Conduct comprehensive code audit and architectural planning

README.md

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+
+## Current System Analysis Report
+
+The Current System Analysis Report provides a detailed overview of the existing system, including core business logic, data flow, system interactions, dependencies, critical paths, and bottlenecks. This report helps in understanding the current state of the system and identifying areas for improvement.
+
+## Code Quality Assessment Report
+
+The Code Quality Assessment Report evaluates the quality of the codebase, identifying violations of SOLID principles, instances of code duplication, naming conventions, documentation quality, security vulnerabilities, resource management, and performance bottlenecks. This report helps in maintaining a high standard of code quality and ensuring the system's reliability and maintainability.
+
+## Architectural Design Document
+
+The Architectural Design Document outlines the system's architecture, including system boundaries, components, data model, database schema, API endpoints, authentication and authorization strategy, caching, and performance optimization strategies. This document serves as a blueprint for the system's design and helps in ensuring a scalable and efficient architecture.
+
+## Migration Strategy Document
+
+The Migration Strategy Document provides a detailed plan for migrating the system to a new architecture, including frontend architecture (React), backend architecture (FastAPI), integration strategy, and development operations. This document helps in ensuring a smooth and efficient migration process.
+
+## Risk Assessment and Mitigation Plan
+
+The Risk Assessment and Mitigation Plan identifies potential risks in the migration process and proposes mitigation strategies for these risks. This document also includes scalability requirements, security considerations, and maintenance and support strategy. This plan helps in minimizing risks and ensuring a successful migration.

docs/Architectural_Design_Document.md

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+# Architectural Design Document
+
+## System Boundaries and Components
+
+The system boundaries and components of the Resume Matcher system are defined as follows:
+
+1. **Frontend**: The frontend component is responsible for providing the user interface for the Resume Matcher system. It is built using React and communicates with the backend via API calls.
+
+2. **Backend**: The backend component is responsible for handling the business logic and data processing of the Resume Matcher system. It is built using FastAPI and provides API endpoints for the frontend to interact with.
+
+3. **Database**: The database component is responsible for storing the data used by the Resume Matcher system. It is designed using a relational database schema and is accessed by the backend component.
+
+4. **External Services**: The system interacts with external services such as machine learning models and third-party APIs for keyword extraction and similarity calculation.
+
+## Data Model and Database Schema
+
+The data model and database schema for the Resume Matcher system are designed as follows:
+
+1. **User Table**: Stores user information such as user ID, name, email, and password.
+
+2. **Resume Table**: Stores resume information such as resume ID, user ID, resume data, and extracted keywords.
+
+3. **Job Description Table**: Stores job description information such as job description ID, user ID, job description data, and extracted keywords.
+
+4. **Similarity Score Table**: Stores similarity scores between resumes and job descriptions, including resume ID, job description ID, and similarity score.
+
+## API Endpoints and Interactions
+
+The API endpoints and their interactions for the Resume Matcher system are defined as follows:
+
+1. **User API**:
+   - `POST /users`: Create a new user.
+   - `GET /users/{user_id}`: Retrieve user information.
+   - `PUT /users/{user_id}`: Update user information.
+   - `DELETE /users/{user_id}`: Delete a user.
+
+2. **Resume API**:
+   - `POST /resumes`: Upload a new resume.
+   - `GET /resumes/{resume_id}`: Retrieve resume information.
+   - `PUT /resumes/{resume_id}`: Update resume information.
+   - `DELETE /resumes/{resume_id}`: Delete a resume.
+
+3. **Job Description API**:
+   - `POST /job_descriptions`: Upload a new job description.
+   - `GET /job_descriptions/{job_description_id}`: Retrieve job description information.
+   - `PUT /job_descriptions/{job_description_id}`: Update job description information.
+   - `DELETE /job_descriptions/{job_description_id}`: Delete a job description.
+
+4. **Similarity Score API**:
+   - `POST /similarity_scores`: Calculate similarity score between a resume and a job description.
+   - `GET /similarity_scores/{similarity_score_id}`: Retrieve similarity score information.
+
+## Authentication and Authorization Strategy
+
+The authentication and authorization strategy for the Resume Matcher system is defined as follows:
+
+1. **Authentication**: The system uses JSON Web Tokens (JWT) for user authentication. Users are required to provide their credentials (email and password) to obtain a JWT, which is then used to authenticate subsequent API requests.
+
+2. **Authorization**: The system uses role-based access control (RBAC) to manage user permissions. Different roles (e.g., admin, user) have different levels of access to the system's resources and functionalities.
+
+## Caching and Performance Optimization Strategies
+
+The caching and performance optimization strategies for the Resume Matcher system are defined as follows:
+
+1. **Caching**: The system uses a caching mechanism (e.g., Redis) to store frequently accessed data and reduce the load on the database. This helps improve the overall performance and responsiveness of the system.
+
+2. **Performance Optimization**: The system employs various performance optimization techniques, such as query optimization, indexing, and load balancing, to ensure efficient resource utilization and minimize response times.

