362 changed files with 66311 additions and 25239 deletions
--- a/.dockerignore
+++ b/.dockerignore
@ -1,18 +0,0 @@
 .env
 .git
 .cache
 .mypy_cache
 .pytest_cache
 .ipynb_checkpoints
 .gitignore
 .dockerignore
 Dockerfile
 .github
 .vscode
 docs
 scripts
 tests
 mkdocs.yaml
 **/.mypy_cache
 **/__pycache__
 **/tracing_database.json
--- a/.forgejo/workflows/docker.yml
+++ b/.forgejo/workflows/docker.yml
@ -1,57 +0,0 @@
 name: Publish on DockerHub
 on:
  push:
    tags: ['*']
  workflow_dispatch:
 jobs:
  publish:
    runs-on: docker
    steps:
      - name: Checkout
        uses: actions/checkout@v4
      # The `docker` runner image is node-based and may not ship the docker CLI
      # that the docker/* actions below drive; install it idempotently, as the
      # sibling repos do for their Docker jobs.
      - name: Ensure Docker CLI
        run: |
          set -eux
          if ! command -v docker >/dev/null 2>&1; then
            ARCH=$(uname -m)
            curl -fsSL --retry 3 --retry-connrefused \
              "https://download.docker.com/linux/static/stable/${ARCH}/docker-27.5.1.tgz" \
              | tar xz --strip-components=1 -C /usr/local/bin docker/docker
          fi
          docker --version
      # Marketplace actions must be referenced by full URL: this instance's
      # DEFAULT_ACTIONS_URL points at code.forgejo.org, so only bare `actions/*`
      # names resolve to GitHub automatically.
      - name: Set up QEMU
        uses: https://github.com/docker/setup-qemu-action@v3
      - name: Set up Docker Buildx
        uses: https://github.com/docker/setup-buildx-action@v3
      - name: Log in to Docker Hub
        uses: https://github.com/docker/login-action@v3
        with:
          username: ${{ secrets.DOCKER_USERNAME }}
          password: ${{ secrets.DOCKER_PASSWORD }}
      - name: Extract metadata for the Docker image
        id: meta
        uses: https://github.com/docker/metadata-action@v5
        with:
          images: schmelczera/great-ai
      - name: Build and push
        uses: https://github.com/docker/build-push-action@v6
        with:
          context: .
          push: true
          tags: ${{ steps.meta.outputs.tags }}
          labels: ${{ steps.meta.outputs.labels }}
          platforms: linux/amd64,linux/arm64
--- a/.forgejo/workflows/docs.yml
+++ b/.forgejo/workflows/docs.yml
@ -1,52 +0,0 @@
 name: Publish documentation
 on:
  push:
    branches: [main]
    paths:
      - 'docs/**'
      - 'great_ai/**'
      - 'mkdocs.yaml'
      - '.forgejo/workflows/docs.yml'
  workflow_dispatch:
 concurrency:
  group: pages
  cancel-in-progress: false
 jobs:
  publish:
    runs-on: docker
    steps:
      - uses: actions/checkout@v4
        with:
          # The mkdocs git-revision-date plugin reads each page's git history.
          fetch-depth: 0
      - name: Install uv
        run: |
          curl -LsSf https://astral.sh/uv/install.sh | sh
          echo "$HOME/.local/bin" >> "$GITHUB_PATH"
      - name: Set up Python
        run: uv python install 3.10
      - name: Install dependencies
        run: |
          uv venv --python 3.10
          uv pip install '.[dev]'
      - name: Build documentation
        run: |
          . .venv/bin/activate
          mkdocs build
      # The old workflow ran `mkdocs gh-deploy` (GitHub Pages); on this instance
      # built sites are rsynced into the host /pages mount that nginx serves,
      # via the shared ci-actions/deploy-pages action — same as photos/reconcile.
      - name: Deploy to pages mount
        if: github.ref == 'refs/heads/main'
        uses: http://forgejo:3000/andras/ci-actions/deploy-pages@main
        with:
          source: site
          target: great-ai
--- a/.forgejo/workflows/publish.yml
+++ b/.forgejo/workflows/publish.yml
@ -1,28 +0,0 @@
 name: Publish on PyPI
 on:
  push:
    tags: ['*']
  workflow_dispatch:
 jobs:
  publish:
    runs-on: docker
    steps:
      - uses: actions/checkout@v4
      - name: Install uv
        run: |
          curl -LsSf https://astral.sh/uv/install.sh | sh
          echo "$HOME/.local/bin" >> "$GITHUB_PATH"
      - name: Set up Python
        run: uv python install 3.10
      # Forgejo cannot use PyPI trusted publishing (OIDC), so authenticate with
      # an API token, the same way the sibling repos publish their packages.
      - name: Build and publish
        env:
          FLIT_USERNAME: __token__
          FLIT_PASSWORD: ${{ secrets.PYPI_TOKEN }}
        run: uvx flit publish
--- a/.forgejo/workflows/test.yml
+++ b/.forgejo/workflows/test.yml
@ -1,73 +0,0 @@
 name: Check
 on:
  push:
    branches: [main, dev]
  pull_request:
    branches: [main, dev]
  workflow_dispatch:
 concurrency:
  group: ${{ gitea.workflow }}-${{ gitea.ref }}
  cancel-in-progress: true
 jobs:
  lint:
    name: Lint, format & type checks
    runs-on: docker
    steps:
      - uses: actions/checkout@v4
      - name: Install uv
        run: |
          curl -LsSf https://astral.sh/uv/install.sh | sh
          echo "$HOME/.local/bin" >> "$GITHUB_PATH"
      - name: Set up Python
        run: uv python install 3.10
      - name: Install dependencies
        # --seed gives the venv its own pip, which scripts/check-python.sh shells
        # out to for its linters; '.[dev]' provides the rest (mypy resolves the
        # package's own imports against the installed tree).
        run: |
          uv venv --seed --python 3.10
          uv pip install '.[dev]'
      - name: Check code and formatting
        run: |
          . .venv/bin/activate
          scripts/check-python.sh great_ai tests
  test:
    name: Test on Python ${{ matrix.python-version }}
    runs-on: docker
    strategy:
      fail-fast: false
      matrix:
        # pyproject declares >= 3.7, but uv's standalone CPython and the current
        # dependency floors (scikit-learn/numpy now require >= 3.9) no longer
        # build on the older interpreters, so we test the current supported set.
        # The windows leg of the old GitHub matrix is dropped: this Forgejo
        # instance only has a Linux `docker` runner.
        python-version: ['3.10', '3.11', '3.12', '3.13']
    steps:
      - uses: actions/checkout@v4
      - name: Install uv
        run: |
          curl -LsSf https://astral.sh/uv/install.sh | sh
          echo "$HOME/.local/bin" >> "$GITHUB_PATH"
      - name: Set up Python ${{ matrix.python-version }}
        run: uv python install ${{ matrix.python-version }}
      - name: Install dependencies
        run: |
          uv venv --python ${{ matrix.python-version }}
          uv pip install '.[dev]'
      - name: Run tests
        run: |
          . .venv/bin/activate
          pytest --doctest-modules --asyncio-mode=strict
--- a/.gitignore
+++ b/.gitignore
@ -1,12 +0,0 @@
 .env
 .venv
 .DS_Store
 __pycache__
 .cache
 .mypy_cache
 .pytest_cache
 .ipynb_checkpoints
 *.egg-info
 build
 tracing_database.json
 .tox
--- a/great_ai/external/init.py
+++ b/great_ai/external/init.py
--- a/.vscode/settings.json
+++ b/.vscode/settings.json
@ -1,84 +0,0 @@
 {
  "cSpell.words": [
    "alru",
    "Analyse",
    "András",
    "basereload",
    "boto",
    "botocore",
    "Convolutional",
    "datatable",
    "deduplicated",
    "displaylogo",
    "downsample",
    "fastapi",
    "finalise",
    "finalised",
    "gridfs",
    "httpx",
    "iloc",
    "initialisation",
    "initialised",
    "initialising",
    "inplace",
    "ipynb",
    "langcodes",
    "lemmatize",
    "levelname",
    "levelno",
    "matplotlib",
    "miniters",
    "Multinomial",
    "multiprocess",
    "nbconvert",
    "nbins",
    "optimisation",
    "organisation's",
    "Parcoords",
    "plotly",
    "pretrained",
    "proba",
    "pydantic",
    "pymongo",
    "pyplot",
    "redoc",
    "scibert",
    "serialise",
    "sklearn",
    "starlette",
    "sublinear",
    "syntok",
    "Tfidf",
    "threadsafe",
    "ticktext",
    "tickvals",
    "tinydb",
    "tqdm",
    "unchunk",
    "uvicorn",
    "Vectorizer",
    "xmargin",
    "xticks"
  ],
  "files.exclude": {
    ".env": true,
    "**/__pycache__": true,
    "**/.ipynb_checkpoints": true,
    "**/.mypy_cache": true,
    "**/.pytest_cache": true,
    "**/*.egg-info": true,
    "**/*.cache": true,
    "**/*.tox": true,
    "**/tracing_database.json": true
  },
  "notebook.output.textLineLimit": 400,
  "python.defaultInterpreterPath": ".env/bin/python",
  "python.testing.pytestArgs": ["tests"],
  "editor.rulers": [88],
  "python.linting.flake8Enabled": false,
  "python.linting.pylintEnabled": false,
  "python.linting.mypyEnabled": true,
  "python.testing.unittestEnabled": false,
  "python.testing.pytestEnabled": true,
  "editor.wordWrap": "on"
 }
--- a/.vscode/tasks.json
+++ b/.vscode/tasks.json
@ -1,56 +0,0 @@
 {
  "version": "2.0.0",
  "tasks": [
    {
      "label": "Format and lint",
      "type": "shell",
      "command": "source .env/bin/activate && scripts/format-python.sh great_ai docs tests",
      "windows": {
        "command": ".env\\bin\\activate.bat; scripts\\format-python.sh great_ai docs tests"
      },
      "group": "test",
      "presentation": {
        "reveal": "always",
        "showReuseMessage": false,
        "panel": "shared"
      },
      "options": {
        "cwd": "${workspaceFolder}"
      }
    },
    {
      "label": "Test (quick)",
      "type": "shell",
      "command": "source .env/bin/activate && python3 -m pytest . --doctest-modules --asyncio-mode=strict",
      "windows": {
        "command": ".env\\bin\\activate.bat; python3 -m pytest . --doctest-modules --asyncio-mode=strict"
      },
      "group": "test",
      "presentation": {
        "reveal": "always",
        "showReuseMessage": false,
        "panel": "shared"
      },
      "options": {
        "cwd": "${workspaceFolder}"
      }
    },
    {
      "label": "Test (all Python versions)",
      "type": "shell",
      "command": "source .env/bin/activate && python3 -m tox",
      "windows": {
        "command": ".env\\bin\\activate.bat; python3 -m tox"
      },
      "group": "test",
      "presentation": {
        "reveal": "always",
        "showReuseMessage": false,
        "panel": "shared"
      },
      "options": {
        "cwd": "${workspaceFolder}"
      }
    }
  ]
 }
--- a/404.html
+++ b/404.html
@ -0,0 +1,875 @@
 <!doctype html>
 <html lang="en" class="no-js">
  <head>
      <meta charset="utf-8">
      <meta name="viewport" content="width=device-width,initial-scale=1">
        <meta name="description" content="GreatAI helps you easily transform your prototype AI code into production-ready software.">
        <meta name="author" content="András Schmelczer">
      <link rel="icon" href="/media/favicon.ico">
      <meta name="generator" content="mkdocs-1.4.2, mkdocs-material-8.5.8">
        <title>GreatAI documentation</title>
      <link rel="stylesheet" href="/assets/stylesheets/main.20d9efc8.min.css">
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      <link rel="stylesheet" href="/assets/_mkdocstrings.css">
    <script>__md_scope=new URL("/",location),__md_hash=e=>[...e].reduce((e,_)=>(e<<5)-e+_.charCodeAt(0),0),__md_get=(e,_=localStorage,t=__md_scope)=>JSON.parse(_.getItem(t.pathname+"."+e)),__md_set=(e,_,t=localStorage,a=__md_scope)=>{try{t.setItem(a.pathname+"."+e,JSON.stringify(_))}catch(e){}}</script>
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--- a/docs/CNAME
+++ b/docs/CNAME
--- a/46
+++ b/46
@ -1,46 +0,0 @@
 # syntax=docker/dockerfile:1.4
 FROM python:3.10.4-slim-bullseye
 LABEL org.opencontainers.image.title="GreatAI package wrapper image"
 LABEL org.opencontainers.image.vendor="ScoutinScience B.V."
 LABEL org.opencontainers.image.authors="andras@schmelczer.dev"
 LABEL org.opencontainers.image.source="https://github.com/schmelczer/great-ai"
 SHELL ["/bin/bash", "-c"]
 ENV ENVIRONMENT=production
 # curl is needed for the healthcheck
 # build-essentials are needed for building packages
 RUN DEBIAN_FRONTEND=noninteractive apt update &&\
 	apt install curl build-essential -y &&\
 	rm -rf /var/lib/apt/lists/*
 WORKDIR /dependencies
 COPY . great_ai
 RUN python3 -m pip --no-cache-dir install --upgrade pip &&\
 	pip install --no-cache-dir ./great_ai &&\
 	rm -rf great_ai
 HEALTHCHECK \
 	--interval=30s \
 	--timeout=180s  \
 	--start-period=60s \
 	--retries=5 \
 	CMD [ "curl", "--fail", "http://localhost:6060/health" ]
 WORKDIR /app
 COPY <<EOF hello_world.py
 from great_ai import GreatAI
 \
@GreatAI.create
 def hello_world(name: str) -> str:
 	"""Learn more about GreatAI at https://great-ai.scoutinscience.com"""
 	return f"Hello {name}!"
 EOF
 EXPOSE 6060
 VOLUME /app
 ENTRYPOINT ["/usr/local/bin/python3", "-m", "great_ai"]
 CMD ["hello_world.py"]
--- a/674
+++ b/674
@ -1,674 +0,0 @@
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--- a/README.md
+++ b/README.md
@ -1,103 +0,0 @@
 # <img src="https://raw.githubusercontent.com/schmelczer/great-ai/main/docs/media/logo.png" alt="logo of great-ai" width=60 /> GreatAI
 > Easily transform your prototype AI code into production-ready software.
 [![PyPI version](https://badge.fury.io/py/great-ai.svg)](https://badge.fury.io/py/great-ai)
 [![Downloads](https://pepy.tech/badge/great-ai/month)](https://pepy.tech/project/great-ai)
 [![Docker Pulls](https://img.shields.io/docker/pulls/schmelczera/great-ai)](https://hub.docker.com/repository/docker/schmelczera/great-ai)
 [![Test](https://github.com/schmelczer/great-ai/actions/workflows/test.yml/badge.svg)](https://github.com/schmelczer/great-ai/actions/workflows/test.yml)
 [![Sonar line coverage](https://sonar.scoutinscience.com/api/project_badges/measure?project=great-ai&metric=coverage)](https://sonar.scoutinscience.com/dashboard?id=great-ai)
 [![Sonar LoC](https://sonar.scoutinscience.com/api/project_badges/measure?project=great-ai&metric=ncloc)](https://sonar.scoutinscience.com/dashboard?id=great-ai)
 Applying AI is becoming increasingly more accessible, but many case studies have shown that these applications are often deployed poorly. This may lead to suboptimal performance and to introducing unintended biases. GreatAI helps fix this by allowing you to easily transform your prototype AI code into production-ready software.
 ## Example
 ```sh
 pip install great-ai
 ```
 Create a new file called `demo.py`
 ```python
 from great_ai import GreatAI
@GreatAI.create
 def greeter(name: str) -> str:
    return f"Hello {name}!"
 ```
 Start it by executing `great-ai demo.py`, and find the dashboard at [http://localhost:6060](http://localhost:6060/dashboard).
 ![demo screen capture](https://raw.githubusercontent.com/schmelczer/great-ai/main/docs/media/demo.gif)
 That's it. Your GreatAI service is _nearly_ ready for production use. Many of the [SE4ML best practices](https://se-ml.github.io) are configured and implemented automatically (of course, these can be customised as well).
 [Check out the full documentation here](https://great-ai.scoutinscience.com).
