Script Comparison Analysis

2024-07-23 08:17 UTC gpt-4o Open in ChatGPT ↗

You

Whats the difference between this script:

import random import argparse import asyncio from typing import Tuple import csv import os import nltk from transformers import pipeline, Pipeline from pythonosc import dispatcher, osc_server, udp_client from pythonosc.udp_client import SimpleUDPClient

Download necessary NLTK data

nltk.download(‘averaged_perceptron_tagger’)

nltk.download(‘wordnet’)

nltk.download(‘punkt’)

Use Local NLTK data

nltk.data.path.append(‘C:/Users/Florence Nightingale/AppData/Roaming/nltk_data’)

Initialize global message counter

new_message = 0

file_path = r’C:\Users\Florence Nightingale\Documents\Florence Nightingale Living Portrait\InputHistory.csv’

class Config: """ Configuration class for default values and settings.

   Attributes:
       LOCAL_IP (str): IP address for the local machine.
       TABLET_IP (str): IP address for the tablet.
       LOCAL_PORT (int): Port for the local machine to listen on.
       TD_PORT (int): Port for the TD (transmitting device).
       TABLET_PORT (int): Port for the tablet.
       OFFENSIVE_MODEL (str): Model name for offensive content classification.
       CONTEXT_MODEL (str): Model name for context classification.
       ZERO_SHOT_LABELS (list): List of labels for zero-shot classification.
   """
LOCAL_IP = "192.168.0.2"
TABLET_IP = "192.168.0.3"
LOCAL_PORT = 8000
TD_PORT = 9000
TABLET_PORT = 10000
OFFENSIVE_MODEL = "unitary/toxic-bert"
CONTEXT_MODEL = "facebook/bart-large-mnli"
ZERO_SHOT_LABELS = ["angel", "hero", "feminist", "rebel"]

class FlorenceAttributes: """A class to store and manipulate sentence classification attributes."""

LABELS = Config.ZERO_SHOT_LABELS

def __init__(self,
             primary: str = None,
             secondary: str = None,
             tertiary: str = None,
             quaternary: str = None,
             primary_value: int = None,
             secondary_value: int = None,
             tertiary_value: int = None,
             quaternary_value: int = None,
             ambiguous: bool = False):
    """
       Initialize the FlorenceAttributes class.

       Args:
           primary (str): Primary label.
           secondary (str): Secondary label.
           tertiary (str): Tertiary label.
           quaternary (str): Quaternary label.
           primary_value (int): Value of the primary label.
           secondary_value (int): Value of the secondary label.
           tertiary_value (int): Value of the tertiary label.
           quaternary_value (int): Value of the quaternary label.
           ambiguous (bool): Whether the attributes are ambiguous.
       """
    self.primary = primary
    self.secondary = secondary
    self.tertiary = tertiary
    self.quaternary = quaternary
    self.primary_value = primary_value
    self.secondary_value = secondary_value
    self.tertiary_value = tertiary_value
    self.quaternary_value = quaternary_value
    self.ambiguous = ambiguous

def __str__(self):
    """Return a string representation of the attributes."""
    if self.ambiguous:
        return "AMBIGUOUS"
    else:
        return (f'PRIMARY: "{self.primary}" = {self.primary_value}, '
                f'SECONDARY: "{self.secondary}" = {self.secondary_value}, '
                f'TERTIARY: "{self.tertiary}" = {self.tertiary_value}, '
                f'QUATERNARY: "{self.quaternary}" = {self.quaternary_value}')

def has_null_labels(self) -> bool:
    """Check if any labels are null or 'None'."""
    return any(label in ("", "None") for label in [self.primary, self.secondary, self.tertiary, self.quaternary])

def has_null_values(self) -> bool:
    """Check if any values are null or zero."""
    return any(value in (0, None) for value in
               [self.primary_value, self.secondary_value, self.tertiary_value, self.quaternary_value])

def fill_missing_labels(self):
    """Fill missing labels with available labels from the predefined list."""
    used_labels = {label for label in [self.primary, self.secondary, self.tertiary, self.quaternary] if label}
    free_labels = [label for label in self.LABELS if label not in used_labels]