docs/Code_Quality_Assessment_Report.md

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+# Code Quality Assessment Report
+
+## Violations of SOLID Principles
+
+1. **Single Responsibility Principle (SRP) Violations**:
+   - The `DataExtractor` class in `resume_matcher/dataextractor/DataExtractor.py` has multiple responsibilities, including extracting links, names, emails, phone numbers, experience, and position years. This violates the SRP as each class should have only one reason to change.
+   - The `Processor` class in `resume_matcher/scripts/processor.py` handles both reading data and writing JSON files. These responsibilities should be separated into different classes.
+
+2. **Open/Closed Principle (OCP) Violations**:
+   - The `KeytermExtractor` class in `resume_matcher/dataextractor/KeyTermExtractor.py` is not easily extendable for new key term extraction algorithms without modifying the existing code. This violates the OCP as the class should be open for extension but closed for modification.
+
+3. **Liskov Substitution Principle (LSP) Violations**:
+   - No violations of the LSP were identified in the current codebase.
+
+4. **Interface Segregation Principle (ISP) Violations**:
+   - No violations of the ISP were identified in the current codebase.
+
+5. **Dependency Inversion Principle (DIP) Violations**:
+   - The `Processor` class in `resume_matcher/scripts/processor.py` directly depends on the `ParseDocumentToJson` class. This violates the DIP as high-level modules should not depend on low-level modules but on abstractions.
+
+## Instances of Code Duplication
+
+1. **Text Cleaning**:
+   - The `TextCleaner` class in `resume_matcher/dataextractor/TextCleaner.py` and `scripts/utils/Utils.py` have similar methods for cleaning text. These methods should be consolidated into a single utility class to avoid code duplication.
+
+2. **Key Term Extraction**:
+   - The `KeytermExtractor` class in `resume_matcher/dataextractor/KeyTermExtractor.py` and `scripts/KeytermsExtraction.py` have similar methods for extracting key terms. These methods should be consolidated into a single class to avoid code duplication.
+
+## Naming Conventions and Code Organization
+
+1. **Inconsistent Naming Conventions**:
+   - The naming conventions for classes and methods are inconsistent across the codebase. For example, some classes use camel case (`DataExtractor`), while others use snake case (`ParseDocumentToJson`). A consistent naming convention should be adopted throughout the codebase.
+
+2. **Code Organization**:
+   - The code organization can be improved by grouping related classes and functions into appropriate modules. For example, all data extraction-related classes and functions should be grouped into a single module.
+
+## Documentation Quality and Completeness
+
+1. **Missing Docstrings**:
+   - Several classes and methods are missing docstrings, making it difficult to understand their purpose and functionality. Docstrings should be added to all classes and methods to improve code readability and maintainability.
+
+2. **Incomplete Documentation**:
+   - The existing documentation is incomplete and does not cover all aspects of the codebase. Comprehensive documentation should be provided for all major functions, algorithms, data structures, error handling, logging mechanisms, and test coverage.
+
+## Security Vulnerabilities and Anti-Patterns
+
+1. **Hardcoded API Keys**:
+   - The `QdrantSearch` class in `scripts/similarity/get_similarity_score.py` contains hardcoded API keys. This is a security vulnerability as API keys should be stored securely and not hardcoded in the codebase.
+
+2. **Lack of Input Validation**:
+   - Several methods lack input validation, making the codebase vulnerable to injection attacks and other security issues. Input validation should be added to all methods to ensure the integrity and security of the system.
+
+## Resource Management and Performance Bottlenecks
+
+1. **Inefficient Parsing**:
+   - The parsing process in the `DataExtractor` class can be a performance bottleneck, especially for large documents. The performance of the parsing process should be optimized to improve the overall performance of the system.
+
+2. **Memory Management**:
+   - The current codebase does not include any memory management mechanisms, which can lead to memory leaks and performance issues. Memory management mechanisms should be implemented to ensure efficient resource utilization.

