InterviewAce AI

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analysis and results

Inspiration

Job seekers often struggle to understand how well their resumes match a specific job description. Preparing for interviews requires identifying skill gaps, understanding strengths, and practicing relevant questions. I wanted to build an AI-powered assistant that could provide personalized feedback and make interview preparation more effective.

What it does

InterviewAce AI analyzes a candidate's resume against a job description using Google Gemini. The platform:

Calculates a resume match score
Identifies strengths and missing skills
Generates interview questions tailored to the role
Provides actionable improvement suggestions
Exports results as a downloadable PDF report

Users can either paste their resume or upload a PDF and receive detailed AI-generated feedback.

How I built it

The project was built using:

React + Vite for the frontend
Flask for the backend API
Google Gemini API for resume analysis and content generation
PyPDF for extracting text from uploaded resumes
jsPDF for generating downloadable reports
Render for backend deployment

The frontend collects resume and job description data and sends it to the Flask backend. The backend extracts resume text, constructs a prompt, sends it to Gemini, and returns structured analysis results to the user interface.

Challenges I ran into

Extracting text reliably from uploaded PDF resumes
Connecting the React frontend with the deployed Flask backend
Handling deployment issues on Render
Managing API integration and prompt design for consistent AI-generated responses
Formatting AI output into a clean and readable report