Week #3

Week #3 #

Implemented MVP features #

  • User login and registration, PR

  • Name of the profile, PR

  • Picture in the profile, PR

  • Possibility to rotate the map, (was implemented on previous week)

  • Possibility to open labels with fish pictures on the map, PR

  • Adding fishing places, PR back

  • Possibility to start the event of fishing, PR Front, PR Back

  • User interface to add pictures of fishes, PR Front, PR Back

  • Test model for ML, PR

Demonstration of the working MVP #

Link to the video: Google drive

ML #

Remove this section if not applicable

Link to dataset: Link

We build UML diagram for the ML part and started implementing some parts of it:

  • Vector DB: Based on Quadrant cloud solution, to store ready embeddings
  • Fish Speices class: to store all information about row in one place
  • Data preparation: to prepare data for tokenization in our embedder

Links to the initial model artifacts: Qwen3-Embedding-0.6B

Internal demo #

All needed comments were provided on the video.

Weekly commitments #

Individual contribution of each participant #

Stepan Vagin:

  • Created UML of the architecture, Link
  • Created classes FishSpeices, VectorDatabase, to connect to vector DB, PR
  • Created first ML endpoint, PR
  • Updated backlog, Link

Kostya Zimin:

  • fix db migrations and add liquibase, PR
  • refactor all controllers for useful explanation in swagger and create API contract, PR
  • fix all methods in service part and add more exceptions, PR
  • fix sql codes, db relationships and docker-compose file for back part

Ivan Vasiliev:

  • defined the architecture of search system with Stepan Vagin(discussed which models, dataset to use, workflow)
  • developed preprocess of the data, added embeddings of the data PR retrieved datasets from the data_prep.py, Link
  • worked on localization of the dataset, compared different models(Helsinki-NLP/opus-mt-en-ru, facebook/nllb-200-distilled-600M, mistral-7b-instruct-v0.1.Q4_K_M.gguf) mess work can be found here, Link

Kirill Greshnov:

  • Connect login and registration with backend, add page for fishing event with statistics, PR
  • Add local image upload for fishing event, PR
  • Add feature to choose fishing location, PR
  • Add functionality to profile page to show actual user data, minor design changes, PR

Egor Belozerov:

  • containerize the ML part + add it to deployment, PR
  • additional build and linting flutter app, PR
  • fix 301 status code for https endpoint, PR
  • deploy the backend, PR
  • fix the issues with runner and sops (recreate the encryption keys)

Damir Sadykov:

  • Started working on final app design (some screens are done, some sketches for other were made, color palette with fonts has been selected) in Figma: Link
  • Research in automated tools for design travel from Figma to Flutter (plugins: “Figma to Flutter”, “Figma2Flutter”, “Supernova”, tools: “FlutterFlow”). Result: no plugins are good enough for this purpose (lot’s of bugs) and tool FlutterFlow is good for transfer design to flutter code.
  • FlutterFlow design for first screen are made, FlutterFLow (subscription required, so screenshots are made (in same Figma file: Figma)
  • Fishing event related requests handle are made: fishing event is now ready, PR
  • Pictures of catches upload request handled too, PR

Adel Gaznanov:

  • Сreate endpoint to add new water places on the map, PR
  • Сreate endpoints to start and end fishing on the specific water place, PR
  • Сreate an opportunity to save caught fish during fishing, PR

Plan for Next Week #

  • Start to implement features, meant for final project
  • Continue building ML architecture
  • Fixing bugs

Confirmation of the code’s operability #

We confirm that the code in the main branch:

  • [+] In working condition.
  • [+] Run via docker-compose (or another alternative described in the README.md).