    if not self.primary:
        self.primary = free_labels.pop() if len(free_labels) == 1 else random.choice(free_labels)
    if not self.secondary:
        self.secondary = free_labels.pop() if len(free_labels) == 1 else random.choice(free_labels)
    if not self.tertiary:
        self.tertiary = free_labels.pop() if len(free_labels) == 1 else random.choice(free_labels)
    if not self.quaternary:
        self.quaternary = free_labels.pop() if len(free_labels) == 1 else random.choice(free_labels)

def reorganize_data(self) -> str:
    """Reorganize the data for display and increment the message counter."""
    global new_message
    new_message += 1

    labels_values = zip([self.primary, self.secondary, self.tertiary, self.quaternary],
                        [self.primary_value, self.secondary_value, self.tertiary_value, self.quaternary_value])

    result = [f"{label}: {value}" for label, value in labels_values if label in self.LABELS and value is not None]
    result.append(f"/new_message {new_message}")
    return ", ".join(result)

def generate_final_message(self, word_response: str) -> str:
    """Generate the final message including the word response and increment the message counter."""
    global new_message
    new_message += 1

    return (f"{self.primary} = {self.primary_value}, "
            f"{self.secondary} = {self.secondary_value}, "
            f"{self.tertiary} = {self.tertiary_value}, "
            f"{self.quaternary} = {self.quaternary_value}, "
            f"newMessage = {new_message},  "
            f"wordResponse = {word_response}")

def parse_args(): """ Parse command-line arguments.

   Returns:
       argparse.Namespace: Parsed arguments with default values.
   """
parser = argparse.ArgumentParser(description="OSC Server for handling Unity messages")
parser.add_argument("--local_ip", default=Config.LOCAL_IP, help="The IP of the python script")
parser.add_argument("--local_port", default=Config.LOCAL_PORT, help="The port of the python script")
parser.add_argument("--td_ip", default=Config.LOCAL_IP, help="The IP of the Touch Designer app")
parser.add_argument("--td_port", type=int, default=Config.TD_PORT, help="The port of the Touch Designer app")
parser.add_argument("--unity_ip", default=Config.TABLET_IP, help="The IP of the Unity client")
parser.add_argument("--unity_port", type=int, default=Config.TABLET_PORT, help="The port of the Unity client")
return parser.parse_args()

def is_offensive(word: str, offensive_classifier: Pipeline) -> bool: """ Check if a word is offensive using a text classification model.

   Args:
       word (str): The word to check.
       offensive_classifier (Pipeline): The classifier for detecting offensive content.

   Returns:
       bool: True if the word is classified as offensive, False otherwise.
"""
result = offensive_classifier(word)
offensive = any(res['label'] == 'toxic' and res['score'] > 0.5 for res in result)
print(f"Checking if word '{word}' is offensive: {result}")
return offensive

def is_contextually_relevant(word: str, context_classifier: Pipeline) -> bool: """ Check if a word is contextually relevant using a zero-shot classification model.

Args:
    word (str): The word to check.
    context_classifier (Pipeline): The classifier for context detection.

Returns:
    bool: True if the word is contextually relevant, False otherwise.
"""

result = context_classifier(word, candidate_labels=Config.ZERO_SHOT_LABELS)
relevant = any(score > 0.3 for score in result['scores'])  # Adjusted threshold for relevance
print(f"Checking if word '{word}' is contextually relevant: {result}")
return relevant

def extract_word_response(sentence: str, offensive_classifier: Pipeline, context_classifier: Pipeline) -> str: """ Extract a contextually relevant and non-offensive word from a sentence.

Args:
    sentence (str): The input sentence.
    offensive_classifier (Pipeline): The classifier for detecting offensive content.
    context_classifier (Pipeline): The classifier for context detection.

Returns:
    str: A relevant word if found, otherwise a blank string.
"""

words = nltk.word_tokenize(sentence)
tagged_words = nltk.pos_tag(words)

print(f"Tagged words: {tagged_words}")

relevant_words = [word for word, pos in tagged_words if
                  pos in ('JJ', 'JJR', 'JJS', 'NN', 'NNS', 'NNP', 'NNPS')
                  and not is_offensive(word, offensive_classifier)]

print(f"Relevant Adjectives, Proper Nouns, and Nouns: {relevant_words}")

for word in relevant_words:
    if is_contextually_relevant(word, context_classifier):
        print(f"Chosen word: {word}")
        return word

return " "  # Return blank if no relevant word is found or if it's offensive

def analyze_sentence(sentence: str) -> dict: """ Analyze a sentence for contextual relevance using a zero-shot classification model.

