REQUIREMENTS
* given a goal, enumerate sub-goals with using a language model, such as GPT-3
* execute the next sub-goal from text by dispatching the goal to call download of a website, writing to a file, visiting a website
write pythonic pseudocode of classes and functions to implement of the requirements!
Here's a Pythonic pseudocode implementation of your requirements using classes and functions. Note that this example assumes you have access to GPT-3 and have the necessary API keys and libraries.
python
import openai
import requests
import os
class GPT3Helper:
def __init__(self, api_key):
self.api_key = api_key
openai.api_key = api_key
def generate_subgoals(self, prompt):
response = openai.Completion.create(
engine="davinci-codex",
prompt=prompt,
max_tokens=100,
n=1,
stop=None,
temperature=0.5,
)
return response.choices[0].text.strip().split('\n')
class TaskExecutor:
def __init__(self, subgoals):
self.subgoals = subgoals
def download_website(self, url):
response = requests.get(url)
return response.text
def write_to_file(self, content, file_name):
with open(file_name, 'w') as f:
f.write(content)
def visit_website(self, url):
# You can use a web browser automation tool like Selenium to visit the website
pass
def execute_subgoals(self):
for subgoal in self.subgoals:
action, args = subgoal.split(" ", 1)
if action == "download":
content = self.download_website(args)
elif action == "write":
file_name = args
self.write_to_file(content, file_name)
elif action == "visit":
self.visit_website(args)
else:
print("Unknown action:", action)
if __name__ == "__main__":
API_KEY = "your_gpt3_api_key_here"
gpt3_helper = GPT3Helper(API_KEY)
goal = "Given a goal, download a website, write its content to a file, and visit the website."
subgoals = gpt3_helper.generate_subgoals(goal)
task_executor = TaskExecutor(subgoals)
task_executor.execute_subgoals()
This pseudocode first initializes a GPT-3 Helper class with an API key, then generates subgoals based on the provided goal. It then initializes a Task Executor class with the generated subgoals, and executes the subgoals one by one.
Note that this example uses the requests library to download websites, but it does not include a library to visit websites. You can use a library like Selenium for that purpose.
please improve: add a loop and do the subgoaling forever!
Here's an updated version of the Pythonic pseudocode with an infinite loop that generates and executes subgoals continuously. Note that the break statement is included to allow stopping the loop when a specific condition is met.
python
import openai
import requests
import os
class GPT3Helper:
def __init__(self, api_key):
self.api_key = api_key
openai.api_key = api_key
def generate_subgoals(self, prompt):
response = openai.Completion.create(
engine="davinci-codex",
prompt=prompt,
max_tokens=100,
n=1,
stop=None,
temperature=0.5,
)
return response.choices[0].text.strip().split('\n')
class TaskExecutor:
def __init__(self):
pass
def download_website(self, url):
response = requests.get(url)
return response.text
def write_to_file(self, content, file_name):
with open(file_name, 'w') as f:
f.write(content)
def visit_website(self, url):
# You can use a web browser automation tool like Selenium to visit the website
pass
def execute_subgoals(self, subgoals):
for subgoal in subgoals:
action, args = subgoal.split(" ", 1)
if action == "download":
content = self.download_website(args)
elif action == "write":
file_name = args
self.write_to_file(content, file_name)
elif action == "visit":
self.visit_website(args)
else:
print("Unknown action:", action)
if __name__ == "__main__":
API_KEY = "your_gpt3_api_key_here"
gpt3_helper = GPT3Helper(API_KEY)
task_executor = TaskExecutor()
goal = "Given a goal, download a website, write its content to a file, and visit the website."
while True:
subgoals = gpt3_helper.generate_subgoals(goal)
task_executor.execute_subgoals(subgoals)
# Add a condition to break the loop if needed
# For example, break when the user inputs 'stop'
user_input = input("Type 'stop' to exit the loop or press Enter to continue: ")
if user_input.lower() == 'stop':
break
In this version, the execute_subgoals() function is called within an infinite loop, and the user can stop the loop by typing 'stop' when prompted.
come up with bulletpoints of ideas to improve the code!
Here are some ideas to improve the code:
Error handling:
Add proper exception handling for potential errors, such as network issues, file I/O errors, or issues with the GPT-3 API.
User-defined goals:
Allow users to input their own goals instead of using a hard-coded goal string.
Extensibility:
Create a more flexible and modular architecture for handling different types of subgoals, making it easier to add new types of actions.
Logging:
Implement a logging system to track the execution of subgoals and any errors that may occur.
Configurable subgoal generation:
Allow users to configure GPT-3 API parameters, such as the number of tokens, the temperature, or the stop sequence, to better tailor subgoal generation to their needs.
Parallel execution:
Implement parallel execution of subgoals when possible, to improve the overall efficiency and speed of the program.
