Using ffmpeg, yt-dlp, and gpt-4o to Automate Extraction and Explanation of Python Code from YouTube Videos

yt-dlp https://www.youtube.com/watch?v=SnbGD677_u0 -o out.mp4
ffmpeg -ss 1:13:00 -to 1:33:00 -i out.mp4 -vf fps=1/60 img%03d.jpg

output=""

for i in {5..8}; do
    img="img00${i}.jpg"
    result=$(sgpt -m "gpt-4o" -t 0 -s 2000 -i "$img" "Extract the Python code from screenshots. Make sure code is extracted as is. Do not extract Jupyter notebook cells output.")
    output="${output}${result}"
done

sgpt -m "gpt-4o" -t 0 -s 2000 "Explain the below Python code ${output}"

Sure, I'd be happy to explain the provided Python code snippets. Here’s a detailed breakdown:

### Part 1

```python
pd.set_option('display.max_colwidth', 1000)
```

This line sets an option in the pandas library to display a maximum column width of 1000 characters. It helps when you have large text data in your dataframe, ensuring that the entire content of a cell can be displayed without truncation.

### Part 2: Loading and Preparing FIB Dataset

```python
# Prepare FIB data

# FIB contains one-sentence summaries on CNN/DM & XSUM news articles.
# Note: We exclude the CNN/Daily Mail data as it is pretty bad.

fib_ds = load_dataset('t-feb/fib', split='test')
fib_df = fib_ds.to_pandas()
logger.info(f'No of rows in FIB: {len(fib_df)}')
```

1. **Comment**: Explains what the FIB dataset is and notes that CNN/Daily Mail data is excluded due to poor quality.
2. **Loading Dataset**: The `load_dataset` function is used to load the FIB dataset (test split) from the `t-feb/fib` configuration.
3. **Conversion to DataFrame**: Converts the loaded dataset into a pandas DataFrame using `to_pandas()`.
4. **Logging**: Logs the number of rows in the FIB DataFrame using `logger.info`.

### Part 3: Selecting Columns

```python
# Only keep selected columns.
fib_df[['dataset' == 'xsun'
fib_df.fib.df[['dataset' == 'cm_dm', 'input', 'list_choices', 'correct_choice']].head(5)
fib_df.df[['input', 'list_choices', 'correct_choice']].head(5)
```

There seems to be a syntax error here. Assuming the intent was to filter and select specific columns, the correct code might look something like:

```python
# Only keep selected columns for the 'xsun' and 'cm_dm' datasets.
xsun_df = fib_df[fib_df['dataset'] == 'xsun'][['input', 'list_choices', 'correct_choice']].head(5)
cm_dm_df = fib_df[fib_df['dataset'] == 'cm_dm'][['input', 'list_choices', 'correct_choice']].head(5)

# Display the first 5 rows of the combined result
combined_df = fib_df[['input', 'list_choices', 'correct_choice']].head(5)
```

### Part 4: Removing Duplicates and Saving Datasets

```python
fib_train = fib_train.drop_duplicates(subset=['input', 'label'])
fib_val = fib_val.drop_duplicates(subset=['input', 'label'])
fib_test = fib_test.drop_duplicates(subset=['input', 'label'])

logger.info(f"Rows in balanced train: {len(fib_train)}, val: {len(fib_val)}, test: {len(fib_test)}")

fib_train.to_csv('/content/drive/My Drive/fib/fib-train.csv', index=False)
fib_val.to_csv('/content/drive/My Drive/fib/fib-val.csv', index=False)
fib_test.to_csv('/content/drive/My Drive/fib/fib-test.csv', index=False)
```

1. **Removing Duplicates**: Deduplicate the training, validation, and test datasets using `drop_duplicates`, based on the 'input' and 'label' columns.
2. **Logging**: Log the number of rows in each deduplicated dataset.
3. **Saving to CSV**: Save the deduplicated training, validation, and test datasets to CSV files on Google Drive.

### Part 5: Re-loading and Renaming Columns in Dataset

```python
# Test loading into dataset
fib_files = {
    'train': '/content/drive/My Drive/fib/fib-train.csv',
    'val': '/content/drive/My Drive/fib/fib-val.csv',
    'test': '/content/drive/My Drive/fib/fib-test.csv'
}
fib_ds = load_dataset('csv', data_files=fib_files)
fib_ds = fib_ds.rename_column('list_choices', 'hypothesis')
fib_ds = fib_ds.rename_column('premise', 'premise_column('list_choices', 'hypothesis').rename_column('label', 'label')

logger.info(f'Label distribution - Train: {Counter(fib_ds["train"]["label"])}, Val: {Counter(fib_ds["val"]["label"])}, Test: {Counter(fib_ds["test"]["label"])}')
```

1. **File Paths Definition**: Define file paths for the saved CSV files.
2. **Loading Dataset**: Reload datasets from the CSV files using `load_dataset`.
3. **Renaming Columns**:
   - Renames the 'list_choices' column to 'hypothesis'.
   - Appears to have a syntax error. Corrected, it might look like:
     ```python
     fib_ds = fib_ds.rename_column('list_choices', 'hypothesis').rename_column('premise', 'premise_column').rename_column('label', 'label')
     ```
4. **Logging**: Log the distribution of labels in training, validation, and test datasets using `Counter`.

### Part 6: Downloading NLTK Data

```python
import nltk
nltk.download('punkt')
```

1. **Importing NLTK**: Imports the NLTK library.
2. **Downloading Dataset**: Downloads the 'punkt' tokenizer models used for tokenizing text into sentences and words.

### Part 7: Processing USB Dataset

```python
usb_train["label"] = usb_train["label"].apply(lambda x: 1 if x == 1 else 2)
usb_val["label"] = usb_val["label"].apply(lambda x: 1 if x == 1 else 2)

logger.info(f"Label distribution (train): \n{usb_train['label'].value_counts()}")
logger.info(f"Label distribution (val): \n{usb_val['label'].value_counts()}")
```

1. **Label Mapping**: Apply a lambda function to map labels. If the label is 1, it remains 1; otherwise, it changes to 2.
2. **Logging**: Log the distribution of labels in the training and validation datasets.

### Part 8: Saving and Loading USB Dataset

```python
usb_train.to_csv('/content/drive/My Drive/usb-train.csv', index=False)
usb_val.to_csv('/content/drive/My Drive/usb-val.csv', index=False)
```

1. **Saving to CSV**: Save the training and validation datasets to CSV files on Google Drive.

```python
usb_files = {
    'train': '/content/drive/My Drive/usb-train.csv',
    'val': '/content/drive/My Drive/usb-val.csv'
}
usb_ds = load_dataset('csv', data_files=usb_files)
usb_ds = usb_ds.select_columns(['source', 'summary', 'label'])
usb_ds = usb_ds.rename_column('source', 'premise').rename_column('summary', 'hypothesis')

logger.info(f"Label distribution - Train: Counter(usb_ds['train']['label']), Val: {Counter(usb_ds['val']['label'])}")
```

1. **File Paths Definition**: Define file paths for the new USB training and validation CSV files.
2. **Loading Dataset**: Reload datasets from the CSV files using `load_dataset`.
3. **Selecting Columns**: Select only the 'source', 'summary', and 'label' columns.
4. **Renaming Columns**:
   - Rename 'source' to 'premise'.
   - Rename 'summary' to 'hypothesis'.
5. **Logging**: Log the distribution of labels in the training and validation datasets for USB.

The code provided aims to preprocess and manage two datasets (FIB and USB), ensuring the datasets are clean, properly formatted, and ready for further analysis or model training.