docs/Current_System_Analysis_Report.md

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+# Current System Analysis Report
+
+## Core Business Logic and Data Flow
+
+The core business logic of the Resume Matcher system involves parsing resumes and job descriptions, extracting key terms and keywords, and comparing them to provide insights and suggestions for improving the resume. The data flow can be summarized as follows:
+
+1. **Input**: Users provide resumes and job descriptions in PDF format.
+2. **Parsing**: The system uses Python to parse the input documents and extract relevant information.
+3. **Keyword Extraction**: The system extracts keywords and key terms from the parsed documents using machine learning algorithms.
+4. **Comparison**: The system compares the extracted keywords from the resume and job description to calculate a similarity score.
+5. **Output**: The system provides insights and suggestions to improve the resume based on the comparison results.
+
+## Key System Interactions and Dependencies
+
+The key system interactions and dependencies in the Resume Matcher system include:
+
+1. **User Interaction**: Users interact with the system by providing input documents and receiving output insights.
+2. **Parsing Libraries**: The system relies on libraries such as `PyPDF2` and `spacy` for parsing and natural language processing.
+3. **Machine Learning Models**: The system uses machine learning models for keyword extraction and similarity calculation.
+4. **Data Storage**: The system stores parsed data and results in JSON format for further processing and analysis.
+
+## Critical Paths and Bottlenecks
+
+The critical paths and bottlenecks in the Resume Matcher system include:
+
+1. **Parsing Performance**: The performance of the parsing process can be a bottleneck, especially for large documents.
+2. **Keyword Extraction Accuracy**: The accuracy of the keyword extraction process is critical for providing meaningful insights.
+3. **Similarity Calculation Efficiency**: The efficiency of the similarity calculation process can impact the overall performance of the system.
+
+## Current Architecture Patterns in Use
+
+The current architecture patterns in use in the Resume Matcher system include:
+
+1. **Modular Design**: The system is designed in a modular way, with separate modules for parsing, keyword extraction, and similarity calculation.
+2. **Pipeline Architecture**: The system follows a pipeline architecture, where data flows through different stages of processing.
+3. **Microservices**: The system can be extended to use microservices for different components, allowing for scalability and maintainability.