Args:
    sentence (str): The input sentence.

Returns:
    dict: A dictionary with labels as keys and their corresponding scores as values.
"""

classifier = pipeline("zero-shot-classification", model=Config.CONTEXT_MODEL)
result = classifier(sentence, candidate_labels=Config.ZERO_SHOT_LABELS)
weightings = {label: int(score * 100) for label, score in zip(result['labels'], result['scores'])}
return weightings

async def classify_sentence(sentence: str, offensive_classifier: Pipeline, context_classifier: Pipeline)
-> Tuple[str, FlorenceAttributes, int, str]: """ Classify a sentence, extract relevant words, and generate a final message.

Args:
    sentence (str): The input sentence to classify.
    offensive_classifier (Pipeline):
    context_classifier (Pipeline):
Returns:
    tuple: A tuple containing the final message, attributes object, message counter, and word response.
"""
# PART 1: Analyze Sentence using one-shot classification
weightings = analyze_sentence(sentence)
sorted_weightings = sorted(weightings.items(), key=lambda item: item[1], reverse=True)

primary, primary_value = sorted_weightings[0]
secondary, secondary_value = sorted_weightings[1]
tertiary, tertiary_value = sorted_weightings[2]
quaternary, quaternary_value = sorted_weightings[3]

attr1 = FlorenceAttributes(
    primary=primary,
    secondary=secondary,
    tertiary=tertiary,
    quaternary=quaternary,
    primary_value=primary_value,
    secondary_value=secondary_value,
    tertiary_value=tertiary_value,
    quaternary_value=quaternary_value,
    ambiguous=False
)

# PART 2: Extract important words to be used as typographic elements
word_response = extract_word_response(sentence, offensive_classifier, context_classifier)

print(f"Weightings: {weightings}, Word response: {word_response}")  # Debug statement

return str(attr1.generate_final_message(word_response)).strip(), attr1, new_message, word_response

async def handle_osc_message(unity_client: SimpleUDPClient, td_client: SimpleUDPClient, unused_addr, offensive_classifier: Pipeline, context_classifier: Pipeline, sentence): """ Handle incoming OSC messages and process them.

Args:
    unity_client: The UDP client to send responses to Unity (tablet).
    td_client: The UDP client to send responses to Touch Designer.
    unused_addr: The address of the sender.
    offensive_classifier: The classifier for detecting offensive content.
    context_classifier: The classifier for context detection.
    *sentence: Additional arguments from the OSC message.
"""
print("I received message from Unity")
response, attribute, new_msg, word_response = await classify_sentence(sentence,
                                                                      offensive_classifier,
                                                                      context_classifier)

print(f"Received response from classify_sentence: {response}, {attribute}, {new_msg}, {word_response}")

save_string_to_csv(file_path, f"{sentence} , {attribute}, {word_response}" )

unity_client.send_message("/response", response)

td_client.send_message(f"/{attribute.primary}", attribute.primary_value)
td_client.send_message(f"/{attribute.secondary}", attribute.secondary_value)
td_client.send_message(f"/{attribute.tertiary}", attribute.tertiary_value)
td_client.send_message(f"/{attribute.quaternary}", attribute.quaternary_value)
td_client.send_message("/newMessage", new_msg)
td_client.send_message("/wordResponse", word_response)
td_client.send_message("/message", f"Message received: '{sentence}'")

def save_string_to_csv(file_path, string_to_save): # Ensure the directory exists os.makedirs(os.path.dirname(file_path), exist_ok=True)

# Open the CSV file in append mode, creating it if it doesn't exist
with open(file_path, mode='a', newline='') as file:
    writer = csv.writer(file)
    # Write the string as a new row
    writer.writerow([string_to_save])

def main(): """ Main function to set up and run the OSC server. """

args = parse_args()

unity_client = udp_client.SimpleUDPClient(args.unity_ip, args.unity_port)
td_client = udp_client.SimpleUDPClient(args.td_ip, args.td_port)

offensive_classifier = pipeline("text-classification", model=Config.OFFENSIVE_MODEL)
context_classifier = pipeline("zero-shot-classification", model=Config.CONTEXT_MODEL)

disp = dispatcher.Dispatcher()
disp.map("/florence_sentence", lambda unused_addr, *osc_args: asyncio.run(
    handle_osc_message(unity_client, td_client, unused_addr,
                       offensive_classifier, context_classifier,
                       *osc_args)))

server = osc_server.ThreadingOSCUDPServer((args.local_ip, args.local_port), disp)
print(f"Serving on {server.server_address}")
server.serve_forever()

if name == “main”: main()

