https://github.com/hahaanisha/placementor
AI-powered interview preparation platform that simulates real-time mock interviews, generates personalized questions from your resume, and provides instant feedback using Agno - based AI agents.
https://github.com/hahaanisha/placementor
agno conversational-bot gemini react
Last synced: 2 months ago
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AI-powered interview preparation platform that simulates real-time mock interviews, generates personalized questions from your resume, and provides instant feedback using Agno - based AI agents.
- Host: GitHub
- URL: https://github.com/hahaanisha/placementor
- Owner: hahaanisha
- Created: 2025-07-06T09:27:35.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2025-07-19T12:52:31.000Z (3 months ago)
- Last Synced: 2025-07-19T17:10:52.994Z (3 months ago)
- Topics: agno, conversational-bot, gemini, react
- Language: Python
- Homepage: https://place-mentor.vercel.app
- Size: 20.6 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
## About
Placementor is an advanced interview preparation platform designed to assist users in practicing and refining their interview skills. Developed during a hackathon, this project utilizes sophisticated AI agents to simulate realistic interview scenarios and provide actionable feedback.
## Flow:
## Demo: [Youtube Video](https://youtu.be/RogdnodHS_4)
## Table of Contents
- [Features](#features)
- [Tech Stack](#tech-stack)
- [Agents](#agents)
- [Feedback Agent](#feedback-agent)
- [Question Fetcher](#question-fetcher)
- [Interview Planner](#interview-planner)
- [Resume Parser](#resume-parser)
- [Installation](#installation)
- [Usage](#usage)
- [Screenshots](#screenshots)
- [Contributing](#contributing)
- [Developed By](#developed-by)
## Features
- **Resume Upload and Parsing**: Users upload their resumes, which are parsed to extract relevant information.
- **Interview Simulation**: Users select a company, role, and interview round to generate a tailored set of interview questions.
- **Speech Recognition**: Users practice answering questions using voice input.
- **Feedback and Evaluation**: AI agents evaluate user responses and provide detailed feedback, including scores and improvement suggestions.
## Tech Stack
- **Frontend**: React.js, Tailwind CSS, Axios
- **Backend**: Python, Flask
- **AI and NLP**: Agno Framework, Gemini, SpeechRecognition, DuckDuckGo(Webscraping)
- **Data Handling**: JSON & localstorage
- **Version Control**: Git
- **Authentication**: Clerk
## Agents
### Resume Parser
- **Purpose**: Extracts structured information from a user's uploaded resume.
- **Logic**:
- Uses the Gemini model within the Agno framework to parse the resume and extract key details such as full name, email, phone, skills, projects, education, and work experience.
- Constructs a prompt that instructs the AI to return the extracted information in a structured JSON format.
- Processes the AI response to extract and return the relevant information in the specified format.
### Interview Planner
- **Purpose**: Generates a structured interview plan based on the user's resume, target company, role, and interview round.
- **Logic**:
- Uses the Gemini model within the Agno framework to create a list of interview questions tailored to the user's selected parameters.
- Constructs a detailed prompt that guides the AI to generate questions simulating a real interview, starting with introductory questions and gradually increasing in difficulty.
- Outputs the interview plan in a structured JSON format, ensuring easy integration with other components.
### Question Fetcher
- **Purpose**: Retrieves specific questions from an interview plan based on the serial number.
- **Logic**:
- Takes an interview plan and a serial number as inputs.
- Converts the serial number to a string to match the format in the JSON data.
- Iterates through the interview plan to find and return the question corresponding to the given serial number.
- Returns an error if the question is not found, ensuring robustness in handling missing data.
### Feedback Agent
- **Purpose**: Evaluates user responses to interview questions and provides constructive feedback.
- **Logic**:
- Utilizes the Gemini model within the Agno framework to analyze user answers based on structure, clarity, relevance, and impact.
- Constructs a prompt that instructs the AI to evaluate answers on a scale of 1 to 10, provide feedback, suggest improvements, and decide if the user should repeat the question.
- Processes the AI response to extract and return a structured JSON format containing the score, feedback, corrected answer, and repeat status.
## Installation
1. Clone the repository:
```bash
git clone https://github.com/hahaanisha/PlaceMentor
```
2. Navigate to the project directory:
```bash
cd Placementor
```
3. Install the required dependencies:
```bash
pip install -r requirements.txt
```
## Usage
1. **Upload Resume**: Upload your resume in PDF format. The system will parse the resume and extract relevant information.
2. **Select Interview Parameters**: Choose the target company, role, and interview round to generate a tailored set of interview questions.
3. **Practice Interview**: Use the speech recognition feature to practice answering the generated interview questions.
4. **Receive Feedback**: After submitting your answers, the AI agents will evaluate your responses and provide detailed feedback, including scores and suggestions for improvement.
## Screenshots
## Contributing
We welcome contributions to Placementor! If you have any ideas, suggestions, or bug reports, please open an issue or submit a pull request.
## Developed By
- [Tejas Gadge](https://github.com/tejasgadge2504)
- [Anisha Shankar](https://github.com/hahaanisha)