update_keywords.py

import re

with open('src/App.jsx', 'r') as f:
    content = f.read()

new_keywords_str = """
    'React.js', 'Node.js', 'MongoDB', 'LangChain', 'production-ready', '15k+ queries/month', 
    '55%', 'semantic search', 'hybrid reranking', 'RESTful APIs', 'asynchronous processing architecture', 
    'AWS Lambda', 'S3', 'SQS', 'maintenance workflows', 'API response times', '85%', '12s → 1.8s', 
    '600+ operations', 'eliminating bottlenecks', 'FastAPI', 'standardizing execution', 'reducing handoffs', 
    'routine maintenance operations', 'improving cross-team collaboration efficiency', 'RAG', 
    'Gemini 2.5 flash', 'gemini-embedding-001', 'ChatGPT APIs', 'with retrieved historical context', 
    'accelerating incident diagnosis', 'LLM-driven', 'Gemini API', 'GCP infrastructure with Terraform (IaC)', 
    'Jenkins', 'test, build, container push, and deployment workflows', 'intelligent health copilot', 
    '30% faster data processing', '350+ test cases', 'containerized environments', '30+ critical defects', 
    '30%', 'Docker', 'DeepAnalyze-8B', 'GCP L4 GPU', 'Gemini Flash', 'Analyze-Code-Execute loop', 
    'AST validation', 'inference pipeline', 'automated HTML report generation', 'hybrid LLM routing', 
    'Pandas-based statistical analysis', 'self-correcting retry loops', 'LangGraph and LangChain', 'LangGraph', 
    'Gemini', 'reasoning accuracy by 30%', 'advanced Pre-Act RAG', 'Corrective RAG', 'ChromaDB', 
    'reducing hallucinations by 25%', 'Tiny LLMs', 'Qwen3-4B', 'Gemma-3-270M', 'Phi-3-mini', 'Edge-IIoTset', 
    'real-time IoT threat classification and mitigation', 'end-to-end fine-tuning and evaluation pipeline', 
    'FLAIRS-39', 'GenAI-driven', 'WaveGAN and SpecGAN', 'WaveGAN', 'SpecGAN', 'synthetic audio patterns', 
    'deep generative models', '22% improvement', 'automated audio deepfake detection accuracy', 
    'AI-driven voice integrity analysis', 'IEEE research paper',
    'MERN Stack', 'Serper API', 'SQLite', 'SMTP', 'Gemini 2.5 Pro', 'Puppeteer', 'Perplexity API', 'Qwen3', 
    'Phi-3', 'LoRA', 'MITRE CAPEC', 'Flask', 'NLP', 'Scikit-Learn', 'BERT', 'Transformers', 'Google Gemini', 
    'Vertex AI', 'Chart.js', 'MediaPipe', 'OpenCV', 'React', 'Tailwind CSS', 'Pinecone', 'Next.js', 'PostgreSQL', 
    '93% Faithfulness score', '100% binary classification accuracy', '60% improvement', '96.81% accuracy'
"""

# Find the keywords array in the source
pattern = r"const keywords = \[\s*(.*?)\s*\];"
match = re.search(pattern, content, flags=re.DOTALL)

if match:
    content = content.replace(match.group(0), f"const keywords = [\n{new_keywords_str}\n  ];")

with open('src/App.jsx', 'w') as f:
    f.write(content)