Upload folder using huggingface_hub

.dockerignore

@@ -0,0 +1,18 @@
+# flyctl launch added from .gitignore
+**/.venv
+**/__pycache__
+**/wandb
+**/*.py[cod]
+**/*$py.class
+**/venv
+**/.eggs
+**/.pytest_cache
+**/*.egg-info
+**/.DS_Store
+**/build
+
+# flyctl launch added from openui/eval/.gitignore
+openui/eval/**/wandb
+openui/eval/**/datasets
+openui/eval/**/components
+fly.toml

.github/workflows/publish.yml

@@ -0,0 +1,52 @@
+name: Publish Python Package
+
+on:
+  release:
+    types: [created]
+
+permissions:
+  contents: read
+
+jobs:
+  test:
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"]
+    steps:
+    - uses: actions/checkout@v4
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v5
+      with:
+        python-version: ${{ matrix.python-version }}
+        cache: pip
+        cache-dependency-path: pyproject.toml
+    - name: Install dependencies
+      run: |
+        pip install '.[test]'
+    - name: Run tests
+      run: |
+        pytest
+  deploy:
+    runs-on: ubuntu-latest
+    needs: [test]
+    environment: release
+    permissions:
+      id-token: write
+    steps:
+    - uses: actions/checkout@v4
+    - name: Set up Python
+      uses: actions/setup-python@v5
+      with:
+        python-version: "3.12"
+        cache: pip
+        cache-dependency-path: pyproject.toml
+    - name: Install dependencies
+      run: |
+        pip install setuptools wheel build
+    - name: Build
+      run: |
+        python -m build
+    - name: Publish
+      uses: pypa/gh-action-pypi-publish@release/v1
+

.github/workflows/test.yml

@@ -0,0 +1,28 @@
+name: Test
+
+on: [push, pull_request]
+
+permissions:
+  contents: read
+
+jobs:
+  test:
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: ["3.8", "3.9", "3.10", "3.11", "3.12"]
+    steps:
+    - uses: actions/checkout@v4
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v5
+      with:
+        python-version: ${{ matrix.python-version }}
+        cache: pip
+        cache-dependency-path: pyproject.toml
+    - name: Install dependencies
+      run: |
+        pip install '.[test]'
+    - name: Run tests
+      run: |
+        pytest
+

.gitignore

@@ -0,0 +1,14 @@
+.venv
+__pycache__/
+wandb/
+*.py[cod]
+*$py.class
+venv
+.eggs
+.pytest_cache
+*.egg-info
+.DS_Store
+build
+eval/components
+eval/datasets
+!eval/datasets/eval.csv

.python-version

@@ -0,0 +1 @@
+openui

.vscode/extensions.json

@@ -0,0 +1,3 @@
+{
+  "recommendations": ["charliermarsh.ruff"]
+}

.vscode/settings.json

@@ -0,0 +1,6 @@
+{
+  "[python]": {
+    "editor.formatOnSave": true,
+    "editor.defaultFormatter": "charliermarsh.ruff"
+  }
+}

Dockerfile

@@ -0,0 +1,16 @@
+# Build the virtualenv as a separate step: Only re-execute this step when pyproject.toml changes
+FROM python:3.12-bookworm AS build-venv
+COPY pyproject.toml /build/pyproject.toml
+RUN python -m venv /venv && \
+    /venv/bin/pip install --upgrade pip setuptools wheel && \
+    mkdir -p /build/openui/util && touch /build/README.md
+RUN /venv/bin/pip install --disable-pip-version-check /build
+
+# Copy the virtualenv into a distroless image
+FROM python:3.12-slim-bookworm
+ENV PATH="/venv/bin:$PATH"
+COPY --from=build-venv /venv /venv
+COPY . /app
+WORKDIR /app
+RUN pip install --no-deps -U /app
+ENTRYPOINT ["python", "-m", "openui"]

LICENSE

@@ -0,0 +1,201 @@
+                                 Apache License
+                           Version 2.0, January 2004
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+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
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+
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+   See the License for the specific language governing permissions and
+   limitations under the License.