Progress indication:
Provide a visual indication of the progress made in executing subgoals, such as a progress bar or a percentage complete.
Support for multiple goals:
Allow users to provide a list of goals to be executed one after the other or concurrently.
Integration with web automation tools:
Integrate with web automation tools like Selenium or Playwright to perform more complex tasks when visiting websites.
User feedback and confirmation:
Allow users to confirm the execution of specific subgoals or to provide feedback on the generated subgoals to improve the GPT-3 model's understanding of their requirements.
These improvements can help make the code more robust, flexible, and user-friendly, while also increasing its overall capabilities.
implement the improvements!
Here is an updated version of the code implementing some of the suggested improvements. Due to the complexity of certain improvements, such as the integration with web automation tools or parallel execution, those have been omitted in this example.
python
import openai
import requests
import os
import logging
# Set up logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
class GPT3Helper:
def __init__(self, api_key):
self.api_key = api_key
openai.api_key = api_key
def generate_subgoals(self, prompt, max_tokens=100, temperature=0.5, stop=None):
try:
response = openai.Completion.create(
engine="davinci-codex",
prompt=prompt,
max_tokens=max_tokens,
n=1,
stop=stop,
temperature=temperature,
)
return response.choices[0].text.strip().split('\n')
except Exception as e:
logging.error(f"Error generating subgoals: {e}")
return []
class TaskExecutor:
def __init__(self):
pass
def download_website(self, url):
try:
response = requests.get(url)
return response.text
except Exception as e:
logging.error(f"Error downloading website: {e}")
return ""
def write_to_file(self, content, file_name):
try:
with open(file_name, 'w') as f:
f.write(content)
except Exception as e:
logging.error(f"Error writing to file: {e}")
def visit_website(self, url):
# You can use a web browser automation tool like Selenium to visit the website
pass
def execute_subgoals(self, subgoals):
for subgoal in subgoals:
action, args = subgoal.split(" ", 1)
if action == "download":
content = self.download_website(args)
elif action == "write":
file_name = args
self.write_to_file(content, file_name)
elif action == "visit":
self.visit_website(args)
else:
logging.warning(f"Unknown action: {action}")
if __name__ == "__main__":
API_KEY = "your_gpt3_api_key_here"
gpt3_helper = GPT3Helper(API_KEY)
task_executor = TaskExecutor()
while True:
goal = input("Enter your goal (or type 'exit' to quit): ")
if goal.lower() == 'exit':
break
subgoals = gpt3_helper.generate_subgoals(goal)
task_executor.execute_subgoals(subgoals)
# Allow the user to provide feedback on the generated subgoals
feedback = input("If you have any feedback on the generated subgoals, please provide it here (or press Enter to continue): ")
if feedback:
gpt3_helper.generate_subgoals(f"The previous subgoals were not satisfactory because: {feedback}")
This version of the code includes:
Error handling for potential exceptions.
User input for goals.
Logging for improved debugging and tracking.
Customizable GPT-3 API parameters.
User feedback on generated subgoals.
Remember to replace the your_gpt3_api_key_here placeholder with your actual GPT-3 API key.
add to execute_subgoals(): summarize()
In order to add a summarize() function to the execute_subgoals() method, we will first need to create the function within the TaskExecutor class. In this example, we will use the GPT-3 API to generate summaries. Here's the updated code:
python
import openai
import requests
import os
import logging
# Set up logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
class GPT3Helper:
def __init__(self, api_key):
self.api_key = api_key
openai.api_key = api_key
def generate_subgoals(self, prompt, max_tokens=100, temperature=0.5, stop=None):
try:
response = openai.Completion.create(
engine="davinci-codex",
prompt=prompt,
max_tokens=max_tokens,
n=1,
stop=stop,
temperature=temperature,
)
return response.choices[0].text.strip().split('\n')
except Exception as e:
logging.error(f"Error generating subgoals: {e}")
return []
class TaskExecutor:
def __init__(self, gpt3_helper):
self.gpt3_helper = gpt3_helper
def download_website(self, url):
try:
response = requests.get(url)
return response.text
except Exception as e:
logging.error(f"Error downloading website: {e}")
return ""
def write_to_file(self, content, file_name):
try:
with open(file_name, 'w') as f:
f.write(content)
except Exception as e:
logging.error(f"Error writing to file: {e}")
def visit_website(self, url):
# You can use a web browser automation tool like Selenium to visit the website
pass
def summarize(self, text):
prompt = f"Please summarize the following text:\n{text}\nSummary:"
summary = self.gpt3_helper.generate_subgoals(prompt, max_tokens=50)
return summary[0] if summary else ""
def execute_subgoals(self, subgoals):
for subgoal in subgoals:
action, args = subgoal.split(" ", 1)
if action == "download":
content = self.download_website(args)
elif action == "write":
file_name = args
self.write_to_file(content, file_name)
elif action == "visit":
self.visit_website(args)
elif action == "summarize":
summary = self.summarize(content)
print(f"Summary: {summary}")
else:
logging.warning(f"Unknown action: {action}")
if __name__ == "__main__":
API_KEY = "your_gpt3_api_key_here"
gpt3_helper = GPT3Helper(API_KEY)
task_executor = TaskExecutor(gpt3_helper)
while True:
goal = input("Enter your goal (or type 'exit' to quit): ")
if goal.lower() == 'exit':
break
subgoals = gpt3_helper.generate_subgoals(goal)
task_executor.execute_subgoals(subgoals)
# Allow the user to provide feedback on the generated subgoals
feedback = input("If you have any feedback on the generated subgoals, please provide it here (or press Enter to continue): ")
if feedback:
gpt3_helper.generate_subgoals(f"The previous subgoals were not satisfactory because: {feedback}")
In this version of the code, we added a summarize() function that takes a text input and uses GPT-3 to generate
come with with bulletpoint of ideas for execute_subgoals()
Here are some ideas for additional functionality that can be added to the execute_subgoals() method:
Translation:
Add a translate() function that translates text from one language to another using GPT-3 or another translation API.
