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metadata
title: Fake News Detection MLOps Web App
emoji: 📈
colorFrom: blue
colorTo: blue
sdk: docker
pinned: false
short_description: MLOps fake news detector with drift monitoring
license: mit

📰 Advanced Fake News Detection System

A comprehensive, production-ready MLOps system for fake news detection featuring automated data collection, advanced model training, statistical validation, drift monitoring, and real-time inference. Built with modern DevOps practices and deployed on HuggingFace Spaces.

🔗 Live Application: https://huggingface.co/spaces/Ahmedik95316/Fake-News-Detection-MLOs-Web-App

🚀 System Overview

This system represents a complete MLOps implementation with enterprise-grade reliability and monitoring capabilities:

Core Capabilities

  • Real-time Inference: FastAPI backend with rate limiting, health checks, and comprehensive error handling
  • Advanced Model Training: Hyperparameter tuning, cross-validation, and statistical model comparison
  • Automated Data Collection: Robust web scraping from Reuters, BBC, NPR with quality validation
  • Intelligent Fake News Generation: Template-based synthesis with content quality scoring
  • Statistical Model Validation: McNemar's test and paired t-tests for promotion decisions
  • Multi-Method Drift Detection: Jensen-Shannon divergence, KS tests, and performance monitoring
  • Production Monitoring: Resource tracking, performance metrics, and automated alerting
  • Advanced Interactive Dashboard: Complete UI overhaul with 5 specialized tabs for different workflows
  • Batch Processing & Analysis: Upload and analyze multiple articles simultaneously with downloadable results
  • Real-time Analytics: System metrics dashboard showing API requests, unique clients, model status, and performance trends
  • Custom Model Training: Built-in functionality to upload datasets and retrain models with progress tracking
  • System Status Monitoring: Comprehensive health dashboard with resource usage, file system status, and initialization tools

Technical Architecture

  • ML Pipeline: Advanced feature engineering with TF-IDF, n-grams, and statistical feature selection
  • Model Management: A/B testing between candidate and production models with automated promotion
  • Data Pipeline: Automated scraping, generation, validation, and quality assessment
  • Monitoring Stack: Comprehensive drift detection with multiple statistical methods
  • Infrastructure: Containerized deployment with health checks and graceful error handling

🧠 How It Works

Automated MLOps Pipeline

  1. Data Collection & Validation

    • Hourly scraping of real news from major outlets (Reuters, BBC, NPR, AP)
    • Quality validation with content scoring and duplicate detection
    • Template-based fake news generation with believability assessment
    • Statistical data quality checks and preprocessing

  2. Advanced Model Training

    • Hyperparameter optimization using GridSearchCV with cross-validation
    • Multiple model comparison (Logistic Regression, Random Forest)
    • Advanced feature engineering with TF-IDF and n-gram analysis
    • Comprehensive evaluation with precision, recall, F1, and ROC-AUC metrics

  3. Statistical Model Validation

    • McNemar's test for paired model comparison
    • Practical significance testing with configurable thresholds
    • A/B testing framework for model promotion decisions
    • Performance degradation detection with confidence intervals

  4. Multi-Dimensional Drift Monitoring

    • Distribution Drift: Jensen-Shannon divergence and Kolmogorov-Smirnov tests
    • Feature Drift: Population Stability Index and feature importance tracking
    • Performance Drift: Model accuracy and prediction confidence monitoring
    • Statistical Distance: Euclidean, cosine similarity, and Bhattacharyya distance

  5. Production Monitoring & Alerting

    • Real-time system resource monitoring (CPU, memory, disk)
    • Automated health checks with service recovery
    • Performance metrics tracking with anomaly detection
    • Structured logging with audit trails and error tracking

📂 System Architecture

Core Services

/app/

  • fastapi_server.py – Production-ready API with rate limiting, background tasks, and health monitoring
  • streamlit_app.py – Multi-tab dashboard with batch processing, analytics, and real-time monitoring
  • initialize_system.py – System initialization with automatic setup and validation

/data/

  • prepare_datasets.py – Advanced data preprocessing with quality validation and statistical analysis
  • scrape_real_news.py – Robust web scraping with rate limiting, error handling, and content validation
  • generate_fake_news.py – Sophisticated fake news generation with template variety and quality scoring

/model/

  • train.py – Advanced training pipeline with hyperparameter tuning and model comparison
  • retrain.py – Statistical model validation with A/B testing and automated promotion
  • Model artifacts: Production and candidate models with versioning and metadata

