flows.md

Application Flows

This document details the key workflows within the AIHawk application using Mermaid diagrams.

1. App Startup Flow

The application initialization process ensures all configurations and dependencies are ready before user interaction.

graph TD
    Start([Start main.py]) --> ValidateData[Validate Data Folder & Files]
    ValidateData -->|Check| Secrets[secrets.yaml]
    ValidateData -->|Check| Config[config.yaml]
    ValidateData -->|Check| Resume[plain_text_resume.yaml]
    
    Secrets -->|Validate| LoadSecrets[Load API Keys]
    Config -->|Validate| LoadConfig[Load User Preferences]
    
    LoadSecrets --> PromptUser[Prompt User for Action]
    LoadConfig --> PromptUser
    
    PromptUser -->|Select Action| HandleInquiries[Handle Inquiries]

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2. Resume Parsing & Tailoring Flow

How the system takes a specific job URL and tailored a resume for it.

sequenceDiagram
    participant User
    participant Facade as ResumeFacade
    participant Browser as Selenium Browser
    participant Parser as LLMJobParser
    participant LLM as LLM Service
    participant Generator as ResumeGenerator

    User->>Facade: Select "Tailor Resume"
    Facade->>User: Request Job URL
    User->>Facade: Provide URL
    
    Facade->>Browser: Navigate to Job URL
    Browser->>Facade: Return Page HTML
    
    Facade->>Parser: Parse HTML
    Parser->>LLM: Extract Role, Company, Description
    LLM-->>Parser: Structured Job Data
    
    Facade->>Generator: Generate Tailored Resume
    Generator->>LLM: Compare Resume vs Job Desc
    LLM-->>Generator: Contextual Suggestions
    
    Generator->>Browser: Render HTML Template
    Browser->>Facade: Return PDF Bytes
    Facade->>User: Save PDF to Output

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3. General Resume Generation Flow

Generating a generic resume without specific job tailoring.

graph TD
    User["User Input"] -->|Select Style| StyleManager["Style Manager"]
    StyleManager -->|Template Path| Generator["Resume Generator"]
    
    subgraph Generation Process
        ResumeData["Load Resume Data"] -->|Inject| Generator
        Generator -->|Render| HTML["HTML Resume"]
        HTML -->|Convert| PDF["PDF Generator (Selenium)"]
    end
    
    PDF --> Output["Output Folder"]

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4. LLM Request Lifecycle

How the system handles requests to the Large Language Model, including logging and error handling.

graph LR
    Request[App Request] --> Adapter[AI Adapter]
    Adapter -->|Select Provider| ModelFactory{Provider?}
    
    ModelFactory -->|OpenAI| OpenAI[OpenAI Model]
    ModelFactory -->|Claude| Claude[Claude Model]
    ModelFactory -->|Ollama| Ollama[Ollama Model]
    
    OpenAI --> API[External API]
    Claude --> API
    Ollama --> Local[Local Inference]
    
    API -->|Response| Logger[LLM Logger]
    Local -->|Response| Logger
    
    Logger -->|Log Token Usage| LogFile[open_ai_calls.json]
    Logger -->|Return Content| App[Application Logic]

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