🧠 AI Interview Coach

An intelligent desktop app built with Python + Tkinter + AI models that analyzes your speech, posture, eye contact, and sentiment in real-time or from recorded videos — helping you improve your interview performance.

🚀 Features

✅ 🎙 Live Interview Mode – Record via webcam + mic with real-time subtitles and live sentiment color feedback (green, red, white). ✅ 📂 Recorded Video Analysis – Upload any pre-recorded interview video to get a complete AI-generated report. ✅ 📊 Smart Report Dashboard – Displays:

Posture Score

Eye Contact Score

Speech Sentiment Score

Overall Interview Confidence ✅ 🧍 Posture & Eye Tracking – Uses MediaPipe to analyze body alignment and gaze direction. ✅ 💬 Speech Sentiment Analysis – Powered by Vosk ASR + HuggingFace Transformers to evaluate positivity and clarity in tone. ✅ 🎨 Beautiful Dynamic UI – Modern Tkinter interface with dark mode, smooth layout, and vivid live feedback colors.

🛠️ Tech Stack Layer Technologies Used Frontend (GUI) Tkinter, Pillow (PIL), Matplotlib Speech Processing Vosk (Offline ASR), SoundDevice Sentiment Analysis Transformers (BERT / RoBERTa), Torch Posture Detection OpenCV, MediaPipe Video Handling MoviePy Backend Logic Python 3.12, Threading, JSON Visualization Matplotlib (Pie Charts), Tkinter Canvas Packaging requirements.txt for reproducible setup ⚙️ Installation 1️⃣ Clone the Repository git clone https://github.com/nihals007/AI-Interview-Coach.git cd AI-Interview-Coach1

2️⃣ Create & Activate a Virtual Environment python -m venv venv venv\Scripts\activate

3️⃣ Install Dependencies pip install -r requirements.txt

4️⃣ Run the App python app.py

🧩 Folder Structure AI-Interview-Coach1/ │ ├── app.py # Main Tkinter Application ├── models/ │ ├── posture_model.py # Body posture and eye-contact analyzer │ ├── speech_model.py # Speech recording & transcription logic │ └── sentiment_model.py # Text sentiment analyzer (Transformers) │ ├── requirements.txt # All dependencies ├── snapshot.jpg # Auto-generated snapshot (from last test) ├── audio.wav # Temporary recorded audio file └── .gitignore # Keeps venv and cache files out of Git

💡 How It Works

Live Mode:

Opens webcam & mic, records video and audio.

Uses Vosk for real-time speech recognition.

Runs live sentiment detection → subtitle colors: 🟢 Positive | ⚪ Neutral | 🔴 Negative

Analyzes facial alignment (eye contact) and posture with MediaPipe.

On stop → generates detailed report with pie chart + transcript.

Recorded Video Mode:

Upload any .mp4 / .avi / .mov file.

Extracts audio, runs transcription + sentiment + posture frame sampling.

Creates a full AI feedback report with realistic scoring.

📊 Example Report Metric Score Description 🧍 Posture 82% Slight slouch but stable presence 👀 Eye Contact 88% Mostly maintained eye contact 💬 Speech 79% Positive and articulate tone 🎯 Overall 83% Great confidence! Minor posture improvement needed 📸 Screenshots

(Add your screenshots here once you present)

🟢 Live Mode with subtitles

📂 Recorded video upload screen

📊 AI-generated report window

👩‍💻 Contributors

Team Interview Architects

AI Models by Open Source Communities (HuggingFace, Vosk, MediaPipe)

🧰 Future Enhancements

Browser-based version using Flask + React

Emotion recognition via facial analysis

Voice modulation + clarity score

Resume-based question simulation