataset, Audio