Mlhbdapp New -

Example: A tiny Flask inference API.

# app.py
from flask import Flask, request, jsonify
import mlhbdapp
app = Flask(__name__)
# Initialise the MLHB agent (auto‑starts background thread)
mlhbdapp.init(
    service_name="demo‑sentiment‑api",
    version="v0.1.3",
    tags="team": "nlp",
    # optional: custom endpoint for the server
    endpoint="http://localhost:8080/api/v1/telemetry"
)
# Example metric: count of requests
request_counter = mlhbdapp.Counter("api_requests_total")
@app.route("/predict", methods=["POST"])
def predict():
    data = request.json
    # Simulate inference latency
    import time, random
    start = time.time()
    sentiment = "positive" if random.random() > 0.5 else "negative"
    latency = time.time() - start
# Record metrics
    request_counter.inc()
    mlhbdapp.Gauge("inference_latency_ms").set(latency * 1000)
    mlhbdapp.Gauge("model_accuracy").set(0.92)   # just for demo
return jsonify("sentiment": sentiment, "latency_ms": latency * 1000)
if __name__ == "__main__":
    app.run(host="0.0.0.0", port=5000)

pip install flask
python app.py

Now the agent automatically streams:

To help you fully, please clarify:

In the meantime, here’s how I would structurally analyze any something new CLI feature: mlhbdapp new

This report outlines the development plan for the "MLHBD App (New)," a comprehensive mobile and web application designed to integrate Mobile Laboratory services with Smart Health Bed monitoring systems. The new version aims to bridge the gap between in-patient monitoring and diagnostic lab results in real-time, providing healthcare professionals with a centralized dashboard for critical decision-making. Example : A tiny Flask inference API