aifeifei

from datasets import load_dataset
import sqlite3

# Load the dataset
dataset = load_dataset('aifeifei798/song_lyrics_min', split='train')

# Define a function to save the dataset to an SQLite database
def save_dataset_to_sqlite(dataset, db_path='temp_dataset.db'):
    # Connect to the SQLite database (creates a new database if it doesn't exist)
    conn = sqlite3.connect(db_path)
    cursor = conn.cursor()

    # Create a table to store the dataset
    cursor.execute('''CREATE TABLE IF NOT EXISTS songs
                      (id INTEGER PRIMARY KEY, title TEXT, tag TEXT, lyrics TEXT)''')

    # Insert each row of the dataset into the database table
    for i, row in enumerate(dataset):
        cursor.execute("INSERT INTO songs (id, title, tag, lyrics) VALUES (?, ?, ?, ?)",
                       (i, row['title'], row['tag'], row['lyrics']))

    # Commit the transaction and close the connection
    conn.commit()
    conn.close()

# Save the dataset to the SQLite database
save_dataset_to_sqlite(dataset)

# Define a function to query the database
def query_database(db_path='temp_dataset.db'):
    # Connect to the SQLite database
    conn = sqlite3.connect(db_path)
    cursor = conn.cursor()

    # Query the first five rows of the database
    cursor.execute("SELECT * FROM songs LIMIT 5")
    rows = cursor.fetchall()

    # Print each row
    for row in rows:
        print(row)

    # Close the connection
    conn.close()

# Query the database
query_database()

You're now a thinking-first LLM. For all inputs:

1. Start with <thinking>
   - Break down problems step-by-step
   - Consider multiple approaches
   - Calculate carefully
   - Identify errors
   - Evaluate critically
   - Explore edge cases
   - Check knowledge accuracy
   - Cite sources when possible

2. End with </thinking>

3. Then respond clearly based on your thinking.

The <thinking> section is invisible to users and helps you produce better answers.

For math: show all work and verify
For coding: reason through logic and test edge cases
For facts: verify information and consider reliability
For creative tasks: explore options before deciding
For analysis: examine multiple interpretations

Example:
<thinking>
[Step-by-step analysis]
[Multiple perspectives]
[Self-critique]
[Final conclusion]
</thinking>

[Clear, concise response to user]

import re

def remove_emojis(text):
    # Define a broader emoji pattern
    emoji_pattern = re.compile(
        "["
        u"\U0001F600-\U0001F64F"  # emoticons
        u"\U0001F300-\U0001F5FF"  # symbols & pictographs
        u"\U0001F680-\U0001F6FF"  # transport & map symbols
        u"\U0001F1E0-\U0001F1FF"  # flags (iOS)
        u"\U00002702-\U000027B0"
        u"\U000024C2-\U0001F251"
        u"\U0001F900-\U0001F9FF"  # supplemental symbols and pictographs
        u"\U0001FA00-\U0001FA6F"  # chess symbols and more emojis
        u"\U0001FA70-\U0001FAFF"  # more symbols and pictographs
        u"\U00002600-\U000026FF"  # miscellaneous symbols
        u"\U00002B50-\U00002B59"  # additional symbols
        u"\U0000200D"             # zero width joiner
        u"\U0000200C"             # zero width non-joiner
        u"\U0000FE0F"             # emoji variation selector
        "]+", flags=re.UNICODE
    )
    return emoji_pattern.sub(r'', text)

from PIL import Image, ImageDraw, ImageFont

def add_watermark(image):
    watermark_text = "AI Generated by DarkIdol FeiFei"

    # Ensure the input is an Image object
    if not isinstance(image, Image.Image):
        raise ValueError("Input must be a PIL Image object")

    width, height = image.size

    # Create a drawing object to draw on the image
    draw = ImageDraw.Draw(image)

    # Set the font size for the watermark text
    font_size = 10  # Set font size to 10
    try:
        # Try to use a common font file
        font = ImageFont.truetype("Iansui-Regular.ttf", font_size)
    except IOError:
        # Use the default font if the specified font file is not found
        font = ImageFont.load_default()

    # Calculate the width and height of the watermark text using textbbox
    bbox = draw.textbbox((0, 0), watermark_text, font=font)
    text_width = bbox[2] - bbox[0]
    text_height = bbox[3] - bbox[1]

    # Calculate the position for the watermark text (bottom-right corner)
    x = width - text_width - 10  # 10 is the right margin
    y = height - text_height - 10  # 10 is the bottom margin

    # Add the watermark text to the image
    draw.text((x, y), watermark_text, font=font, fill=(255, 255, 255, 128))

    # Return the modified image object
    return image