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#add find text and write category function
#this code modifies the categorize function

def extract_find_and_write_category(html_code, output_filename):
category_letter = char_to_table_category(html_code)

# Extract find and write parts
find_parts, write_parts = extract_find_write(html_code)

# Group find and write parts into columns for the first row
find_parts_row1 = find_parts[:2]
write_parts_row1 = write_parts[:2]
find_parts_row2 = find_parts[2:]
write_parts_row2 = write_parts[2:]

with open(output_filename, ‘w’, encoding=’utf-8′) as html_file:
# Write category letter as the first line
html_file.write(f’

{category_letter}

n’)

# Write first row for the two remaining categories
html_file.write(‘

‘)
html_file.write(‘

Key Word

‘)
for category in find_parts_row1:
html_file.write(f’

{category}

‘)
for category in write_parts_row1:
html_file.write(f’

{category}

‘)
html_file.write(‘

n’)

# Write first row for the two remaining categories
html_file.write(‘

‘)
html_file.write(‘

Key Word

‘)
for category in find_parts_row2:
html_file.write(f’

{category}

‘)
for category in write_parts_row2:
html_file.write(f’

{category}

‘)
html_file.write(‘

n’)

extract_find_and_write_category(점코드, ‘output_table.html’)
#this code converts the html table to jpg

import imgkit
from PIL import Image

def html_table_to_jpg(html_content, output_filename, width, height):
try:
# Use imgkit to convert HTML to an image (JPEG)
imgkit.from_string(html_content, output_filename, options={‘format’: ‘jpg’})

# Open the generated image file
image = Image.open(output_filename)

# Resize the image to the specified width and height
resized_image = image.resize((width, height), Image.LANCZOS)

# Save the resized image back to the file
resized_image.save(output_filename, ‘JPEG’)

print(f”HTML table converted, resized, and saved as {output_filename}”)
except Exception as e:
print(f”An error occurred: {str(e)}”)

# Example usage:
# Read the HTML content from the file
with open(‘output_table.html’, ‘r’) as html_file:
html_content = html_file.read()

# Specify the output filename for the resized image
output_filename = ‘output_resized_table.jpg’

# Specify the desired width and height for the resized image
width = 1366
height = 768

# Convert, resize, and save the HTML table as a resized image
html_table_to_jpg(html_content, output_filename, width, height)
#this code the image find and write

import os
import openai
from openai import OpenAI
import requests
from PIL import Image
from io import BytesIO
from lambda_netlify import text_to_img

# Replace ‘YOUR_API_KEY’ with your actual OpenAI API key
client = OpenAI(api_key = “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”)

# Create or open the “combine” folder (create if it doesn’t exist)
folder_name = “images”
if not os.path.exists(folder_name):
os.makedirs(folder_name)

# Read the text from the “words” file
with open(“words.txt”, “r”) as file:
lines = file.readlines()

# Generate and save images for words in the “words” file with count information
current_count = 1
total_count = len(lines)

for line in lines:
line = line.strip() # Remove leading/trailing whitespace
if line:
parts = line.split(maxsplit=1)
if len(parts) == 2:
category_initial, word = parts
category = f”_{category_initial}”
prompt = word

# Generate an image based on the prompt
generated_image = text_to_img(prompt) # Use your actual function to generate the image
image_response = generated_image.data[0]
revised_prompt = generated_image.data[1]

# Get the image content as bytes
image_bytes = requests.get(image_response).content
# Create a PIL Image object from bytes
image = Image.open(BytesIO(image_bytes))

# Crop the image from the bottom left with dimensions 1000×1000 pixels
cropped_image = image.crop((0, 0, 1024, 525)) # (left, upper, right, lower)

# Save the cropped image with count information
image_filename = f”{category}/{prompt}.jpg” if category else f”{prompt}.jpg”
image_path = os.path.join(folder_name, image_filename)

# Check if the category folder exists, and create it if not
category_folder = os.path.join(folder_name, category_initial)
if not os.path.exists(category_folder):
os.makedirs(category_folder)

# save the cropped image to the category folder
cropped_image_path = os.path.join(category_folder, f”{prompt}.jpg”)
cropped_image.save(cropped_image_path, “JPEG”)

# Display the count information
print(f”Saving image {current_count}/{total_count}: {category_initial} – {prompt}.jpg”)
current_count += 1

#this code the image find and write
import os
import openai
from openai import OpenAI
import requests
from PIL import Image
from io import BytesIO
from lambda_netlify import text_to_img

