I want to build the whole product (backend,frontend,APIs, models) with the standard structure: Overview
Emailer is an email management application that leverages Large Language Models (LLMs) to provide intelligent email classification, analysis, and reporting. Specifically Llama 3.1 served via the Ollama server on localhost, Emailer ensures data security while providing robust features for users to manage their emails efficiently.
Scope
Emailer is intended for individual users and organizations seeking an intelligent solution to manage large volumes of emails. The application provides features such as real-time email processing, customizable classification, detailed reporting, and secure data handling.
Features
1.    User Account Management
Create Account: Users can sign up by selecting their email provider, entering a username, email, and password.
2.    Email Integration
Email Fetching: Support for multiple email providers (Gmail, Outlook, etc.) using IMAP and POP3 protocols.
Real-Time Processing: Immediate fetching and processing of incoming emails.
3.    Email Pre-processing
Parsing: Extraction of email headers, body content, and attachments for thorough analysis.
4.    User Customization
Custom Classification Categories: Users can define their own categories for email classification.
User-Defined Filters: Filters based on sender, date, subject, and topic.
Manager Mail Addition: Ability to add manager email addresses for report distributions.
5.    Intelligent Classification & Analysis
LLM Integration: Utilization of Llama 3.1 for email classification.
6.    Data Storage
Database Management: Use of SQLite to store user profiles, emails, and classification results securely.
7.    User Interface
Email Display: Emails displayed with classification tags for easy identification.
Visualization: Use of tables and charts to represent classification results.
8.    Reporting & Notifications
Scheduled Reports: Automatic generation and sending of email classification summaries to manager emails.
Urgent Email Reminders: Notifications for emails marked as urgent, negative, or important based on user-defined thresholds.
Technology Stack
§  Programming Language: Python
§  Large Language Model (LLM): Llama 3.1
§  Model Server: Ollama
§  Database: SQLite
§  Email Protocols: IMAP, POP3 (based on email provider support)
§  Frontend Framework: PyQt6
§  Backend Framework: FastAPI
System Architected

Data Management
§  User Profiles: Store user information securely in the SQLite database.
§  Email Data: Store fetched emails, including headers, body, and attachments.
§  Classification Results: Store classification tags and analysis data linked to each email.
§  Data Backup: Implement regular backups of the SQLite database to prevent data loss.
User Interface Requirements
Dashboard: Overview of email statistics, recent classifications, and notifications.
Email Viewer: Interface to view individual emails with classification tags.
Filters and Search: Tools to filter and search emails based on user-defined criteria.
Customization Settings: Sections for users to define classification categories and filters.
Reporting Section: Access to generated reports and notification settings.
Account Management: Interface for users to manage their account details and preferences.
Integration and APIs
Email Providers Integration: Utilize IMAP and POP3 protocols to fetch emails from supported providers.
LLM Integration: Interface with the Ollama server to access and utilize Llama 3.1 for classification tasks.
Reporting Services: Integrate with email services to send scheduled reports and urgent notifications.
API Gateway (FastAPI): Centralizes API endpoints, facilitating communication between the frontend and backend services.
Deployment
Local Deployment: The application is designed to run locally on user machines, ensuring data remains secure.
Environment Requirements:
§  Python environment with necessary dependencies.
§  Ollama server setup for Llama 3.1.
§  SQLite database setup.
§  FastAPI server setup.
 
 
Installation Process:
§  Package the application with an installer that sets up the Python environment, dependencies, Ollama server, and FastAPI server.
§  Provide configuration guides for connecting to email providers.  Week 1: Setup & User Management
Objectives:
·       Establish the development environment.
·       Implement user account management.
Key Tasks:
·       Project Initialization:
o   Configure the development environment (Python, FastAPI, PyQt6, Ollama).
·       System Architecture:
o   Finalize a high-level architecture diagram.
o   Design the initial SQLite database schema for user profiles.
·       User Account Management:
o   Develop backend endpoints for user signup and login using FastAPI.
o   Create frontend UI components in PyQt6 for account creation and login.
Deliverables:
·       Functional user signup and login features.
·       Basic system architecture documentation.
Week 2: Email Integration & Pre-processing
Objectives:
·       Enable email fetching from multiple providers.
·       Develop email preprocessing capabilities.
 
 
 
Key Tasks:
·       Email Integration:
o   Implement backend services to connect with email providers (Gmail, Outlook) using IMAP and POP3.
o   Set up real-time email fetching mechanisms.
·       Email Pre-processing:
o   Develop modules to parse email headers, body, and attachments.
o   Normalize and store fetched emails in the SQLite database.
·       Frontend Enhancements:
o   Update the UI to allow users to connect their email accounts.
o   Display status indicators for email fetching and pre-processing.
Deliverables:
·       Ability to fetch and pre-process emails from supported providers.
·       Updated frontend for email integration.
·       Stored emails in the database.
Week 3: Customization & Intelligent Classification
Objectives:
·       Implement user customization features.
·       Integrate LLM for email classification.
Key Tasks:
·       User Customization:
o   Develop functionalities for users to define custom classification categories and filters (sender, date, subject, topic).
o   Enable addition of manager email addresses for report distributions.
 
 
·       LLM Integration:
o   Interface with Llama 3.1 via the Ollama server for email classification.
o   Develop backend logic to send pre-processed emails to the LLM and handle classification results.
·       Store classification tags in the SQLite database.
·       Frontend Enhancements:
o   Update the email viewer to display classification tags.
o   Implement basic visualization (tables) for classification results.
Deliverables:
·       User-defined classification categories and filters.
·       Integrated LLM-based classification system.
·       Enhanced email viewer with classification tags.
Week 4: Reporting, Notifications & Deployment
Objectives:
·       Implement reporting and notification features.
·       Prepare for deployment and finalize the user interface.
Key Tasks:
·       Reporting & Notifications:
o   Develop backend services to generate and send scheduled reports summarizing email classifications.
o   Implement notifications for urgent, negative, or important emails based on user-defined thresholds.
·       Final UI Enhancements:
o   Polish the dashboard with email statistics and recent classifications.
o   Implement search and advanced filtering tools.
o   Ensure all UI components are user-friendly and intuitive.
 
·       Deployment Preparation:
o   Create an installer package that sets up the Python environment, dependencies, Ollama server, FastAPI server, and initializes the SQLite database.
o   Develop configuration guides for users to connect their email providers and set up the application.
·       Final Testing & Launch:
o   Conduct end-to-end testing of all features.
o   Perform security checks to ensure data privacy and secure communications.
o   Launch the application to initial users.
Deliverables:
·       Functional reporting and notification systems.
·       Polished and fully functional user interface.
·       Ready-to-deploy installer package with user guides.
·       Successfully deployed Emailer application.