ML_IMPLEMENTATION.md

ML Implementation

MediFlow now has two NLP implementation paths:

1. ml/hf_intent_demo.mjs runs a Hugging Face Transformers.js zero-shot classifier.
2. ml/mediflow_nlp.py runs a lightweight local baseline using a Naive Bayes intent classifier plus rule-based department and FAQ routing.

The Hugging Face path is the profile-relevant implementation. The Python baseline is kept as a transparent fallback and testing harness.

Hugging Face Demo

Install dependencies:

npm install

Run model inference:

npm run demo

The script downloads and runs Xenova/all-MiniLM-L6-v2 through @huggingface/transformers for sentence embeddings. A nearest-centroid classifier is built from the labeled examples in data/intent_dataset.jsonl. It predicts:

• User intent: booking, cancellation, FAQ, feedback, or human handoff.
• Department for booking requests: Cardiology, Orthopedics, Dermatology, Pediatrics, or Internal Medicine.

Results are written to:

output/hf-demo-results.json

Latest verified run:

Model: Xenova/all-MiniLM-L6-v2
Task: feature-extraction
Classifier: nearest-centroid over labeled project examples
Evaluation: leave-one-out
Examples: 28
Accuracy: 0.8571

The first attempted zero-shot MNLI classifier over-routed examples to handoff, so it was replaced with the embedding retrieval approach above. That failure is useful evidence: the project now shows model selection, error analysis, and iteration instead of only a polished happy path.

Why This Matters

This moves the project from "chatbot plan" to "implemented ML inference demo":

• Uses a real model from Hugging Face.
• Produces reproducible inference outputs.
• Separates model inference from UI state management.
• Includes a small labeled dataset and evaluation report.
• Shows an explicit model-selection iteration from weak zero-shot classification to embedding-based routing.
• Gives a clear upgrade path to a Hugging Face Space, Botpress Action, or API endpoint.

Current Boundary

The current implementation is an inference demo, not a fine-tuned production model. A stronger ML engineering version would add:

• A larger labeled dataset.
• Fine-tuning or embedding-based retrieval.
• Evaluation split with precision, recall, F1, and confusion matrix.
• Model card with limitations and intended use.
• API endpoint consumed by the chatbot UI.
• Deployment as a Hugging Face Space.