README.md

@@ -1,12 +1,65 @@
 ---
-title: Open Ui
-emoji: 🐢
-colorFrom: purple
-colorTo: indigo
+title: open-ui
+app_file: openui
 sdk: gradio
 sdk_version: 4.25.0
-app_file: app.py
-pinned: false
 ---
+# OpenUI
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+[![PyPI](https://img.shields.io/pypi/v/wandb-openui.svg)](https://pypi.org/project/wandb-openui/)
+[![Changelog](https://img.shields.io/github/v/release/wandb/openui?include_prereleases&label=changelog)](https://github.com/wandb/openui/releases)
+[![License](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](https://github.com/wandb/openui/blob/main/LICENSE)
+
+A backend service for generating HTML components with AI
+
+## Installation
+
+Clone this repo, then install using `pip`.  You'll probably want to create a virtual env.
+
+```bash
+git clone https://github.com/wandb/openui
+cd openui/backend
+pip install .
+```
+
+## Usage
+
+You must set the `OPENAI_API_KEY` even if you just want to try Ollama models.  Just set it to `xxx` in that case like below.
+
+```bash
+OPENAI_API_KEY=xxx python -m openui
+```
+
+### Docker
+
+You can build and run the docker file from the `/backend` directory:
+
+```bash
+docker build . -t wandb/openui --load
+docker run -p 7878:7878 -e OPENAI_API_KEY wandb/openui
+```
+
+## Development
+
+First be sure to install the package as editable, then passing `--dev` as an argument will live reload any local changes.
+
+```bash
+pip install -e .
+python -m openui --dev
+```
+
+Now install the dependencies and test dependencies:
+
+```bash
+pip install -e '.[test]'
+```
+
+To run the tests:
+
+```bash
+pytest
+```
+
+## Evaluation
+
+The [eval](./openui/eval) folder contains scripts for evaluating the performance of a model.  It automates generating UI, taking screenshots of the UI, then asking `gpt-4-vision-preview` to rate the elements.  More details about the eval pipeline coming soon...

fly.toml

@@ -0,0 +1,29 @@
+# fly.toml app configuration file generated for openui on 2024-03-15T15:53:15-07:00
+#
+# See https://fly.io/docs/reference/configuration/ for information about how to use this file.
+#
+
+app = 'openui'
+primary_region = 'sjc'
+
+[http_service]
+internal_port = 7878
+force_https = true
+auto_stop_machines = true
+auto_start_machines = true
+min_machines_running = 0
+processes = ['app']
+
+[mounts]
+source = "openui_data"
+destination = "/root/.openui"
+
+[env]
+OPENUI_ENVIRONMENT = "production"
+OPENUI_HOST = "https://openui.fly.dev"
+GITHUB_CLIENT_ID = "3af8fbeb90d06484dff0"
+
+[[vm]]
+memory = '1gb'
+cpu_kind = 'shared'
+cpus = 1

openui/__main__.py

@@ -0,0 +1,60 @@
+from pathlib import Path
+from .logs import setup_logger
+from . import server
+from . import config
+import uvicorn
+from uvicorn import Config
+import sys
+
+
+if __name__ == "__main__":
+    ui = any([arg == "-i" for arg in sys.argv])
+    logger = setup_logger("/tmp/openui.log" if ui else None)
+    logger.info("Starting OpenUI AI Server created by W&B...")
+
+    reload = any([arg == "--dev" for arg in sys.argv])
+    if reload:
+        config.ENV = config.Env.DEV
+        logger.info("Running in dev mode")
+
+    try:
+        from .tui.app import OpenUIApp
+
+        app = OpenUIApp()
+        server.queue = app.queue
+    except ImportError:
+        if ui:
+            logger.warning(
+                "Install OpenUI with pip install .[tui] to use the terminal UI"
+            )
+        ui = False
+
+    config_file = Path(__file__).parent / "log_config.yaml"
+    api_server = server.Server(
+        Config(
+            "openui.server:app",
+            host="0.0.0.0" if config.ENV == config.Env.PROD else "127.0.0.1",
+            log_config=str(config_file) if ui else None,
+            port=7878,
+            reload=reload,
+        )
+    )
+    if ui:
+        with api_server.run_in_thread():
+            logger.info("Running Terminal UI App")
+            app.run()
+    else:
+        logger.info("Running API Server")
+        mkcert_dir = Path.home() / ".vite-plugin-mkcert"
+
+        if reload:
+            # TODO: hot reload wasn't working with the server approach, and ctrl-C doesn't
+            # work with the uvicorn.run approach, so here we are
+            uvicorn.run(
+                "openui.server:app",
+                host="0.0.0.0" if config.ENV == config.Env.PROD else "127.0.0.1",
+                port=7878,
+                reload=reload,
+            )
+        else:
+            api_server.run_with_wandb()

