AI Systems & Backend Engineer | RAG & Agent
Project Title
NeuroNLP — Cognitive & Emotional Text Analysis Engine
Project Link
https://github.com/shoaib1522/NeuroNLP-Brain-Cognition-Scoring-from-Natural-Language-Text
- Overview: NeuroNLP is a full-stack, microservice-based text analysis platform engineered to evaluate human written communication across cognitive, emotional, and structural dimensions. The system processes user text in real-time through an asynchronous Python backend to compute a composite "Brain Cognition Score" alongside multi-faceted analytics visualized in an interactive Next.js chat interface.
- Coherence & Logical Flow Engine: Converts sentences into dense vector embeddings using all-MiniLM-L6-v2 (Sentence-Transformers) and evaluates sentence-to-sentence topical continuity via pairwise cosine similarity matrices.
- Granular Emotion Classifier: Utilizes a fine-tuned roberta-base-go_emotions transformer model running on PyTorch to detect probability distributions across 27 distinct emotional states.
- Lexical Diversity Pipeline: Computes a normalized Type-Token Ratio (TTR) using spaCy tokenization pipelines to evaluate vocabulary richness and prevent stylistic redundancy.
- Automated Grammar Scoring: Integrates language-tool-python to identify syntactic errors and derive an inverted error-rate metric normalized to a 100-point scale.
- Backend Microservice: Developed with Python and FastAPI (running Uvicorn) to execute asynchronous ML inference and return structured JSON analytics payloads.
- Frontend Application: Built with Next.js 14 (App Router), React 18, and TypeScript, styled via Tailwind CSS, and rendering real-time analytical charts using Recharts.
- ML Infrastructure: Powered by PyTorch, Hugging Face Transformers, Sentence-Transformers, and spaCy.
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