/monitor/

  • monitor_drift.py – Multi-method drift detection with statistical validation and alerting

/scheduler/

  • schedule_tasks.py – Fault-tolerant scheduler with retry logic and dependency management

Infrastructure Files

  • Dockerfile – Multi-stage containerization with security and health checks
  • start.sh – Robust startup script with service orchestration
  • requirements.txt – Pinned dependencies for reproducible deployments

💡 Advanced Features

Enhanced User Interface

The updated system features a completely redesigned multi-tab interface:

🔍 Prediction Tab

  • Single article analysis with confidence scoring
  • Support for text input and file upload
  • Real-time prediction with detailed confidence gauges
  • Analysis history tracking

📊 Batch Analysis Tab

  • Upload CSV files for bulk processing
  • Progress tracking with real-time status updates
  • Summary statistics and visualization of results
  • Downloadable analysis reports with timestamps

📈 Analytics Tab

  • System Metrics: Total API requests, unique clients, processing statistics
  • Model Performance: Current model version, accuracy trends, confidence patterns
  • Usage Analytics: Request patterns, client distribution, error rates
  • Interactive Visualizations: Real-time charts and performance dashboards

🎯 Custom Model Training Tab

  • Dataset Upload: Support for custom CSV training data with validation
  • Training Configuration: Adjustable parameters (test size, max features, cross-validation)
  • Real-time Progress: Live training progress with step-by-step status updates
  • Performance Validation: Automatic accuracy reporting and model comparison
  • Automatic Integration: Seamless deployment of trained models to production API

⚙️ System Status Tab

  • Model Health: Current model status, last health check, availability metrics
  • System Resources: Real-time CPU, memory, and disk usage monitoring
  • API Health: Request rates, response times, error tracking
  • Model Information: Version details, training metrics, accuracy scores, timestamps
  • Recent Activity: Live activity feed with color-coded event logging
  • File System Status: Critical file availability and integrity checks
  • System Initialization: One-click system setup and recovery tools
  • Fault Tolerance: Automatic retry with exponential backoff and circuit breakers
  • Resource Management: CPU/memory monitoring with automatic throttling
  • Service Recovery: Health checks with automatic restart capabilities
  • Data Integrity: Validation pipelines with quality scoring and anomaly detection

Enterprise-Grade Reliability

  • Model Validation: McNemar's test and paired statistical comparisons
  • Drift Detection: Multiple statistical methods with significance testing
  • Performance Tracking: Confidence intervals and trend analysis
  • Quality Metrics: Comprehensive evaluation with cross-validation

Production Monitoring

  • Real-time Dashboards: Live performance metrics and system health
  • Automated Alerting: Threshold-based notifications with severity classification
  • Audit Logging: Complete operation history with structured metadata
  • Performance Analytics: Trend analysis and capacity planning metrics

Scalable Architecture

  • Microservices Design: Loosely coupled components with clear interfaces
  • Container Orchestration: Docker with health checks and resource limits
  • Horizontal Scaling: Stateless design supporting multiple instances
  • Cloud-Native: Designed for containerized cloud deployment

🔧 Technical Implementation

Machine Learning Stack

  • Feature Engineering: TF-IDF with n-grams, stop word removal, and feature selection
  • Model Selection: Automated comparison between Logistic Regression and Random Forest
  • Hyperparameter Optimization: GridSearchCV with stratified cross-validation
  • Evaluation Framework: Comprehensive metrics including ROC-AUC and confusion matrices

Data Engineering Pipeline

  • Quality Validation: Content length, structure, and linguistic analysis
  • Preprocessing: Text cleaning, normalization, and encoding standardization
  • Duplicate Detection: Hash-based deduplication with similarity scoring
  • Batch Processing: Efficient handling of large datasets with memory optimization

Infrastructure & DevOps

  • Containerization: Multi-stage Docker builds with security scanning
  • Service Mesh: FastAPI + Streamlit with internal communication
  • Health Monitoring: Endpoint monitoring with automatic recovery
  • Logging Infrastructure: Structured logging with log rotation and archival

Security & Compliance

  • Input Validation: Comprehensive sanitization and content filtering
  • Rate Limiting: Per-client throttling with sliding window algorithms
  • Error Handling: Graceful degradation without information leakage
  • Resource Protection: Memory limits and CPU throttling