 ## Why is this GREAT?
 ![scope of GreatAI](https://raw.githubusercontent.com/schmelczer/great-ai/main/docs/media/scope-simple.drawio.svg)
 GreatAI fits between the prototype and deployment phases of your AI development lifecycle. This is highlighted in blue in the diagram. Here, several best practices can be automatically implemented, aiming to achieve the following attributes:
 - **G**eneral: use any Python library without restriction
 - **R**obust: have error-handling and well-tested utilities out-of-the-box
 - **E**nd-to-end: utilise end-to-end feedback as a built-in, first-class concept
 - **A**utomated: focus only on what actually requires your attention
 - **T**rustworthy: deploy models that you and society can confidently trust
 ## Why GreatAI?
 There are other existing solutions aiming to facilitate this phase. [Amazon SageMaker](https://aws.amazon.com/sagemaker) and [Seldon Core](https://www.seldon.io/solutions/open-source-projects/core) provide the most comprehensive suite of features. If you have the opportunity to use them, do that because they're great.
 However, [research indicates](https://great-ai.scoutinscience.com) that professionals rarely use them. This may be due to their inherent setup and operational complexity. **GreatAI is designed to be as simple to use as possible.** Its straightforward, high-level API and sensible default configuration make it easy to start using. Despite its relative simplicity over Seldon Core, it still implements many of the [SE4ML best practices](https://se-ml.github.io), and thus, can meaningfully improve your deployment without requiring prohibitively great effort.
 ## [Learn more](https://great-ai.scoutinscience.com)
 [Check out the full documentation here](https://great-ai.scoutinscience.com).
 ## Find `great-ai` on [PyPI](https://pypi.org/project/great-ai/)
 ```sh
 pip install great-ai
 ```
 ## Find `great-ai` on [DockerHub](https://hub.docker.com/repository/docker/schmelczera/great-ai)
 ```sh
 docker run -p6060:6060 schmelczera/great-ai
 ```
 ## Contribute
 Contributions are welcome.
 ### Install for development
 ```sh
 python3 -m venv --copies .env
 source .env/bin/activate
 pip install --upgrade flit pip
 flit install --symlink
 ```
 ### Develop
 ```sh
 scripts/format-python.sh great_ai docs tests
 ```
 > Format code.
 ```sh
 python3 -m pytest --doctest-modules --asyncio-mode=strict .
 ```
 > Run tests.
 ```sh
 mkdocs serve
 ```
 > Serve documentation.
--- a/assets/_mkdocstrings.css
+++ b/assets/_mkdocstrings.css
@ -0,0 +1,36 @@
 /* Don't capitalize names. */
 h5.doc-heading {
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 .doc .md-typeset__table table {
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 /* Avoid line breaks in rendered fields. */
 .field-body p {
  display: inline;
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 /* Defaults in Spacy table style. */
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  float: right;
 }
--- a/assets/images/favicon.png
+++ b/assets/images/favicon.png
--- a/assets/javascripts/bundle.d6c3db9e.min.js
+++ b/assets/javascripts/bundle.d6c3db9e.min.js
--- a/assets/javascripts/bundle.d6c3db9e.min.js.map
+++ b/assets/javascripts/bundle.d6c3db9e.min.js.map
--- a/assets/javascripts/lunr/min/lunr.ar.min.js
+++ b/assets/javascripts/lunr/min/lunr.ar.min.js
--- a/assets/javascripts/lunr/min/lunr.da.min.js
+++ b/assets/javascripts/lunr/min/lunr.da.min.js
@ -0,0 +1,18 @@
 /*!
 * Lunr languages, `Danish` language
 * https://github.com/MihaiValentin/lunr-languages
 *
 * Copyright 2014, Mihai Valentin
 * http://www.mozilla.org/MPL/
 */
 /*!
 * based on
 * Snowball JavaScript Library v0.3
 * http://code.google.com/p/urim/
 * http://snowball.tartarus.org/
 *
 * Copyright 2010, Oleg Mazko
 * http://www.mozilla.org/MPL/
 */
 !function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");e.da=function(){this.pipeline.reset(),this.pipeline.add(e.da.trimmer,e.da.stopWordFilter,e.da.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.da.stemmer))},e.da.wordCharacters="A-Za-zªºÀ-ÖØ-öø-ʸˠ-ˤᴀ-ᴥᴬ-ᵜᵢ-ᵥᵫ-ᵷᵹ-ᶾḀ-ỿⁱⁿₐ-ₜKÅℲⅎⅠ-ↈⱠ-ⱿꜢ-ꞇꞋ-ꞭꞰ-ꞷꟷ-ꟿꬰ-ꭚꭜ-ꭤﬀ-ﬆＡ-Ｚａ-ｚ",e.da.trimmer=e.trimmerSupport.generateTrimmer(e.da.wordCharacters),e.Pipeline.registerFunction(e.da.trimmer,"trimmer-da"),e.da.stemmer=function(){var r=e.stemmerSupport.Among,i=e.stemmerSupport.SnowballProgram,n=new function(){function e(){var e,r=f.cursor+3;if(d=f.limit,0<=r&&r<=f.limit){for(a=r;;){if(e=f.cursor,f.in_grouping(w,97,248)){f.cursor=e;break}if(f.cursor=e,e>=f.limit)return;f.cursor++}for(;!f.out_grouping(w,97,248);){if(f.cursor>=f.limit)return;f.cursor++}d=f.cursor,d<a&&(d=a)}}function n(){var e,r;if(f.cursor>=d&&(r=f.limit_backward,f.limit_backward=d,f.ket=f.cursor,e=f.find_among_b(c,32),f.limit_backward=r,e))switch(f.bra=f.cursor,e){case 1:f.slice_del();break;case 2:f.in_grouping_b(p,97,229)&&f.slice_del()}}function t(){var e,r=f.limit-f.cursor;f.cursor>=d&&(e=f.limit_backward,f.limit_backward=d,f.ket=f.cursor,f.find_among_b(l,4)?(f.bra=f.cursor,f.limit_backward=e,f.cursor=f.limit-r,f.cursor>f.limit_backward&&(f.cursor--,f.bra=f.cursor,f.slice_del())):f.limit_backward=e)}function s(){var e,r,i,n=f.limit-f.cursor;if(f.ket=f.cursor,f.eq_s_b(2,"st")&&(f.bra=f.cursor,f.eq_s_b(2,"ig")&&f.slice_del()),f.cursor=f.limit-n,f.cursor>=d&&(r=f.limit_backward,f.limit_backward=d,f.ket=f.cursor,e=f.find_among_b(m,5),f.limit_backward=r,e))switch(f.bra=f.cursor,e){case 1:f.slice_del(),i=f.limit-f.cursor,t(),f.cursor=f.limit-i;break;case 2:f.slice_from("løs")}}function o(){var e;f.cursor>=d&&(e=f.limit_backward,f.limit_backward=d,f.ket=f.cursor,f.out_grouping_b(w,97,248)?(f.bra=f.cursor,u=f.slice_to(u),f.limit_backward=e,f.eq_v_b(u)&&f.slice_del()):f.limit_backward=e)}var a,d,u,c=[new r("hed",-1,1),new r("ethed",0,1),new r("ered",-1,1),new r("e",-1,1),new r("erede",3,1),new r("ende",3,1),new r("erende",5,1),new r("ene",3,1),new r("erne",3,1),new r("ere",3,1),new r("en",-1,1),new r("heden",10,1),new r("eren",10,1),new r("er",-1,1),new r("heder",13,1),new r("erer",13,1),new r("s",-1,2),new r("heds",16,1),new r("es",16,1),new r("endes",18,1),new r("erendes",19,1),new r("enes",18,1),new r("ernes",18,1),new r("eres",18,1),new r("ens",16,1),new r("hedens",24,1),new r("erens",24,1),new r("ers",16,1),new r("ets",16,1),new r("erets",28,1),new r("et",-1,1),new r("eret",30,1)],l=[new r("gd",-1,-1),new r("dt",-1,-1),new r("gt",-1,-1),new r("kt",-1,-1)],m=[new r("ig",-1,1),new r("lig",0,1),new r("elig",1,1),new r("els",-1,1),new r("løst",-1,2)],w=[17,65,16,1,0,0,0,0,0,0,0,0,0,0,0,0,48,0,128],p=[239,254,42,3,0,0,0,0,0,0,0,0,0,0,0,0,16],f=new i;this.setCurrent=function(e){f.setCurrent(e)},this.getCurrent=function(){return f.getCurrent()},this.stem=function(){var r=f.cursor;return e(),f.limit_backward=r,f.cursor=f.limit,n(),f.cursor=f.limit,t(),f.cursor=f.limit,s(),f.cursor=f.limit,o(),!0}};return function(e){return"function"==typeof e.update?e.update(function(e){return n.setCurrent(e),n.stem(),n.getCurrent()}):(n.setCurrent(e),n.stem(),n.getCurrent())}}(),e.Pipeline.registerFunction(e.da.stemmer,"stemmer-da"),e.da.stopWordFilter=e.generateStopWordFilter("ad af alle alt anden at blev blive bliver da de dem den denne der deres det dette dig din disse dog du efter eller en end er et for fra ham han hans har havde have hende hendes her hos hun hvad hvis hvor i ikke ind jeg jer jo kunne man mange med meget men mig min mine mit mod ned noget nogle nu når og også om op os over på selv sig sin sine sit skal skulle som sådan thi til ud under var vi vil ville vor være været".split(" ")),e.Pipeline.registerFunction(e.da.stopWordFilter,"stopWordFilter-da")}});
--- a/assets/javascripts/lunr/min/lunr.de.min.js
+++ b/assets/javascripts/lunr/min/lunr.de.min.js
--- a/assets/javascripts/lunr/min/lunr.du.min.js
+++ b/assets/javascripts/lunr/min/lunr.du.min.js
--- a/assets/javascripts/lunr/min/lunr.es.min.js
+++ b/assets/javascripts/lunr/min/lunr.es.min.js
--- a/assets/javascripts/lunr/min/lunr.fi.min.js
+++ b/assets/javascripts/lunr/min/lunr.fi.min.js
--- a/assets/javascripts/lunr/min/lunr.fr.min.js
+++ b/assets/javascripts/lunr/min/lunr.fr.min.js
--- a/assets/javascripts/lunr/min/lunr.hi.min.js
+++ b/assets/javascripts/lunr/min/lunr.hi.min.js
@ -0,0 +1 @@
 !function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");e.hi=function(){this.pipeline.reset(),this.pipeline.add(e.hi.trimmer,e.hi.stopWordFilter,e.hi.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.hi.stemmer))},e.hi.wordCharacters="ऀ-ःऄ-एऐ-टठ-यर-िी-ॏॐ-य़ॠ-९॰-ॿa-zA-Zａ-ｚＡ-Ｚ0-9０-９",e.hi.trimmer=e.trimmerSupport.generateTrimmer(e.hi.wordCharacters),e.Pipeline.registerFunction(e.hi.trimmer,"trimmer-hi"),e.hi.stopWordFilter=e.generateStopWordFilter("अत अपना अपनी अपने अभी अंदर आदि आप इत्यादि इन इनका इन्हीं इन्हें इन्हों इस इसका इसकी इसके इसमें इसी इसे उन उनका उनकी उनके उनको उन्हीं उन्हें उन्हों उस उसके उसी उसे एक एवं एस ऐसे और कई कर करता करते करना करने करें कहते कहा का काफ़ी कि कितना किन्हें किन्हों किया किर किस किसी किसे की कुछ कुल के को कोई कौन कौनसा गया घर जब जहाँ जा जितना जिन जिन्हें जिन्हों जिस जिसे जीधर जैसा जैसे जो तक तब तरह तिन तिन्हें तिन्हों तिस तिसे तो था थी थे दबारा दिया दुसरा दूसरे दो द्वारा न नके नहीं ना निहायत नीचे ने पर पहले पूरा पे फिर बनी बही बहुत बाद बाला बिलकुल भी भीतर मगर मानो मे में यदि यह यहाँ यही या यिह ये रखें रहा रहे ऱ्वासा लिए लिये लेकिन व वग़ैरह वर्ग वह वहाँ वहीं वाले वुह वे वो सकता सकते सबसे सभी साथ साबुत साभ सारा से सो संग ही हुआ हुई हुए है हैं हो होता होती होते होना होने".split(" ")),e.hi.stemmer=function(){return function(e){return"function"==typeof e.update?e.update(function(e){return e}):e}}();var r=e.wordcut;r.init(),e.hi.tokenizer=function(i){if(!arguments.length||null==i||void 0==i)return[];if(Array.isArray(i))return i.map(function(r){return isLunr2?new e.Token(r.toLowerCase()):r.toLowerCase()});var t=i.toString().toLowerCase().replace(/^\s+/,"");return r.cut(t).split("|")},e.Pipeline.registerFunction(e.hi.stemmer,"stemmer-hi"),e.Pipeline.registerFunction(e.hi.stopWordFilter,"stopWordFilter-hi")}});
--- a/assets/javascripts/lunr/min/lunr.hu.min.js
+++ b/assets/javascripts/lunr/min/lunr.hu.min.js
--- a/assets/javascripts/lunr/min/lunr.it.min.js
+++ b/assets/javascripts/lunr/min/lunr.it.min.js
--- a/assets/javascripts/lunr/min/lunr.ja.min.js
+++ b/assets/javascripts/lunr/min/lunr.ja.min.js
@ -0,0 +1 @@
 !function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");var r="2"==e.version[0];e.ja=function(){this.pipeline.reset(),this.pipeline.add(e.ja.trimmer,e.ja.stopWordFilter,e.ja.stemmer),r?this.tokenizer=e.ja.tokenizer:(e.tokenizer&&(e.tokenizer=e.ja.tokenizer),this.tokenizerFn&&(this.tokenizerFn=e.ja.tokenizer))};var t=new e.TinySegmenter;e.ja.tokenizer=function(i){var n,o,s,p,a,u,m,l,c,f;if(!arguments.length||null==i||void 0==i)return[];if(Array.isArray(i))return i.map(function(t){return r?new e.Token(t.toLowerCase()):t.toLowerCase()});for(o=i.toString().toLowerCase().replace(/^\s+/,""),n=o.length-1;n>=0;n--)if(/\S/.test(o.charAt(n))){o=o.substring(0,n+1);break}for(a=[],s=o.length,c=0,l=0;c<=s;c++)if(u=o.charAt(c),m=c-l,u.match(/\s/)||c==s){if(m>0)for(p=t.segment(o.slice(l,c)).filter(function(e){return!!e}),f=l,n=0;n<p.length;n++)r?a.push(new e.Token(p[n],{position:[f,p[n].length],index:a.length})):a.push(p[n]),f+=p[n].length;l=c+1}return a},e.ja.stemmer=function(){return function(e){return e}}(),e.Pipeline.registerFunction(e.ja.stemmer,"stemmer-ja"),e.ja.wordCharacters="一二三四五六七八九十百千万億兆一-龠々〆ヵヶぁ-んァ-ヴーｱ-ﾝﾞa-zA-Zａ-ｚＡ-Ｚ0-9０-９",e.ja.trimmer=e.trimmerSupport.generateTrimmer(e.ja.wordCharacters),e.Pipeline.registerFunction(e.ja.trimmer,"trimmer-ja"),e.ja.stopWordFilter=e.generateStopWordFilter("これ それ あれ この その あの ここ そこ あそこ こちら どこ だれ なに なん 何 私 貴方 貴方方 我々 私達 あの人 あのかた 彼女 彼 です あります おります います は が の に を で え から まで より も どの と し それで しかし".split(" ")),e.Pipeline.registerFunction(e.ja.stopWordFilter,"stopWordFilter-ja"),e.jp=e.ja,e.Pipeline.registerFunction(e.jp.stemmer,"stemmer-jp"),e.Pipeline.registerFunction(e.jp.trimmer,"trimmer-jp"),e.Pipeline.registerFunction(e.jp.stopWordFilter,"stopWordFilter-jp")}});
--- a/assets/javascripts/lunr/min/lunr.jp.min.js
+++ b/assets/javascripts/lunr/min/lunr.jp.min.js
@ -0,0 +1 @@
 module.exports=require("./lunr.ja");
--- a/assets/javascripts/lunr/min/lunr.ko.min.js
+++ b/assets/javascripts/lunr/min/lunr.ko.min.js
--- a/assets/javascripts/lunr/min/lunr.multi.min.js
+++ b/assets/javascripts/lunr/min/lunr.multi.min.js
@ -0,0 +1 @@
 !function(e,t){"function"==typeof define&&define.amd?define(t):"object"==typeof exports?module.exports=t():t()(e.lunr)}(this,function(){return function(e){e.multiLanguage=function(){for(var t=Array.prototype.slice.call(arguments),i=t.join("-"),r="",n=[],s=[],p=0;p<t.length;++p)"en"==t[p]?(r+="\\w",n.unshift(e.stopWordFilter),n.push(e.stemmer),s.push(e.stemmer)):(r+=e[t[p]].wordCharacters,e[t[p]].stopWordFilter&&n.unshift(e[t[p]].stopWordFilter),e[t[p]].stemmer&&(n.push(e[t[p]].stemmer),s.push(e[t[p]].stemmer)));var o=e.trimmerSupport.generateTrimmer(r);return e.Pipeline.registerFunction(o,"lunr-multi-trimmer-"+i),n.unshift(o),function(){this.pipeline.reset(),this.pipeline.add.apply(this.pipeline,n),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add.apply(this.searchPipeline,s))}}}});
--- a/assets/javascripts/lunr/min/lunr.nl.min.js
+++ b/assets/javascripts/lunr/min/lunr.nl.min.js
--- a/assets/javascripts/lunr/min/lunr.no.min.js
+++ b/assets/javascripts/lunr/min/lunr.no.min.js
@ -0,0 +1,18 @@
 /*!