Sentiment analysis:
Add a sentiment_analysis() function that analyzes the sentiment of the text (positive, negative, or neutral) using GPT-3 or another NLP library.
Text extraction:
Add a text_extraction() function that extracts specific information from the text, such as names, dates, or locations, using regular expressions or NLP techniques.
Data scraping:
Add a scrape_data() function that extracts structured data from websites, such as tables or lists, using web scraping techniques or libraries like Beautiful Soup.
Content generation:
Add a generate_content() function that creates new content, such as blog posts or social media updates, using GPT-3 or another text generation API.
Text to speech:
Add a text_to_speech() function that converts text into spoken audio using a text-to-speech API or library.
Speech to text:
Add a speech_to_text() function that transcribes spoken audio into text using a speech-to-text API or library.
Image recognition:
Add an image_recognition() function that identifies objects, people, or text within images using an image recognition API or library.
File conversion:
Add a file_conversion() function that converts files from one format to another, such as PDF to Word or HTML to Markdown.
Email automation:
Add an email_automation() function that sends emails, schedules meetings, or performs other email-related tasks using an email API or library.
These additional functions can help make the execute_subgoals() method more versatile and capable of handling a wider range of tasks.
implement 3 of the most needed improvements!
Here's an updated version of the code implementing three additional improvements: translation, sentiment analysis, and text extraction. For sentiment analysis, we will use the TextBlob library, while for translation and text extraction, we will utilize GPT-3.
python
import openai
import requests
import os
import logging
from textblob import TextBlob
# Set up logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
class GPT3Helper:
def __init__(self, api_key):
self.api_key = api_key
openai.api_key = api_key
def generate_subgoals(self, prompt, max_tokens=100, temperature=0.5, stop=None):
try:
response = openai.Completion.create(
engine="davinci-codex",
prompt=prompt,
max_tokens=max_tokens,
n=1,
stop=stop,
temperature=temperature,
)
return response.choices[0].text.strip().split('\n')
except Exception as e:
logging.error(f"Error generating subgoals: {e}")
return []
class TaskExecutor:
def __init__(self, gpt3_helper):
self.gpt3_helper = gpt3_helper
# ... (other methods) ...
def translate(self, text, target_language):
prompt = f"Translate the following text to {target_language}:\n{text}\nTranslation:"
translation = self.gpt3_helper.generate_subgoals(prompt, max_tokens=50)
return translation[0] if translation else ""
def sentiment_analysis(self, text):
blob = TextBlob(text)
sentiment = blob.sentiment.polarity
if sentiment > 0:
return "positive"
elif sentiment < 0:
return "negative"
else:
return "neutral"
def text_extraction(self, text, pattern):
prompt = f"Extract {pattern} from the following text:\n{text}\nExtraction:"
extraction = self.gpt3_helper.generate_subgoals(prompt, max_tokens=50)
return extraction[0] if extraction else ""
def execute_subgoals(self, subgoals):
for subgoal in subgoals:
action, args = subgoal.split(" ", 1)
# ... (previous actions) ...
elif action == "translate":
target_language = args
translation = self.translate(content, target_language)
print(f"Translation: {translation}")
elif action == "sentiment_analysis":
sentiment = self.sentiment_analysis(content)
print(f"Sentiment: {sentiment}")
elif action == "text_extraction":
pattern = args
extraction = self.text_extraction(content, pattern)
print(f"Extraction: {extraction}")
else:
logging.warning(f"Unknown action: {action}")
if __name__ == "__main__":
# ... (previous code) ...
In this version of the code, we added three new actions in the execute_subgoals() method: translation, sentiment analysis, and text extraction. The translate() function uses GPT-3 to translate text, the sentiment_analysis() function uses TextBlob to analyze sentiment, and the text_extraction() function uses GPT-3 to extract specific patterns from the text.
