Revert "Build an upgraded, feature-rich, user-friendly executive scheduling assistant with improved functionality and a clean repository."

LICENSE.md

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+MIT License
+
+Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

README.md

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+# DonnaSpecter
+
+Welcome to the world of DonnaSpecter, an open-source software (OSS) project that's as sharp, adaptable, and indispensable as Donna Paulson herself. As a tribute to the legendary secretary from the TV show "Suits", this AI-powered personal assistant is ready to handle your daily tasks with the same flair and proficiency.
+
+## Features
+
+### Email Handling
+
+Just as Donna manages the communications for Pearson Hardman, the `email_handler` module takes charge of your incoming and outgoing emails, ensuring not a single important message slips through the cracks.
+
+### Task Scheduling
+
+Ever wonder how Donna keeps track of all those appointments and meetings? With the `scheduler` module, DonnaSpecter has an impeccable sense of timing, ensuring you never miss a task or deadline.
+
+### AI Modeling
+
+Donna always seems to know exactly what's needed, doesn't she? The `ai_model` directory houses the intelligence behind DonnaSpecter, enabling her to anticipate your needs and offer solutions.
+
+### Frontend and Backend
+
+Every law firm needs its front-of-house and its backroom experts. The `frontend` and `backend` directories contain the code that keeps DonnaSpecter running smoothly, from the interface you see to the data processing happening behind the scenes.
+
+### Security
+
+In the world of legal drama, confidentiality is paramount. Our `security` module is designed to protect your data with as much diligence as Donna protects the secrets of Pearson Hardman.
+
+### Database Management
+
+Every case detail, every clause, every precedent - Donna remembers them all. The `database` module is the digital equivalent, storing and managing your data with precision.
+
+### Microservices Architecture
+
+Just as a law firm relies on the expertise of various departments, DonnaSpecter is built on a microservices architecture for scalable, reliable, and independent deployment of services.
+
+### DevOps
+
+Pearson Hardman wouldn't be a top law firm without its streamlined processes. The `cicd`, `kubernetes`, and `docker` directories reflect our commitment to efficiency and modern development practices.
+
+## Getting Started
+
+Ready to bring the efficiency of Pearson Hardman to your daily life? Here's how to start:
+
+1. Clone the DonnaSpecter repository - no legal paperwork required. You can find the repository at [this link](https://github.com/shadowaxe99/DonnaSpecter).
+2. Enter the `src` directory, the heart of our operation.
+3. Run the `main.py` script to wake DonnaSpecter and get started with your new personal assistant.
+
+Ensure you have set up the necessary environment variables as specified in `shared_dependencies.md` and that your system is equipped with all the necessary dependencies.
+
+## Contributing
+
+Just as Mike Ross found his place at Pearson Hardman, we welcome new contributors to DonnaSpecter. Check out `CONTRIBUTING.md` for your orientation.
+
+## License
+
+DonnaSpecter operates under the MIT License. For the legalities, see `LICENSE.md`.
+
+## Reporting Issues
+
+Just like Harvey Specter, we believe in taking matters into our own hands. If you encounter an issue, don't wait around - "fix it yourself." However, if you believe that the issue may affect other users or require a more substantial fix, don't hesitate to raise an issue. This way, we can all contribute to improving DonnaSpecter and making it an even more effective assistant. After all, we're a team, and "that's how we win."
+
+## How to Use - A Guide for the Mikes of the World
+
+Ever feel like a fraud in a world of Harveys? Don't worry, Mike. We've got your back. Here's a simple guide to using DonnaSpecter:
+
+
+### Step 1: Get the Goods
+First, you need to get DonnaSpecter onto your computer. This is called "cloning" the repository. Don't worry, it's perfectly legal. In your terminal, navigate to the directory where you want to put DonnaSpecter, and enter:
+
+                              git clone https://github.com/shadowaxe99/DonnaSpecter.git
+
+Now you've got your own copy of DonnaSpecter!
+
+### Step 2: Enter the World
+Navigate into the heart of the operation, the `src` directory. Just type:
+
+                                cd DonnaSpecter/src
+
+You're in.
+
+### Step 3: Wake Donna Up
+Start the program by running the `main.py` script. This is like waking Donna up in the morning. Type:
+
+          python main.py
+
+DonnaSpecter should now be running and ready to assist you.
+
+### Step 4: Ask for Help
+DonnaSpecter has a lot of functionalities. If you're not sure where to start, just ask for help. Donna is here to assist you, and she's got a whole lot of tricks up her sleeve.
+
+Remember, as a wise man once said, "When you are backed against the wall, break the goddamn thing down." So if you encounter any obstacles in your journey with DonnaSpecter, don't hesitate to reach out and report any issues. We're in this together, and we'll break down those walls as a team. Don't be afraid to dive in and learn as you go. In the immortal words of Harvey Specter and I am sure McKay would agree, "the only time success comes before work is in the dictionary." DonnaSpecter is here to make your work more manageable. "Remember, the road to success is still yours to travel and it is measured by how many lines of code you write" - McKay Wrigley, somewhere, so you definitely needed this to help you catch up.
+
+This guide assumes that the user has basic knowledge of how to use a terminal and has Python installed on their computer. If this isn't the case, they may need to look up some additional resources to get started.
+
+"Life is this, I like this." - Harvey Specter, and hopefully you after using DonnaSpecter. Enjoy your journey with your new AI-powered assistant. It's time to suit up and get to work!