 * Lunr languages, `Norwegian` language
 * https://github.com/MihaiValentin/lunr-languages
 *
 * Copyright 2014, Mihai Valentin
 * http://www.mozilla.org/MPL/
 */
 /*!
 * based on
 * Snowball JavaScript Library v0.3
 * http://code.google.com/p/urim/
 * http://snowball.tartarus.org/
 *
 * Copyright 2010, Oleg Mazko
 * http://www.mozilla.org/MPL/
 */
 !function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");e.no=function(){this.pipeline.reset(),this.pipeline.add(e.no.trimmer,e.no.stopWordFilter,e.no.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.no.stemmer))},e.no.wordCharacters="A-Za-zªºÀ-ÖØ-öø-ʸˠ-ˤᴀ-ᴥᴬ-ᵜᵢ-ᵥᵫ-ᵷᵹ-ᶾḀ-ỿⁱⁿₐ-ₜKÅℲⅎⅠ-ↈⱠ-ⱿꜢ-ꞇꞋ-ꞭꞰ-ꞷꟷ-ꟿꬰ-ꭚꭜ-ꭤﬀ-ﬆＡ-Ｚａ-ｚ",e.no.trimmer=e.trimmerSupport.generateTrimmer(e.no.wordCharacters),e.Pipeline.registerFunction(e.no.trimmer,"trimmer-no"),e.no.stemmer=function(){var r=e.stemmerSupport.Among,n=e.stemmerSupport.SnowballProgram,i=new function(){function e(){var e,r=w.cursor+3;if(a=w.limit,0<=r||r<=w.limit){for(s=r;;){if(e=w.cursor,w.in_grouping(d,97,248)){w.cursor=e;break}if(e>=w.limit)return;w.cursor=e+1}for(;!w.out_grouping(d,97,248);){if(w.cursor>=w.limit)return;w.cursor++}a=w.cursor,a<s&&(a=s)}}function i(){var e,r,n;if(w.cursor>=a&&(r=w.limit_backward,w.limit_backward=a,w.ket=w.cursor,e=w.find_among_b(m,29),w.limit_backward=r,e))switch(w.bra=w.cursor,e){case 1:w.slice_del();break;case 2:n=w.limit-w.cursor,w.in_grouping_b(c,98,122)?w.slice_del():(w.cursor=w.limit-n,w.eq_s_b(1,"k")&&w.out_grouping_b(d,97,248)&&w.slice_del());break;case 3:w.slice_from("er")}}function t(){var e,r=w.limit-w.cursor;w.cursor>=a&&(e=w.limit_backward,w.limit_backward=a,w.ket=w.cursor,w.find_among_b(u,2)?(w.bra=w.cursor,w.limit_backward=e,w.cursor=w.limit-r,w.cursor>w.limit_backward&&(w.cursor--,w.bra=w.cursor,w.slice_del())):w.limit_backward=e)}function o(){var e,r;w.cursor>=a&&(r=w.limit_backward,w.limit_backward=a,w.ket=w.cursor,e=w.find_among_b(l,11),e?(w.bra=w.cursor,w.limit_backward=r,1==e&&w.slice_del()):w.limit_backward=r)}var s,a,m=[new r("a",-1,1),new r("e",-1,1),new r("ede",1,1),new r("ande",1,1),new r("ende",1,1),new r("ane",1,1),new r("ene",1,1),new r("hetene",6,1),new r("erte",1,3),new r("en",-1,1),new r("heten",9,1),new r("ar",-1,1),new r("er",-1,1),new r("heter",12,1),new r("s",-1,2),new r("as",14,1),new r("es",14,1),new r("edes",16,1),new r("endes",16,1),new r("enes",16,1),new r("hetenes",19,1),new r("ens",14,1),new r("hetens",21,1),new r("ers",14,1),new r("ets",14,1),new r("et",-1,1),new r("het",25,1),new r("ert",-1,3),new r("ast",-1,1)],u=[new r("dt",-1,-1),new r("vt",-1,-1)],l=[new r("leg",-1,1),new r("eleg",0,1),new r("ig",-1,1),new r("eig",2,1),new r("lig",2,1),new r("elig",4,1),new r("els",-1,1),new r("lov",-1,1),new r("elov",7,1),new r("slov",7,1),new r("hetslov",9,1)],d=[17,65,16,1,0,0,0,0,0,0,0,0,0,0,0,0,48,0,128],c=[119,125,149,1],w=new n;this.setCurrent=function(e){w.setCurrent(e)},this.getCurrent=function(){return w.getCurrent()},this.stem=function(){var r=w.cursor;return e(),w.limit_backward=r,w.cursor=w.limit,i(),w.cursor=w.limit,t(),w.cursor=w.limit,o(),!0}};return function(e){return"function"==typeof e.update?e.update(function(e){return i.setCurrent(e),i.stem(),i.getCurrent()}):(i.setCurrent(e),i.stem(),i.getCurrent())}}(),e.Pipeline.registerFunction(e.no.stemmer,"stemmer-no"),e.no.stopWordFilter=e.generateStopWordFilter("alle at av bare begge ble blei bli blir blitt både båe da de deg dei deim deira deires dem den denne der dere deres det dette di din disse ditt du dykk dykkar då eg ein eit eitt eller elles en enn er et ett etter for fordi fra før ha hadde han hans har hennar henne hennes her hjå ho hoe honom hoss hossen hun hva hvem hver hvilke hvilken hvis hvor hvordan hvorfor i ikke ikkje ikkje ingen ingi inkje inn inni ja jeg kan kom korleis korso kun kunne kva kvar kvarhelst kven kvi kvifor man mange me med medan meg meget mellom men mi min mine mitt mot mykje ned no noe noen noka noko nokon nokor nokre nå når og også om opp oss over på samme seg selv si si sia sidan siden sin sine sitt sjøl skal skulle slik so som som somme somt så sånn til um upp ut uten var vart varte ved vere verte vi vil ville vore vors vort vår være være vært å".split(" ")),e.Pipeline.registerFunction(e.no.stopWordFilter,"stopWordFilter-no")}});
--- a/assets/javascripts/lunr/min/lunr.pt.min.js
+++ b/assets/javascripts/lunr/min/lunr.pt.min.js
--- a/assets/javascripts/lunr/min/lunr.ro.min.js
+++ b/assets/javascripts/lunr/min/lunr.ro.min.js
--- a/assets/javascripts/lunr/min/lunr.ru.min.js
+++ b/assets/javascripts/lunr/min/lunr.ru.min.js
--- a/assets/javascripts/lunr/min/lunr.stemmer.support.min.js
+++ b/assets/javascripts/lunr/min/lunr.stemmer.support.min.js
@ -0,0 +1 @@
 !function(r,t){"function"==typeof define&&define.amd?define(t):"object"==typeof exports?module.exports=t():t()(r.lunr)}(this,function(){return function(r){r.stemmerSupport={Among:function(r,t,i,s){if(this.toCharArray=function(r){for(var t=r.length,i=new Array(t),s=0;s<t;s++)i[s]=r.charCodeAt(s);return i},!r&&""!=r||!t&&0!=t||!i)throw"Bad Among initialisation: s:"+r+", substring_i: "+t+", result: "+i;this.s_size=r.length,this.s=this.toCharArray(r),this.substring_i=t,this.result=i,this.method=s},SnowballProgram:function(){var r;return{bra:0,ket:0,limit:0,cursor:0,limit_backward:0,setCurrent:function(t){r=t,this.cursor=0,this.limit=t.length,this.limit_backward=0,this.bra=this.cursor,this.ket=this.limit},getCurrent:function(){var t=r;return r=null,t},in_grouping:function(t,i,s){if(this.cursor<this.limit){var e=r.charCodeAt(this.cursor);if(e<=s&&e>=i&&(e-=i,t[e>>3]&1<<(7&e)))return this.cursor++,!0}return!1},in_grouping_b:function(t,i,s){if(this.cursor>this.limit_backward){var e=r.charCodeAt(this.cursor-1);if(e<=s&&e>=i&&(e-=i,t[e>>3]&1<<(7&e)))return this.cursor--,!0}return!1},out_grouping:function(t,i,s){if(this.cursor<this.limit){var e=r.charCodeAt(this.cursor);if(e>s||e<i)return this.cursor++,!0;if(e-=i,!(t[e>>3]&1<<(7&e)))return this.cursor++,!0}return!1},out_grouping_b:function(t,i,s){if(this.cursor>this.limit_backward){var e=r.charCodeAt(this.cursor-1);if(e>s||e<i)return this.cursor--,!0;if(e-=i,!(t[e>>3]&1<<(7&e)))return this.cursor--,!0}return!1},eq_s:function(t,i){if(this.limit-this.cursor<t)return!1;for(var s=0;s<t;s++)if(r.charCodeAt(this.cursor+s)!=i.charCodeAt(s))return!1;return this.cursor+=t,!0},eq_s_b:function(t,i){if(this.cursor-this.limit_backward<t)return!1;for(var s=0;s<t;s++)if(r.charCodeAt(this.cursor-t+s)!=i.charCodeAt(s))return!1;return this.cursor-=t,!0},find_among:function(t,i){for(var s=0,e=i,n=this.cursor,u=this.limit,o=0,h=0,c=!1;;){for(var a=s+(e-s>>1),f=0,l=o<h?o:h,_=t[a],m=l;m<_.s_size;m++){if(n+l==u){f=-1;break}if(f=r.charCodeAt(n+l)-_.s[m])break;l++}if(f<0?(e=a,h=l):(s=a,o=l),e-s<=1){if(s>0||e==s||c)break;c=!0}}for(;;){var _=t[s];if(o>=_.s_size){if(this.cursor=n+_.s_size,!_.method)return _.result;var b=_.method();if(this.cursor=n+_.s_size,b)return _.result}if((s=_.substring_i)<0)return 0}},find_among_b:function(t,i){for(var s=0,e=i,n=this.cursor,u=this.limit_backward,o=0,h=0,c=!1;;){for(var a=s+(e-s>>1),f=0,l=o<h?o:h,_=t[a],m=_.s_size-1-l;m>=0;m--){if(n-l==u){f=-1;break}if(f=r.charCodeAt(n-1-l)-_.s[m])break;l++}if(f<0?(e=a,h=l):(s=a,o=l),e-s<=1){if(s>0||e==s||c)break;c=!0}}for(;;){var _=t[s];if(o>=_.s_size){if(this.cursor=n-_.s_size,!_.method)return _.result;var b=_.method();if(this.cursor=n-_.s_size,b)return _.result}if((s=_.substring_i)<0)return 0}},replace_s:function(t,i,s){var e=s.length-(i-t),n=r.substring(0,t),u=r.substring(i);return r=n+s+u,this.limit+=e,this.cursor>=i?this.cursor+=e:this.cursor>t&&(this.cursor=t),e},slice_check:function(){if(this.bra<0||this.bra>this.ket||this.ket>this.limit||this.limit>r.length)throw"faulty slice operation"},slice_from:function(r){this.slice_check(),this.replace_s(this.bra,this.ket,r)},slice_del:function(){this.slice_from("")},insert:function(r,t,i){var s=this.replace_s(r,t,i);r<=this.bra&&(this.bra+=s),r<=this.ket&&(this.ket+=s)},slice_to:function(){return this.slice_check(),r.substring(this.bra,this.ket)},eq_v_b:function(r){return this.eq_s_b(r.length,r)}}}},r.trimmerSupport={generateTrimmer:function(r){var t=new RegExp("^[^"+r+"]+"),i=new RegExp("[^"+r+"]+$");return function(r){return"function"==typeof r.update?r.update(function(r){return r.replace(t,"").replace(i,"")}):r.replace(t,"").replace(i,"")}}}}});
--- a/assets/javascripts/lunr/min/lunr.sv.min.js
+++ b/assets/javascripts/lunr/min/lunr.sv.min.js
@ -0,0 +1,18 @@
 /*!
 * Lunr languages, `Swedish` language
 * https://github.com/MihaiValentin/lunr-languages
 *
 * Copyright 2014, Mihai Valentin
 * http://www.mozilla.org/MPL/
 */
 /*!