Remember to install the TextBlob library by running pip install textblob and replace the your_gpt3_api_key_here placeholder with your actual GPT-3 API key.
come with with bulletpoint of ideas for execute_subgoals()
Here are some additional ideas for enhancing the execute_subgoals() method with new functionalities:
Text summarization:
Add a summarize() function that generates a concise summary of a given text using GPT-3 or another NLP library.
Text classification:
Add a classify_text() function that categorizes text into predefined categories using machine learning techniques or pre-trained models.
Keyword extraction:
Add a keyword_extraction() function that identifies and extracts the most important keywords or phrases from a given text using NLP techniques or libraries.
Spell checking and correction:
Add a spell_check() function that detects and corrects spelling errors in a given text using NLP libraries or APIs.
Readability analysis:
Add a readability_analysis() function that measures the readability of a given text using readability metrics like Flesch-Kincaid, Gunning Fog, or SMOG.
Paraphrasing:
Add a paraphrase() function that rephrases a given text while preserving its original meaning using GPT-3 or another NLP library.
Content moderation:
Add a moderate_content() function that filters out inappropriate or offensive content from a given text using content moderation APIs or libraries.
Question-answering:
Add a question_answering() function that provides answers to user-posed questions based on a given text or context using GPT-3 or another NLP library.
Syntax and grammar checking:
Add a syntax_check() function that detects and corrects syntax and grammar errors in a given text using NLP libraries or APIs.
Named entity recognition (NER):
Add a named_entity_recognition() function that identifies and extracts entities like names, organizations, or locations from a given text using NLP techniques or libraries.
These enhancements can further expand the capabilities of the execute_subgoals() method, allowing it to handle a broader range of text-related tasks.
implement 3 of the most needed improvements!
In this updated version of the code, I have added three additional improvements: text summarization, paraphrasing, and named entity recognition (NER). For text summarization and paraphrasing, we will use GPT-3, while for NER, we will use the spaCy library.
python
import openai
import requests
import os
import logging
import spacy
# Set up logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
class GPT3Helper:
# ... (existing code) ...
class TaskExecutor:
def __init__(self, gpt3_helper):
self.gpt3_helper = gpt3_helper
self.nlp = spacy.load("en_core_web_sm")
# ... (other methods) ...
def summarize(self, text):
prompt = f"Please summarize the following text:\n{text}\nSummary:"
summary = self.gpt3_helper.generate_subgoals(prompt, max_tokens=50)
return summary[0] if summary else ""
def paraphrase(self, text):
prompt = f"Please paraphrase the following text:\n{text}\nParaphrase:"
paraphrase = self.gpt3_helper.generate_subgoals(prompt, max_tokens=50)
return paraphrase[0] if paraphrase else ""
def named_entity_recognition(self, text):
doc = self.nlp(text)
entities = [(ent.text, ent.label_) for ent in doc.ents]
return entities
def execute_subgoals(self, subgoals):
for subgoal in subgoals:
action, args = subgoal.split(" ", 1)
# ... (previous actions) ...
elif action == "summarize":
summary = self.summarize(content)
print(f"Summary: {summary}")
elif action == "paraphrase":
paraphrase = self.paraphrase(content)
print(f"Paraphrase: {paraphrase}")
elif action == "named_entity_recognition":
entities = self.named_entity_recognition(content)
print(f"Entities: {entities}")
else:
logging.warning(f"Unknown action: {action}")
if __name__ == "__main__":
# ... (previous code) ...
In this version of the code, we added three new actions in the execute_subgoals() method: text summarization, paraphrasing, and named entity recognition. The summarize() function uses GPT-3 to generate summaries, the paraphrase() function uses GPT-3 to create paraphrases, and the named_entity_recognition() function uses spaCy to identify and extract named entities from the text.
Remember to install the spaCy library and its English model by running pip install spacy and python -m spacy download en_core_web_sm, and replace the your_gpt3_api_key_here placeholder with your actual GPT-3 API key.
come with with bulletpoint of ideas for execute_subgoals()
Here are some additional ideas for enhancing the execute_subgoals() method with new functionalities:
Text classification:
Add a classify_text() function that categorizes text into predefined categories using machine learning techniques or pre-trained models.
Keyword extraction:
Add a keyword_extraction() function that identifies and extracts the most important keywords or phrases from a given text using NLP techniques or libraries.
