chore: readme

README.md

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-# interview-ai-detector
+# Interview AI Detector
+
+## 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.
+- The repository is meant for deployment purposes only and does not support local installation for development.
+
+## Author
+Yakobus Iryanto Prasethio