 * based on
 * Snowball JavaScript Library v0.3
 * http://code.google.com/p/urim/
 * http://snowball.tartarus.org/
 *
 * Copyright 2010, Oleg Mazko
 * http://www.mozilla.org/MPL/
 */
 !function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");e.sv=function(){this.pipeline.reset(),this.pipeline.add(e.sv.trimmer,e.sv.stopWordFilter,e.sv.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.sv.stemmer))},e.sv.wordCharacters="A-Za-zªºÀ-ÖØ-öø-ʸˠ-ˤᴀ-ᴥᴬ-ᵜᵢ-ᵥᵫ-ᵷᵹ-ᶾḀ-ỿⁱⁿₐ-ₜKÅℲⅎⅠ-ↈⱠ-ⱿꜢ-ꞇꞋ-ꞭꞰ-ꞷꟷ-ꟿꬰ-ꭚꭜ-ꭤﬀ-ﬆＡ-Ｚａ-ｚ",e.sv.trimmer=e.trimmerSupport.generateTrimmer(e.sv.wordCharacters),e.Pipeline.registerFunction(e.sv.trimmer,"trimmer-sv"),e.sv.stemmer=function(){var r=e.stemmerSupport.Among,n=e.stemmerSupport.SnowballProgram,t=new function(){function e(){var e,r=w.cursor+3;if(o=w.limit,0<=r||r<=w.limit){for(a=r;;){if(e=w.cursor,w.in_grouping(l,97,246)){w.cursor=e;break}if(w.cursor=e,w.cursor>=w.limit)return;w.cursor++}for(;!w.out_grouping(l,97,246);){if(w.cursor>=w.limit)return;w.cursor++}o=w.cursor,o<a&&(o=a)}}function t(){var e,r=w.limit_backward;if(w.cursor>=o&&(w.limit_backward=o,w.cursor=w.limit,w.ket=w.cursor,e=w.find_among_b(u,37),w.limit_backward=r,e))switch(w.bra=w.cursor,e){case 1:w.slice_del();break;case 2:w.in_grouping_b(d,98,121)&&w.slice_del()}}function i(){var e=w.limit_backward;w.cursor>=o&&(w.limit_backward=o,w.cursor=w.limit,w.find_among_b(c,7)&&(w.cursor=w.limit,w.ket=w.cursor,w.cursor>w.limit_backward&&(w.bra=--w.cursor,w.slice_del())),w.limit_backward=e)}function s(){var e,r;if(w.cursor>=o){if(r=w.limit_backward,w.limit_backward=o,w.cursor=w.limit,w.ket=w.cursor,e=w.find_among_b(m,5))switch(w.bra=w.cursor,e){case 1:w.slice_del();break;case 2:w.slice_from("lös");break;case 3:w.slice_from("full")}w.limit_backward=r}}var a,o,u=[new r("a",-1,1),new r("arna",0,1),new r("erna",0,1),new r("heterna",2,1),new r("orna",0,1),new r("ad",-1,1),new r("e",-1,1),new r("ade",6,1),new r("ande",6,1),new r("arne",6,1),new r("are",6,1),new r("aste",6,1),new r("en",-1,1),new r("anden",12,1),new r("aren",12,1),new r("heten",12,1),new r("ern",-1,1),new r("ar",-1,1),new r("er",-1,1),new r("heter",18,1),new r("or",-1,1),new r("s",-1,2),new r("as",21,1),new r("arnas",22,1),new r("ernas",22,1),new r("ornas",22,1),new r("es",21,1),new r("ades",26,1),new r("andes",26,1),new r("ens",21,1),new r("arens",29,1),new r("hetens",29,1),new r("erns",21,1),new r("at",-1,1),new r("andet",-1,1),new r("het",-1,1),new r("ast",-1,1)],c=[new r("dd",-1,-1),new r("gd",-1,-1),new r("nn",-1,-1),new r("dt",-1,-1),new r("gt",-1,-1),new r("kt",-1,-1),new r("tt",-1,-1)],m=[new r("ig",-1,1),new r("lig",0,1),new r("els",-1,1),new r("fullt",-1,3),new r("löst",-1,2)],l=[17,65,16,1,0,0,0,0,0,0,0,0,0,0,0,0,24,0,32],d=[119,127,149],w=new n;this.setCurrent=function(e){w.setCurrent(e)},this.getCurrent=function(){return w.getCurrent()},this.stem=function(){var r=w.cursor;return e(),w.limit_backward=r,w.cursor=w.limit,t(),w.cursor=w.limit,i(),w.cursor=w.limit,s(),!0}};return function(e){return"function"==typeof e.update?e.update(function(e){return t.setCurrent(e),t.stem(),t.getCurrent()}):(t.setCurrent(e),t.stem(),t.getCurrent())}}(),e.Pipeline.registerFunction(e.sv.stemmer,"stemmer-sv"),e.sv.stopWordFilter=e.generateStopWordFilter("alla allt att av blev bli blir blivit de dem den denna deras dess dessa det detta dig din dina ditt du där då efter ej eller en er era ert ett från för ha hade han hans har henne hennes hon honom hur här i icke ingen inom inte jag ju kan kunde man med mellan men mig min mina mitt mot mycket ni nu när någon något några och om oss på samma sedan sig sin sina sitta själv skulle som så sådan sådana sådant till under upp ut utan vad var vara varför varit varje vars vart vem vi vid vilka vilkas vilken vilket vår våra vårt än är åt över".split(" ")),e.Pipeline.registerFunction(e.sv.stopWordFilter,"stopWordFilter-sv")}});
--- a/assets/javascripts/lunr/min/lunr.ta.min.js
+++ b/assets/javascripts/lunr/min/lunr.ta.min.js
@ -0,0 +1 @@
 !function(e,t){"function"==typeof define&&define.amd?define(t):"object"==typeof exports?module.exports=t():t()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");e.ta=function(){this.pipeline.reset(),this.pipeline.add(e.ta.trimmer,e.ta.stopWordFilter,e.ta.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.ta.stemmer))},e.ta.wordCharacters="஀-உஊ-ஏஐ-ஙச-ட஠-னப-யர-ஹ஺-ிீ-௉ொ-௏ௐ-௙௚-௟௠-௩௪-௯௰-௹௺-௿a-zA-Zａ-ｚＡ-Ｚ0-9０-９",e.ta.trimmer=e.trimmerSupport.generateTrimmer(e.ta.wordCharacters),e.Pipeline.registerFunction(e.ta.trimmer,"trimmer-ta"),e.ta.stopWordFilter=e.generateStopWordFilter("அங்கு அங்கே அது அதை அந்த அவர் அவர்கள் அவள் அவன் அவை ஆக ஆகவே ஆகையால் ஆதலால் ஆதலினால் ஆனாலும் ஆனால் இங்கு இங்கே இது இதை இந்த இப்படி இவர் இவர்கள் இவள் இவன் இவை இவ்வளவு உனக்கு உனது உன் உன்னால் எங்கு எங்கே எது எதை எந்த எப்படி எவர் எவர்கள் எவள் எவன் எவை எவ்வளவு எனக்கு எனது எனவே என் என்ன என்னால் ஏது ஏன் தனது தன்னால் தானே தான் நாங்கள் நாம் நான் நீ நீங்கள்".split(" ")),e.ta.stemmer=function(){return function(e){return"function"==typeof e.update?e.update(function(e){return e}):e}}();var t=e.wordcut;t.init(),e.ta.tokenizer=function(r){if(!arguments.length||null==r||void 0==r)return[];if(Array.isArray(r))return r.map(function(t){return isLunr2?new e.Token(t.toLowerCase()):t.toLowerCase()});var i=r.toString().toLowerCase().replace(/^\s+/,"");return t.cut(i).split("|")},e.Pipeline.registerFunction(e.ta.stemmer,"stemmer-ta"),e.Pipeline.registerFunction(e.ta.stopWordFilter,"stopWordFilter-ta")}});
--- a/assets/javascripts/lunr/min/lunr.th.min.js
+++ b/assets/javascripts/lunr/min/lunr.th.min.js
@ -0,0 +1 @@
 !function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");var r="2"==e.version[0];e.th=function(){this.pipeline.reset(),this.pipeline.add(e.th.trimmer),r?this.tokenizer=e.th.tokenizer:(e.tokenizer&&(e.tokenizer=e.th.tokenizer),this.tokenizerFn&&(this.tokenizerFn=e.th.tokenizer))},e.th.wordCharacters="[฀-๿]",e.th.trimmer=e.trimmerSupport.generateTrimmer(e.th.wordCharacters),e.Pipeline.registerFunction(e.th.trimmer,"trimmer-th");var t=e.wordcut;t.init(),e.th.tokenizer=function(i){if(!arguments.length||null==i||void 0==i)return[];if(Array.isArray(i))return i.map(function(t){return r?new e.Token(t):t});var n=i.toString().replace(/^\s+/,"");return t.cut(n).split("|")}}});
--- a/assets/javascripts/lunr/min/lunr.tr.min.js
+++ b/assets/javascripts/lunr/min/lunr.tr.min.js
--- a/assets/javascripts/lunr/min/lunr.vi.min.js
+++ b/assets/javascripts/lunr/min/lunr.vi.min.js
@ -0,0 +1 @@
 !function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");e.vi=function(){this.pipeline.reset(),this.pipeline.add(e.vi.stopWordFilter,e.vi.trimmer)},e.vi.wordCharacters="[A-Za-ẓ̀͐́͑̉̃̓ÂâÊêÔôĂ-ăĐ-đƠ-ơƯ-ư]",e.vi.trimmer=e.trimmerSupport.generateTrimmer(e.vi.wordCharacters),e.Pipeline.registerFunction(e.vi.trimmer,"trimmer-vi"),e.vi.stopWordFilter=e.generateStopWordFilter("là cái nhưng mà".split(" "))}});
--- a/assets/javascripts/lunr/min/lunr.zh.min.js
+++ b/assets/javascripts/lunr/min/lunr.zh.min.js
@ -0,0 +1 @@
 !function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r(require("@node-rs/jieba")):r()(e.lunr)}(this,function(e){return function(r,t){if(void 0===r)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===r.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");var i="2"==r.version[0];r.zh=function(){this.pipeline.reset(),this.pipeline.add(r.zh.trimmer,r.zh.stopWordFilter,r.zh.stemmer),i?this.tokenizer=r.zh.tokenizer:(r.tokenizer&&(r.tokenizer=r.zh.tokenizer),this.tokenizerFn&&(this.tokenizerFn=r.zh.tokenizer))},r.zh.tokenizer=function(n){if(!arguments.length||null==n||void 0==n)return[];if(Array.isArray(n))return n.map(function(e){return i?new r.Token(e.toLowerCase()):e.toLowerCase()});t&&e.load(t);var o=n.toString().trim().toLowerCase(),s=[];e.cut(o,!0).forEach(function(e){s=s.concat(e.split(" "))}),s=s.filter(function(e){return!!e});var u=0;return s.map(function(e,t){if(i){var n=o.indexOf(e,u),s={};return s.position=[n,e.length],s.index=t,u=n,new r.Token(e,s)}return e})},r.zh.wordCharacters="\\w一-龥",r.zh.trimmer=r.trimmerSupport.generateTrimmer(r.zh.wordCharacters),r.Pipeline.registerFunction(r.zh.trimmer,"trimmer-zh"),r.zh.stemmer=function(){return function(e){return e}}(),r.Pipeline.registerFunction(r.zh.stemmer,"stemmer-zh"),r.zh.stopWordFilter=r.generateStopWordFilter("的 一 不 在 人 有 是 为 以 于 上 他 而 后 之 来 及 了 因 下 可 到 由 这 与 也 此 但 并 个 其 已 无 小 我 们 起 最 再 今 去 好 只 又 或 很 亦 某 把 那 你 乃 它 吧 被 比 别 趁 当 从 到 得 打 凡 儿 尔 该 各 给 跟 和 何 还 即 几 既 看 据 距 靠 啦 了 另 么 每 们 嘛 拿 哪 那 您 凭 且 却 让 仍 啥 如 若 使 谁 虽 随 同 所 她 哇 嗡 往 哪 些 向 沿 哟 用 于 咱 则 怎 曾 至 致 着 诸 自".split(" ")),r.Pipeline.registerFunction(r.zh.stopWordFilter,"stopWordFilter-zh")}});
--- a/assets/javascripts/lunr/tinyseg.js
+++ b/assets/javascripts/lunr/tinyseg.js
@ -0,0 +1,206 @@
 /**
 * export the module via AMD, CommonJS or as a browser global
 * Export code from https://github.com/umdjs/umd/blob/master/returnExports.js
 */
 ;(function (root, factory) {
    if (typeof define === 'function' && define.amd) {
        // AMD. Register as an anonymous module.
        define(factory)
    } else if (typeof exports === 'object') {
        /**
         * Node. Does not work with strict CommonJS, but
         * only CommonJS-like environments that support module.exports,
         * like Node.
         */
        module.exports = factory()
    } else {
        // Browser globals (root is window)
        factory()(root.lunr);
    }
 }(this, function () {
    /**
     * Just return a value to define the module export.
     * This example returns an object, but the module
     * can return a function as the exported value.
     */
    return function(lunr) {
        // TinySegmenter 0.1 -- Super compact Japanese tokenizer in Javascript
        // (c) 2008 Taku Kudo <taku@chasen.org>
        // TinySegmenter is freely distributable under the terms of a new BSD licence.
        // For details, see http://chasen.org/~taku/software/TinySegmenter/LICENCE.txt
        function TinySegmenter() {
          var patterns = {
            "[一二三四五六七八九十百千万億兆]":"M",
            "[一-龠々〆ヵヶ]":"H",
            "[ぁ-ん]":"I",
            "[ァ-ヴーｱ-ﾝﾞｰ]":"K",
            "[a-zA-Zａ-ｚＡ-Ｚ]":"A",
            "[0-9０-９]":"N"
          }
          this.chartype_ = [];
          for (var i in patterns) {
            var regexp = new RegExp(i);
            this.chartype_.push([regexp, patterns[i]]);
          }
          this.BIAS__ = -332
          this.BC1__ = {"HH":6,"II":2461,"KH":406,"OH":-1378};
          this.BC2__ = {"AA":-3267,"AI":2744,"AN":-878,"HH":-4070,"HM":-1711,"HN":4012,"HO":3761,"IA":1327,"IH":-1184,"II":-1332,"IK":1721,"IO":5492,"KI":3831,"KK":-8741,"MH":-3132,"MK":3334,"OO":-2920};
          this.BC3__ = {"HH":996,"HI":626,"HK":-721,"HN":-1307,"HO":-836,"IH":-301,"KK":2762,"MK":1079,"MM":4034,"OA":-1652,"OH":266};
          this.BP1__ = {"BB":295,"OB":304,"OO":-125,"UB":352};
          this.BP2__ = {"BO":60,"OO":-1762};
          this.BQ1__ = {"BHH":1150,"BHM":1521,"BII":-1158,"BIM":886,"BMH":1208,"BNH":449,"BOH":-91,"BOO":-2597,"OHI":451,"OIH":-296,"OKA":1851,"OKH":-1020,"OKK":904,"OOO":2965};
          this.BQ2__ = {"BHH":118,"BHI":-1159,"BHM":466,"BIH":-919,"BKK":-1720,"BKO":864,"OHH":-1139,"OHM":-181,"OIH":153,"UHI":-1146};
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            var c6 = ctype[i+2];
            score += this.ts_(this.UP1__[p1]);
            score += this.ts_(this.UP2__[p2]);
            score += this.ts_(this.UP3__[p3]);
            score += this.ts_(this.BP1__[p1 + p2]);
            score += this.ts_(this.BP2__[p2 + p3]);
            score += this.ts_(this.UW1__[w1]);
            score += this.ts_(this.UW2__[w2]);
            score += this.ts_(this.UW3__[w3]);
            score += this.ts_(this.UW4__[w4]);
            score += this.ts_(this.UW5__[w5]);
            score += this.ts_(this.UW6__[w6]);
            score += this.ts_(this.BW1__[w2 + w3]);
            score += this.ts_(this.BW2__[w3 + w4]);
            score += this.ts_(this.BW3__[w4 + w5]);
            score += this.ts_(this.TW1__[w1 + w2 + w3]);
            score += this.ts_(this.TW2__[w2 + w3 + w4]);
            score += this.ts_(this.TW3__[w3 + w4 + w5]);
            score += this.ts_(this.TW4__[w4 + w5 + w6]);
            score += this.ts_(this.UC1__[c1]);
            score += this.ts_(this.UC2__[c2]);
            score += this.ts_(this.UC3__[c3]);
            score += this.ts_(this.UC4__[c4]);
            score += this.ts_(this.UC5__[c5]);
            score += this.ts_(this.UC6__[c6]);
            score += this.ts_(this.BC1__[c2 + c3]);
            score += this.ts_(this.BC2__[c3 + c4]);
            score += this.ts_(this.BC3__[c4 + c5]);
            score += this.ts_(this.TC1__[c1 + c2 + c3]);
            score += this.ts_(this.TC2__[c2 + c3 + c4]);
            score += this.ts_(this.TC3__[c3 + c4 + c5]);
            score += this.ts_(this.TC4__[c4 + c5 + c6]);
        //  score += this.ts_(this.TC5__[c4 + c5 + c6]);    
            score += this.ts_(this.UQ1__[p1 + c1]);
            score += this.ts_(this.UQ2__[p2 + c2]);
            score += this.ts_(this.UQ3__[p3 + c3]);
            score += this.ts_(this.BQ1__[p2 + c2 + c3]);
            score += this.ts_(this.BQ2__[p2 + c3 + c4]);
            score += this.ts_(this.BQ3__[p3 + c2 + c3]);
            score += this.ts_(this.BQ4__[p3 + c3 + c4]);
            score += this.ts_(this.TQ1__[p2 + c1 + c2 + c3]);
            score += this.ts_(this.TQ2__[p2 + c2 + c3 + c4]);
            score += this.ts_(this.TQ3__[p3 + c1 + c2 + c3]);
            score += this.ts_(this.TQ4__[p3 + c2 + c3 + c4]);
            var p = "O";
            if (score > 0) {
              result.push(word);
              word = "";
              p = "B";
            }
            p1 = p2;
            p2 = p3;
            p3 = p;
            word += seg[i];
          }
          result.push(word);
          return result;
        }
        lunr.TinySegmenter = TinySegmenter;
    };
 }));
--- a/assets/javascripts/lunr/wordcut.js
+++ b/assets/javascripts/lunr/wordcut.js
--- a/assets/javascripts/workers/search.16e2a7d4.min.js
+++ b/assets/javascripts/workers/search.16e2a7d4.min.js
--- a/assets/javascripts/workers/search.16e2a7d4.min.js.map
+++ b/assets/javascripts/workers/search.16e2a7d4.min.js.map
--- a/assets/stylesheets/main.20d9efc8.min.css
+++ b/assets/stylesheets/main.20d9efc8.min.css
--- a/assets/stylesheets/main.20d9efc8.min.css.map
+++ b/assets/stylesheets/main.20d9efc8.min.css.map
--- a/assets/stylesheets/palette.815d1a91.min.css
+++ b/assets/stylesheets/palette.815d1a91.min.css
--- a/assets/stylesheets/palette.815d1a91.min.css.map
+++ b/assets/stylesheets/palette.815d1a91.min.css.map
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--- a/docs/thesis/figures/design-cycle2.drawio.png
+++ b/docs/thesis/figures/design-cycle2.drawio.png
--- a/examples/scibert/additional-files/additional-files.md
+++ b/examples/scibert/additional-files/additional-files.md
--- a/examples/scibert/additional-files/index.html
+++ b/examples/scibert/additional-files/index.html
--- a/examples/scibert/data/data.ipynb
+++ b/examples/scibert/data/data.ipynb
--- a/examples/scibert/data/index.html
+++ b/examples/scibert/data/index.html
--- a/examples/scibert/deploy/deploy.ipynb
+++ b/examples/scibert/deploy/deploy.ipynb
--- a/examples/scibert/deploy/index.html
+++ b/examples/scibert/deploy/index.html
--- a/examples/scibert/index.html
+++ b/examples/scibert/index.html
--- a/docs/examples/scibert/index.md
+++ b/docs/examples/scibert/index.md
--- a/examples/scibert/train/index.html
+++ b/examples/scibert/train/index.html
--- a/examples/scibert/train/train.ipynb
+++ b/examples/scibert/train/train.ipynb
--- a/docs/examples/simple-mag/mag-confusion.png
+++ b/docs/examples/simple-mag/mag-confusion.png
--- a/docs/examples/simple-mag/mag-distribution.png
+++ b/docs/examples/simple-mag/mag-distribution.png
--- a/examples/simple-mag/train/index.html
+++ b/examples/simple-mag/train/index.html
--- a/examples/simple-mag/train/train.ipynb
+++ b/examples/simple-mag/train/train.ipynb
--- a/examples/simple/data/data.ipynb
+++ b/examples/simple/data/data.ipynb
--- a/examples/simple/data/index.html
+++ b/examples/simple/data/index.html
--- a/examples/simple/deploy/deploy.ipynb
+++ b/examples/simple/deploy/deploy.ipynb
--- a/examples/simple/deploy/index.html
+++ b/examples/simple/deploy/index.html
--- a/docs/examples/simple/ss-confusion.png
+++ b/docs/examples/simple/ss-confusion.png
--- a/docs/examples/simple/ss-distribution.png
+++ b/docs/examples/simple/ss-distribution.png
--- a/examples/simple/stacked-bars/index.html
+++ b/examples/simple/stacked-bars/index.html
--- a/examples/simple/stacked-bars/stacked-bars.ipynb
+++ b/examples/simple/stacked-bars/stacked-bars.ipynb
--- a/examples/simple/train/index.html
+++ b/examples/simple/train/index.html
--- a/examples/simple/train/train.ipynb
+++ b/examples/simple/train/train.ipynb
--- a/explanation/explanation.md
+++ b/explanation/explanation.md
--- a/explanation/index.html
+++ b/explanation/index.html
@ -0,0 +1,950 @@
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        <meta name="description" content="GreatAI helps you easily transform your prototype AI code into production-ready software.">
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 <h1 id="explanation">Explanation<a class="headerlink" href="#explanation" title="Permanent link">#</a></h1>
 <p>A lot more details and discussion about the problem context and approaches of GreatAI, along with its evaluation, can be found in my thesis.</p>
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      <div class="md-dialog__inner md-typeset"></div>
    </div>
    <script id="__config" type="application/json">{"base": "..", "features": ["content.code.annotate", "content.tooltips"], "search": "../assets/javascripts/workers/search.16e2a7d4.min.js", "translations": {"clipboard.copied": "Copied to clipboard", "clipboard.copy": "Copy to clipboard", "search.config.lang": "en", "search.config.pipeline": "trimmer, stopWordFilter", "search.config.separator": "[\\s\\-]+", "search.placeholder": "Search", "search.result.more.one": "1 more on this page", "search.result.more.other": "# more on this page", "search.result.none": "No matching documents", "search.result.one": "1 matching document", "search.result.other": "# matching documents", "search.result.placeholder": "Type to start searching", "search.result.term.missing": "Missing", "select.version.title": "Select version"}}</script>
      <script src="../assets/javascripts/bundle.d6c3db9e.min.js"></script>
  </body>
 </html>
--- a/great_ai/init.py
+++ b/great_ai/init.py
@ -1,32 +0,0 @@
 """Transform your prototype AI code into production-ready software."""