And this one:

import random import argparse import asyncio import csv import os import nltk from transformers import pipeline from pythonosc import dispatcher, osc_server, udp_client

Use Local NLTK data

nltk.data.path.append(‘C:/Users/Florence Nightingale/AppData/Roaming/nltk_data’)

Initialize global message counter

new_message = 0

file_path = r’C:\Users\Florence Nightingale\Documents\Florence Nightingale Living Portrait\InputHistory.csv’

class Config: LOCAL_IP = “192.168.0.2” TABLET_IP = “192.168.0.3” LOCAL_PORT = 8000 TD_PORT = 9000 TABLET_PORT = 10000 OFFENSIVE_MODEL = “unitary/toxic-bert” CONTEXT_MODEL = “facebook/bart-large-mnli” ZERO_SHOT_LABELS = [“angel”, “hero”, “feminist”, “rebel”]

class FlorenceAttributes: LABELS = Config.ZERO_SHOT_LABELS

def __init__(self, primary=None, secondary=None, tertiary=None, quaternary=None, primary_value=None,
             secondary_value=None, tertiary_value=None, quaternary_value=None, ambiguous=False):
    self.primary = primary
    self.secondary = secondary
    self.tertiary = tertiary
    self.quaternary = quaternary
    self.primary_value = primary_value
    self.secondary_value = secondary_value
    self.tertiary_value = tertiary_value
    self.quaternary_value = quaternary_value
    self.ambiguous = ambiguous

def __str__(self):
    if self.ambiguous:
        return "AMBIGUOUS"
    else:
        return (f'PRIMARY: "{self.primary}" = {self.primary_value}, '
                f'SECONDARY: "{self.secondary}" = {self.secondary_value}, '
                f'TERTIARY: "{self.tertiary}" = {self.tertiary_value}, '
                f'QUATERNARY: "{self.quaternary}" = {self.quaternary_value}')

def has_null_labels(self):
    return any(label in ("", "None") for label in [self.primary, self.secondary, self.tertiary, self.quaternary])

def has_null_values(self):
    return any(value in (0, None) for value in [self.primary_value, self.secondary_value, self.tertiary_value, self.quaternary_value])

def fill_missing_labels(self):
    used_labels = {label for label in [self.primary, self.secondary, self.tertiary, self.quaternary] if label}
    free_labels = [label for label in self.LABELS if label not in used_labels]

    if not self.primary:
        self.primary = free_labels.pop() if len(free_labels) == 1 else random.choice(free_labels)
    if not self.secondary:
        self.secondary = free_labels.pop() if len(free_labels) == 1 else random.choice(free_labels)
    if not self.tertiary:
        self.tertiary = free_labels.pop() if len(free_labels) == 1 else random.choice(free_labels)
    if not self.quaternary:
        self.quaternary = free_labels.pop() if len(free_labels) == 1 else random.choice(free_labels)

def reorganize_data(self):
    global new_message
    new_message += 1

    labels_values = zip([self.primary, self.secondary, self.tertiary, self.quaternary],
                        [self.primary_value, self.secondary_value, self.tertiary_value, self.quaternary_value])

    result = [f"{label}: {value}" for label, value in labels_values if label in self.LABELS and value is not None]
    result.append(f"/new_message {new_message}")
    return ", ".join(result)

def generate_final_message(self, word_response):
    global new_message
    new_message += 1

    return (f"{self.primary} = {self.primary_value}, "
            f"{self.secondary} = {self.secondary_value}, "
            f"{self.tertiary} = {self.tertiary_value}, "
            f"{self.quaternary} = {self.quaternary_value}, "
            f"newMessage = {new_message},  "
            f"wordResponse = {word_response}")

