AI Job Candidates CRM

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$683.90
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appdrew
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Jun 16, 2024

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Product Details

Our enhanced Flask CV Generation App is a comprehensive and user-friendly web application designed to streamline the process of creating professional CVs and managing candidate data.


This app not only allows users to input their personal and professional details, upload images, and generate customized CVs in DOCX format but also leverages AI to generate recommendation letters and welcome messages for candidates. Users can manage these documents, import and export candidate data, and perform mass resume generation.



Key Features:



  1. User-Friendly Form:


    • The app provides a simple HTML form for users to input their details.

    • Fields include personal information, contact details, professional experience, and more.

    • Users can upload an image to be included in the CV.



  2. Dynamic CV Generation:


    • Utilizes the Python docx library to generate CVs in DOCX format.

    • Automatically formats text and images within the document.

    • Customizable templates allow for personalized and professional CV design.



  3. Database Integration:


    • Generated CVs are stored in an SQLite database using SQLAlchemy.

    • Easily retrieve and manage CVs through the app interface.



  4. File Management:


    • Generated CV files are saved with a naming convention that includes the user's name and the date of generation.

    • Users can download their generated CVs directly from the app.



  5. API Endpoint:


    • The app provides a RESTful API endpoint for creating CVs via POST requests.

    • Ideal for integrating with other applications or for automated CV generation.


Key Features:

User-Friendly Form:
The app provides a simple HTML form for users to input their details.
Fields include personal information, contact details, professional experience, and more.
Users can upload an image to be included in the CV.

Dynamic CV Generation:
Utilizes the Python docx library to generate CVs in DOCX format.
Automatically formats text and images within the document.
Customizable templates allow for personalized and professional CV design.

AI-Powered Document Generation:
Generates recommendation letters and welcome messages for candidates using AI.
Provides personalized content based on the candidate's details and qualifications.

Database Integration:
Generated CVs and candidate data are stored in an SQLite database using SQLAlchemy.
Easily retrieve, edit, and manage CVs and related documents through the app interface.

File Management:
Generated CV files are saved with a naming convention that includes the user's name and the date of generation.
Users can download their generated CVs directly from the app.

API Endpoint:
The app provides a RESTful API endpoint for creating CVs via POST requests.
Ideal for integrating with other applications or for automated CV generation.

Import and Export:
Import candidate data from a CSV file and generate CVs for all candidates in the list.
Export candidate data and generated CVs to a CSV file for easy sharing and backup.

Edit, delete, and manage generated recommendation letters and welcome messages. Allows for efficient document handling and customization. Developed using Flask, a lightweight and powerful Python web framework, SQLAlchemy ORM for database interactions with SQLite as for front end was made in bootstrap 5 .

Installation Instructions

How It Works:

User Input:
Users fill out a form with their details and upload an image.

CV and Document Generation:
The app processes the input data and generates a CV in DOCX format.
AI generates recommendation letters and welcome messages.
Text formatting (e.g., titles for name and signature) and image insertion are handled programmatically.

Storage and Retrieval:
The generated CV and related documents are saved in the cv_generate folder, and their metadata is stored in the SQLite database.
Users can download their CVs or generate new ones as needed.

Import and Export:
Users can import candidate data from a CSV file and generate CVs for all candidates.
Users can export all candidate data and generated documents to a CSV file.

Change and Adaptation Instructions

This section provides detailed instructions on how to change and adapt the Flask CV Generation App. You will learn how to modify various components of the application, including forms, templates, models, routes, and functionality. This will help you customize the app to meet specific needs or integrate additional features.

Prerequisites

Basic understanding of Python and Flask.
Familiarity with SQLAlchemy for database management.
Basic knowledge of HTML and CSS for front-end customization.

Structure of the Application

The application is organized as follows:

app.py: Main application file where routes and main logic are defined.
models.py: Contains the SQLAlchemy models for database tables.
templates/: Folder containing HTML templates.
static/: Folder for static files like CSS, JavaScript, and images.
cv_generate/: Folder where generated CV files are saved.
uploads/ : Folder where all uploaded files are saved.

Open templates/form.html.
Add new fields or modify existing ones as needed

In app.py, update the route that handles the form submission to include new form data.
In app.py, modify the update_doc function to adjust how the DOCX file is created and formatted.
In app.py, you can define the new route and its logic.
In app.py, update or add new functions that utilize AI models.

Create migration scripts to update the database schema:
flask db migrate -m "Add new fields to Candidate model"
flask db upgrade

These instructions provide a comprehensive guide for modifying and adapting the Flask CV Generation App. By following these steps, you can customize the application to meet specific requirements, integrate new features, and ensure a seamless experience for users. Whether adding new form fields, updating the database schema, or enhancing AI functionalities, this guide equips you with the knowledge to make the necessary changes effectively.

pip install -r requirements.txt
list containing required Python dependencies.

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