/how-to-build-lovable

How to build Resume parser with Lovable?

Learn how to build a robust resume parser with Lovable. Our step-by-step guide covers setup, integration, and customization for a seamless hiring process.

Matt Graham, CEO of Rapid Developers

Book a call with an Expert

Starting a new venture? Need to upgrade your web app? RapidDev builds application with your growth in mind.

Book a free No-Code consultation

How to build Resume parser with Lovable?

 
Setting Up Your Lovable Project for a Resume Parser
 

Start by creating a new project in Lovable. In the project dashboard, click on "New Project" and give it a meaningful name (for instance, "ResumeParser"). Since Lovable does not have a terminal, all dependency management will be handled through code configuration files.

 
Declaring Dependencies
 

Create a new file in your project called dependencies.txt. In Lovable, this file will act as your configuration file for installing dependencies. Add the following lines to instruct Lovable which Python libraries to load:


Flask
python-docx
regex
# You may add any other dependencies required for resume parsing here.
  

Lovable will automatically read this file and install the listed dependencies when your project runs.

 
Creating the Main Application File
 

Create a new file called app.py. This file will serve as the primary entry point for your resume parser application. Insert the following code into app.py:


from flask import Flask, request, render\_template, jsonify
import re
from docx import Document

app = Flask(name)

Function to extract basic information from a resume

def parse_resume(file_content, filename):
text = ""
if filename.endswith('.docx'):
document = Document(file_content)
for para in document.paragraphs:
text += para.text + "\n"
else:
# Assuming plain text resume
text = file_content.read().decode("utf-8")

# Use simple regex to extract email address as an example
email_pattern = r"[a-zA-Z0-9+._%-]+@[a-zA-Z0-9.-]+.[a-zA-Z]{2,4}"
emails = re.findall(email\_pattern, text)

# Extract full name based on a heuristic (first line as name if words are capitalized)
lines = text.splitlines()
name = lines[0] if len(lines) > 0 and lines[0].istitle() else "Not Found"

return {
    "name": name,
    "emails": emails,
    "raw\_text": text
}

@app.route("/", methods=["GET", "POST"])
def index():
if request.method == "POST":
if "resume" not in request.files:
return "No resume file provided", 400
file = request.files["resume"]
parsed_data = parse_resume(file, file.filename)
return jsonify(parsed_data)
return render_template("index.html")

if name == "main":
# Lovable will automatically bind to the appropriate host and port.
app.run()


This code sets up a basic Flask application with an endpoint to upload a resume. The parse_resume function shows an example of how to process both .docx files and plain text uploads.

 
Creating the HTML Template for Resume Upload
 

Create a new folder in your project named templates. Inside the templates folder, create a file called index.html and add the following HTML code:





    
    Resume Parser


    

Upload Your Resume

This HTML file creates a simple user interface for uploading resumes. The form sends a POST request to the root endpoint, which is handled by the Flask application.

 
Integrating and Running Your Application on Lovable
 

Since Lovable does not support terminal commands, all dependency installations and server initializations are automatically handled by the platform using the dependencies.txt file and the app.py's entry point. To run your application:

  • Ensure all files (dependencies.txt, app.py, and templates/index.html) are saved in your project.
  • Click on the Run button provided by Lovable's interface.
  • Your Flask server will start automatically. Lovable binds to the required host and port, and your application becomes accessible via the provided URL.

If any errors occur, review the error messages in the Lovable console pane to debug and make necessary adjustments in your code.

 
Testing the Resume Parser
 

Access the live URL generated by Lovable in your browser. The home page should display the resume upload form. Perform the following test:

  • Upload a sample resume in either .docx or .txt format.
  • Upon submission, the application processes the file and returns a JSON response with parsed details such as the extracted name and email addresses.
  • Review the output to ensure that the resume parser functions as expected and tweak the parsing logic in the parse\_resume function if needed.

This completes the setup of your resume parser in Lovable.

Want to explore opportunities to work with us?

Connect with our team to unlock the full potential of no-code solutions with a no-commitment consultation!

Book a Free Consultation

How to Build Your Own Resume Parsing API with Lovable?


const express = require('express');
const multer = require('multer');
const { parseResume } = require('lovable-resume-parser'); // Hypothetical library
const app = express();
const upload = multer();

app.post('/api/parse-resume', upload.single('resume'), async (req, res) => {
  if (!req.file) {
    return res.status(400).json({ error: 'No file uploaded' });
  }
  try {
    const resumeBuffer = req.file.buffer;
    const parsedData = await parseResume(resumeBuffer);
    
    const structuredResume = {
      personal\_info: {
        name: parsedData.name || '',
        email: parsedData.email || '',
        phone: parsedData.phone || ''
      },
      work\_experience: parsedData.experiences ? parsedData.experiences.map(exp => ({
        company: exp.company,
        role: exp.title,
        start\_date: exp.start,
        end\_date: exp.end,
        description: exp.summary
      })) : [],
      education: parsedData.education ? parsedData.education.map(edu => ({
        institution: edu.institution,
        degree: edu.degree,
        start\_date: edu.start,
        end\_date: edu.end
      })) : []
    };

    return res.json(structuredResume);
  } catch (error) {
    console.error(error);
    return res.status(500).json({ error: 'Failed to parse resume' });
  }
});

app.listen(3000, () => {
  console.log('Resume parser API running on port 3000');
});