# Replace ‘YOUR_API_KEY’ with your actual OpenAI API key
client = OpenAI(api_key=”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″)
##
# Create or open the “combine” folder (create if it doesn’t exist)
folder_name = “images”
if not os.path.exists(folder_name):
os.makedirs(folder_name)

# Read the text from the “words” file
with open(“words.txt”, “r”) as file:
lines = file.readlines()

# Create an HTML file to write the combined content
with open(“combined.html”, “w”) as html_file:
# Initialize counts for success and failure
success_count = 0
failure_count = 0

# Write the beginning of the HTML structure
html_file.write(“””

“””)

# Define keywords to filter
keywords_to_filter = [‘의미’, ‘정의’]

# Initialize HTML structure for tables
tables_html = “”

# Iterate through each line in the “words” file
for line_index, line in enumerate(lines):
line = line.strip() # Remove leading/trailing whitespace

if line:
parts = line.split(maxsplit=1)
if len(parts) == 2:
# Parse category_initial and word
category_initial, word = parts
category = f”_{category_initial}”
prompt = word
print(prompt)
# Generate an image based on the prompt
generated_image = text_to_img(prompt) # Use your actual function to generate the image
image_response = generated_image.data[0]
revised_prompt = generated_image.data[1]

# Get the image content as bytes
image_bytes = requests.get(image_response).content

# Create a PIL Image object from bytes
image = Image.open(BytesIO(image_bytes))

# Crop the image from the bottom left with dimensions 1000×1000 pixels
cropped_image = image.crop((0, 0, 1024, 575)) # (left, upper, right, lower)

# Save the cropped image with count information
image_filename = f”{prompt}” if category else f”{prompt}”
image_path = os.path.join(folder_name, image_filename)

# Check if the category folder exists, and create it if not
category_folder = os.path.join(folder_name, category_initial)
if not os.path.exists(category_folder):
os.makedirs(category_folder)

# Save the cropped image to the category folder
if not any(keyword in prompt for keyword in keywords_to_filter): cropped_image_path = os.path.join(category_folder, f”{prompt}.jpg”)
cropped_image.save(cropped_image_path, “JPEG”)

if any(keyword in prompt for keyword in keywords_to_filter):
print(f”Skipping {prompt} as it contains a filtered keyword.”)
html_file.write(“
“)
else:
print(f”Skipping {prompt.lower()} as it contains a filtered keyword.”)

if any(keyword in prompt for keyword in keywords_to_filter):
print(f”Skipping {prompt} as it contains a filtered keyword.”)
html_file.write(“
“)
else:
print(f”Skipping {prompt.lower()} as it contains a filtered keyword.”)

# Update counts based on success or failure
if revised_prompt is None:
failure_count += 1
else:
success_count += 1

# Write the table structure to the HTML file
html_file.write(f”

Du Tem Bland -Precision Knowledge

“)
html_file.write(
f”

n”
f”

n”
f”

n”
f”

n”
)

# Iterate through each line in “words.txt”
for line_index, line in enumerate(lines):
parts = line.strip().split(maxsplit=1)
if len(parts) == 2:
category, word = parts

# Extract the first letter from the category
category_initial = category[0] if category else “”

# Append image to the corresponding row and column in the table
image_filename = f”{category_initial}/{word}.jpg”
image_path = os.path.join(folder_name, image_filename)
if os.path.exists(image_path):
with Image.open(image_path) as image:
# Resize the image to 800×800 pixels
resized_image = image.resize((500, 500))
# Save the resized image temporarily
resized_image_path = “resized_image.png”
resized_image.save(resized_image_path)

# Check if it’s the first image in the row to determine row opening
if line_index % 8 == 0:
html_file.write(“

n”)

# Add the image tag to the HTML file
html_file.write(f’

n’)

# Check if it’s the last image in the row to determine row closing
if line_index % 8 == 7 or line_index == len(lines) – 1:
html_file.write(“

n”)
# Check if it’s the last 4 images to determine row closing
if (line_index + 1) % 4 == 0:
html_file.write(“

n”)

# Write the end of the HTML structure
html_file.write(“””

“””)

# Print success and failure counts
print(“Success:”, success_count)
print(“Failure:”, failure_count)

#this code the image find and write
import os
import openai
from openai import OpenAI
import requests
from PIL import Image
from io import BytesIO
from lambda_netlify import text_to_img

