https://github.com/kingabzpro/redis-ml-project
Model serving with FastAPI and Redis caching to enable fast and efficient inference by reducing latency for repeated predictions.
https://github.com/kingabzpro/redis-ml-project
async fastapi machine-learning phishing-classcification redis redis-cache sklearn text-classification
Last synced: about 1 year ago
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Model serving with FastAPI and Redis caching to enable fast and efficient inference by reducing latency for repeated predictions.
- Host: GitHub
- URL: https://github.com/kingabzpro/redis-ml-project
- Owner: kingabzpro
- Created: 2025-03-17T12:12:50.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-03-29T11:19:10.000Z (over 1 year ago)
- Last Synced: 2025-04-30T08:13:40.494Z (about 1 year ago)
- Topics: async, fastapi, machine-learning, phishing-classcification, redis, redis-cache, sklearn, text-classification
- Language: Python
- Homepage: https://www.kdnuggets.com/accelerate-machine-learning-model-serving-with-fastapi-and-redis-caching
- Size: 19.8 MB
- Stars: 7
- Watchers: 1
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Phishing Email Classification App with Redis
This project demonstrates a phishing email classification application using machine learning. It includes four main components:
- **Model Training (`train.py`)**: Processes the dataset, trains a model using TF-IDF vectorization and logistic regression, and saves the trained model.
- **Model Validation (`validate.py`)**: Evaluates the saved model on a test dataset and outputs performance metrics such as accuracy, precision, and recall.
- **Model Serving (`serve.py`)**: Implements a FastAPI-based service to provide real-time phishing email predictions. Redis caching is used to optimize performance by speeding up repeated requests.
- **Redis Check (`check_redis.py`)**: A utility script to inspect the Redis database, displaying the number of cached entries and sample predictions.
---
## Application Workflow
The following diagram illustrates how the application works, including the interaction between the machine learning model, FastAPI, and Redis caching:
---
## Project Structure
```
.
├── data/
│ └── phishing_emails.csv # Kaggle dataset
├── phishing_model.pkl # Saved model file
├── train.py # Script for training the model
├── validate.py # Script for validating the model
├── serve.py # FastAPI app for model serving with Redis caching
└── check_redis.py # Script to inspect Redis cache entries
```
---
## Prerequisites
- Python 3.7+
- Pip
- Redis (see instructions below for running Redis on Windows)
Install the required Python packages with:
```bash
pip install -r requirements.txt
```
---
## Dataset
The dataset is sourced from [Kaggle](https://www.kaggle.com/datasets/subhajournal/phishingemails). Download the dataset and place it in the `data/` directory.
---
## Usage
### 1. Training the Model
Run the training script to process the dataset and generate the model file:
```bash
python train.py
```
### 2. Validating the Model
Run the validation script to evaluate the model's performance:
```bash
python validate.py
```
### 3. Serving Predictions
Ensure Redis is running (see instructions below for Windows), then start the FastAPI server:
```bash
python serve.py
```
Access the API at `http://localhost:8000` and send POST requests to the `/predict` endpoint with JSON payloads:
```json
{
"text": "Your email content here..."
}
```
### 4. Checking Redis
Use the `check_redis.py` script to inspect the Redis cache. This script displays the number of cached entries and the first five email texts along with their predictions:
```bash
python check_redis.py
```
---
## Running Redis on Windows
1. **Enable WSL**: Follow [Microsoft's guide](https://docs.microsoft.com/en-us/windows/wsl/install) to enable WSL and install a Linux distribution (e.g., Ubuntu) from the Microsoft Store.
2. **Install Redis**: Open your WSL terminal and run:
```bash
sudo apt update
sudo apt install redis-server
```
3. **Start Redis**:
```bash
sudo service redis-server start
```
4. **Access Redis from Windows**: With Redis running in WSL (default on `localhost:6379`), the Python Redis client in your FastAPI app can connect without extra configuration.
---
## API Usage
Send a POST request to `http://localhost:8000/predict` with the following JSON payload:
```bash
curl -X 'POST' \
'http://localhost:8000/predict' \
-H 'accept: application/json' \
-H 'Content-Type: application/json' \
-d '{
"text": "todays floor meeting you may get a few pointed questions about today article about lays potential severance of $ 80 mm"
}'
```
The API responds with a JSON object containing:
- **`prediction`**: The predicted class name (e.g., "Phishing Email" or "Safe Email").
- **`probability`**: The confidence score of the prediction.
Example response:
```json
{
"prediction": "Safe Email",
"probability": 0.7791625553383463
}
```