🎤 AI Interview Practice Partner

A conversational AI agent built for the Eightfold Agentic AI Internship assignment.
This project allows users to practice interviews with a realistic AI interviewer and receive detailed, professional feedback.

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🚀 Features

• Conducts mock interviews for selected job roles
• Asks contextual follow-up questions
• Adapts to user response style (efficient, confused, chatty)
• Provides detailed structured feedback (strengths, areas to improve, tips, rating)
• Voice support (optional – but included in frontend design)
• Fast, free LLM using Groq API (Llama-3 models)

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🛠️ Tech Stack

Backend (Python – FastAPI)

• FastAPI
• Groq API (llama-3.1-8b-instant model – FREE)
• Pydantic
• Uvicorn
• dotenv

Frontend

• HTML
• CSS
• JavaScript
• Browser Speech-to-Text (Web Speech API)
• Live Server

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📂 Project Structure

interview-practice-agent/
│
├── backend/
│   ├── main.py
│   ├── requirements.txt
│   ├── .env
│   └── venv/
│
└── frontend/
    ├── index.html
    ├── style.css
    └── script.js

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🔧 Installation & Setup

1️⃣ Clone the Repository

git clone <your-repo-url>
cd interview-practice-agent

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🧠 Backend Setup (FastAPI)

2️⃣ Create Virtual Environment

cd backend
python -m venv venv
venv\Scripts\activate

3️⃣ Install Dependencies

pip install -r requirements.txt

4️⃣ Add Your Groq API Key

Create a .env file inside backend/:

GROQ_API_KEY=your_key_here

Get your free API key: https://console.groq.com/keys
(No credit card required)

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5️⃣ Run the Backend

uvicorn main:app --reload

Backend will run at:

👉 http://127.0.0.1:8000

Check health:

👉 http://127.0.0.1:8000/health

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💻 Frontend Setup

6️⃣ Open the frontend folder

cd ../frontend

Right-click index.html →
👉 Open With Live Server

Or open manually:

👉 http://127.0.0.1:5500/frontend/index.html

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🧪 How to Use

1. Select job role, experience level, and mode
2. Click Start Interview
3. Answer questions (text or voice)
4. AI interviewer asks smart follow-ups
5. Click Get Feedback
6. Receive:
   • Strengths
   • Areas to improve
   • Actionable tips
   • Rating (0–5)

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📌 Architecture Overview

Frontend (HTML/JS) 
     ↓ REST API calls (fetch)
Backend (FastAPI)
     ↓
Groq LLM (Llama 3.1 Models)

• Frontend sends /start, /message, /finish
• Backend stores session → generates next question
• Feedback is created using structured prompt engineering

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🤖 Design Decisions

1. Why Groq Llama-3.1?

• Fastest free inference
• No quota issues
• Great for conversational tasks

2. Why FastAPI?

• Lightweight
• Easy routing
• Async for smooth processing

3. Why Web Speech API for Voice?

• No external dependency
• Works in browser
• Simple to integrate

4. Stateful Sessions

Used in-memory dictionary:

SESSIONS = { sessionId: { role, level, mode, qa[] } }

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🎥 Demo Instructions (for submission)

Your demo video (max 10 minutes) should show:

✔ Starting backend

✔ Running frontend

✔ Multiple interview styles:

• Efficient user
• Chatty user
• Confused user
• Off-topic user

✔ Getting feedback

✔ Explain architecture & design choices

No slides needed — only screen recording with voice.

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✅ This meets all Eightfold Assignment Requirements

• Conversational quality
• Agentic behaviour
• Multi-persona handling
• Technical implementation
• Natural interaction
• Detailed README
• Clear architecture reasoning

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🙌 Author

Modi Sireesha
Eightfold Agentic AI Internship – Assignment Submission