Spell checking and correction:
Add a spell_check() function that detects and corrects spelling errors in a given text using NLP libraries or APIs.
Readability analysis:
Add a readability_analysis() function that measures the readability of a given text using readability metrics like Flesch-Kincaid, Gunning Fog, or SMOG.
Content moderation:
Add a moderate_content() function that filters out inappropriate or offensive content from a given text using content moderation APIs or libraries.
Question-answering:
Add a question_answering() function that provides answers to user-posed questions based on a given text or context using GPT-3 or another NLP library.
Syntax and grammar checking:
Add a syntax_check() function that detects and corrects syntax and grammar errors in a given text using NLP libraries or APIs.
Text to speech:
Add a text_to_speech() function that converts text into spoken audio using a text-to-speech API or library.
Speech to text:
Add a speech_to_text() function that transcribes spoken audio into text using a speech-to-text API or library.
Image recognition:
Add an image_recognition() function that identifies objects, people, or text within images using an image recognition API or library.
These enhancements can further expand the capabilities of the execute_subgoals() method, allowing it to handle a broader range of tasks.
implement 3 of the most needed improvements!
In this updated version of the code, I have added three additional improvements: text classification, keyword extraction, and spell checking. For text classification, we will use a pre-trained model from the scikit-learn library. For keyword extraction, we will use the RAKE-NLTK library. For spell checking, we will use the pyspellchecker library.
python
import openai
import requests
import os
import logging
import spacy
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.naive_bayes import MultinomialNB
from rake_nltk import Rake
from spellchecker import SpellChecker
# Set up logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
class GPT3Helper:
# ... (existing code) ...
class TaskExecutor:
def __init__(self, gpt3_helper):
self.gpt3_helper = gpt3_helper
self.nlp = spacy.load("en_core_web_sm")
self.rake = Rake()
self.spell_checker = SpellChecker()
# ... (other methods) ...
def classify_text(self, text, model, vectorizer):
transformed_text = vectorizer.transform([text])
label = model.predict(transformed_text)
return label[0]
def keyword_extraction(self, text):
self.rake.extract_keywords_from_text(text)
keywords = self.rake.get_ranked_phrases()
return keywords
def spell_check(self, text):
words = text.split()
misspelled = self.spell_checker.unknown(words)
corrected_text = [self.spell_checker.correction(word) if word in misspelled else word for word in words]
return ' '.join(corrected_text)
def execute_subgoals(self, subgoals):
for subgoal in subgoals:
action, args = subgoal.split(" ", 1)
# ... (previous actions) ...
elif action == "classify_text":
model, vectorizer = args # Load or pass the pre-trained model and vectorizer
label = self.classify_text(content, model, vectorizer)
print(f"Classification: {label}")
elif action == "keyword_extraction":
keywords = self.keyword_extraction(content)
print(f"Keywords: {keywords}")
elif action == "spell_check":
corrected_text = self.spell_check(content)
print(f"Corrected text: {corrected_text}")
else:
logging.warning(f"Unknown action: {action}")
if __name__ == "__main__":
# ... (previous code) ...
In this version of the code, we added three new actions in the execute_subgoals() method: text classification, keyword extraction, and spell checking. The classify_text() function uses a pre-trained model from scikit-learn to classify text, the keyword_extraction() function uses the RAKE-NLTK library to extract keywords, and the spell_check() function uses the pyspellchecker library to check and correct spelling errors in the text.
Remember to install the required libraries by running pip install scikit-learn rake_nltk pyspellchecker, and replace the your_gpt3_api_key_here placeholder with your actual GPT-3 API key. Note that you'll also need to load or pass the pre-trained text classification model and vectorizer in the classify_text action.
come with with bulletpoint of ideas for execute_subgoals()
Here are some additional ideas for enhancing the execute_subgoals() method with new functionalities:
Readability analysis:
Add a readability_analysis() function that measures the readability of a given text using readability metrics like Flesch-Kincaid, Gunning Fog, or SMOG.
Content moderation:
Add a moderate_content() function that filters out inappropriate or offensive content from a given text using content moderation APIs or libraries.
Question-answering:
Add a question_answering() function that provides answers to user-posed questions based on a given text or context using GPT-3 or another NLP library.
Syntax and grammar checking:
Add a syntax_check() function that detects and corrects syntax and grammar errors in a given text using NLP libraries or APIs.
Text to speech:
Add a text_to_speech() function that converts text into spoken audio using a text-to-speech API or library.
Speech to text:
Add a speech_to_text() function that transcribes spoken audio into text using a speech-to-text API or library.
Image recognition:
Add an image_recognition() function that identifies objects, people, or text within images using an image recognition API or library.
Optical Character Recognition (OCR):
Add an ocr() function that extracts text from images or scanned documents using OCR libraries or APIs.
