added standard error response#916
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Engine fixes: - Engine.Dockerfile: deploy echo_engine_iot.py as echo_engine.py so the IoT engine actually runs in Docker - Engine/requirements.txt: add scikit-learn (imported but missing) - echo_engine.json: add API_URL field to replace hardcoded endpoint - echo_engine_iot.py: read API URL from config, remove duplicate import, add _handle_edge_prediction() for edge devices, route on payload type - test_iot_integration.py: fix import so unit tests run locally Light engine MQTT fix: - light_echo_engine.json: correct MQTT_CLIENT_URL from mqtt-broker to ts-mqtt-server-cont, fixing DNS resolution error on startup - torch_impl/requirements.txt: add paho-mqtt==1.6.1, pymongo, geopy, google-cloud-storage (all imported but missing) Edge inference (new): - IoT/edge_inference/iot_edge_client.py: RPi script that records audio, runs EfficientNetV2 TFLite on-device, publishes only prediction result over MQTT (no audio transmitted) - IoT/edge_inference/requirements.txt: RPi deps - IoT/edge_inference/README.md: deployment guide
Signed-off-by: mraula <96328895+mraula@users.noreply.github.com>
Added error_analysis folder with analysis notebook and resulting output that covers core essentials of production model strengths and weaknesses.
…g-task Completed onboarding task - added bird descriptions
…g-task Add onboarding task
…lity-guide Add engine maintainability guide and handover workbook
…diness Sprint 2 Task 4 Dataset Readiness Workflow
…diness Ee/ds/dataset readinessSprint 2 Task 4 Dataset Readiness Workflow
…apping Added ALA label mapping validation workflow
…nfidence-eval Add Sprint 2 Task 2 deliverable: calibration evaluation report
Created final Error Analysis Report from data gathered from the error_analysis Notebook.
…ysis Create Error Analysis Report.pdf
…standardisation Ee/ka/calibrated confidence standardisation
Added three tutorial files to the Tutorials pool for future students. One contains the extension to the PE Engine benchmarking we did earlier in the trimester, in order to give new students a feel for how the engine of the project works, and why certain decisions were made. The second contains a walk-through from first principles on digital audio and its use in Machine Learning, designed to work alongside the Benchmarking notebook to give better understanding of the concepts at play. The third is a quick leadership skills refresher/buff for new team leads, building on personal experience from PMing teams across public and private sector.
Signed-off-by: mraula <96328895+mraula@users.noreply.github.com>
…shboard Enhance sensor health UI for Sprint 2
EIOT/SP - Recreate device onboarding system + Unique device ID
Added tutorial files
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