 __version__ = "0.1.11"
 from .context import configure
 from .deploy import GreatAI
 from .errors import (
    ArgumentValidationError,
    MissingArgumentError,
    RemoteCallError,
    WrongDecoratorOrderError,
 )
 from .models.save_model import save_model
 from .models.use_model import use_model
 from .parameters.log_metric import log_metric
 from .parameters.parameter import parameter
 from .persistence.mongodb_driver import MongoDbDriver
 from .persistence.parallel_tinydb_driver import ParallelTinyDbDriver
 from .persistence.tracing_database_driver import TracingDatabaseDriver
 from .remote.call_remote_great_ai import call_remote_great_ai
 from .remote.call_remote_great_ai_async import call_remote_great_ai_async
 from .tracing.add_ground_truth import add_ground_truth
 from .tracing.delete_ground_truth import delete_ground_truth
 from .tracing.query_ground_truth import query_ground_truth
 from .views import RouteConfig, Trace
 from .views.outputs.classification_output import ClassificationOutput
 from .views.outputs.multi_label_classification_output import (
    MultiLabelClassificationOutput,
 )
 from .views.outputs.regression_output import RegressionOutput
 from .views.outputs.sequence_labeling_output import SequenceLabelingOutput
--- a/great_ai/main.py
+++ b/great_ai/main.py
@ -1,223 +0,0 @@
 #!/usr/bin/env python3
 import logging
 import re
 from importlib import import_module, reload
 from pathlib import Path
 from threading import Event
 from typing import Optional
 import uvicorn
 from uvicorn._subprocess import get_subprocess
 from uvicorn.config import LOGGING_CONFIG, Config
 from uvicorn.supervisors import BaseReload, Multiprocess
 from watchdog.events import FileSystemEvent, PatternMatchingEventHandler
 from watchdog.observers import Observer
 from great_ai.constants import SERVER_NAME
 from great_ai.context import _is_in_production_mode
 from great_ai.deploy import GreatAI
 from great_ai.errors import ArgumentValidationError, MissingArgumentError
 from great_ai.utilities import get_logger
 from .parse_arguments import parse_arguments
 logger = get_logger(SERVER_NAME)
 GREAT_AI_LOGGING_CONFIG = {
    **LOGGING_CONFIG,
    "formatters": {
        "default": {
            "()": "great_ai.utilities.logger.get_logger.CustomFormatter",
            "fmt": "%(asctime)s | %(levelname)8s | %(message)s",
        },
        "access": {
            "()": "great_ai.utilities.logger.get_logger.CustomFormatter",
            "fmt": "%(asctime)s | %(levelname)8s | %(message)s",
        },
    },
 }
 def serve() -> None:
    args = parse_arguments()
    should_auto_reload = not _is_in_production_mode(logger=None)
    if args.worker_count > 1 and should_auto_reload:
        raise ArgumentValidationError(
            "Cannot use auto-reload with multiple worker_count: set the `--worker_count=1` CLI argument,"
            + "or set the ENVIRONMENT environment variable to `production`."
        )
    common_config = dict(
        host=args.host,
        port=args.port,
        timeout_keep_alive=args.timeout_keep_alive,
        workers=args.worker_count,
        server_header=False,
        reload=False,
        log_config=GREAT_AI_LOGGING_CONFIG,
    )
    if not should_auto_reload:
        file_name = get_script_name(args.file_name)
        app = find_app(file_name)
        logger.info(f"Starting uvicorn server with app={app}")
        config = Config(app, **common_config)
        socket = config.bind_socket()
        try:
            Multiprocess(
                config, target=uvicorn.Server(config=config).run, sockets=[socket]
            ).run()
        finally:
            if args.file_name.endswith(".ipynb"):
                try:
                    Path(get_script_name_of_notebook(args.file_name)).unlink()
                except FileNotFoundError:
                    # missing_ok only exists >= Python 3.8
                    pass
    else:
        class EventHandler(PatternMatchingEventHandler):
            def __init__(self) -> None:
                super().__init__(
                    patterns=["*.py", "*.ipynb"], ignore_patterns=["__*.py"]
                )
                self.server: Optional[GreatAIReload] = None
                self.restart()
            def on_closed(self, event: FileSystemEvent) -> None:
                logger.warning(f"File {event.src_path} has triggered a restart")
                self.restart()
            def restart(self) -> None:
                file_name = get_script_name(args.file_name)
                app = find_app(file_name)
                if app is None:
                    logger.warning("Auto-reloading skipped")
                    return
                self.stop_server()
                config = Config(app, **common_config)
                socket = config.bind_socket()
                self.server = GreatAIReload(
                    config, target=uvicorn.Server(config=config).run, sockets=[socket]
                )
                self.server.startup()
            def stop_server(self) -> None:
                if self.server:
                    self.server.shutdown()
        restart_handler = EventHandler()
        observer = Observer()
        observer.schedule(restart_handler, path=".", recursive=True)
        observer.start()
        try:
            Event().wait()
        finally:
            observer.stop()
            restart_handler.stop_server()
            if args.file_name.endswith(".ipynb"):
                try:
                    Path(get_script_name_of_notebook(args.file_name)).unlink()
                except FileNotFoundError:
                    # missing_ok only exists >= Python 3.8
                    pass
            observer.join()
 def get_script_name(file_name_argument: str) -> str:
    if file_name_argument.endswith(".ipynb"):
        logger.info("Converting notebook to Python script")
        from nbconvert import PythonExporter
        exporter = PythonExporter()
        content, _ = exporter.from_filename(file_name_argument)
        content = re.sub(r".*get_ipython\(.*", "", content)
        file_name_argument = get_script_name_of_notebook(file_name_argument)
        with open(file_name_argument, "w", encoding="utf-8") as f:
            f.write(content)
    return re.sub(r"\.(py|ipynb)$", "", file_name_argument)
 def get_script_name_of_notebook(notebook_name: str) -> str:
    base_name = re.sub(r"\.ipynb$", "", notebook_name)
    return f"__{base_name}__.py"
 module = None
 def find_app(file_name: str) -> Optional[str]:
    global module
    logging.disable(logging.CRITICAL)
    try:
        if module is None:
            module = import_module(file_name)
        else:
            module = reload(module)
    except Exception:
        logging.disable(logging.NOTSET)
        logger.exception("Could not load file because of an exception: fix your code")
        return None
    finally:
        logging.disable(logging.NOTSET)
    app_name = None
    for name, value in module.__dict__.items():
        if isinstance(value, GreatAI):
            app_name = name
    if app_name:
        logger.info(f"Found `{app_name}` to be the GreatAI app ")
    else:
        raise MissingArgumentError(
            "GreatAI app could not be found, make sure to use `@GreatAI.create` on your prediction function"
        )
    return f"{file_name}:{app_name}.app"
 class GreatAIReload(BaseReload):
    def startup(self) -> None:
        self.process = get_subprocess(
            config=self.config, target=self.target, sockets=self.sockets
        )
        self.process.start()
    def shutdown(self) -> None:
        self.process.terminate()
        self.process.join()
        for sock in self.sockets:
            sock.close()
 def main() -> None:
    import os
    import sys
    sys.path.append(os.getcwd())
    try:
        serve()
    except KeyboardInterrupt:
        exit()
    except Exception as e:
        logger.error(e)
 if __name__ == "__main__":
    main()
--- a/great_ai/constants.py
+++ b/great_ai/constants.py
@ -1,31 +0,0 @@
 from .large_file import LargeFileMongo, LargeFileS3
 from .persistence.mongodb_driver import MongoDbDriver
 ENV_VAR_KEY = "ENVIRONMENT"
 PRODUCTION_KEY = "production"
 DASHBOARD_PATH = "/dashboard"
 MONGO_CONFIG_PATHS = ["mongodb.ini", "mongo.ini", "mongo_db.ini", "mongo-db.ini"]
 DEFAULT_TRACING_DATABASE_CONFIG_PATHS = {
    MongoDbDriver: MONGO_CONFIG_PATHS,
 }
 DEFAULT_LARGE_FILE_CONFIG_PATHS = {
    LargeFileS3: ["s3.ini", "b2.ini"],
    LargeFileMongo: MONGO_CONFIG_PATHS,
 }
 GITHUB_LINK = "https://github.com/schmelczer/great_ai"
 TRAIN_SPLIT_TAG_NAME = "train"
 TEST_SPLIT_TAG_NAME = "test"
 VALIDATION_SPLIT_TAG_NAME = "validation"
 GROUND_TRUTH_TAG_NAME = "ground_truth"
 PRODUCTION_TAG_NAME = "production"
 DEVELOPMENT_TAG_NAME = "development"
 ONLINE_TAG_NAME = "online"
 SERVER_NAME = "GreatAI-Server"
 SE4ML_WEBSITE = "https://se-ml.github.io/practices"
 LIST_ITEM_PREFIX = "  🔩 "
--- a/great_ai/context.py
+++ b/great_ai/context.py
@ -1,242 +0,0 @@
 import os
 import random
 from logging import DEBUG, Logger
 from pathlib import Path
 from typing import Any, Dict, Optional, Type, Union, cast
 from pydantic import BaseModel, ConfigDict
 from great_ai import __version__
 from .constants import (
    DEFAULT_LARGE_FILE_CONFIG_PATHS,
    DEFAULT_TRACING_DATABASE_CONFIG_PATHS,
    ENV_VAR_KEY,
    LIST_ITEM_PREFIX,
    PRODUCTION_KEY,
    SE4ML_WEBSITE,
 )
 from .large_file import LargeFileBase, LargeFileLocal
 from .persistence.parallel_tinydb_driver import ParallelTinyDbDriver
 from .persistence.tracing_database_driver import TracingDatabaseDriver
 from .utilities import get_logger
 from .views import RouteConfig
 class Context(BaseModel):
    version: Union[int, str]
    tracing_database: TracingDatabaseDriver
    large_file_implementation: Type[LargeFileBase]
    is_production: bool
    logger: Logger
    should_log_exception_stack: bool
    prediction_cache_size: int
    dashboard_table_size: int
    route_config: RouteConfig
    model_config = ConfigDict(arbitrary_types_allowed=True)
    def to_flat_dict(self) -> Dict[str, Any]:
        return {
            "tracing_database": type(self.tracing_database).__name__,
            "large_file_implementation": self.large_file_implementation.__name__,
            "is_production": self.is_production,
            "should_log_exception_stack": self.should_log_exception_stack,
            "prediction_cache_size": self.prediction_cache_size,
            "dashboard_table_size": self.dashboard_table_size,
        }
 _context: Optional[Context] = None
 def get_context() -> Context:
    if _context is None:
        configure()
    return cast(Context, _context)
 def configure(
    *,
    version: Union[int, str] = "0.0.1",
    log_level: int = DEBUG,
    seed: int = 42,
    tracing_database_factory: Optional[Type[TracingDatabaseDriver]] = None,
    large_file_implementation: Optional[Type[LargeFileBase]] = None,
    should_log_exception_stack: Optional[bool] = None,
    prediction_cache_size: int = 512,
    disable_se4ml_banner: bool = False,
    dashboard_table_size: int = 50,
    route_config: RouteConfig = RouteConfig(),
 ) -> None:
    """Set the global configuration used by the great-ai library.
    You must call `configure` before calling (or decorating with) any other great-ai
    function.
    If `tracing_database_factory` or `large_file_implementation` is not specified, their
    default value is determined based on which TracingDatabase and LargeFile has been
    configured (e.g.: LargeFileS3.configure_credentials_from_file('s3.ini')), or whether
    there is any file named s3.ini or mongo.ini in the working directory.
    Examples:
        >>> configure(prediction_cache_size=0)
    Arguments:
        version: The version of your application (using SemVer is recommended).
        log_level: Set the default logging level of `logging`.
        seed: Set seed of `random` (and `numpy` if installed) for reproducibility.
        tracing_database_factory: Specify a different TracingDatabaseDriver than the one
            already configured.
        large_file_implementation: Specify a different LargeFile than the one already
            configured.
        should_log_exception_stack: Log the traces of unhandled exceptions.
        prediction_cache_size: Size of the LRU cache applied over the prediction
            functions.
        disable_se4ml_banner: Turn off the warning about the importance of SE4ML best-
            practices.
        dashboard_table_size: Number of rows to display in the dashboard's table.
        route_config: Enable or disable specific HTTP API endpoints.
    """
    global _context
    logger = get_logger("great_ai", level=log_level)
    if _context is not None:
        logger.error(
            "Configuration has been already initialised, overwriting.\n"
            + "Make sure to call `configure()` before importing your application code."