Sentiment analysis enhancement:
Improve the sentiment_analysis() function by using more advanced NLP libraries or models for increased accuracy.
Language detection:
Add a detect_language() function that identifies the language of a given text using NLP libraries or APIs.
These enhancements can further expand the capabilities of the execute_subgoals() method, allowing it to handle a broader range of tasks.
implement 3 of the most needed improvements!
In this updated version of the code, I have added three additional improvements: readability analysis, text-to-speech, and language detection. For readability analysis, we will use the textstat library. For text-to-speech, we will use the gTTS (Google Text-to-Speech) library. For language detection, we will use the langdetect library.
python
import openai
import requests
import os
import logging
import spacy
from textstat import textstat
from gtts import gTTS
from langdetect import detect
# Set up logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
class GPT3Helper:
# ... (existing code) ...
class TaskExecutor:
def __init__(self, gpt3_helper):
self.gpt3_helper = gpt3_helper
self.nlp = spacy.load("en_core_web_sm")
# ... (other methods) ...
def readability_analysis(self, text):
fk_score = textstat.flesch_kincaid_grade(text)
gf_score = textstat.gunning_fog(text)
smog_score = textstat.smog_index(text)
return {"Flesch-Kincaid": fk_score, "Gunning Fog": gf_score, "SMOG": smog_score}
def text_to_speech(self, text, filename):
tts = gTTS(text)
tts.save(filename)
def detect_language(self, text):
language = detect(text)
return language
def execute_subgoals(self, subgoals):
for subgoal in subgoals:
action, args = subgoal.split(" ", 1)
# ... (previous actions) ...
elif action == "readability_analysis":
scores = self.readability_analysis(content)
print(f"Readability scores: {scores}")
elif action == "text_to_speech":
filename = args
self.text_to_speech(content, filename)
print(f"Text-to-speech saved to: {filename}")
elif action == "detect_language":
language = self.detect_language(content)
print(f"Detected language: {language}")
else:
logging.warning(f"Unknown action: {action}")
if __name__ == "__main__":
# ... (previous code) ...
In this version of the code, we added three new actions in the execute_subgoals() method: readability analysis, text-to-speech, and language detection. The readability_analysis() function uses the textstat library to compute readability scores, the text_to_speech() function uses the gTTS library to convert text into spoken audio and save it as an audio file, and the detect_language() function uses the langdetect library to identify the language of a given text.
Remember to install the required libraries by running pip install textstat gtts langdetect, and replace the your_gpt3_api_key_here placeholder with your actual GPT-3 API key.
come with with bulletpoint of ideas for execute_subgoals()
Here are some additional ideas for enhancing the execute_subgoals() method with new functionalities:
Content moderation:
Add a moderate_content() function that filters out inappropriate or offensive content from a given text using content moderation APIs or libraries.
Question-answering:
Add a question_answering() function that provides answers to user-posed questions based on a given text or context using GPT-3 or another NLP library.
Syntax and grammar checking:
Add a syntax_check() function that detects and corrects syntax and grammar errors in a given text using NLP libraries or APIs.
Speech to text:
Add a speech_to_text() function that transcribes spoken audio into text using a speech-to-text API or library.
Image recognition:
Add an image_recognition() function that identifies objects, people, or text within images using an image recognition API or library.
Optical Character Recognition (OCR):
Add an ocr() function that extracts text from images or scanned documents using OCR libraries or APIs.
Sentiment analysis enhancement:
Improve the sentiment_analysis() function by using more advanced NLP libraries or models for increased accuracy.
Machine translation:
Add a translate_text() function that translates text from one language to another using NLP libraries or APIs.
Voice recognition:
Add a voice_recognition() function that identifies the speaker in an audio recording using voice recognition APIs or libraries.
Emotion analysis:
Add an emotion_analysis() function that detects emotions expressed in a given text using NLP libraries or models.
These enhancements can further expand the capabilities of the execute_subgoals() method, allowing it to handle a broader range of tasks.
implement 3 of the most needed improvements!
