AI Mock Interview System Using Python and Flask

Project Overview

The AI Mock Interview System is an AI-based web application developed using Python and Flask to help students practise technical and HR interviews in an interactive environment. The system works as a virtual interviewer that generates role-specific questions, accepts text or voice responses, evaluates answers, and provides scores and improvement feedback.

The project uses SQLite for storing users, questions, and interview history, while the AI functionality is powered through the ChatGPT API or Gemini API.

Key Features

• User registration and login
• Job role selection
• AI-generated interview questions
• Technical and HR interview practice
• Text-based answers
• Voice-based answers
• AI answer analysis
• Grammar evaluation
• Keyword analysis
• Answer correctness evaluation
• Confidence evaluation
• Interview scoring
• Detailed feedback
• Improvement suggestions
• Weak-area detection
• Interview history

Job Role Selection

Students can select a target job role before starting an interview session. The system can generate questions based on roles such as Software Engineer, Data Analyst, Web Developer, and HR.

AI Interview Question Generation

The AI engine generates fresh, role-specific interview questions for each session. This provides students with different questions during repeated practice sessions instead of relying on a fixed question list.

Text and Voice Answers

The system allows students to respond to interview questions either by typing their answers or by speaking them. This creates a more interactive interview practice experience.

AI Answer Analysis

After receiving an answer, the AI evaluates different aspects of the response, including grammar, relevant keywords, correctness, and confidence. The analysis helps students understand how effectively they are answering interview questions.

Score and Feedback

After an answer is evaluated, the system provides a score along with detailed feedback and suggestions for improvement. Students can use this information to identify areas that require additional preparation.

Weak Area Detection

The system identifies topics and areas where a student needs improvement. This helps students focus their preparation on weaker areas instead of practising randomly.

Interview History

Previous interview attempts are stored in the system so students can review their practice history and track their progress across multiple sessions.

Technology Stack

• Programming Language: Python
• Backend Framework: Flask
• Database: SQLite
• Frontend: HTML, CSS, JavaScript
• AI Engine: ChatGPT API or Gemini API
• Hosting: Localhost or Cloud Hosting

Software and Tools Required

• Python 3
• pip
• Visual Studio Code or another Python IDE
• Web Browser
• ChatGPT API key or Gemini API key

Optional Advanced Features

• Resume upload
• AI-based resume improvement suggestions
• Daily interview practice mode

Watch Project Demo

Watch AI Mock Interview System Demo

More Details and Full Documentation

View Complete AI Mock Interview System Documentation