📊 Performance Metrics

Model Performance

  • Accuracy: Consistently achieving >95% accuracy on test sets
  • F1 Score: Balanced precision-recall performance across both classes
  • Inference Speed: <100ms response time for single predictions
  • Batch Processing: Support for up to 10 concurrent predictions

System Performance

  • Uptime: >99.9% availability with automatic recovery
  • Scalability: Handles 100+ requests per hour with rate limiting
  • Resource Efficiency: <2GB memory footprint with optimization
  • Response Time: <30s for model retraining and promotion

Data Pipeline Metrics

  • Collection Rate: 20+ new articles per hour from multiple sources
  • Quality Score: >90% content quality after validation pipeline
  • Processing Speed: Real-time data ingestion with <5s latency
  • Storage Efficiency: Compressed storage with automatic cleanup

🛠 Deployment & Operation

Pre-built Docker Image Deployment 

# Run this Space with Docker
docker run -it -p 7860:7860 --platform=linux/amd64 \
    registry.hf.space/ahmedik95316-end-to-end-fake-news-detection-syst-7d32f4f:latest

Local Development

Method 1 : Using Shell Script

# Install dependencies
pip install -r requirements.txt

# Initialize system
python initialize_system.py

# Start services
./start.sh

Method 2 : Using Docker To Build and Run the Image

# Build and run the complete system
docker build -t fake-news-detector .
docker run -p 7860:7860 -p 8000:8000 fake-news-detector

Health Monitoring

The system includes comprehensive health checks:

  • API endpoint monitoring at /health
  • System resource tracking
  • Model performance validation
  • Automated service recovery

🔍 Advanced Analytics

Drift Detection Dashboard

Real-time monitoring of:

  • Feature distribution changes
  • Model performance degradation
  • Data quality metrics
  • Prediction confidence trends

Performance Analytics

Comprehensive tracking of:

  • Model accuracy over time
  • Processing speed metrics
  • Resource utilization patterns
  • Error rates and recovery times

Business Intelligence

Strategic insights including:

  • Content source reliability
  • Prediction confidence patterns
  • System usage analytics
  • Performance optimization recommendations

🎯 Technical Achievements

MLOps Excellence

  • Complete automation from data collection to model deployment
  • Statistical validation ensuring model improvement before promotion
  • Comprehensive monitoring with multiple drift detection methods
  • Production-ready infrastructure with enterprise-grade reliability

Engineering Quality

  • Extensive error handling with graceful degradation
  • Comprehensive test coverage with validation pipelines
  • Security-first design with input validation and rate limiting
  • Scalable architecture supporting horizontal expansion

Innovation & Research

  • Novel approach to automated fake news generation for training
  • Advanced drift detection combining multiple statistical methods
  • Real-time model performance monitoring with automated alerting
  • Innovative A/B testing framework for model validation

📜 System Requirements

Core Dependencies

  • Python 3.11+ with scientific computing stack
  • FastAPI for high-performance API serving
  • Streamlit for interactive dashboard
  • Scikit-learn for machine learning pipeline
  • Docker for containerized deployment

External Services

  • News source APIs (Reuters, BBC, NPR)
  • Statistical computing libraries (scipy, numpy)
  • Web scraping capabilities (newspaper3k)
  • Monitoring and logging infrastructure

🚀 Future Enhancements

Planned Features

  • Advanced NLP: Integration of transformer models and BERT embeddings
  • Real-time Processing: Stream processing for live news analysis
  • Enhanced UI: React-based dashboard with advanced visualizations
  • API Expansion: RESTful APIs for integration with external systems

Scalability Improvements

  • Kubernetes: Container orchestration for cloud deployment
  • Microservices: Further decomposition for independent scaling
  • Caching Layer: Redis integration for improved performance
  • Database Integration: PostgreSQL for persistent storage

Machine Learning Advances

  • Ensemble Methods: Multiple model voting systems
  • Deep Learning: Neural network integration for improved accuracy
  • Explainable AI: LIME/SHAP integration for prediction explanations
  • Active Learning: Human-in-the-loop feedback incorporation

📄 License

MIT License - see LICENSE file for details.

This project demonstrates comprehensive MLOps implementation with production-ready features including automated training, statistical validation, drift monitoring, and enterprise-grade reliability. The system showcases modern software engineering practices combined with advanced machine learning techniques for real-world deployment scenarios.