In this updated version of the code, I have added three additional improvements: content moderation, question-answering, and machine translation. For content moderation, we will use the perspective-api-client library. For question-answering, we will use the GPT-3 API. For machine translation, we will use the googletrans library.
python
import openai
import requests
import os
import logging
import spacy
from perspective import PerspectiveAPI
from googletrans import Translator
# Set up logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
class GPT3Helper:
# ... (existing code) ...
class TaskExecutor:
def __init__(self, gpt3_helper):
self.gpt3_helper = gpt3_helper
self.nlp = spacy.load("en_core_web_sm")
self.perspective_api = PerspectiveAPI('your_perspective_api_key_here')
self.translator = Translator()
# ... (other methods) ...
def moderate_content(self, text):
moderation_result = self.perspective_api.score(text, tests=["TOXICITY"])
toxicity = moderation_result["attributeScores"]["TOXICITY"]["summaryScore"]["value"]
return toxicity
def question_answering(self, context, question):
answer = self.gpt3_helper.ask_question(context, question)
return answer
def translate_text(self, text, target_language):
translated_text = self.translator.translate(text, dest=target_language).text
return translated_text
def execute_subgoals(self, subgoals):
for subgoal in subgoals:
action, args = subgoal.split(" ", 1)
# ... (previous actions) ...
elif action == "moderate_content":
toxicity = self.moderate_content(content)
print(f"Toxicity score: {toxicity}")
elif action == "question_answering":
question = args
answer = self.question_answering(content, question)
print(f"Answer: {answer}")
elif action == "translate_text":
target_language = args
translated_text = self.translate_text(content, target_language)
print(f"Translated text: {translated_text}")
else:
logging.warning(f"Unknown action: {action}")
if __name__ == "__main__":
# ... (previous code) ...
In this version of the code, we added three new actions in the execute_subgoals() method: content moderation, question-answering, and machine translation. The moderate_content() function uses the perspective-api-client library to detect the toxicity level of a given text, the question_answering() function uses the GPT-3 API to answer a user-posed question based on a given context, and the translate_text() function uses the googletrans library to translate text from one language to another.
Remember to install the required libraries by running pip install perspective-api-client googletrans==4.0.0-rc1, and replace the your_gpt3_api_key_here and your_perspective_api_key_here placeholders with your actual GPT-3 and Perspective API keys.
come with with bulletpoint of ideas for execute_subgoals()
Here are some additional ideas for enhancing the execute_subgoals() method with new functionalities:
Syntax and grammar checking:
Add a syntax_check() function that detects and corrects syntax and grammar errors in a given text using NLP libraries or APIs.
Speech to text:
Add a speech_to_text() function that transcribes spoken audio into text using a speech-to-text API or library.
Image recognition:
Add an image_recognition() function that identifies objects, people, or text within images using an image recognition API or library.
Optical Character Recognition (OCR):
Add an ocr() function that extracts text from images or scanned documents using OCR libraries or APIs.
Sentiment analysis enhancement:
Improve the sentiment_analysis() function by using more advanced NLP libraries or models for increased accuracy.
Voice recognition:
Add a voice_recognition() function that identifies the speaker in an audio recording using voice recognition APIs or libraries.
Emotion analysis:
Add an emotion_analysis() function that detects emotions expressed in a given text using NLP libraries or models.
Text summarization:
Add a summarize_text() function that generates a summary of a given text using NLP libraries or models.
Named-entity recognition enhancement:
Improve the named_entity_recognition() function by using more advanced NLP libraries or models for increased accuracy.
Topic modeling:
Add a topic_modeling() function that identifies the main topics in a given text using NLP libraries or models.
These enhancements can further expand the capabilities of the execute_subgoals() method, allowing it to handle a broader range of tasks.
implement 3 of the most needed improvements!
In this updated version of the code, I have added three additional improvements: syntax and grammar checking, speech to text, and text summarization. For syntax and grammar checking, we will use the language_tool_python library. For speech to text, we will use the speech_recognition library. For text summarization, we will use the GPT-3 API.
python
import openai
import requests
import os
import logging
import spacy
import language_tool_python
import speech_recognition as sr
# Set up logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
class GPT3Helper:
# ... (existing code) ...
class TaskExecutor:
def __init__(self, gpt3_helper):
self.gpt3_helper = gpt3_helper
self.nlp = spacy.load("en_core_web_sm")
self.tool = language_tool_python.LanguageTool('en-US')
self.recognizer = sr.Recognizer()
# ... (other methods) ...
def syntax_check(self, text):
matches = self.tool.check(text)
corrected_text = language_tool_python.utils.correct(text, matches)
return corrected_text
def speech_to_text(self, audio_file):
with sr.AudioFile(audio_file) as source:
audio_data = self.recognizer.record(source)
text = self.recognizer.recognize_google(audio_data)
return text
def summarize_text(self, text, summary_length):
summary = self.gpt3_helper.generate_summary(text, summary_length)
return summary
def execute_subgoals(self, subgoals):
for subgoal in subgoals:
action, args = subgoal.split(" ", 1)
# ... (previous actions) ...
elif action == "syntax_check":
corrected_text = self.syntax_check(content)
print(f"Corrected text: {corrected_text}")
elif action == "speech_to_text":
audio_file = args
transcribed_text = self.speech_to_text(audio_file)
print(f"Transcribed text: {transcribed_text}")
elif action == "summarize_text":
summary_length = int(args)
summary = self.summarize_text(content, summary_length)
print(f"Summary: {summary}")
else:
logging.warning(f"Unknown action: {action}")
if __name__ == "__main__":
# ... (previous code) ...
In this version of the code, we added three new actions in the execute_subgoals() method: syntax and grammar checking, speech to text, and text summarization. The syntax_check() function uses the language_tool_python library to detect and correct syntax and grammar errors in a given text, the speech_to_text() function uses the speech_recognition library to transcribe spoken audio into text, and the summarize_text() function uses the GPT-3 API to generate a summary of a given text.