availability_analysis.py

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+```python
+import datetime
+from ai_assistant.scheduler import schedule
+
+def check_availability(user_profile, start_time, end_time):
+    """
+    Check the availability of the user within a given time frame.
+    """
+    user_schedule = schedule[user_profile]
+    for event in user_schedule:
+        if event['start_time'] <= start_time < event['end_time'] or event['start_time'] < end_time <= event['end_time']:
+            return False
+    return True
+
+def find_free_slots(user_profile, duration, start_date=datetime.datetime.now(), end_date=datetime.datetime.now() + datetime.timedelta(days=7)):
+    """
+    Find free slots in the user's schedule that are at least as long as the specified duration.
+    """
+    free_slots = []
+    current_time = start_date
+    while current_time + duration <= end_date:
+        if check_availability(user_profile, current_time, current_time + duration):
+            free_slots.append((current_time, current_time + duration))
+        current_time += duration
+    return free_slots
+
+def suggest_times(user_profile, duration, num_suggestions=5):
+    """
+    Suggest a number of free slots in the user's schedule that are at least as long as the specified duration.
+    """
+    free_slots = find_free_slots(user_profile, duration)
+    return free_slots[:num_suggestions]
+```

cognitive_load_balancing.py

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+```python
+import datetime
+from ai_assistant.scheduler import schedule
+from ai_assistant.task_automation import task_list
+
+def balanceLoad(user_profile, meeting_data, task_list):
+    """
+    Function to balance cognitive load by distributing tasks and meetings evenly
+    """
+    # Get the total number of tasks and meetings
+    total_items = len(task_list) + len(meeting_data)
+
+    # Calculate the average load per day
+    average_load = total_items / 7
+
+    # Distribute the tasks and meetings evenly across the week
+    for i in range(7):
+        day_load = 0
+        while day_load < average_load:
+            if len(task_list) > 0:
+                schedule(user_profile, task_list.pop(0), datetime.datetime.now() + datetime.timedelta(days=i))
+                day_load += 1
+            if len(meeting_data) > 0:
+                schedule(user_profile, meeting_data.pop(0), datetime.datetime.now() + datetime.timedelta(days=i))
+                day_load += 1
+
+    return user_profile
+
+def updateLoadBalancing(user_profile, meeting_data, task_list):
+    """
+    Function to update the load balancing when a new task or meeting is added
+    """
+    # Add the new task or meeting to the schedule
+    balanceLoad(user_profile, meeting_data, task_list)
+
+    return user_profile
+```

content_curation.py

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+```python
+import os
+import json
+from ai_assistant.shared_dependencies import user_profile, content_data
+
+class ContentCuration:
+    def __init__(self, user_profile, content_data):
+        self.user_profile = user_profile
+        self.content_data = content_data
+
+    def curate_content(self):
+        curated_content = []
+        for content in self.content_data:
+            if self.user_profile['interests'] in content['tags']:
+                curated_content.append(content)
+        return curated_content
+
+    def save_curated_content(self, curated_content):
+        with open('curated_content.json', 'w') as json_file:
+            json.dump(curated_content, json_file)
+
+    def load_curated_content(self):
+        if os.path.exists('curated_content.json'):
+            with open('curated_content.json') as json_file:
+                curated_content = json.load(json_file)
+            return curated_content
+        else:
+            return []
+
+if __name__ == "__main__":
+    content_curation = ContentCuration(user_profile, content_data)
+    curated_content = content_curation.curate_content()
+    content_curation.save_curated_content(curated_content)
+    loaded_content = content_curation.load_curated_content()
+    print(loaded_content)
+```

