Analyst Feedback on Model Result

[MaxenceGui]

Issue Description

When seed analysts use Nachet, they should be able to give their retroaction on the result. A pipeline of action needs to be integrated from the Frontend to the database to be able to register the user feedback. The possible feedbacks types are:

• No seed: The user indicates that the seed detected by the model is not a seed (soil peds for example)
• Wrong classification: The user indicates that the seed is wrongfully classified by selecting the right seed or guess.
• Perfect Inference: The guess of the model was correct and the box hasn't been changed.

Architecture:

---
title: Nachet Architecture for Inference
---
erDiagram
    seed{
        uuid id
        text name
    }
    inference{
        uuid id PK
        json inference 
        uuid picture_id FK
        uuid user_id FK
        timestamp upload_date
    }
    object{
        uuid id PK
        json box_metadata
        uuid inference_id FK
        integer type_id
        boolean verified
        boolean modified
        uuid top_guess FK
        timestamp upload_date
        timestamp updated_at
    }
    seed_object{
        uuid id PK
        uuid seed_id FK 
        uuid object_id FK
        float score
    }

  user ||--o{ inference: requests
  inference ||--|| picture: infers
  inference }o--|| pipeline: creates
  inference ||--o{ object: detects
  object ||--o{ seed_object: is
  seed_object }o--|| seed: is

Work to do

1. Tweak the current classification/inference process in the Backend to use the Datastore.

Sequence of saving the inference:

Note the picture must of already been uploaded and registered in the DB

sequenceDiagram;
  actor User
  box grey Ai-Lab services
  participant Frontend
  participant Backend
  participant Datastore
  participant ML
  end
  box grey Storage services
  participant PostgreSQL Database
  participant Azure Storage
  end

    User ->> Frontend: Classify picture
    Frontend -) Backend: Classify_picture(user_id,picture_id,pipeline_id)
    Backend -) Datastore: get_picture_url(picture_id)
    Datastore ->> Backend : picture_url
    Backend -) ML: inference_request(pipeline,picture)
    ML ->> Backend : inference.json
    Backend -) Datastore: register_inference_result(inference,user_id,picture_id,pipeline_id)
    Datastore ->> Datastore: trim_inference
    Datastore -) PostgreSQL Database: new_inference(trimmed_inference)
    Datastore ->> Datastore: Add {inference_id: uuid}
    loop each box 
        Datastore ->> Datastore: build_box_metadata(box_metadata)
        Datastore ->> PostgreSQL: new_inference_object(box_metadata)
        Datastore ->> Datastore: Add {box_id: uuid}
        loop each guess
            Datastore -) PostgreSQL: get_seed_id(seed_name)
            Datastore ->> PostgreSQL: new_seed_object(box_id,seed_id)
            Datastore ->> Datastore: Add {object_id: uuid}
        end
        Datastore  ->> PostgreSQL: set_inference_object_top_id(object_id, top_seed_object_id)
    end
  Datastore ->> Backend: inference_with_id.json
  Backend ->> Frontend: Display picture with inference results

2. Implement a process to enable users to submit their inference feedback/validation

Sequence of saving the inference feedback

sequenceDiagram;
  actor User
  participant Frontend
  participant Backend
  participant Datastore
  participant Database
  

    User ->> Frontend: Validate inference
    alt Perfect Inference
    Frontend -) Backend: Inference result positive (user_id,inference_id)
    Backend -) Datastore: Inference result positive (user_id,inference_id)
    Datastore ->> Database: Set each object.verified = True & object.modified=False
    else Annotated Inference
    Frontend -) Backend: Inference feedback (inference_feedback.json,user_id,inference_id)
    Backend ->> Datastore: Inference feedback (inference_feedback.json, user_id, inference_id)
    Datastore -> Database: Get Inference_result(inference_id)
        loop each Boxes
            alt box has an id value
                alt inference_feedback.box.verified= False
                    Datastore --> Datastore: Next box & flag_all_box_verified=False
                else
                    Datastore -) Database: Set object.verified=True & object.verified_by=user_id
                    Datastore -) Datastore: Compare label & box coordinate
                    alt label value empty
                        Datastore -) Database: Set object.top_inference=Null
                        Datastore -) Database: Set object.modified=False                   
                    else label or box coordinate are changed & not empty
                        Datastore -) Database: Update object.top_inference & object.box_metadata
                        Note over Datastore,Database: if the top label is not part of the seed_object guesses, <br>we will need to create a new instance of seed_object.
                        Datastore -) Database: Set object.modified=true
                    else label and box haven't changed
                        Datastore -) Database: Set object.modified=False
                    end
                end
            else box has no id value
                Datastore -) Database: Create new object and seed_object
            end
        end
        alt if flag_all_box_verified=True
            Datastore -) Database: Set Inference.verified=true
        end
    end

Acceptance Criteria

• Users can annotate their retroaction on the model results
• Retroaction is saved into the database and helps data scientists with the model training
• Data from retroaction is recorded and saved in the database
• An endpoint in the backend is set up and allows the transit of the data from the frontend to the database and the model

Tasks

Frontend:

• Implement the ability for users to give their retroaction in the frontend #129
  • Implement the ability for users to give their retroaction in the frontend
    • Related to add ability for user to identify classification error and document it for continuous training nachet-frontend#40
    • Provide positive feedback to accept an inference as good data
    • Provide negative feedback to reject an inference box completely
    • Provide negative feedback with a correction to the inference box position and dimensions
    • Provide negative feedback with a correction to the species label (species in training set)
    • Provide negative feedback with a correction to the species label (species not in training set)
    • Provide negative feedback no seed in the inference box
    • Provide negative feedback seed not detected
    • Add API calls for uuid and seed list
    • New Component Tests
    • Update Documentation

Backend:

• Create an endpoint in the backend to save the data to the database for training and statistics purposes #87
• Species List API Endpoint #88

Database:

• Create inference structure for the Classification and feedback #23

[ChromaticPanic]

Some ideas for potential feedback mechanism

• click a prediction box, show a callout with more information
  • could be model explanation, if our models do that
  • ranked predictions / or other info users want to see
  • the callout can have check or X for feedback
  • selecting X would give the option to specify if:
    • bounding box issue
      • Let them redraw
      • or let them say "there are multiple seeds in this box" (If that's useful for negative reinforcement)
    • wrong species - specify correct one from list