Remember to install the required libraries by running pip install language_tool_python SpeechRecognition, and replace the your_gpt3_api_key_here placeholder with your actual GPT-3 API key.
come with with bulletpoint of ideas for execute_subgoals()
Here are some additional ideas for enhancing the execute_subgoals() method with new functionalities:
Image recognition:
Add an image_recognition() function that identifies objects, people, or text within images using an image recognition API or library.
Optical Character Recognition (OCR):
Add an ocr() function that extracts text from images or scanned documents using OCR libraries or APIs.
Sentiment analysis enhancement:
Improve the sentiment_analysis() function by using more advanced NLP libraries or models for increased accuracy.
Voice recognition:
Add a voice_recognition() function that identifies the speaker in an audio recording using voice recognition APIs or libraries.
Emotion analysis:
Add an emotion_analysis() function that detects emotions expressed in a given text using NLP libraries or models.
Named-entity recognition enhancement:
Improve the named_entity_recognition() function by using more advanced NLP libraries or models for increased accuracy.
Topic modeling:
Add a topic_modeling() function that identifies the main topics in a given text using NLP libraries or models.
Text classification:
Add a text_classification() function that classifies a given text into pre-defined categories using NLP libraries or models.
Keyword extraction:
Add a keyword_extraction() function that extracts the most relevant keywords from a given text using NLP libraries or models.
Text generation:
Add a text_generation() function that generates text based on a given context or prompt using NLP libraries or models.
These enhancements can further expand the capabilities of the execute_subgoals() method, allowing it to handle a broader range of tasks.
implement 3 of the most needed improvements!
In this updated version of the code, I have added three additional improvements: image recognition, Optical Character Recognition (OCR), and text classification. For image recognition, we will use the Google Cloud Vision API. For OCR, we will use the Tesseract OCR library. For text classification, we will use the GPT-3 API.
python
import openai
import requests
import os
import logging
import spacy
from google.cloud import vision
import pytesseract
from pytesseract import Output
# Set up logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
class GPT3Helper:
# ... (existing code) ...
class TaskExecutor:
def __init__(self, gpt3_helper):
self.gpt3_helper = gpt3_helper
self.nlp = spacy.load("en_core_web_sm")
self.vision_client = vision.ImageAnnotatorClient()
pytesseract.pytesseract.tesseract_cmd = r'<path_to_tesseract_executable>'
# ... (other methods) ...
def image_recognition(self, image_path):
with open(image_path, 'rb') as image_file:
content = image_file.read()
image = vision.Image(content=content)
response = self.vision_client.label_detection(image=image)
labels = response.label_annotations
return [label.description for label in labels]
def ocr(self, image_path):
text = pytesseract.image_to_string(image_path)
return text
def text_classification(self, text, categories):
category_scores = self.gpt3_helper.classify_text(text, categories)
return category_scores
def execute_subgoals(self, subgoals):
for subgoal in subgoals:
action, args = subgoal.split(" ", 1)
# ... (previous actions) ...
elif action == "image_recognition":
image_path = args
labels = self.image_recognition(image_path)
print(f"Labels: {', '.join(labels)}")
elif action == "ocr":
image_path = args
extracted_text = self.ocr(image_path)
print(f"Extracted text: {extracted_text}")
elif action == "text_classification":
category_args = args.split(", ")
categories = category_args[:-1]
text = category_args[-1]
category_scores = self.text_classification(text, categories)
print(f"Category scores: {category_scores}")
else:
logging.warning(f"Unknown action: {action}")
if __name__ == "__main__":
# ... (previous code) ...
In this version of the code, we added three new actions in the execute_subgoals() method: image recognition, OCR, and text classification. The image_recognition() function uses the Google Cloud Vision API to identify objects, people, or text within images, the ocr() function uses the Tesseract OCR library to extract text from images or scanned documents, and the text_classification() function uses the GPT-3 API to classify a given text into pre-defined categories.
Remember to install the required libraries by running pip install google-cloud-vision pytesseract, set up authentication for the Google Cloud Vision API, and replace the your_gpt3_api_key_here placeholder with your actual GPT-3 API key. Also, provide the path to the Tesseract OCR executable by updating the path_to_tesseract_executable in the code.
