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• Utilizes Python and machine learning for text classification.
• Implements text preprocessing steps, including tokenization, stop word removal, and stemming.
• Employs TF-IDF for feature extraction to convert text into numerical representations.
• Experiments with classifiers like logistic regression, support vector machines, and neural networks.
• Features a console-based interface for real-time hate speech detection.
• Achieves high accuracy in identifying harmful language.
• Contributes to safer digital spaces through automated content moderation.

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• Leverages Groq's Whisper for high-accuracy video transcription and FFmpeg for audio processing.
• Uses Llama 3.1 for nuanced, multi-language summaries supporting over 12 languages.
• Implements Langchain for advanced text chunking, organizing summaries into structured sections.
• Features an intuitive Streamlit interface for real-time YouTube URL input and summary generation.
• Integrates yt-dlp for reliable video processing and authentication cookies for private video access.
• Includes robust error handling for invalid URLs, transcript issues, and API errors.
• Automates temporary file cleanup to optimize system resource usage.

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• Leverages Kubernetes' Ingress controllers and service discovery for dynamic traffic management.
• Implements a custom Python script to enforce a perfect round-robin load balancing pattern.
• Optimizes resource usage to prevent pod exhaustion and enhance fault tolerance.
• Supports bidirectional communication for seamless data flow between clients and services.
• Enhances scalability and resilience for high-traffic microservices-based applications.
• Demonstrates Kubernetes' effectiveness in managing distributed systems in production environments.

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