UDF

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.