        )
    is_production = _is_in_production_mode(logger=logger)
    _set_seed(seed)
    tracing_database_factory = _initialize_tracing_database(
        tracing_database_factory, logger=logger
    )
    tracing_database = tracing_database_factory()
    if not tracing_database.is_production_ready:
        message = f"""The selected tracing database ({
            tracing_database_factory.__name__
        }) is not recommended for production"""
        if is_production:
            logger.error(message)
        else:
            logger.warning(message)
    _context = Context(
        version=version,
        tracing_database=tracing_database,
        large_file_implementation=_initialize_large_file(
            large_file_implementation, logger=logger
        ),
        is_production=is_production,
        logger=logger,
        should_log_exception_stack=(
            not is_production
            if should_log_exception_stack is None
            else should_log_exception_stack
        ),
        prediction_cache_size=prediction_cache_size,
        dashboard_table_size=dashboard_table_size,
        route_config=route_config,
    )
    logger.info(f"GreatAI (v{__version__}): configured ✅")
    for k, v in get_context().to_flat_dict().items():
        logger.info(f"{LIST_ITEM_PREFIX}{k}: {v}")
    if not is_production and not disable_se4ml_banner:
        logger.warning(
            "You still need to check whether you follow all best practices before "
            "trusting your deployment."
        )
        logger.warning(f"> Find out more at {SE4ML_WEBSITE}")
 def _is_in_production_mode(logger: Optional[Logger]) -> bool:
    environment = os.environ.get(ENV_VAR_KEY)
    if environment is None:
        if logger:
            logger.warning(
                f"Environment variable {ENV_VAR_KEY} is not set, "
                "defaulting to development mode ‼️"
            )
        is_production = False
    else:
        is_production = environment.lower() == PRODUCTION_KEY
        if logger:
            if not is_production:
                logger.info(
                    f"Value of {ENV_VAR_KEY} is `{environment}` which is not equal to"
                    + f"`{PRODUCTION_KEY}` defaulting to development mode ‼️"
                )
            else:
                logger.info("Running in production mode ✅")
    return is_production
 def _initialize_tracing_database(
    selected: Optional[Type[TracingDatabaseDriver]], logger: Logger
 ) -> Type[TracingDatabaseDriver]:
    for tracing_driver, paths in DEFAULT_TRACING_DATABASE_CONFIG_PATHS.items():
        if selected is None or selected == tracing_driver:
            if tracing_driver.initialized:
                logger.info(
                    f"{tracing_driver.__name__} has been already configured: "
                    "skipping initialisation"
                )
                return tracing_driver
            for p in paths:
                if Path(p).exists():
                    logger.info(
                        f"""Found credentials file ({Path(p).absolute()}), initialising {
                            tracing_driver.__name__
                        }"""
                    )
                    tracing_driver.configure_credentials_from_file(p)
                    return tracing_driver
    logger.warning(
        "Cannot find credentials files, defaulting to using ParallelTinyDbDriver"
    )
    return ParallelTinyDbDriver
 def _initialize_large_file(
    selected: Optional[Type[LargeFileBase]], logger: Logger
 ) -> Type[LargeFileBase]:
    for large_file, paths in DEFAULT_LARGE_FILE_CONFIG_PATHS.items():
        if selected is None or selected == large_file:
            if large_file.initialized:
                logger.info(
                    f"{large_file.__name__} has been already configured: skipping initialisation"
                )
                return large_file
            for p in paths:
                if Path(p).exists():
                    logger.info(
                        f"""Found credentials file ({Path(p).absolute()}), initialising {
                            large_file.__name__
                        }"""
                    )
                    large_file.configure_credentials_from_file(p)
                    return large_file
    logger.warning("Cannot find credentials files, defaulting to using LargeFileLocal")
    return LargeFileLocal
 def _set_seed(seed: int) -> None:
    random.seed(seed)
    try:
        import numpy
        numpy.random.seed(seed + 1)
    except ImportError:
        pass
--- a/great_ai/deploy/init.py
+++ b/great_ai/deploy/init.py
@ -1 +0,0 @@
 from .great_ai import GreatAI
--- a/great_ai/deploy/great_ai.py
+++ b/great_ai/deploy/great_ai.py
@ -1,319 +0,0 @@
 from functools import lru_cache, wraps
 from textwrap import dedent
 from typing import (
    Any,
    Awaitable,
    Callable,
    Generic,
    List,
    Literal,
    Optional,
    Sequence,
    Tuple,
    TypeVar,
    Union,
    cast,
    overload,
 )
 from fastapi import FastAPI
 from tqdm import tqdm
 from ..constants import DASHBOARD_PATH
 from ..context import get_context
 from ..external.async_lru import alru_cache
 from ..helper import freeze_arguments, get_function_metadata_store, snake_case_to_text
 from ..models.use_model import model_versions
 from ..parameters.automatically_decorate_parameters import (
    automatically_decorate_parameters,
 )
 from ..tracing.tracing_context import TracingContext
 from ..utilities import parallel_map
 from ..views import ApiMetadata, Trace
 from .routes.bootstrap_dashboard import bootstrap_dashboard
 from .routes.bootstrap_docs_endpoints import bootstrap_docs_endpoints
 from .routes.bootstrap_feedback_endpoints import bootstrap_feedback_endpoints
 from .routes.bootstrap_meta_endpoints import bootstrap_meta_endpoints
 from .routes.bootstrap_prediction_endpoint import bootstrap_prediction_endpoint
 from .routes.bootstrap_trace_endpoints import bootstrap_trace_endpoints
 T = TypeVar("T", bound=Union[Trace, Awaitable[Trace]])
 V = TypeVar("V")
 class GreatAI(Generic[T, V]):
    """Wrapper for a prediction function providing the implementation of SE4ML best practices.
    Provides caching (with argument freezing), a TracingContext during execution, the
    scaffolding of HTTP endpoints using FastAPI and a dashboard using Dash.
    IMPORTANT: when a request is served from cache, no new trace is created. Thus, the
    same trace can be returned multiple times. If this is undesirable turn off caching
    using `configure(prediction_cache_size=0)`.
    Supports wrapping async and synchronous functions while also maintaining correct
    typing.
    Attributes:
        app: FastAPI instance wrapping the scaffolded endpoints and the Dash app.
        version: SemVer derived from the app's version and the model names and versions
            registered through use_model.
    """
    __name__: str  # help for MyPy
    __doc__: str  # help for MyPy
    def __init__(
        self,
        func: Callable[..., Union[V, Awaitable[V]]],
    ):
        """Do not call this function directly, use GreatAI.create instead."""
        func = automatically_decorate_parameters(func)
        get_function_metadata_store(func).is_finalised = True
        self._cached_func = self._get_cached_traced_function(func)
        self._wrapped_func = wraps(func)(freeze_arguments(self._cached_func))
        wraps(func)(self)
        self.__doc__ = (
            f"GreatAI wrapper for interacting with the `{self.__name__}` "
            + f"function.\n\n{dedent(self.__doc__ or '')}"
        )
        self.version = str(get_context().version)
        flat_model_versions = ".".join(f"{k}-v{v}" for k, v in model_versions)
        if flat_model_versions:
            self.version += f"+{flat_model_versions}"
        self.app = FastAPI(
            title=snake_case_to_text(self.__name__),
            version=self.version,
            description=self.__doc__
            + f"\n\nFind out more in the [dashboard]({DASHBOARD_PATH}).",
            docs_url=None,
            redoc_url=None,
        )
        self._bootstrap_rest_api()
    @overload
    @staticmethod
    def create(  # type: ignore
        # Overloaded function signatures 1 and 2 overlap with incompatible return types
        # https://github.com/python/mypy/issues/12759
        func: Callable[..., Awaitable[V]],
    ) -> "GreatAI[Awaitable[Trace[V]], V]": ...
    @overload
    @staticmethod
    def create(
        func: Callable[..., V],
    ) -> "GreatAI[Trace[V], V]": ...
    @staticmethod
    def create(
        func: Union[Callable[..., Awaitable[V]], Callable[..., V]],
    ) -> Union["GreatAI[Awaitable[Trace[V]], V]", "GreatAI[Trace[V], V]"]:
        """Decorate a function by wrapping it in a GreatAI instance.
        The function can be typed, synchronous or async. If it has
        unwrapped parameters (parameters not affected by a
        [@parameter][great_ai.parameter] or [@use_model][great_ai.use_model] decorator),
        those will be automatically wrapped.
        The return value is replaced by a Trace (or Awaitable[Trace]),
        while the original return value is available under the `.output`
        property.
        For configuration options, see [great_ai.configure][].
        Examples:
            >>> @GreatAI.create
            ... def my_function(a):
            ...     return a + 2
            >>> my_function(3).output
            5
            >>> @GreatAI.create
            ... def my_function(a: int) -> int:
            ...     return a + 2
            >>> my_function(3)
            Trace[int]...
            >>> my_function('3').output
            Traceback (most recent call last):
                ...
            TypeError: argument a is not of the expected type: ...
        Args:
            func: The prediction function that needs to be decorated.
        Returns:
            A GreatAI instance wrapping `func`.
        """
        return GreatAI[Trace[V], V](
            func,
        )
    def __call__(self, *args: Any, **kwargs: Any) -> T:
        return self._wrapped_func(*args, **kwargs)
    @overload
    def process_batch(
        self,
        batch: Sequence[Tuple],
        *,
        concurrency: Optional[int] = None,
        unpack_arguments: Literal[True],
        do_not_persist_traces: bool = ...,
    ) -> List[Trace[V]]: ...
    @overload
    def process_batch(
        self,
        batch: Sequence,
        *,
        concurrency: Optional[int] = None,
        unpack_arguments: Literal[False] = ...,
        do_not_persist_traces: bool = ...,
    ) -> List[Trace[V]]: ...
    def process_batch(
        self,
        batch: Sequence,
        *,
        concurrency: Optional[int] = None,
        unpack_arguments: bool = False,
        do_not_persist_traces: bool = False,
    ) -> List[Trace[V]]:
        """Map the wrapped function over a list of input_values (`batch`).
        A wrapper over [parallel_map][great_ai.utilities.parallel_map.parallel_map.parallel_map]
        providing type-safety and a progressbar through tqdm.
        Args:
            batch: A list of arguments for the original (wrapped) function. If the
                function expects multiple arguments, provide a list of tuples and set
                `unpack_arguments=True`.
            concurrency: Number of processes to start. Don't set it too much higher than
                the number of available CPU cores.
            unpack_arguments: Expect a list of tuples and unpack the tuples before
                giving them to the wrapped function.
            do_not_persist_traces: Don't save the traces in the database. Useful for
                evaluations run part of the CI.
        """
        wrapped_function = self._wrapped_func
        def inner(value: Any) -> T:
            return (
                wrapped_function(*value, do_not_persist_traces=do_not_persist_traces)
                if unpack_arguments
                else wrapped_function(
                    value, do_not_persist_traces=do_not_persist_traces
                )
            )
        async def inner_async(value: Any) -> T:
            return await cast(
                Awaitable,
                (
                    wrapped_function(
                        *value, do_not_persist_traces=do_not_persist_traces
                    )
                    if unpack_arguments
                    else wrapped_function(
                        value, do_not_persist_traces=do_not_persist_traces
                    )
                ),
            )
        # inner/inner_async return the class' T (bound to Trace | Awaitable[Trace]);
        # in this method T resolves to Trace[V], but that is not provable to mypy, so
        # cast to the concrete shape parallel_map expects.
        map_function = cast(
            Callable[[Any], Union[Trace[V], Awaitable[Trace[V]]]],
            inner_async if get_function_metadata_store(self).is_asynchronous else inner,
        )
        return list(
            tqdm(
                parallel_map(map_function, batch, concurrency=concurrency),
                total=len(batch),
            )
        )
    @staticmethod
    def _get_cached_traced_function(
        func: Callable[..., Union[V, Awaitable[V]]],
    ) -> Callable[..., T]:
        @lru_cache(maxsize=get_context().prediction_cache_size)
        def func_in_tracing_context_sync(
            *args: Any,
            do_not_persist_traces: bool = False,
            **kwargs: Any,
        ) -> T:
            with TracingContext[V](
                func.__name__, do_not_persist_traces=do_not_persist_traces
            ) as t:
                result = func(*args, **kwargs)
                return cast(T, t.finalise(output=result))
        @alru_cache(maxsize=get_context().prediction_cache_size)
        async def func_in_tracing_context_async(
            *args: Any,
            do_not_persist_traces: bool = False,
            **kwargs: Any,
        ) -> T:
            with TracingContext[V](
                func.__name__, do_not_persist_traces=do_not_persist_traces
            ) as t:
                result = await cast(Callable[..., Awaitable], func)(*args, **kwargs)
                return cast(T, t.finalise(output=result))
        return cast(
            Callable[..., T],
            (
                func_in_tracing_context_async
                if get_function_metadata_store(func).is_asynchronous
                else func_in_tracing_context_sync
            ),
        )
    def _bootstrap_rest_api(
        self,
    ) -> None:
        route_config = get_context().route_config
        if route_config.prediction_endpoint_enabled:
            bootstrap_prediction_endpoint(self.app, self._wrapped_func)
        if route_config.docs_endpoints_enabled:
            bootstrap_docs_endpoints(self.app)
        if route_config.dashboard_enabled:
            bootstrap_dashboard(
                self.app,
                function_name=self.__name__,
                documentation=self.__doc__,
            )
        if route_config.trace_endpoints_enabled:
            bootstrap_trace_endpoints(self.app)
        if route_config.feedback_endpoints_enabled:
            bootstrap_feedback_endpoints(self.app)
        if route_config.meta_endpoints_enabled:
            bootstrap_meta_endpoints(
                self.app,
                self._cached_func,
                ApiMetadata(
                    name=self.__name__,
                    version=self.version,
                    documentation=self.__doc__,
                    configuration=get_context().to_flat_dict(),
                ),
            )
--- a/great_ai/deploy/routes/init.py
+++ b/great_ai/deploy/routes/init.py
@ -1,6 +0,0 @@
 from .bootstrap_dashboard import bootstrap_dashboard
 from .bootstrap_docs_endpoints import bootstrap_docs_endpoints
 from .bootstrap_feedback_endpoints import bootstrap_feedback_endpoints
 from .bootstrap_meta_endpoints import bootstrap_meta_endpoints
 from .bootstrap_prediction_endpoint import bootstrap_prediction_endpoint
 from .bootstrap_trace_endpoints import bootstrap_trace_endpoints
--- a/great_ai/deploy/routes/bootstrap_dashboard.py
+++ b/great_ai/deploy/routes/bootstrap_dashboard.py
@ -1,35 +0,0 @@
 from pathlib import Path
 from a2wsgi import WSGIMiddleware
 from fastapi import FastAPI
 from fastapi.responses import FileResponse, RedirectResponse
 from fastapi.staticfiles import StaticFiles
 from ...constants import DASHBOARD_PATH
 from .dashboard.create_dash_app import create_dash_app
 PATH = Path(__file__).parent.resolve()
 def bootstrap_dashboard(app: FastAPI, function_name: str, documentation: str) -> None:
    dash_app = create_dash_app(function_name, app.version, documentation)
    @app.get("/favicon.ico", include_in_schema=False)
    @app.get(f"{DASHBOARD_PATH}/_favicon.ico", include_in_schema=False)
    def get_favicon() -> FileResponse:
        return FileResponse(PATH / "dashboard/assets/favicon.ico")
