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---
title: Interview AI Detector
emoji: 🧐
colorFrom: red
colorTo: green
sdk: docker
pinned: false
---
# Interview AI Detector
## Playground
https://panduwana-interview-ai-detector.hf.space/docs
## Dev setup
Requirements:
- Python 3.10.x (may have problems with higher versions)
- Accept the agreement for https://huggingface.co/google/gemma-2b
```
git clone [email protected]:rekrutmen_plus/interview-ai-detector.git
cd interview-ai-detector
```
### With Docker
<details>
<summary>Local build/run</summary>
```
cp COPY_THEN_EDIT.env .env # then FILL IT OUT
pip install python-dotenv transformers huggingface_hub
python download-huggingface-model.py google/gemma-2b
docker build -t interview-ai-detector --secret id=dotenv,src=.env .
PORT=8080 docker run -p $PORT:7860 interview-ai-detector
```
Then open: http://localhost:8080/docs/
</details>
<details>
<summary>Using Huggingface Spaces</summary>
- define a secret: HUGGINGFACE_TOKEN in the space's settings
- git push --force
</details>
### Without Docker
```
python --version # ensure 3.10.x
pip install -r requirements.txt
python huggingface-download-model.py google/gemma-2b
python -m nltk.downloader punkt wordnet averaged_perceptron_tagger
unzip ~/nltk_data/corpora/wordnet.zip -d ~/nltk_data/corpora/
PORT=8080 uvicorn prediction:app --host 0.0.0.0 --port $PORT
```
Then open: http://localhost:8080/docs/
## Overview
Interview AI Detector is a machine learning model designed to distinguish between human and AI-generated responses during interviews. The system is composed of two models:
1. **ALBERT Model**: Processes text features extracted from responses.
2. **Logistic Regression Model (LogReg)**: Utilizes the output from the ALBERT model along with additional behavioral features to make the final prediction.
The model is deployed on Google Vertex AI, with integration managed by a Kafka consumer deployed on Google Compute Engine. Both the model and Kafka consumer utilize FastAPI for API management.
## Architecture
### ALBERT Model
- **Source**: HuggingFace
- **Input**: 25 numerical features extracted from the text, including:
- Part-of-Speech (POS) tags
- Readability scores
- Sentiment analysis
- Perplexity numbers
- **Output**: Features used as input for the Logistic Regression model
### Logistic Regression Model
- **Input**:
- Output from the ALBERT model
- 4 additional features, including typing behavior metrics such as backspace count and key presses per letter
- **Output**: Final prediction indicating whether the response is human or AI-generated
## Deployment
- **Model Deployment**: Vertex AI
- **Kafka Consumer Deployment**: Compute Engine
- **API Framework**: FastAPI
- **Training**:
- **Epochs**: 8
- **Dataset**: 2000 data points (1000 human responses, 1000 AI-generated responses)
- **Framework**: PyTorch
## Usage
### API Endpoints
- **POST /predict**:
- **Description**: Receives a pair of question and answer, along with typing behavior metrics. Runs the prediction pipeline and returns the result.
- **Input**:
```json
{
"question": "Your question text",
"answer": "The given answer",
"backspace_count": 5,
"letter_click_counts": {"a": 27, "b": 4, "c": 9, "d": 17, "e": 54, "f": 12, "g": 4, "h": 15, "i": 25, "j": 2, "k": 2, "l": 14, "m": 10, "n": 23, "o": 23, "p": 9, "q": 1, "r": 24, "s": 19, "t": 36, "u": 9, "v": 6, "w": 8, "x": 1, "y": 7, "z": 0}
}
```
- **Output**:
```json
{
"predicted_class": "HUMAN" or "AI",
"main_model_probability": "0.85",
"secondary_model_probability": "0.75",
"confidence": "High Confidence" or "Partially Confident" or "Low Confidence"
}
```
## Limitations
- The model is not designed for retraining. The current implementation focuses solely on deployment and prediction.
## Author
Yakobus Iryanto Prasethio |