openui/config.py

@@ -0,0 +1,51 @@
+import os
+from pathlib import Path
+import secrets
+from urllib.parse import urlparse
+from enum import Enum
+
+
+class Env(Enum):
+    LOCAL = 1
+    PROD = 2
+    DEV = 3
+
+
+try:
+    env = os.getenv("OPENUI_ENVIRONMENT", "local")
+    if env == "production":
+        env = "prod"
+    elif env == "development":
+        env = "dev"
+    ENV = Env[env.upper()]
+except KeyError:
+    print("Invalid environment, defaulting to running locally")
+    ENV = Env.LOCAL
+
+default_db = Path.home() / ".openui" / "db.sqlite"
+default_db.parent.mkdir(exist_ok=True)
+DB = os.getenv("DATABASE", default_db)
+HOST = os.getenv(
+    "OPENUI_HOST",
+    "https://localhost:5173" if ENV == Env.DEV else "http://localhost:7878",
+)
+RP_ID = urlparse(HOST).hostname
+SESSION_KEY = os.getenv("OPENUI_SESSION_KEY")
+if SESSION_KEY is None:
+    env_path = Path.home() / ".openui" / ".env"
+    if env_path.exists():
+        SESSION_KEY = env_path.read_text().splitlines()[0].split("=")[1]
+    else:
+        SESSION_KEY = secrets.token_hex(32)
+        with env_path.open("w") as f:
+            f.write("OPENUI_SESSION_KEY={SESSION_KEY}")
+# GPT 3.5 is 0.0005 per 1k tokens input and 0.0015 output
+# 700k puts us at a max of $1.00 spent per user over a 48 hour period
+MAX_TOKENS = int(os.getenv("OPENUI_MAX_TOKENS", "700000"))
+GITHUB_CLIENT_ID = os.getenv("GITHUB_CLIENT_ID")
+GITHUB_CLIENT_SECRET = os.getenv("GITHUB_CLIENT_SECRET")
+
+AWS_ENDPOINT_URL_S3 = os.getenv("AWS_ENDPOINT_URL_S3")
+AWS_ACCESS_KEY_ID = os.getenv("AWS_ACCESS_KEY_ID")
+AWS_SECRET_ACCESS_KEY = os.getenv("AWS_SECRET_ACCESS_KEY")
+BUCKET_NAME = os.getenv("BUCKET_NAME", "openui")