def parse_args(): parser = argparse.ArgumentParser(description=“OSC Server for handling Unity messages”) parser.add_argument(“—local_ip”, default=Config.LOCAL_IP, help=“The IP of the python script”) parser.add_argument(“—local_port”, default=Config.LOCAL_PORT, help=“The port of the python script”) parser.add_argument(“—td_ip”, default=Config.LOCAL_IP, help=“The IP of the Touch Designer app”) parser.add_argument(“—td_port”, type=int, default=Config.TD_PORT, help=“The port of the Touch Designer app”) parser.add_argument(“—unity_ip”, default=Config.TABLET_IP, help=“The IP of the Unity client”) parser.add_argument(“—unity_port”, type=int, default=Config.TABLET_PORT, help=“The port of the Unity client”) return parser.parse_args()

def is_offensive(word, offensive_classifier): result = offensive_classifier(word) offensive = any(res[‘label’] == ‘toxic’ and res[‘score’] > 0.5 for res in result) print(f”Checking if word ‘{word}’ is offensive: {result}”) return offensive

def is_contextually_relevant(word, context_classifier): result = context_classifier(word, candidate_labels=Config.ZERO_SHOT_LABELS) relevant = any(score > 0.3 for score in result[‘scores’]) # Adjusted threshold for relevance print(f”Checking if word ‘{word}’ is contextually relevant: {result}”) return relevant

def extract_word_response(sentence, offensive_classifier, context_classifier): words = nltk.word_tokenize(sentence) tagged_words = nltk.pos_tag(words)

print(f"Tagged words: {tagged_words}")

relevant_words = [word for word, pos in tagged_words if
                  pos in ('JJ', 'JJR', 'JJS', 'NN', 'NNS', 'NNP', 'NNPS')
                  and not is_offensive(word, offensive_classifier)]

print(f"Relevant Adjectives, Proper Nouns, and Nouns: {relevant_words}")

for word in relevant_words:
    if is_contextually_relevant(word, context_classifier):
        print(f"Chosen word: {word}")
        return word

return " "  # Return blank if no relevant word is found or if it's offensive

def analyze_sentence(sentence): classifier = pipeline(“zero-shot-classification”, model=Config.CONTEXT_MODEL) result = classifier(sentence, candidate_labels=Config.ZERO_SHOT_LABELS) weightings = {label: int(score * 100) for label, score in zip(result[‘labels’], result[‘scores’])} return weightings

async def classify_sentence(sentence, offensive_classifier, context_classifier): weightings = analyze_sentence(sentence) sorted_weightings = sorted(weightings.items(), key=lambda item: item[1], reverse=True)

primary, primary_value = sorted_weightings[0]
secondary, secondary_value = sorted_weightings[1]
tertiary, tertiary_value = sorted_weightings[2]
quaternary, quaternary_value = sorted_weightings[3]

attr1 = FlorenceAttributes(
    primary=primary,
    secondary=secondary,
    tertiary=tertiary,
    quaternary=quaternary,
    primary_value=primary_value,
    secondary_value=secondary_value,
    tertiary_value=tertiary_value,
    quaternary_value=quaternary_value,
    ambiguous=False
)

word_response = extract_word_response(sentence, offensive_classifier, context_classifier)

print(f"Weightings: {weightings}, Word response: {word_response}")

return str(attr1.generate_final_message(word_response)).strip(), attr1, new_message, word_response

async def handle_osc_message(unity_client, td_client, unused_addr, offensive_classifier, context_classifier, sentence): print(“I received message from Unity”) response, attribute, new_msg, word_response = await classify_sentence(sentence, offensive_classifier, context_classifier)

print(f"Received response from classify_sentence: {response}, {attribute}, {new_msg}, {word_response}")

save_string_to_csv(file_path, f"{sentence} , {attribute}, {word_response}")

unity_client.send_message("/response", response)

td_client.send_message(f"/{attribute.primary}", attribute.primary_value)
td_client.send_message(f"/{attribute.secondary}", attribute.secondary_value)
td_client.send_message(f"/{attribute.tertiary}", attribute.tertiary_value)
td_client.send_message(f"/{attribute.quaternary}", attribute.quaternary_value)
td_client.send_message("/newMessage", new_msg)
td_client.send_message("/wordResponse", word_response)
td_client.send_message("/message", f"Message received: '{sentence}'")

