UDF

Creating a full complex version of the code requires a deep integration of various features, including web scraping for reference data, complex decision-making based on historical data, and extensive HTML forms. The complexity of such a system is substantial, and it's essential to consider potential security risks when dealing with user input and external data sources.

Below is a comprehensive version that includes all the features mentioned. Please note that for web scraping, you should choose a suitable library like BeautifulSoup or Selenium based on the specific requirements.

```python
from flask import Flask, render_template, request
import requests
import re
import nltk
from nltk.sentiment.vader import SentimentIntensityAnalyzer
import datetime
import os
from bs4 import BeautifulSoup # Use BeautifulSoup for web scraping

app = Flask(__name__)

class UniversalDecisionFormula:
def __init__(self, api_key):
self.api_key = api_key
self.base_url = "https://api.dictionaryapi.dev/api/v2/entries/en_US/"
self.sid = SentimentIntensityAnalyzer()

def get_word_definition(self, word):
url = f"{self.base_url}{word}"
headers = {"Authorization": f"Token {self.api_key}"}
response = requests.get(url, headers=headers)

if response.status_code == 200:
data = response.json()
if 'definitions' in data:
return data['definitions'][0]['definition']
return f"No definition found for {word}"

def calculate_sentiment_score(self, text):
sentiment_scores = self.sid.polarity_scores(text)
return sentiment_scores['compound']

def web_scrape_reference_data(self, word):
# Add web scraping logic here to gather reference data from the internet
# Use BeautifulSoup or another suitable library

# Example: scraping Wikipedia for word summary
wikipedia_url = f"https://en.wikipedia.org/wiki/{word}"
response = requests.get(wikipedia_url)
if response.status_code == 200:
soup = BeautifulSoup(response.text, 'html.parser')
summary_paragraphs = soup.find_all('p')[:2] # Take the first two paragraphs as a summary
summary_text = '\n'.join([paragraph.get_text() for paragraph in summary_paragraphs])
return summary_text
else:
return f"Failed to fetch reference data from {wikipedia_url}"

def generate_information(self, word, output_directory):
definition = self.get_word_definition(word)
if definition:
examples = re.findall(r'(?i)\b(?:for example|e\.g\.|example)\b(.*?)[\.;]', definition)
sentiment_info = ""

for example in examples:
word_score = self.calculate_sentiment_score(example.strip())
sentiment_info += f"Example: {example.strip()} - Sentiment Score: {word_score}\n"

information = f"Word: {word}\nDefinition: {definition}\n\nSentiment Information:\n{sentiment_info}"

# Web scrape reference data
reference_data = self.web_scrape_reference_data(word)
information += f"\n\nReference Data:\n{reference_data}"

# Save the information to a text file
file_path = os.path.join(output_directory, f"{word}_profile.txt")
with open(file_path, 'w') as file:
file.write(information)

return information

return f"No information found for {word}"

def evaluate_decision(self, decision, word_profiles, output_directory):
total_score = 0

for word in nltk.word_tokenize(decision):
information = self.generate_information(word, output_directory)
print(information) # Modify as needed, you can store or use this information

if information:
examples = re.findall(r'(?i)\b(?:for example|e\.g\.|example)\b(.*?)[\.;]', information)
for example in examples:
word_score = self.calculate_sentiment_score(example.strip())
total_score += word_score

if word in word_profiles:
total_score += word_profiles[word]['historical_score']

return total_score

def generate_question(self, word_profiles):
highest_score_word = max(word_profiles, key=lambda k: word_profiles[k].get('historical_score', 0))
return f"What can you tell me about {highest_score_word}?"

class DecisionMaker:
def __init__(self, udf, output_directory):
self.udf = udf
self.word_profiles = {}
self.output_directory = output_directory

# Create the output directory if it doesn't exist
os.makedirs(self.output_directory, exist_ok=True)

# Load data from existing files in the output directory
self.load_word_profiles()

def load_word_profiles(self):
for file_name in os.listdir(self.output_directory):
if file_name.endswith("_profile.txt"):
word = file_name.replace("_profile.txt", "")
with open(os.path.join(self.output_directory, file_name), 'r') as file:
content = file.read()
# Update the word_profiles dictionary with loaded data
self.word_profiles[word] = {'historical_score': 0} # Modify as needed based on your data structure

def make_decision(self, choices):
best_choice = None
highest_roi = float('-inf')

for choice in choices:
decision_score = self.udf.evaluate_decision(choice, self.word_profiles, self.output_directory)
roi = decision_score

if roi > highest_roi:
highest_roi = roi
best_choice = choice

for word in nltk.word_tokenize(best_choice):
if word in self.word_profiles:
self.word_profiles[word]['historical_score'] += highest_roi
else:
self.word_profiles[word] = {'historical_score': highest_roi}

return best_choice

def get_generated_question(self):
return self.udf.generate_question(self.word_profiles)

def log_query(query, decision):
timestamp = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
with open("query_log.txt", "a") as log_file:
log_file.write(f"{timestamp} - Query: {query}, Decision: {decision}\n")

def get_verbose_information(choices, decision, generated_question, word_profiles, output_directory):
verbose_info = ""

for choice in choices:
verbose_info += f"\nChoice: {choice}\n"
decision_score = udf.evaluate_decision(choice, word_profiles, output_directory)
verbose_info += f"Decision Score: {decision_score}\n"
# Add more details as needed

verbose_info += f"\nBest Decision: {decision}\n"
verbose_info += f"Generated Question: {generated_question}\n"

return verbose_info
route('/', methods=['GET', 'POST'])
def index():
if request.method == 'POST':
choices = [request.form['choice1'], request.form['choice2'], request.form['choice3']]
user_desired_features = [request.form['feature1'], request.form['feature2']]

udf = UniversalDecisionFormula("YOUR_DICTIONARY_API_KEY")
output_directory = "word_profiles"
decision_maker = DecisionMaker(udf, output_directory)

best_decision = decision_maker.make_decision(choices)

log_query(str(choices), best_decision)

generated_question = decision_maker.get_generated_question()

Certainly! To store the output into its own directory with a text file for each word, you can modify the code as follows. This version of the code creates a directory named 'word_profiles' and saves individual text files for each word in that directory:

```python
from flask import Flask, render_template, request
import requests
import re
import nltk
from nltk.sentiment.vader import SentimentIntensityAnalyzer
import datetime
import os

app = Flask(__name__)

class UniversalDecisionFormula:
def __init__(self, api_key):
self.api_key = api_key
self.base_url = "https://api.dictionaryapi.dev/api/v2/entries/en_US/"
self.sid = SentimentIntensityAnalyzer()

def get_word_definition(self, word):
url = f"{self.base_url}{word}"
headers = {"Authorization": f"Token {self.api_key}"}
response = requests.get(url, headers=headers)

if response.status_code == 200:
data = response.json()
if 'definitions' in data:
return data['definitions'][0]['definition']
return f"No definition found for {word}"

def calculate_sentiment_score(self, text):
sentiment_scores = self.sid.polarity_scores(text)
return sentiment_scores['compound']

def generate_information(self, word, output_directory):
definition = self.get_word_definition(word)
if definition:
examples = re.findall(r'(?i)\b(?:for example|e\.g\.|example)\b(.*?)[\.;]', definition)
sentiment_info = ""

for example in examples:
word_score = self.calculate_sentiment_score(example.strip())
sentiment_info += f"Example: {example.strip()} - Sentiment Score: {word_score}\n"

information = f"Word: {word}\nDefinition: {definition}\n\nSentiment Information:\n{sentiment_info}"

# Save the information to a text file
file_path = os.path.join(output_directory, f"{word}_profile.txt")
with open(file_path, 'w') as file:
file.write(information)

return information

return f"No information found for {word}"

def evaluate_decision(self, decision, word_profiles, output_directory):
total_score = 0

for word in nltk.word_tokenize(decision):
information = self.generate_information(word, output_directory)
print(information) # Modify as needed, you can store or use this information

if information:
examples = re.findall(r'(?i)\b(?:for example|e\.g\.|example)\b(.*?)[\.;]', information)
for example in examples:
word_score = self.calculate_sentiment_score(example.strip())
total_score += word_score

if word in word_profiles:
total_score += word_profiles[word]['historical_score']

return total_score

def generate_question(self, word_profiles):
highest_score_word = max(word_profiles, key=lambda k: word_profiles[k].get('historical_score', 0))
return f"What can you tell me about {highest_score_word}?"

class DecisionMaker:
def __init__(self, udf, output_directory):
self.udf = udf
self.word_profiles = {}
self.output_directory = output_directory

# Create the output directory if it doesn't exist
os.makedirs(self.output_directory, exist_ok=True)

def make_decision(self, choices):
best_choice = None
highest_roi = float('-inf')

for choice in choices:
decision_score = self.udf.evaluate_decision(choice, self.word_profiles, self.output_directory)
roi = decision_score

if roi > highest_roi:
highest_roi = roi
best_choice = choice

for word in nltk.word_tokenize(best_choice):
if word in self.word_profiles:
self.word_profiles[word]['historical_score'] += highest_roi
else:
self.word_profiles[word] = {'historical_score': highest_roi}

return best_choice

def get_generated_question(self):
return self.udf.generate_question(self.word_profiles)

def log_query(query, decision):
timestamp = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
with open("query_log.txt", "a") as log_file:
log_file.write(f"{timestamp} - Query: {query}, Decision: {decision}\n")
route('/', methods=['GET', 'POST'])
def index():
if request.method == 'POST':
choices = [request.form['choice1'], request.form['choice2'], request.form['choice3']]
user_desired_features = [request.form['feature1'], request.form['feature2']]

udf = UniversalDecisionFormula("YOUR_DICTIONARY_API_KEY")
output_directory = "word_profiles"
decision_maker = DecisionMaker(udf, output_directory)

best_decision = decision_maker.make_decision(choices)

log_query(str(choices), best_decision)

generated_question = decision_maker.get_generated_question()

return render_template('index.html', choices=choices, best_decision=best_decision, generated_question=generated_question)

return render_template('index.html', choices=None, best_decision=None, generated_question=None)
route('/decision_log')
def decision_log():
with open("query_log.txt", "r") as log_file:
log_entries = log_file.readlines()
return render_template('decision_log.html', log_entries=log_entries)

if __name__ == '__main__':
app.run(debug=True)
```

In this version, the `generate_information` method saves the information into a text file for each word in the 'word_profiles' directory. The `DecisionMaker` class is modified to include the `output_directory` parameter, and the directory is created if it doesn't exist. The text files are named based on the word, and the information is written to these files.