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https://github.com/robinmillford/reddit-content-classifier

This repository contains the source code for a complete, end-to-end MLOps project that automatically trains, evaluates, and deploys a machine learning model to classify Reddit content as Safe-For-Work (SFW) or Not-Safe-For-Work (NSFW).
https://github.com/robinmillford/reddit-content-classifier

content-classification end-to-end-machine-learning mlops-project mlops-workflow nsfw reddit-api

Last synced: 9 months ago
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This repository contains the source code for a complete, end-to-end MLOps project that automatically trains, evaluates, and deploys a machine learning model to classify Reddit content as Safe-For-Work (SFW) or Not-Safe-For-Work (NSFW).

Awesome Lists containing this project

README 

          
# AI Content Classifier: Production MLOps Pipeline



[![Scheduled Model Retraining](https://github.com/RobinMillford/Reddit-content-classifier/actions/workflows/main.yml/badge.svg)](https://github.com/RobinMillford/Reddit-content-classifier/actions/workflows/main.yml)

[![Python 3.11](https://img.shields.io/badge/python-3.11-blue.svg)](https://www.python.org/downloads/)

[![Streamlit](https://img.shields.io/badge/streamlit-1.28+-red.svg)](https://streamlit.io/)

[![MLOps](https://img.shields.io/badge/MLOps-automated-green.svg)](https://github.com/RobinMillford/Reddit-content-classifier)



A production-ready MLOps pipeline that automatically classifies Reddit content using advanced multi-label machine learning with enterprise-level automation and continuous learning.



🌐 **[Live Application](https://reddit-content-classifier.streamlit.app/)**



## 🎯 Project Overview



- **Multi-Label Classification**: Simultaneous analysis across 5 dimensions (Safety, Toxicity, Sentiment, Topic, Engagement)

- **Automated MLOps**: Weekly retraining with model selection and deployment

- **Production Scaling**: Handles 25,000+ posts per training cycle

- **Real-time Inference**: Sub-second response times with 88%+ accuracy



## 📊 Multi-Label Classification



| Category       | Description                | Classifications                      |

| -------------- | -------------------------- | ------------------------------------ |

| **Safety**     | Content safety assessment  | Safe, NSFW                           |

| **Toxicity**   | Harmful content detection  | Non-toxic, Toxic                     |

| **Sentiment**  | Emotional tone analysis    | Positive, Neutral, Negative          |

| **Topic**      | Content categorization     | Technology, Gaming, Business, Health |

| **Engagement** | Viral potential prediction | High, Low Engagement                 |



## 🏗️ Architecture



![MLOps Pipeline Workflow](Workflow.png)



```

Reddit API → Data Pipeline → ML Training → Model Deployment → Web Application

    │              │              │               │                │

 PRAW API       GitHub Actions   Ensemble ML      Git LFS        Streamlit

```



**MLOps Pipeline**:



1. **Data Collection**: Weekly automated Reddit data ingestion (25,000+ posts)

2. **Feature Engineering**: TF-IDF vectorization (10k features, 1-2 grams)

3. **Model Training**: Multi-algorithm competition (Logistic Regression, SVM, Neural Networks, LightGBM)

4. **Model Selection**: Ensemble creation from top-performing models

5. **Deployment**: Automated Git LFS versioning and cloud deployment



## 🎯 Performance Metrics



| Metric                  | Value   | Description                        |

| ----------------------- | ------- | ---------------------------------- |

| **Binary F1-Score**     | 88.3%   | SFW/NSFW classification accuracy   |

| **Multi-Label Jaccard** | 82.7%   | Overall multi-category performance |

| **Training Data**       | 25,000+ | Reddit posts per training cycle    |

| **Inference Speed**     | <100ms  | Real-time response capability      |

| **Model Size**          | ~150MB  | Optimized for cloud deployment     |

| **Automation**          | Weekly  | Continuous learning and updates    |



## 🛠️ Technology Stack



**Core Technologies**:



- **Python 3.11**, Scikit-learn, LightGBM, Pandas, NumPy

- **Streamlit**, Plotly (Visualization)

- **PRAW** (Reddit API), TF-IDF (NLP)



**MLOps & Infrastructure**:



- **GitHub Actions** (CI/CD), **Git LFS** (Model Versioning)

- **Docker** (Containerization), **Streamlit Cloud** (Deployment)



## 🚀 Local Development Setup



### Prerequisites



- **Python 3.11+**

- **Git** with **Git LFS** support

- **Reddit API credentials** (for data collection and training)



### Step 1: Clone Repository



```bash

git clone https://github.com/RobinMillford/Reddit-content-classifier.git

cd Reddit-content-classifier



# Setup Git LFS for model files

git lfs install

git lfs pull