def save_string_to_csv(file_path, string_to_save): os.makedirs(os.path.dirname(file_path), exist_ok=True) with open(file_path, mode=‘a’, newline=”) as file: writer = csv.writer(file) writer.writerow([string_to_save])

def main(): args = parse_args()

unity_client = udp_client.SimpleUDPClient(args.unity_ip, args.unity_port)
td_client = udp_client.SimpleUDPClient(args.td_ip, args.td_port)

offensive_classifier = pipeline("text-classification", model=Config.OFFENSIVE_MODEL)
context_classifier = pipeline("zero-shot-classification", model=Config.CONTEXT_MODEL)

disp = dispatcher.Dispatcher()
disp.map("/florence_sentence", lambda unused_addr, *osc_args: asyncio.run(handle_osc_message(unity_client, td_client, unused_addr, offensive_classifier, context_classifier, *osc_args)))

server = osc_server.ThreadingOSCUDPServer((args.local_ip, args.local_port), disp)
print(f"Serving on {server.server_address}")
server.serve_forever()

if name == “main”: main()

ChatGPT

The two scripts you provided are very similar but have some differences in terms of imports, class definitions, functions, and how the code is structured. Here’s a detailed comparison:

Imports

1. The first script has some extra imports:

   • from typing import Tuple
   • from pythonosc.udp_client import SimpleUDPClient

2. The second script has fewer imports, removing the ones mentioned above.

Download NLTK Data

1. The first script includes comments for downloading NLTK data:

   # nltk.download('averaged_perceptron_tagger')
   # nltk.download('wordnet')
   # nltk.download('punkt')

2. The second script does not include these comments.

Configuration Class (Config)

Both scripts have a Config class, but it is formatted differently in each:

1. The first script has a docstring explaining each attribute.

   class Config:
       """
       Configuration class for default values and settings.
   
       Attributes:
           LOCAL_IP (str): IP address for the local machine.
           TABLET_IP (str): IP address for the tablet.
           LOCAL_PORT (int): Port for the local machine to listen on.
           TD_PORT (int): Port for the TD (transmitting device).
           TABLET_PORT (int): Port for the tablet.
           OFFENSIVE_MODEL (str): Model name for offensive content classification.
           CONTEXT_MODEL (str): Model name for context classification.
           ZERO_SHOT_LABELS (list): List of labels for zero-shot classification.
       """
       LOCAL_IP = "192.168.0.2"
       TABLET_IP = "192.168.0.3"
       LOCAL_PORT = 8000
       TD_PORT = 9000
       TABLET_PORT = 10000
       OFFENSIVE_MODEL = "unitary/toxic-bert"
       CONTEXT_MODEL = "facebook/bart-large-mnli"
       ZERO_SHOT_LABELS = ["angel", "hero", "feminist", "rebel"]

2. The second script has a more concise Config class without the docstring.

   class Config:
       LOCAL_IP = "192.168.0.2"
       TABLET_IP = "192.168.0.3"
       LOCAL_PORT = 8000
       TD_PORT = 9000
       TABLET_PORT = 10000
       OFFENSIVE_MODEL = "unitary/toxic-bert"
       CONTEXT_MODEL = "facebook/bart-large-mnli"
       ZERO_SHOT_LABELS = ["angel", "hero", "feminist", "rebel"]

FlorenceAttributes Class

The FlorenceAttributes class is defined similarly in both scripts, but there are minor differences in formatting:

1. The first script includes docstrings for the class and its methods.

   class FlorenceAttributes:
       """A class to store and manipulate sentence classification attributes."""
   
       LABELS = Config.ZERO_SHOT_LABELS
   
       def __init__(self, primary=None, secondary=None, tertiary=None, quaternary=None, primary_value=None,
                    secondary_value=None, tertiary_value=None, quaternary_value=None, ambiguous=False):
           """
           Initialize the FlorenceAttributes class.
   