How to build a resume parser with Lovable and enrich insights


const express = require('express');
const multer = require('multer');
const axios = require('axios');
const { parseResume } = require('lovable-resume-parser-ex'); // Hypothetical Lovable extendable parser library
const app = express();
const upload = multer();

app.post('/api/enrich-resume', upload.single('resume'), async (req, res) => {
  if (!req.file) {
    return res.status(400).json({ error: 'Resume file is required.' });
  }
  try {
    const resumeBuffer = req.file.buffer;
    const parsedResume = await parseResume(resumeBuffer);

    // Send parsed resume data to an external enrichment API for personality and skills insights
    const enrichmentResponse = await axios.post('https://api.external-enrichment.com/insights', {
      email: parsedResume.email,
      skills: parsedResume.skills
    });

    const enrichedInsights = enrichmentResponse.data;

    const structuredResume = {
      personal\_info: {
        name: parsedResume.name || '',
        email: parsedResume.email || '',
        phone: parsedResume.phone || ''
      },
      work\_experience: parsedResume.experiences || [],
      education: parsedResume.education || [],
      enriched\_insights: enrichedInsights
    };

    return res.json(structuredResume);
  } catch (error) {
    console.error('Error processing resume:', error);
    return res.status(500).json({ error: 'Failed to process the resume.' });
  }
});

app.listen(3001, () => {
  console.log('Resume enrichment API running on port 3001');
});

How to Securely Stream-Parse Resumes with Lovable and Express.js


const express = require('express');
const multer = require('multer');
const jwt = require('jsonwebtoken');
const AWS = require('aws-sdk');
const { streamParseResume } = require('lovable-resume-parser'); // Hypothetical stream-based parser

const app = express();
const upload = multer({ storage: multer.memoryStorage() });

const s3 = new AWS.S3({
  accessKeyId: process.env.AWS_ACCESS_KEY,
  secretAccessKey: process.env.AWS_SECRET_KEY,
  region: process.env.AWS\_REGION
});

function authenticateToken(req, res, next) {
  const authHeader = req.headers['authorization'];
  const token = authHeader && authHeader.split(' ')[1];
  if (!token) return res.status(401).json({ error: 'Missing token' });
  jwt.verify(token, process.env.JWT\_SECRET, (err, user) => {
    if (err) return res.status(403).json({ error: 'Invalid token' });
    req.user = user;
    next();
  });
}

app.post('/api/secure/parse-s3-resume', authenticateToken, upload.single('resume'), async (req, res) => {
  if (!req.file) {
    return res.status(400).json({ error: 'No resume provided' });
  }

  const s3Params = {
    Bucket: process.env.AWS_S3_BUCKET,
    Key: `resumes/${Date.now()}_${req.file.originalname}`,
    Body: req.file.buffer,
    ContentType: req.file.mimetype
  };

  try {
    const s3Upload = await s3.upload(s3Params).promise();
    const s3Stream = s3.getObject({ Bucket: process.env.AWS_S3_BUCKET, Key: s3Params.Key }).createReadStream();

    streamParseResume(s3Stream, (err, parsedData) => {
      if (err) {
        return res.status(500).json({ error: 'Error parsing resume' });
      }
      res.json({
        s3Location: s3Upload.Location,
        parsedResume: {
          personal: parsedData.personal || {},
          experience: parsedData.experience || [],
          education: parsedData.education || []
        }
      });
    });
  } catch (error) {
    console.error(error);
    res.status(500).json({ error: 'Server error processing resume' });
  }
});

app.listen(4000, () => {
  console.log('Secure resume parser running on port 4000');
});

Want to explore opportunities to work with us?

Connect with our team to unlock the full potential of no-code solutions with a no-commitment consultation!

Book a Free Consultation
Matt Graham, CEO of Rapid Developers

Book a call with an Expert

Starting a new venture? Need to upgrade your web app? RapidDev builds application with your growth in mind.

Book a free No-Code consultation

Best Practices for Building a Resume parser with AI Code Generators

 
Understanding the Resume Parser Challenge
 

  • This guide explains how to build a resume parser using AI code generators.
  • A resume parser extracts key information from resumes (e.g., name, email, skills) using text processing techniques.
  • AI code generators can accelerate development by suggesting code snippets and automation rules.

 
Prerequisites
 

  • A basic computer with Python installed.
  • An internet connection and a web browser.
  • An account with access to an AI code generator or a platform such as GitHub Copilot or ChatGPT.
  • Basic familiarity with copying, pasting, and running simple Python scripts.

