UDF

UDF The Universal Decision Formula (UDF) is designed to make decision making easier and more accurate. Based on the book, Universal Morals, by Jeromy Hess.

UDF is the ultimate formula for making decisions in the existence.

02/25/2024

Detecting objects and tracking them over IoT involves integrating computer vision and IoT technologies. This task is complex and usually requires specialized hardware and software. For the sake of illustration, I'll provide a simplified example using OpenCV for object detection and Flask for the IoT communication.

Please note that real-world implementations may require more sophisticated solutions, and security considerations are crucial.

First, install the necessary libraries:

```bash
pip install opencv-python Flask
```

Now, you can create a basic Python script:

```python
import cv2
from flask import Flask, render_template, Response
import threading
import time

app = Flask(__name__)
video_capture = cv2.VideoCapture(0) # Use 0 for the default camera

object_detected = False
object_name = ""

def detect_objects(frame):
# Replace this with a proper object detection model (e.g., YOLO, SSD, etc.)
# For simplicity, using OpenCV's Haarcascades for face detection
face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))

if len(faces) > 0:
global object_detected, object_name
object_detected = True
object_name = "Face"

# Log the detection or send data to IoT platform
print("Face detected!")
else:
object_detected = False
object_name = ""

def video_stream():
while True:
success, frame = video_capture.read()
if not success:
break

if object_detected:
# Draw a rectangle around the detected object
cv2.rectangle(frame, (0, 0), (frame.shape[1], frame.shape[0]), (0, 255, 0), 2)
cv2.putText(frame, f"Object Detected: {object_name}", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2, cv2.LINE_AA)

ret, jpeg = cv2.imencode('.jpg', frame)
frame_bytes = jpeg.tobytes()
yield (b'--frame\r\n'
b'Content-Type: image/jpeg\r\n\r\n' + frame_bytes + b'\r\n\r\n')

# Add a delay to control the frame rate
time.sleep(0.1)
route('/')
def index():
return render_template('index.html')
route('/video_feed')
def video_feed():
return Response(video_stream(), mimetype='multipart/x-mixed-replace; boundary=frame')

if __name__ == '__main__':
video_thread = threading.Thread(target=video_stream)
video_thread.daemon = True
video_thread.start()

app.run(host='0.0.0.0', port=5000, debug=True)
```

This code sets up a basic web server using Flask to stream video from your camera and detect faces using Haarcascades. Replace the face detection logic with a more robust object detection model suitable for your needs.

Create an HTML file (templates/index.html):

```html





Object Detection and IoT


Object Detection and IoT



```

This is a basic example, and depending on your specific requirements, you may need more advanced object detection models and IoT communication mechanisms.

02/25/2024

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()

02/25/2024

Combining all the requested features, including the Universal Decision Formula (UDF), HTML forms for queries and decisions, logging, profile generation, and generating verbose information, can result in a large and complex codebase. Below is a condensed version that includes all the requested features:

```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)

# 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")
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)
```

Please note that this code may still need further adjustments based on your specific requirements and the exact structure of your data. Ensure you replace "YOUR_DICTIONARY_API_KEY" with your actual Dictionary.com API key.

02/25/2024

Certainly! To parse non-log data generated in previous saved text files in each appropriate directory, you can add a function to read the data from these files and update the `word_profiles` accordingly. Here's how you can modify the code:

```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)

# 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")
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 `load_word_profiles` function is added to the `DecisionMaker` class. It reads each file in the 'word_profiles' directory, extracts the word, and updates the `word_profiles` dictionary with the historical score information. Modify the code inside `load_word_profiles` based on your data structure stored in these files.

02/25/2024

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.

02/25/2024

Certainly! To generate information in a verbose and well-written form, you can modify the `generate_information` function in the `UniversalDecisionFormula` class. The following code includes a simple modification to create more verbose information:

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

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):
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}"
return information

return f"No information found for {word}"

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

for word in nltk.word_tokenize(decision):
information = self.generate_information(word)
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):
self.udf = udf
self.word_profiles = {}

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)
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")
decision_maker = DecisionMaker(udf)

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)
```

This modification includes a `generate_information` method in the `UniversalDecisionFormula` class, which provides more verbose information about each word. You can adapt this method further to suit the desired format or level of detail for your information.

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