    # a2wsgi types its WSGI input (Flask) and ASGI output more strictly than
    # Starlette's loose WSGIApp/ASGIApp aliases, so mypy flags the mount despite
    # this being the canonical, runtime-correct way to bridge the Dash app.
    app.mount(DASHBOARD_PATH, WSGIMiddleware(dash_app))  # type: ignore[arg-type]
    @app.get("/", include_in_schema=False)
    def redirect_to_entrypoint() -> RedirectResponse:
        return RedirectResponse(DASHBOARD_PATH)
    app.mount(
        "/assets",
        StaticFiles(directory=PATH / "dashboard/assets"),
        name="static",
    )
--- a/great_ai/deploy/routes/bootstrap_docs_endpoints.py
+++ b/great_ai/deploy/routes/bootstrap_docs_endpoints.py
@ -1,18 +0,0 @@
 from fastapi import FastAPI
 from fastapi.openapi.docs import get_swagger_ui_html
 from fastapi.responses import RedirectResponse
 from starlette.responses import HTMLResponse
 def bootstrap_docs_endpoints(app: FastAPI) -> None:
    @app.get("/docs", include_in_schema=False)
    def custom_swagger_ui_html() -> HTMLResponse:
        return get_swagger_ui_html(
            openapi_url="openapi.json",
            title=app.title,
            swagger_favicon_url="/favicon.ico",
        )
    @app.get("/docs/index.html", include_in_schema=False)
    def redirect_to_docs() -> RedirectResponse:
        return RedirectResponse("/docs")
--- a/great_ai/deploy/routes/bootstrap_feedback_endpoints.py
+++ b/great_ai/deploy/routes/bootstrap_feedback_endpoints.py
@ -1,44 +0,0 @@
 from typing import Any
 from fastapi import APIRouter, FastAPI, HTTPException, Response, status
 from ...context import get_context
 from ...views import EvaluationFeedbackRequest
 def bootstrap_feedback_endpoints(app: FastAPI) -> None:
    router = APIRouter(
        prefix="/traces/{trace_id}/feedback",
        tags=["feedback"],
    )
    @router.put("/", status_code=status.HTTP_202_ACCEPTED)
    def set_feedback(trace_id: str, input: EvaluationFeedbackRequest) -> Response:
        trace = get_context().tracing_database.get(trace_id)
        if trace is None:
            raise HTTPException(status_code=status.HTTP_404_NOT_FOUND)
        trace.feedback = input.feedback
        get_context().tracing_database.update(trace_id, trace)
        return Response(status_code=status.HTTP_202_ACCEPTED)
    @router.get("/", status_code=status.HTTP_200_OK)
    def get_feedback(trace_id: str) -> Any:
        trace = get_context().tracing_database.get(trace_id)
        if trace is None:
            raise HTTPException(status_code=status.HTTP_404_NOT_FOUND)
        return trace.feedback
    @router.delete("/", status_code=status.HTTP_204_NO_CONTENT)
    def delete_feedback(trace_id: str) -> Response:
        trace = get_context().tracing_database.get(trace_id)
        if trace is None:
            raise HTTPException(status_code=status.HTTP_404_NOT_FOUND)
        trace.feedback = None
        get_context().tracing_database.update(trace_id, trace)
        return Response(status_code=status.HTTP_204_NO_CONTENT)
    app.include_router(router)
--- a/great_ai/deploy/routes/bootstrap_meta_endpoints.py
+++ b/great_ai/deploy/routes/bootstrap_meta_endpoints.py
@ -1,26 +0,0 @@
 from typing import Any
 from fastapi import APIRouter, FastAPI, status
 from ...views import ApiMetadata, CacheStatistics, HealthCheckResponse
 def bootstrap_meta_endpoints(app: FastAPI, func: Any, metadata: ApiMetadata) -> None:
    router = APIRouter(
        tags=["meta"],
    )
    @router.get("/health", status_code=status.HTTP_200_OK)
    def check_health() -> HealthCheckResponse:
        hits, misses, maxsize, cache_size = func.cache_info()
        cache_statistics = CacheStatistics(
            hits=hits, misses=misses, size=cache_size, max_size=maxsize
        )
        return HealthCheckResponse(is_healthy=True, cache_statistics=cache_statistics)
    @router.get("/version", response_model=ApiMetadata, status_code=status.HTTP_200_OK)
    def get_version() -> ApiMetadata:
        return metadata
    app.include_router(router)
--- a/great_ai/deploy/routes/bootstrap_prediction_endpoint.py
+++ b/great_ai/deploy/routes/bootstrap_prediction_endpoint.py
@ -1,51 +0,0 @@
 import inspect
 from typing import Any, Awaitable, Callable, Type, Union, cast
 from fastapi import APIRouter, FastAPI, HTTPException, status
 from pydantic import BaseModel, create_model
 from ...helper import get_function_metadata_store
 from ...views import Trace
 def bootstrap_prediction_endpoint(
    app: FastAPI, func: Callable[..., Union[Trace, Awaitable[Trace]]]
 ) -> None:
    router = APIRouter(
        tags=["predictions"],
    )
    schema = _get_schema(func)
    @router.post("/predict", status_code=status.HTTP_200_OK, response_model=Trace)
    async def predict(input_value: schema) -> Trace:  # type: ignore
        try:
            if inspect.iscoroutinefunction(func):
                return await cast(Callable[..., Awaitable[Trace]], func)(
                    **cast(BaseModel, input_value).model_dump()
                )
            return cast(Callable[..., Trace], func)(
                **cast(BaseModel, input_value).model_dump()
            )
        except Exception as e:
            raise HTTPException(
                status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
                detail=f"The following exception has occurred: {type(e).__name__}: {e}",
            )
    app.include_router(router)
 def _get_schema(func: Callable) -> Type[BaseModel]:
    signature = inspect.signature(func)
    parameters = {
        p.name: (
            p.annotation if p.annotation != inspect._empty else Any,
            p.default if p.default != inspect._empty else ...,
        )
        for p in signature.parameters.values()
        if p.name in get_function_metadata_store(func).input_parameter_names
    }
    schema: Type[BaseModel] = create_model("InputModel", **parameters)  # type: ignore
    return schema
--- a/great_ai/deploy/routes/bootstrap_trace_endpoints.py
+++ b/great_ai/deploy/routes/bootstrap_trace_endpoints.py
@ -1,44 +0,0 @@
 from typing import List
 from fastapi import APIRouter, FastAPI, HTTPException, Response, status
 from ...context import get_context
 from ...views import Query, Trace
 def bootstrap_trace_endpoints(app: FastAPI) -> None:
    router = APIRouter(
        prefix="/traces",
        tags=["traces"],
    )
    @router.post("", status_code=status.HTTP_200_OK, response_model=List[Trace])
    def query_traces(
        query: Query,
        skip: int = 0,
        take: int = 100,
    ) -> List[Trace]:
        return get_context().tracing_database.query(
            conjunctive_filters=query.filter,
            conjunctive_tags=query.conjunctive_tags,
            since=query.since,
            until=query.until,
            has_feedback=query.has_feedback,
            sort_by=query.sort,
            skip=skip,
            take=take,
        )[0]
    @router.get("/{trace_id}", status_code=status.HTTP_200_OK, response_model=Trace)
    def get_trace(trace_id: str) -> Trace:
        result = get_context().tracing_database.get(trace_id)
        if result is None:
            raise HTTPException(status_code=status.HTTP_404_NOT_FOUND)
        return result
    @router.delete("/{trace_id}", status_code=status.HTTP_204_NO_CONTENT)
    def delete_trace(trace_id: str) -> Response:
        get_context().tracing_database.delete(trace_id)
        return Response(status_code=status.HTTP_204_NO_CONTENT)
    app.include_router(router)
--- a/great_ai/deploy/routes/dashboard/init.py
+++ b/great_ai/deploy/routes/dashboard/init.py
@ -1 +0,0 @@
 from .create_dash_app import create_dash_app
--- a/great_ai/deploy/routes/dashboard/assets/favicon.ico
+++ b/great_ai/deploy/routes/dashboard/assets/favicon.ico
--- a/great_ai/deploy/routes/dashboard/assets/index.css
+++ b/great_ai/deploy/routes/dashboard/assets/index.css
@ -1,235 +0,0 @@
 :root {
  --background-color: #edf5f6;
  --dark-background-color: #e7eced;
  --light-text-color: #747879;
  --small-padding: 10px;
  --medium-padding: 20px;
  --large-padding: 40px;
  --border-radius: 10px;
  --shadow: 0 4px 6px -1px rgb(0 0 0 / 10%), 0 2px 4px -1px rgb(0 0 0 / 6%);
  --disclaimer-width: 180px;
  --disclaimer-height: 35px;
 }
@media (max-width: 900px) {
  body {
    zoom: 0.8;
  }
 }
@media (max-width: 550px) {
  :root {
    --small-padding: 5px;
    --medium-padding: 10px;
    --large-padding: 20px;
    --border-radius: 8px;
  }
  .environment {
    margin-top: calc(-1 * var(--large-padding));
    margin-bottom: var(--large-padding);
  }
 }
@media (min-width: 551px) {
  .environment {
    position: absolute;
    width: var(--disclaimer-width);
    height: var(--disclaimer-height);
    transform: rotate(-45deg);
    top: calc(
      var(--disclaimer-width) / 1.4142 - var(--disclaimer-height) / 1.4142
    );
    left: calc(-1 * var(--disclaimer-height) / 1.4142);
    transform-origin: top left;
    z-index: 100;
  }
 }
 * {
  margin: 0;
  box-sizing: border-box;
  word-break: break-word;
 }
 body {
  background-color: var(--background-color);
  font-family: Arial, Helvetica, sans-serif;
 }
 h1,
 h2,
 h3,
 h4,
 h5,
 h6 {
  margin: var(--medium-padding) 0 var(--small-padding) 0;
 }
 h6 {
  margin-top: 0;
  font-size: 3rem;
 }
 html,
 body,
 #react-entry-point,
 main {
  height: 100%;
 }
 main {
  padding-top: var(--large-padding);
  display: flex;
  flex-direction: column;
 }
 .environment {
  color: white;
  text-align: center;
  display: flex;
  align-items: center;
  justify-content: center;
 }
 main > header,
 .configuration-container,
 .traces-table-container,
 .parallel-coordinates,
 main > footer {
  padding: var(--large-padding);
  flex-shrink: 0;
  overflow: hidden;
 }
 main > header,
 .configuration-container,
 .traces-table-container,
 .parallel-coordinates {
  margin: 0 var(--large-padding) var(--large-padding) var(--large-padding);
  border-radius: var(--border-radius);
  box-shadow: var(--shadow);
  background-color: white;
 }
 main > header {
  display: flex;
  align-items: center;
  flex-wrap: wrap;
  justify-content: space-between;
 }
 main > header > div:nth-child(1) {
  min-width: 350px;
  max-width: 450px;
  flex: 1;
 }
 main > header > div > h1 {
  margin-top: 0;
 }
 .version-tag {
  border-radius: var(--border-radius);
  display: inline-block;
  font-size: 1rem;
  padding: 3px 8px;
  margin-left: var(--small-padding)
 }
 main > header .version-tag {
  background: var(--background-color);
  vertical-align: 4px;
 }
 main > header > *:nth-child(2) {
  min-width: 250px;
  max-width: 550px;
  flex: 1;
 }
 main > header .placeholder {
  color: var(--light-text-color);
  font-size: 1.5rem;
  text-align: center;
  display: block;
  min-width: 250px;
  width: 60%;
  margin: auto;
 }
 .configuration-container {
  display: flex;
  justify-content: space-between;
  flex-wrap: wrap;
 }
 .configuration-item {
  padding-left: var(--small-padding);
  margin: var(--medium-padding);
 }
 .configuration-item h4 {
  font-weight: bold;
  margin: 0 0 var(--small-padding) 0;
 }
 .traces-table-container {
  padding: 0;
 }
 .traces-table-container header {
  padding: var(--large-padding);
 }
 .traces-table-container header h2 {
  margin-top: 0;
 }
 .dash-filter--case {
  display: none;
 }
 .traces-table-container td > div {
  white-space: pre !important;
  max-height: 150px !important;
  overflow: auto !important;
  display: inline-block !important;
  text-align: left !important;
 }
 .traces-table-container th > div {
  text-align: left !important;
 }
 .space-filler {
  flex-grow: 1;
 }
 main > footer {
  display: flex;
  justify-content: space-between;
  align-items: center;
  padding: var(--large-padding);
  background-color: var(--dark-background-color);
  color: var(--light-text-color);
  position: relative;
 }
 .parallel-coordinates {
  padding: 0;
 }
 a img {
  display: block;
  margin-left: var(--large-padding);
  width: 80px;
  height: 80px;
  cursor: pointer;
  transition: transform 300ms;
  opacity: 0.6;
 }
 a img:hover {
  transform: scale(1.1);
 }
--- a/great_ai/deploy/routes/dashboard/assets/logo.png
+++ b/great_ai/deploy/routes/dashboard/assets/logo.png
--- a/great_ai/deploy/routes/dashboard/assets/logo.svg
+++ b/great_ai/deploy/routes/dashboard/assets/logo.svg
@ -1,34 +0,0 @@
 <?xml version="1.0" encoding="utf-8"?>
 <svg viewBox="-8 -8 516 516" xmlns="http://www.w3.org/2000/svg" xmlns:bx="https://boxy-svg.com">
  <defs>
    <style bx:fonts="Baloo 2">@import url(https://fonts.googleapis.com/css2?family=Baloo+2%3Aital%2Cwght%400%2C400%3B0%2C500%3B0%2C600%3B0%2C700%3B0%2C800&amp;display=swap);</style>
    <g id="arrow">
      <polyline style="stroke-linecap: round; stroke-linejoin: round; stroke-width: 16px; fill: none;" points="0 0 150 0 150 150"/>
      <polyline style="stroke-linecap: round; stroke-width: 16px;" points="150 0 75 75"/>
    </g>
    <defs>
      <linearGradient id="foreground">
        <stop offset="0" stop-color="#44BBA4"/>
        <stop offset="1" stop-color="#0C6291"/>
      </linearGradient>
    </defs>
    <filter id="brightness-filter-0" x="-500%" y="-500%" width="1000%" height="1000%" bx:preset="brightness 1 1.6">
      <feComponentTransfer>
        <feFuncR type="linear" slope="1.6"/>
        <feFuncG type="linear" slope="1.6"/>
        <feFuncB type="linear" slope="1.6"/>
      </feComponentTransfer>
    </filter>
  </defs>
  <g style="filter: url(#brightness-filter-0);">
    <g transform="matrix(1, 0, 0, 1, 350, 0)" stroke="#44BBA4">
      <polyline style="stroke-linecap: round; stroke-linejoin: round; stroke-width: 16px; fill: none;" points="0 0 150 0 150 150"/>
      <polyline style="stroke-linecap: round; stroke-width: 16px;" points="150 0 75 75"/>
    </g>
    <g transform="matrix(-1, 0, 0, -1, 150.136902, 499.826965)" stroke="#0C6291">
      <polyline style="stroke-linecap: round; stroke-linejoin: round; stroke-width: 16px; fill: none;" points="0 0 150 0 150 150"/>
      <polyline style="stroke-linecap: round; stroke-width: 16px;" points="150 0 75 75"/>
    </g>
  </g>
  <text style="fill: url(#foreground); font-family: &quot;Baloo 2&quot;; font-size: 450px; letter-spacing: -12.3px; white-space: pre; filter: url(#brightness-filter-0);" x="78" y="388">AI</text>
 </svg>
--- a/great_ai/deploy/routes/dashboard/create_dash_app.py
+++ b/great_ai/deploy/routes/dashboard/create_dash_app.py
@ -1,281 +0,0 @@
 from math import ceil
 from typing import Any, Dict, List, Optional, Sequence, Tuple, Union
 import pandas as pd
 import plotly.express as px
 import plotly.graph_objects as go
 from dash import Dash, dcc, html
 from dash.dependencies import Input, Output
 from flask import Flask
 from ....constants import DASHBOARD_PATH, ONLINE_TAG_NAME
 from ....context import get_context
 from ....helper import freeze, snake_case_to_text, text_to_hex_color
 from ....utilities import unique
 from ....views import SortBy, Trace
 from .get_description import get_description
 from .get_filter_from_datatable import get_filter_from_datatable
 from .get_footer import get_footer
 from .get_traces_table import get_traces_table
 def create_dash_app(function_name: str, version: str, function_docs: str) -> Flask:
    accent_color = text_to_hex_color(function_name)
    function_name = snake_case_to_text(function_name)
    app = Dash(
        function_name,
        requests_pathname_prefix=DASHBOARD_PATH + "/",
        server=Flask(__name__),  # type: ignore[arg-type]
        title=function_name,
        update_title=None,  # type: ignore[arg-type]
        external_stylesheets=[
            "/assets/index.css",
        ],
    )
    execution_time_histogram_container = html.Div()
    configuration_container = html.Div(
        className="configuration-container",
    )
    table = get_traces_table()
    traces_table_container = html.Div(
        [
            html.Header(
                [
                    html.H2("Latest traces"),
                    html.P(
                        "Recent traces and aggregated metrics are presented below. Try filtering the table."