docs/Migration_Strategy_Document.md

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+# Migration Strategy Document
+
+## Frontend Architecture (React)
+
+### Component Hierarchy and State Management Approach
+- Define a clear component hierarchy to ensure modularity and reusability.
+- Use React's Context API or Redux for state management to handle global state and avoid prop drilling.
+- Implement hooks for managing local component state and side effects.
+
+### Routing and Navigation Structure
+- Use React Router for client-side routing and navigation.
+- Define routes for different pages and components, ensuring a smooth user experience.
+- Implement lazy loading for routes to optimize performance.
+
+### Data Fetching and Caching Strategy
+- Use libraries like Axios or Fetch API for data fetching.
+- Implement caching mechanisms using libraries like React Query or SWR to reduce redundant API calls and improve performance.
+- Handle loading states and errors gracefully during data fetching.
+
+### Error Handling and Loading States
+- Implement error boundaries to catch and handle errors in the component tree.
+- Display user-friendly error messages and fallback UI when errors occur.
+- Show loading indicators while data is being fetched or processed.
+
+### UI/UX Considerations and Responsive Design Approach
+- Follow best practices for UI/UX design to ensure a user-friendly interface.
+- Use CSS frameworks like Tailwind CSS or styled-components for styling.
+- Implement responsive design techniques to ensure the application works well on different screen sizes and devices.
+
+## Backend Architecture (FastAPI)
+
+### API Structure and Endpoint Organization
+- Define a clear structure for API endpoints, following RESTful principles.
+- Organize endpoints based on resource types and functionalities.
+- Use FastAPI's dependency injection system to manage dependencies and middleware.
+
+### Database Integration and ORM Setup
+- Choose a suitable database (e.g., PostgreSQL, MySQL) for storing application data.
+- Use an ORM like SQLAlchemy or Tortoise ORM for database integration and query management.
+- Define database models and relationships to represent the application's data schema.
+
+### Middleware Requirements
+- Implement middleware for tasks like authentication, logging, and request validation.
+- Use FastAPI's middleware system to add custom middleware functions.
+- Ensure middleware functions are efficient and do not introduce performance bottlenecks.
+
+### Background Task Processing
+- Use libraries like Celery or FastAPI's built-in background tasks for handling background processing.
+- Define tasks for time-consuming operations like sending emails or processing large datasets.
+- Ensure background tasks are executed asynchronously to avoid blocking the main application thread.
+
+### API Documentation Strategy
+- Use FastAPI's built-in support for generating API documentation using OpenAPI and Swagger.
+- Document all API endpoints, request parameters, and response formats.
+- Ensure the API documentation is up-to-date and easily accessible to developers.
+
+## Integration Strategy
+
+### API Contract Design and Versioning Strategy
+- Define a clear API contract with detailed specifications for each endpoint.
+- Use versioning to manage changes and updates to the API.
+- Ensure backward compatibility for existing clients when introducing new versions.
+
+### Real-time Communication Requirements (WebSocket vs REST)
+- Determine the need for real-time communication based on application requirements.
+- Use WebSockets for real-time features like live updates or notifications.
+- Use RESTful APIs for standard CRUD operations and data retrieval.
+
+### Data Serialization and Validation Approach
+- Use FastAPI's Pydantic models for data serialization and validation.
+- Define schemas for request and response data to ensure consistency and type safety.
+- Validate incoming data to prevent security vulnerabilities and data corruption.
+
+### Error Handling and Status Code Standards
+- Implement a consistent error handling strategy across the application.
+- Use appropriate HTTP status codes for different types of responses (e.g., 200 for success, 400 for client errors, 500 for server errors).
+- Provide detailed error messages and logs for debugging and troubleshooting.
+
+### Cross-Origin Resource Sharing (CORS) Configuration
+- Configure CORS to allow cross-origin requests from trusted domains.
+- Use FastAPI's CORS middleware to handle CORS settings.
+- Ensure CORS configuration is secure and does not expose the application to unauthorized access.
+
+## Development Operations
+
+### Deployment Strategy and Environment Setup
+- Define a deployment strategy for different environments (e.g., development, staging, production).
+- Use containerization tools like Docker to create consistent and reproducible environments.
+- Automate deployment processes using CI/CD pipelines.
+
+### Monitoring and Logging Requirements
+- Implement monitoring and logging to track application performance and detect issues.
+- Use tools like Prometheus, Grafana, or ELK stack for monitoring and logging.
+- Define metrics and alerts to proactively address performance bottlenecks and errors.
+
+### CI/CD Pipeline Design
+- Set up a CI/CD pipeline to automate the build, test, and deployment processes.
+- Use tools like GitHub Actions, Jenkins, or GitLab CI for pipeline automation.
+- Ensure the pipeline includes steps for code quality checks, testing, and deployment.
+
+### Testing Strategy (Unit, Integration, e2e)
+- Define a comprehensive testing strategy to ensure the application's reliability and stability.
+- Write unit tests for individual components and functions.
+- Implement integration tests to verify interactions between different parts of the application.
+- Use end-to-end (e2e) tests to simulate user interactions and validate the application's behavior.
+
+### Performance Metrics and Optimization Targets
+- Define performance metrics to measure the application's efficiency and responsiveness.
+- Use tools like Lighthouse, WebPageTest, or New Relic to monitor performance.
+- Set optimization targets and continuously improve the application's performance based on metrics and user feedback.