# Replace ‘YOUR_API_KEY’ with your actual OpenAI API key
client = OpenAI(api_key=”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″)
##
# Create or open the “combine” folder (create if it doesn’t exist)
folder_name = “images”
if not os.path.exists(folder_name):
os.makedirs(folder_name)

# Read the text from the “words” file
with open(“words.txt”, “r”) as file:
lines = file.readlines()

# Create an HTML file to write the combined content
with open(“combined.html”, “w”) as html_file:
# Initialize counts for success and failure
success_count = 0
failure_count = 0

# Write the beginning of the HTML structure
html_file.write(“””

“””)

# Define keywords to filter
keywords_to_filter = [‘의미’, ‘정의’]

# Initialize HTML structure for tables
tables_html = “”

# Iterate through each line in the “words” file
for line_index, line in enumerate(lines):
line = line.strip() # Remove leading/trailing whitespace

if line:
parts = line.split(maxsplit=1)
if len(parts) == 2:
# Parse category_initial and word
category_initial, word = parts
category = f”_{category_initial}”
prompt = word
print(prompt)
# Generate an image based on the prompt
generated_image = text_to_img(prompt) # Use your actual function to generate the image
image_response = generated_image.data[0]
revised_prompt = generated_image.data[1]

# Get the image content as bytes
image_bytes = requests.get(image_response).content

# Create a PIL Image object from bytes
image = Image.open(BytesIO(image_bytes))

# Crop the image from the bottom left with dimensions 1000×1000 pixels
cropped_image = image.crop((0, 0, 1024, 575)) # (left, upper, right, lower)

# Save the cropped image with count information
image_filename = f”{prompt}” if category else f”{prompt}”
image_path = os.path.join(folder_name, image_filename)

# Check if the category folder exists, and create it if not
category_folder = os.path.join(folder_name, category_initial)
if not os.path.exists(category_folder):
os.makedirs(category_folder)

# Save the cropped image to the category folder
if not any(keyword in prompt for keyword in keywords_to_filter): cropped_image_path = os.path.join(category_folder, f”{prompt}.jpg”)
cropped_image.save(cropped_image_path, “JPEG”)

if any(keyword in prompt for keyword in keywords_to_filter):
print(f”Skipping {prompt} as it contains a filtered keyword.”)
html_file.write(“
“)
else:
print(f”Skipping {prompt.lower()} as it contains a filtered keyword.”)

if any(keyword in prompt for keyword in keywords_to_filter):
print(f”Skipping {prompt} as it contains a filtered keyword.”)
html_file.write(“
“)
else:
print(f”Skipping {prompt.lower()} as it contains a filtered keyword.”)

# Update counts based on success or failure
if revised_prompt is None:
failure_count += 1
else:
success_count += 1

# Write the table structure to the HTML file
html_file.write(f”

Du Tem Bland -Precision Knowledge

“)
html_file.write(
f”

Search

n”
f”

n”
f”

n”
f”

n”
f”

n”
)

# Iterate through each line in “words.txt”
for line_index, line in enumerate(lines):
parts = line.strip().split(maxsplit=1)
if len(parts) == 2:
category, word = parts
print(word)
# Check if the word matches a filtered keyword or category initial
if any(keyword == word for keyword in keywords_to_filter) or any(keyword.lower() == word for keyword in category_initial):
print(f”Skipping {word} as it matches a filtered keyword or category initial.”)
continue

# Extract the first letter from the category
category_initial = category[0] if category else “”

# Append image to the corresponding row and column in the table
image_filename = f”{category_initial}/{word}.jpg”
image_path = os.path.join(folder_name, image_filename)
if os.path.exists(image_path):
with Image.open(image_path) as image:
# Resize the image to 800×800 pixels
resized_image = image.resize((500, 500))
# Save the resized image temporarily
resized_image_path = “resized_image.png”
resized_image.save(resized_image_path)

# Check if it’s the first image in the row to determine row opening
if line_index % 8 == 0:
html_file.write(“

n”)

# Add the image tag to the HTML file
html_file.write(f’

n’)

# Check if it’s the last image in the row to determine row closing
if line_index % 8 == 7 or line_index == len(lines) – 1:
html_file.write(“

n”)
# Check if it’s the last 4 images to determine row closing
if (line_index + 1) % 4 == 0:
html_file.write(“

n”)

# Write the end of the HTML structure
html_file.write(“””

“””)

# Print success and failure counts
print(“Success:”, success_count)
print(“Failure:”, failure_count)

Search