contextual_understanding.py

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+```python
+import nltk
+from nltk.corpus import stopwords
+from nltk.tokenize import word_tokenize, sent_tokenize
+
+# Importing shared dependencies
+from shared_dependencies import user_profile, meeting_data
+
+class ContextUnderstanding:
+    def __init__(self):
+        self.stop_words = set(stopwords.words('english'))
+
+    def tokenize(self, text):
+        return word_tokenize(text)
+
+    def remove_stopwords(self, tokenized_text):
+        return [word for word in tokenized_text if word not in self.stop_words]
+
+    def understand_context(self, text):
+        tokenized = self.tokenize(text)
+        tokenized = self.remove_stopwords(tokenized)
+
+        tagged = nltk.pos_tag(tokenized)
+
+        namedEnt = nltk.ne_chunk(tagged)
+        namedEnt.draw()
+
+        return namedEnt
+
+context_understanding = ContextUnderstanding()
+
+def update_context():
+    for meeting in meeting_data:
+        context = context_understanding.understand_context(meeting['description'])
+        meeting['context'] = context
+
+def get_context(user_id):
+    user_meetings = [meeting for meeting in meeting_data if meeting['user_id'] == user_id]
+    user_context = [meeting['context'] for meeting in user_meetings]
+    return user_context
+```

continuous_learning.py

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+```python
+import numpy as np
+from sklearn.model_selection import train_test_split
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.metrics import accuracy_score
+from sklearn.metrics import classification_report
+import pandas as pd
+
+class ContinuousLearning:
+    def __init__(self, user_profile, learning_progress):
+        self.user_profile = user_profile
+        self.learning_progress = learning_progress
+        self.model = None
+
+    def load_data(self):
+        # Load data from user_profile and learning_progress
+        data = pd.concat([self.user_profile, self.learning_progress], axis=1)
+        return data
+
+    def preprocess_data(self, data):
+        # Preprocess data, handle missing values, convert categorical data to numerical, etc.
+        data = data.dropna()
+        data = pd.get_dummies(data)
+        return data
+
+    def split_data(self, data):
+        # Split data into training and testing sets
+        X = data.drop('target', axis=1)
+        y = data['target']
+        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+        return X_train, X_test, y_train, y_test
+
+    def train_model(self, X_train, y_train):
+        # Train model using RandomForestClassifier
+        self.model = RandomForestClassifier(n_estimators=100)
+        self.model.fit(X_train, y_train)
+
+    def evaluate_model(self, X_test, y_test):
+        # Evaluate model performance
+        y_pred = self.model.predict(X_test)
+        print("Accuracy:", accuracy_score(y_test, y_pred))
+        print(classification_report(y_test, y_pred))
+
+    def update_learning_progress(self, learning_progress):
+        # Update learning progress
+        self.learning_progress = learning_progress
+
+    def run(self):
+        # Run continuous learning process
+        data = self.load_data()
+        data = self.preprocess_data(data)
+        X_train, X_test, y_train, y_test = self.split_data(data)
+        self.train_model(X_train, y_train)
+        self.evaluate_model(X_test, y_test)
+
+if __name__ == "__main__":
+    user_profile = pd.read_csv('user_profile.csv')
+    learning_progress = pd.read_csv('learning_progress.csv')
+    continuous_learning = ContinuousLearning(user_profile, learning_progress)
+    continuous_learning.run()
+```

cross_platform_syncing.py

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+```python
+import os
+import json
+from ai_assistant.shared_dependencies import user_profile, meeting_data, notification_settings, task_list
+
+class CrossPlatformSync:
+    def __init__(self):
+        self.user_profile = user_profile
+        self.meeting_data = meeting_data
+        self.notification_settings = notification_settings
+        self.task_list = task_list
+
+    def sync_data(self, platform):
+        if platform == 'google':
+            self.sync_google()
+        elif platform == 'apple':
+            self.sync_apple()
+        elif platform == 'microsoft':
+            self.sync_microsoft()
+        else:
+            print("Invalid platform")
+
+    def sync_google(self):
+        # Logic to sync data with Google services
+        pass
+
+    def sync_apple(self):
+        # Logic to sync data with Apple services
+        pass
+
+    def sync_microsoft(self):
+        # Logic to sync data with Microsoft services
+        pass
+
+    def export_data(self, file_path):
+        data = {
+            'user_profile': self.user_profile,
+            'meeting_data': self.meeting_data,
+            'notification_settings': self.notification_settings,
+            'task_list': self.task_list
+        }
+        with open(file_path, 'w') as f:
+            json.dump(data, f)
+
+    def import_data(self, file_path):
+        if os.path.exists(file_path):
+            with open(file_path, 'r') as f:
+                data = json.load(f)
+            self.user_profile = data['user_profile']
+            self.meeting_data = data['meeting_data']
+            self.notification_settings = data['notification_settings']
+            self.task_list = data['task_list']
+        else:
+            print("File does not exist")
+
+if __name__ == "__main__":
+    cross_platform_sync = CrossPlatformSync()
+    cross_platform_sync.sync_data('google')
+    cross_platform_sync.export_data('data.json')
+    cross_platform_sync.import_data('data.json')
+```