 
Planning the Parser Structure
 

  • Decide which information to extract from resumes (e.g., name, email address, phone number, skills, work experience).
  • Outline the workflow: input resume text → preprocessing → information extraction → output structured data.
  • Plan to use AI code generators to help write repetitive or complex parts of parsing logic.

 
Setting Up the Development Environment
 

  • Install Python from the official website if not already available.
  • Use any text editor like VS Code, Sublime Text, or Notepad++ for writing your code.
  • If using an AI code generator plugin, install and configure it within your editor.

 
Preprocessing Resume Data
 

  • Standardize resume text by removing unwanted characters and formatting issues.
  • Convert PDFs or DOCX files into plain text using libraries like PyPDF2 or python-docx, if necessary.
  • This step ensures that the input text is clean and easier for the parser to analyze.

 
Generating Code with an AI Tool
 

  • When you are ready to code, ask your AI code generator to create functions for extracting items using regular expressions.
  • For example, prompt the AI to write a function to extract a candidate's name, email, and phone number.
  • This saves time and prevents common coding mistakes.

 
Writing the Resume Parsing Code
 

  • Create a new Python file (for example, resume\_parser.py).
  • Below is a sample code snippet to build a simple resume parser:
  • 
    import re
    
    

    def parse_resume(text):
    # Define regex patterns for key information
    name_pattern = re.compile(r'Name:\s_(._)')
    email_pattern = re.compile(r'Email:\s*([\w.-]+@[\w.-]+)')
    phone_pattern = re.compile(r'Phone:\s*([\d-]+)')

    name = re.search(name\_pattern, text)
    email = re.search(email\_pattern, text)
    phone = re.search(phone\_pattern, text)
    
    return {
        'name': name.group(1).strip() if name else None,
        'email': email.group(1).strip() if email else None,
        'phone': phone.group(1).strip() if phone else None,
    }
    

    if name == "main":
    resume_text = """
    Name: Jane Doe
    Email: jane.doe@example.com
    Phone: 123-456-7890
    """
    details = parse_resume(resume_text)
    print(details)


  • Examine the generated code and adjust it if needed. Use AI suggestions to refine patterns or add new fields.

 
Enhancing the Parser with AI-Generated Code
 

  • Refine the extraction logic by prompting the AI to include additional data such as skills and work experience entries.
  • Ask the AI to generate parsing logic for complex patterns that may vary across resumes.
  • Integrate fallback mechanisms to handle cases where expected information is missing or formatted differently.

 
Handling Different Resume Formats
 

  • Resumes can come in various formats (plain text, PDF, DOCX). Create functions to convert these formats into plain text.
  • For example, you may create a PDF processing function using libraries like PyPDF2:
  • 
    import PyPDF2
    
    

    def extract_text_from_pdf(pdf_path):
    with open(pdf_path, 'rb') as file:
    reader = PyPDF2.PdfReader(file)
    text = ""
    for page in reader.pages:
    text += page.extract_text()
    return text


  • Use similar techniques to handle other file types.

 
Testing and Debugging the Parser
 

  • Run your code with multiple sample resumes to test its robustness.
  • Check the output for accuracy and completeness of the extracted data.
  • Use manual testing and debugging techniques. Adjust regex patterns and code logic based on test results.

 
Iterating and Improving the Code
 

  • Collect user feedback on parser accuracy and usability.
  • Iterate on the development, possibly incorporating machine learning models for better extraction if needed.
  • Engage your AI code generator to refactor or improve

Client trust and success are our top priorities

When it comes to serving you, we sweat the little things. That’s why our work makes a big impact.

Rapid Dev was an exceptional project management organization and the best development collaborators I've had the pleasure of working with. They do complex work on extremely fast timelines and effectively manage the testing and pre-launch process to deliver the best possible product. I'm extremely impressed with their execution ability.

CPO, Praction - Arkady Sokolov

May 2, 2023

Working with Matt was comparable to having another co-founder on the team, but without the commitment or cost. He has a strategic mindset and willing to change the scope of the project in real time based on the needs of the client. A true strategic thought partner!

Co-Founder, Arc - Donald Muir

Dec 27, 2022

Rapid Dev are 10/10, excellent communicators - the best I've ever encountered in the tech dev space. They always go the extra mile, they genuinely care, they respond quickly, they're flexible, adaptable and their enthusiasm is amazing.

Co-CEO, Grantify - Mat Westergreen-Thorne

Oct 15, 2022

Rapid Dev is an excellent developer for no-code and low-code solutions.
We’ve had great success since launching the platform in November 2023. In a few months, we’ve gained over 1,000 new active users. We’ve also secured several dozen bookings on the platform and seen about 70% new user month-over-month growth since the launch.

Co-Founder, Church Real Estate Marketplace - Emmanuel Brown

May 1, 2024 

Matt’s dedication to executing our vision and his commitment to the project deadline were impressive. 
This was such a specific project, and Matt really delivered. We worked with a really fast turnaround, and he always delivered. The site was a perfect prop for us!

Production Manager, Media Production Company - Samantha Fekete

Sep 23, 2022