           Args:
               primary (str): Primary label.
               secondary (str): Secondary label.
               tertiary (str): Tertiary label.
               quaternary (str): Quaternary label.
               primary_value (int): Value of the primary label.
               secondary_value (int): Value of the secondary label.
               tertiary_value (int): Value of the tertiary label.
               quaternary_value (int): Value of the quaternary label.
               ambiguous (bool): Whether the attributes are ambiguous.
           """
           self.primary = primary
           self.secondary = secondary
           self.tertiary = tertiary
           self.quaternary = quaternary
           self.primary_value = primary_value
           self.secondary_value = secondary_value
           self.tertiary_value = tertiary_value
           self.quaternary_value = quaternary_value
           self.ambiguous = ambiguous
   
       def __str__(self):
           """Return a string representation of the attributes."""
           if self.ambiguous:
               return "AMBIGUOUS"
           else:
               return (f'PRIMARY: "{self.primary}" = {self.primary_value}, '
                       f'SECONDARY: "{self.secondary}" = {self.secondary_value}, '
                       f'TERTIARY: "{self.tertiary}" = {self.tertiary_value}, '
                       f'QUATERNARY: "{self.quaternary}" = {self.quaternary_value}')

2. The second script has a more concise FlorenceAttributes class without the docstrings.

   class FlorenceAttributes:
       LABELS = Config.ZERO_SHOT_LABELS
   
       def __init__(self, primary=None, secondary=None, tertiary=None, quaternary=None, primary_value=None,
                    secondary_value=None, tertiary_value=None, quaternary_value=None, ambiguous=False):
           self.primary = primary
           self.secondary = secondary
           self.tertiary = tertiary
           self.quaternary = quaternary
           self.primary_value = primary_value
           self.secondary_value = secondary_value
           self.tertiary_value = tertiary_value
           self.quaternary_value = quaternary_value
           self.ambiguous = ambiguous
   
       def __str__(self):
           if self.ambiguous:
               return "AMBIGUOUS"
           else:
               return (f'PRIMARY: "{self.primary}" = {self.primary_value}, '
                       f'SECONDARY: "{self.secondary}" = {self.secondary_value}, '
                       f'TERTIARY: "{self.tertiary}" = {self.tertiary_value}, '
                       f'QUATERNARY: "{self.quaternary}" = {self.quaternary_value}')

Function Definitions

The function definitions are very similar between the two scripts, with only minor differences in formatting and some additional comments in the first script.

1. The first script includes docstrings for each function, providing a description of the function, its arguments, and return values.

   def parse_args():
       """
       Parse command-line arguments.
   
       Returns:
           argparse.Namespace: Parsed arguments with default values.
       """
       parser = argparse.ArgumentParser(description="OSC Server for handling Unity messages")
       parser.add_argument("--local_ip", default=Config.LOCAL_IP, help="The IP of the python script")
       parser.add_argument("--local_port", default=Config.LOCAL_PORT, help="The port of the python script")
       parser.add_argument("--td_ip", default=Config.LOCAL_IP, help="The IP of the Touch Designer app")
       parser.add_argument("--td_port", type=int, default=Config.TD_PORT, help="The port of the Touch Designer app")
       parser.add_argument("--unity_ip", default=Config.TABLET_IP, help="The IP of the Unity client")
       parser.add_argument("--unity_port", type=int, default=Config.TABLET_PORT, help="The port of the Unity client")
       return parser.parse_args()

2. The second script does not include these docstrings.

   def parse_args():
       parser = argparse.ArgumentParser(description="OSC Server for handling Unity messages")
       parser.add_argument("--local_ip", default=Config.LOCAL_IP, help="The IP of the python script")
       parser.add_argument("--local_port", default=Config.LOCAL_PORT, help="The port of the python script")
       parser.add_argument("--td_ip", default=Config.LOCAL_IP, help="The IP of the Touch Designer app")
       parser.add_argument("--td_port", type=int, default=Config.TD_PORT, help="The port of the Touch Designer app")
       parser.add_argument("--unity_ip", default=Config.TABLET_IP, help="The IP of the Unity client")
       parser.add_argument("--unity_port", type=int, default=Config.TABLET_PORT, help="The port of the Unity client")
       return parser.parse_args()

Overall Structure

The overall structure and logic of both scripts are identical, with both having the same functions, class definitions, and logic flow. The main difference lies in the presence of docstrings and additional imports in the first script.

Summary

The first script is more verbose and includes detailed docstrings for most classes and functions, providing a better understanding of their purpose and usage. The second script is more concise and lacks these docstrings, making it slightly shorter but potentially less clear for someone new to the code. The extra imports in the first script might also suggest some unused or future functionality, whereas the second script sticks to only the necessary imports.