                    ),
                    html.A(
                        "Filtering syntax.",
                        href="https://dash.plotly.com/datatable/filtering",
                        target="_blank",
                    ),
                ]
            ),
            table,
        ],
        className="traces-table-container",
    )
    parallel_coordinates = dcc.Graph(
        className="parallel-coordinates", config={"displaylogo": False}
    )
    interval = dcc.Interval(
        interval=2 * 1000,  # in milliseconds
        n_intervals=0,
        max_intervals=(
            1 if get_context().is_production else -1
        ),  # will be incremented in production upon each successful request
    )
    app.layout = html.Main(
        [
            html.Div(
                html.P("PRODUCTION" if get_context().is_production else "DEVELOPMENT"),
                className="environment",
                style={"background": accent_color},
            ),
            html.Header(
                [
                    get_description(
                        function_name=function_name,
                        version=version,
                        function_docs=function_docs,
                        accent_color=accent_color,
                    ),
                    execution_time_histogram_container,
                ],
            ),
            configuration_container,
            traces_table_container,
            parallel_coordinates,
            html.Div(className="space-filler"),
            get_footer(),
            interval,
        ]
    )
    @app.callback(
        Output(configuration_container, "children"),
        Input(interval, "n_intervals"),
    )
    def update_configuration(
        n_intervals: int,
    ) -> List[html.Div]:
        config = get_context().to_flat_dict()
        return [
            html.Div(
                [
                    html.H4(snake_case_to_text(key)),
                    html.P(str(value)),
                ],
                className="configuration-item",
                style={"border-left": f"2px solid {accent_color}"},
            )
            for key, value in config.items()
        ]
    @app.callback(
        Output(table, "data"),
        Output(table, "page_count"),
        Output(table, "columns"),
        Output(traces_table_container, "style"),
        Output(execution_time_histogram_container, "children"),
        Output(parallel_coordinates, "figure"),
        Output(parallel_coordinates, "style"),
        Output(interval, "max_intervals"),
        Input(table, "page_current"),
        Input(table, "page_size"),
        Input(table, "sort_by"),
        Input(table, "filter_query"),
        Input(interval, "n_intervals"),
    )
    def update_page(
        page_current: int,
        page_size: int,
        sort_by: List[Dict[str, Union[str, int]]],
        filter_query: str,
        n_intervals: Optional[int],
    ) -> Tuple[
        List[Dict[str, Any]],
        int,
        List[Dict[str, Sequence[str]]],
        Dict[str, Any],
        Any,
        go.Figure,
        Dict[str, Any],
        int,
    ]:
        conjunctive_filters = (
            [get_filter_from_datatable(f) for f in filter_query.split(" && ")]
            if filter_query
            else []
        )
        non_null_conjunctive_filters = [f for f in conjunctive_filters if f is not None]
        elements, count = get_context().tracing_database.query(
            skip=page_current * page_size,
            take=page_size,
            conjunctive_filters=non_null_conjunctive_filters,
            conjunctive_tags=[ONLINE_TAG_NAME],
            sort_by=[SortBy.model_validate(s) for s in sort_by],
        )
        if non_null_conjunctive_filters:
            all_elements, _ = get_context().tracing_database.query(
                take=1, conjunctive_tags=[ONLINE_TAG_NAME]
            )
        else:
            all_elements = elements
        columns, style = update_layout(all_elements[0] if all_elements else None)
        execution_time_histogram, parallel_coords_fig, parallel_style = update_charts(
            elements=elements, accent_color=accent_color
        )
        return (
            [
                {k: str(v) for k, v in e.to_flat_dict(include_original=False).items()}
                for e in elements
            ],
            max(1, ceil(count / page_size)),
            columns,
            style,
            execution_time_histogram,
            parallel_coords_fig,
            parallel_style,
            ((n_intervals or 0) + 1) if get_context().is_production else -1,
        )
    return app.server
 def update_layout(
    first_element: Optional[Trace],
 ) -> Tuple[List[Dict[str, Sequence[str]]], Dict[str, Any]]:
    if first_element:
        keys = list(first_element.to_flat_dict(include_original=False).keys())
        header_height = max(len(i.split(":")) for i in keys)
        columns = [
            {
                "name": [""] * (header_height - len(k.split(":")))
                + k.replace("_flat", "").split(":"),
                "id": k,
            }
            for k in keys
        ]
    else:
        columns = []
    return (
        columns,
        {"display": "none" if first_element is None else "block"},
    )
 def update_charts(
    elements: List[Trace], accent_color: str
 ) -> Tuple[Any, go.Figure, Dict[str, Any]]:
    if not elements:
        return (
            html.Span(
                "No matching traces.",
                className="placeholder",
            ),
            go.Figure(),
            {"display": "none"},
        )
    flat_elements = [e.to_flat_dict(include_original=False) for e in elements]
    execution_time_histogram = dcc.Graph(config={"displaylogo": False})
    df = pd.DataFrame(flat_elements)
    fig = px.histogram(
        df,
        x="original_execution_time_ms",
        labels={"original_execution_time_ms": "Execution time (ms)"},
        nbins=20,
        height=400,
        log_y=True,
        color_discrete_sequence=[accent_color],
    )
    fig.update_layout(
        margin=dict(l=0, r=0, b=0, t=0, pad=0),
    )
    execution_time_histogram.figure = fig  # type: ignore[attr-defined]
    parallel_coords_fig = go.Figure(
        go.Parcoords(
            dimensions=[
                get_dimension_descriptor(df, c)
                for c in df.columns
                if c not in {"trace_id", "created", "output", "exception", "feedback"}
                and "_flat" not in c
            ],
            line_color=accent_color,
        )
    )
    return execution_time_histogram, parallel_coords_fig, {}
 def get_dimension_descriptor(df: pd.DataFrame, column: str) -> Dict[str, Any]:
    dimension: Dict[str, Any] = {
        "label": snake_case_to_text(column),
    }
    values = df[column]
    try:
        dimension["values"] = [float(v) for v in values]
    except (TypeError, ValueError):
        MAX_LENGTH = 40
        unique_values = unique(values, key=freeze)
        value_mapping = {str(v)[:MAX_LENGTH]: i for i, v in enumerate(unique_values)}
        dimension["values"] = [value_mapping[str(v)[:MAX_LENGTH]] for v in values]
        dimension["tickvals"] = list(value_mapping.values())
        dimension["ticktext"] = [k[:MAX_LENGTH] for k in value_mapping.keys()]
    return dimension
--- a/great_ai/deploy/routes/dashboard/get_description.py
+++ b/great_ai/deploy/routes/dashboard/get_description.py
@ -1,35 +0,0 @@
 from dash import dcc, html
 from ....helper import snake_case_to_text, strip_lines
 def get_description(
    function_name: str, version: str, function_docs: str, accent_color: str
 ) -> html.Div:
    return html.Div(
        [
            html.H1(
                [
                    f"{snake_case_to_text(function_name)} - dashboard",
                    html.Span(version, className="version-tag"),
                ],
                style={"color": accent_color},
            ),
            dcc.Markdown(
                strip_lines(
                    f"""
                    > View the live data of your deployment here.
                    ## Using the API
                    You can find the available endpoints at [/docs](/docs).
                    ## Details
                    {function_docs}
                """
                ),
                className="description",
            ),
        ]
    )
--- a/great_ai/deploy/routes/dashboard/get_filter_from_datatable.py
+++ b/great_ai/deploy/routes/dashboard/get_filter_from_datatable.py
@ -1,23 +0,0 @@
 from typing import Optional, Union
 from ....views import Filter, operators
 def get_filter_from_datatable(description: str) -> Optional[Filter]:
    for operator in operators:
        if operator in description:
            name_part, value_part = description.split(operator, 1)
            value_part = value_part.strip()
            name_part = name_part[name_part.find("{") + 1 : name_part.rfind("}")]
            v0 = value_part[0]
            if v0 == value_part[-1] and v0 in ("'", '"', "`"):
                value: Union[str, float] = value_part[1:-1].replace("\\" + v0, v0)
            else:
                try:
                    value = float(value_part)
                except ValueError:
                    value = value_part
            return Filter(property=name_part, operator=operator, value=value)
    return None
--- a/Show more
+++ b/Show more
		`@ -0,0 +1 @@`
							!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");e.hi=function(){this.pipeline.reset(),this.pipeline.add(e.hi.trimmer,e.hi.stopWordFilter,e.hi.stemmer),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add(e.hi.stemmer))},e.hi.wordCharacters="ऀ-ःऄ-एऐ-टठ-यर-िी-ॏॐ-य़ॠ-९॰-ॿa-zA-Zａ-ｚＡ-Ｚ0-9０-９",e.hi.trimmer=e.trimmerSupport.generateTrimmer(e.hi.wordCharacters),e.Pipeline.registerFunction(e.hi.trimmer,"trimmer-hi"),e.hi.stopWordFilter=e.generateStopWordFilter("अत अपना अपनी अपने अभी अंदर आदि आप इत्यादि इन इनका इन्हीं इन्हें इन्हों इस इसका इसकी इसके इसमें इसी इसे उन उनका उनकी उनके उनको उन्हीं उन्हें उन्हों उस उसके उसी उसे एक एवं एस ऐसे और कई कर करता करते करना करने करें कहते कहा का काफ़ी कि कितना किन्हें किन्हों किया किर किस किसी किसे की कुछ कुल के को कोई कौन कौनसा गया घर जब जहाँ जा जितना जिन जिन्हें जिन्हों जिस जिसे जीधर जैसा जैसे जो तक तब तरह तिन तिन्हें तिन्हों तिस तिसे तो था थी थे दबारा दिया दुसरा दूसरे दो द्वारा न नके नहीं ना निहायत नीचे ने पर पहले पूरा पे फिर बनी बही बहुत बाद बाला बिलकुल भी भीतर मगर मानो मे में यदि यह यहाँ यही या यिह ये रखें रहा रहे ऱ्वासा लिए लिये लेकिन व वग़ैरह वर्ग वह वहाँ वहीं वाले वुह वे वो सकता सकते सबसे सभी साथ साबुत साभ सारा से सो संग ही हुआ हुई हुए है हैं हो होता होती होते होना होने".split(" ")),e.hi.stemmer=function(){return function(e){return"function"==typeof e.update?e.update(function(e){return e}):e}}();var r=e.wordcut;r.init(),e.hi.tokenizer=function(i){if(!arguments.length\|\|null==i\|\|void 0==i)return[];if(Array.isArray(i))return i.map(function(r){return isLunr2?new e.Token(r.toLowerCase()):r.toLowerCase()});var t=i.toString().toLowerCase().replace(/^\s+/,"");return r.cut(t).split("\|")},e.Pipeline.registerFunction(e.hi.stemmer,"stemmer-hi"),e.Pipeline.registerFunction(e.hi.stopWordFilter,"stopWordFilter-hi")}});
		`@ -0,0 +1 @@`
							!function(e,t){"function"==typeof define&&define.amd?define(t):"object"==typeof exports?module.exports=t():t()(e.lunr)}(this,function(){return function(e){e.multiLanguage=function(){for(var t=Array.prototype.slice.call(arguments),i=t.join("-"),r="",n=[],s=[],p=0;p<t.length;++p)"en"==t[p]?(r+="\\w",n.unshift(e.stopWordFilter),n.push(e.stemmer),s.push(e.stemmer)):(r+=e[t[p]].wordCharacters,e[t[p]].stopWordFilter&&n.unshift(e[t[p]].stopWordFilter),e[t[p]].stemmer&&(n.push(e[t[p]].stemmer),s.push(e[t[p]].stemmer)));var o=e.trimmerSupport.generateTrimmer(r);return e.Pipeline.registerFunction(o,"lunr-multi-trimmer-"+i),n.unshift(o),function(){this.pipeline.reset(),this.pipeline.add.apply(this.pipeline,n),this.searchPipeline&&(this.searchPipeline.reset(),this.searchPipeline.add.apply(this.searchPipeline,s))}}}});
		`@ -0,0 +1 @@`
							!function(r,t){"function"==typeof define&&define.amd?define(t):"object"==typeof exports?module.exports=t():t()(r.lunr)}(this,function(){return function(r){r.stemmerSupport={Among:function(r,t,i,s){if(this.toCharArray=function(r){for(var t=r.length,i=new Array(t),s=0;s<t;s++)i[s]=r.charCodeAt(s);return i},!r&&""!=r\|\|!t&&0!=t\|\|!i)throw"Bad Among initialisation: s:"+r+", substring_i: "+t+", result: "+i;this.s_size=r.length,this.s=this.toCharArray(r),this.substring_i=t,this.result=i,this.method=s},SnowballProgram:function(){var r;return{bra:0,ket:0,limit:0,cursor:0,limit_backward:0,setCurrent:function(t){r=t,this.cursor=0,this.limit=t.length,this.limit_backward=0,this.bra=this.cursor,this.ket=this.limit},getCurrent:function(){var t=r;return r=null,t},in_grouping:function(t,i,s){if(this.cursor<this.limit){var e=r.charCodeAt(this.cursor);if(e<=s&&e>=i&&(e-=i,t[e>>3]&1<<(7&e)))return this.cursor++,!0}return!1},in_grouping_b:function(t,i,s){if(this.cursor>this.limit_backward){var e=r.charCodeAt(this.cursor-1);if(e<=s&&e>=i&&(e-=i,t[e>>3]&1<<(7&e)))return this.cursor--,!0}return!1},out_grouping:function(t,i,s){if(this.cursor<this.limit){var e=r.charCodeAt(this.cursor);if(e>s\|\|e<i)return this.cursor++,!0;if(e-=i,!(t[e>>3]&1<<(7&e)))return this.cursor++,!0}return!1},out_grouping_b:function(t,i,s){if(this.cursor>this.limit_backward){var e=r.charCodeAt(this.cursor-1);if(e>s\|\|e<i)return this.cursor--,!0;if(e-=i,!(t[e>>3]&1<<(7&e)))return this.cursor--,!0}return!1},eq_s:function(t,i){if(this.limit-this.cursor<t)return!1;for(var s=0;s<t;s++)if(r.charCodeAt(this.cursor+s)!=i.charCodeAt(s))return!1;return this.cursor+=t,!0},eq_s_b:function(t,i){if(this.cursor-this.limit_backward<t)return!1;for(var s=0;s<t;s++)if(r.charCodeAt(this.cursor-t+s)!=i.charCodeAt(s))return!1;return this.cursor-=t,!0},find_among:function(t,i){for(var s=0,e=i,n=this.cursor,u=this.limit,o=0,h=0,c=!1;;){for(var a=s+(e-s>>1),f=0,l=o<h?o:h,_=t[a],m=l;m<_.s_size;m++){if(n+l==u){f=-1;break}if(f=r.charCodeAt(n+l)-_.s[m])break;l++}if(f<0?(e=a,h=l):(s=a,o=l),e-s<=1){if(s>0\|\|e==s\|\|c)break;c=!0}}for(;;){var _=t[s];if(o>=_.s_size){if(this.cursor=n+_.s_size,!_.method)return _.result;var b=_.method();if(this.cursor=n+_.s_size,b)return _.result}if((s=_.substring_i)<0)return 0}},find_among_b:function(t,i){for(var s=0,e=i,n=this.cursor,u=this.limit_backward,o=0,h=0,c=!1;;){for(var a=s+(e-s>>1),f=0,l=o<h?o:h,_=t[a],m=_.s_size-1-l;m>=0;m--){if(n-l==u){f=-1;break}if(f=r.charCodeAt(n-1-l)-_.s[m])break;l++}if(f<0?(e=a,h=l):(s=a,o=l),e-s<=1){if(s>0\|\|e==s\|\|c)break;c=!0}}for(;;){var _=t[s];if(o>=_.s_size){if(this.cursor=n-_.s_size,!_.method)return _.result;var b=_.method();if(this.cursor=n-_.s_size,b)return _.result}if((s=_.substring_i)<0)return 0}},replace_s:function(t,i,s){var e=s.length-(i-t),n=r.substring(0,t),u=r.substring(i);return r=n+s+u,this.limit+=e,this.cursor>=i?this.cursor+=e:this.cursor>t&&(this.cursor=t),e},slice_check:function(){if(this.bra<0\|\|this.bra>this.ket\|\|this.ket>this.limit\|\|this.limit>r.length)throw"faulty slice operation"},slice_from:function(r){this.slice_check(),this.replace_s(this.bra,this.ket,r)},slice_del:function(){this.slice_from("")},insert:function(r,t,i){var s=this.replace_s(r,t,i);r<=this.bra&&(this.bra+=s),r<=this.ket&&(this.ket+=s)},slice_to:function(){return this.slice_check(),r.substring(this.bra,this.ket)},eq_v_b:function(r){return this.eq_s_b(r.length,r)}}}},r.trimmerSupport={generateTrimmer:function(r){var t=new RegExp("^[^"+r+"]+"),i=new RegExp("[^"+r+"]+$");return function(r){return"function"==typeof r.update?r.update(function(r){return r.replace(t,"").replace(i,"")}):r.replace(t,"").replace(i,"")}}}}});
		`@ -0,0 +1 @@`
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		`@ -1 +0,0 @@`
			`from .create_dash_app import create_dash_app`