```



### Step 2: Environment Setup



```bash

# Create virtual environment

python -m venv venv



# Activate virtual environment

# Windows:

venv\Scripts\activate

# macOS/Linux:

source venv/bin/activate



# Install dependencies

pip install -r requirements.txt

```



### Step 3: Reddit API Configuration



**Get Reddit API Credentials**:



1. Go to [Reddit App Preferences](https://www.reddit.com/prefs/apps)

2. Click **"Create App"** or **"Create Another App"**

3. Fill in the form:

   - **Name**: Your app name (e.g., "Content Classifier")

   - **App type**: Select **"script"**

   - **Description**: Optional

   - **About URL**: Leave blank

   - **Redirect URI**: `http://localhost:8080`

4. Click **"Create app"**

5. Note down the **Client ID** (under app name) and **Client Secret**



**Setup Environment Variables**:



Create a `.env` file in the project root:



```bash

# Create .env file

touch .env  # Linux/macOS

# or create manually on Windows

```



Add your Reddit API credentials to `.env`:



```env

REDDIT_CLIENT_ID=your_client_id_here

REDDIT_CLIENT_SECRET=your_client_secret_here

REDDIT_USER_AGENT=YourAppName/1.0

```



**Alternative: Export Environment Variables**:



```bash

# Export variables (Linux/macOS)

export REDDIT_CLIENT_ID="your_client_id"

export REDDIT_CLIENT_SECRET="your_client_secret"

export REDDIT_USER_AGENT="YourAppName/1.0"



# Windows Command Prompt

set REDDIT_CLIENT_ID=your_client_id

set REDDIT_CLIENT_SECRET=your_client_secret

set REDDIT_USER_AGENT=YourAppName/1.0



# Windows PowerShell

$env:REDDIT_CLIENT_ID="your_client_id"

$env:REDDIT_CLIENT_SECRET="your_client_secret"

$env:REDDIT_USER_AGENT="YourAppName/1.0"

```



### Step 4: Run Application



```bash

# Start the web application

streamlit run app.py

```



🌐 **Access**: Application runs at `http://localhost:8501`



### Step 5: Custom Model Training (Optional)



```bash

# Collect fresh training data

python src/ingest_data.py



# Train and evaluate models

python src/train.py



# Models are automatically saved and can be loaded by app.py

```



### Troubleshooting



**Common Issues**:



1. **Git LFS files not downloading**: Run `git lfs pull`

2. **Reddit API errors**: Verify your `.env` credentials

3. **Model files missing**: Ensure Git LFS is installed and configured

4. **Import errors**: Check virtual environment activation



**Verify Setup**:



```bash

# Check Git LFS status

git lfs ls-files



# Verify environment variables

python -c "import os; print(os.getenv('REDDIT_CLIENT_ID'))"



# Test Reddit API connection

python -c "import praw; reddit = praw.Reddit(client_id='test', client_secret='test', user_agent='test'); print('PRAW imported successfully')"

```



## 📁 Project Structure



```

├── src/

│   ├── ingest_data.py      # Reddit data collection

│   └── train.py            # ML model training

├── .github/workflows/      # CI/CD automation

├── app.py                  # Streamlit web application

├── champion_model.pkl      # Production binary model (Git LFS)

├── multi_label_model.pkl   # Production multi-label model (Git LFS)

├── vectorizer.joblib       # Text preprocessing pipeline (Git LFS)

└── model_metadata.joblib   # Model performance metrics (Git LFS)

```



## 💼 Professional Impact



**Business Value**: Demonstrates end-to-end ML engineering capabilities with production-ready automation and scalable infrastructure design.



**Technical Expertise**: Showcases expertise in MLOps, automated pipelines, multi-label classification, and cloud deployment strategies.



**Results Delivered**: 88%+ accuracy system processing 25,000+ posts weekly with zero-downtime continuous deployment.



## 🤝 Contributing



This project is **open source** and welcomes contributions from the community.



**How to Contribute**:



1. Fork the repository

2. Create a feature branch: `git checkout -b feature/enhancement`

3. Make your changes with proper testing

4. Submit a pull request with detailed description



**Areas for Contribution**:



- Model performance improvements

- New classification categories

- Enhanced MLOps automation

- Documentation and testing



**Development Setup**:



```bash

git clone https://github.com/RobinMillford/Reddit-content-classifier.git

cd Reddit-content-classifier

pip install -r requirements.txt

streamlit run app.py

```



**Project Repository**: [github.com/RobinMillford/Reddit-content-classifier](https://github.com/RobinMillford/Reddit-content-classifier)



---



_This project demonstrates production-ready MLOps implementation suitable for enterprise content moderation systems._