openui/config.yaml

@@ -0,0 +1,2 @@
+litellm_settings:
+#  callbacks: callbacks.weave_handler

openui/db/models.py

@@ -0,0 +1,149 @@
+from peewee import (
+    Model,
+    BinaryUUIDField,
+    BooleanField,
+    CharField,
+    IntegerField,
+    DateField,
+    CompositeKey,
+    DateTimeField,
+    ForeignKeyField,
+    OperationalError,
+    fn,
+)
+import uuid
+import datetime
+from playhouse.sqlite_ext import SqliteExtDatabase, JSONField
+from playhouse.migrate import SqliteMigrator, migrate
+from openui import config
+
+database = SqliteExtDatabase(
+    config.DB,
+    pragmas=(
+        ("cache_size", -1024 * 64),  # 64MB page-cache.
+        ("journal_mode", "wal"),  # Use WAL-mode
+        ("foreign_keys", 1),
+    ),
+)
+migrator = SqliteMigrator(database)
+
+
+class BaseModel(Model):
+    class Meta:
+        database = database
+
+
+class SchemaMigration(BaseModel):
+    version = CharField()
+
+
+class User(BaseModel):
+    id = BinaryUUIDField(primary_key=True)
+    username = CharField(unique=True)
+    email = CharField(null=True)
+    created_at = DateTimeField()
+
+
+class Credential(BaseModel):
+    credential_id = CharField(primary_key=True)
+    public_key = CharField()
+    sign_count = IntegerField()
+    aaguid = CharField(null=True)
+    user_verified = BooleanField(default=False)
+    user = ForeignKeyField(User, backref="credentials")
+
+
+class Session(BaseModel):
+    id = BinaryUUIDField(primary_key=True)
+    user = ForeignKeyField(User, backref="sessions")
+    data = JSONField()
+    created_at = DateTimeField()
+    updated_at = DateTimeField()
+
+
+class Component(BaseModel):
+    id = BinaryUUIDField(primary_key=True)
+    name = CharField()
+    user = ForeignKeyField(User, backref="components")
+    data = JSONField()
+
+
+class Usage(BaseModel):
+    input_tokens = IntegerField()
+    output_tokens = IntegerField()
+    day = DateField()
+    user = ForeignKeyField(User, backref="usage")
+
+    class Meta:
+        primary_key = CompositeKey("user", "day")
+
+    @classmethod
+    def update_tokens(cls, user_id: str, input_tokens: int, output_tokens: int):
+        Usage.insert(
+            user_id=uuid.UUID(user_id).bytes,
+            day=datetime.datetime.now().date(),
+            input_tokens=input_tokens,
+            output_tokens=output_tokens,
+        ).on_conflict(
+            conflict_target=[Usage.user_id, Usage.day],
+            update={
+                Usage.input_tokens: Usage.input_tokens + input_tokens,
+                Usage.output_tokens: Usage.output_tokens + output_tokens,
+            },
+        ).execute()
+
+    @classmethod
+    def tokens_since(cls, user_id: str, day: datetime.date) -> int:
+        return (
+            Usage.select(
+                fn.SUM(Usage.input_tokens + Usage.output_tokens).alias("tokens")
+            )
+            .where(Usage.user_id == uuid.UUID(user_id).bytes, Usage.day >= day)
+            .get()
+            .tokens
+            or 0
+        )
+
+
+CURRENT_VERSION = "2024-03-12"
+
+
+def alter(schema: SchemaMigration, ops: list[list], version: str) -> bool:
+    try:
+        migrate(*ops)
+    except OperationalError as e:
+        print("Migration failed", e)
+        return False
+    schema.version = version
+    schema.save()
+    print(f"Migrated {version}")
+    return version != CURRENT_VERSION
+
+
+def perform_migration(schema: SchemaMigration) -> bool:
+    if schema.version == "2024-03-08":
+        version = "2024-03-12"
+        aaguid = CharField(null=True)
+        user_verified = BooleanField(default=False)
+        altered = alter(
+            schema,
+            [
+                migrator.add_column("credential", "aaguid", aaguid),
+                migrator.add_column("credential", "user_verified", user_verified),
+            ],
+            version,
+        )
+        if altered:
+            perform_migration(schema)
+
+
+def ensure_migrated():
+    if not config.DB.exists():
+        database.create_tables(
+            [User, Credential, Session, Component, SchemaMigration, Usage]
+        )
+        SchemaMigration.create(version=CURRENT_VERSION)
+    else:
+        schema = SchemaMigration.select().first()
+        if schema.version != CURRENT_VERSION:
+            perform_migration(schema)

openui/dist/annotator/index.html

@@ -0,0 +1,343 @@
+<html>
+	<head>
+		<script src="/openui/tailwindcss.min.js"></script>
+		<script type="text/javascript">
+			window.tailwind.config.darkMode = ['class', '.darkMode']
+		</script>
+		<style type="text/css">
+			.selected {
+				position: absolute;
+				z-index: 49;
+				border: 2px dashed orange;
+				border-radius: 3px;
+			}
+			.inspector-element {
+				position: absolute;
+				z-index: 100;
+				pointer-events: none;
+				border: 1px dashed rgb(139 92 246);
+				box-shadow: 0 0 3px rgb(139 92 246);
+				animation: pulseGlow 1s infinite ease-in-out;
+				border-radius: 2px;
+				transition: all 250ms ease-in-out;
+				background-color: rgba(250, 250, 250, 0.1);
+			}
+			#wrapper {
+				cursor: pointer;
+				padding-top: 20px;
+			}
+			@keyframes pulseGlow {
+				0% {
+					box-shadow: 0 0 3px rgb(139 92 246);
+				}
+				50% {
+					box-shadow: 0 0 8px rgb(139 92 200);
+				}
+				100% {
+					box-shadow: 0 0 3px rgb(139 92 246);
+				}
+			}
+		</style>
+	</head>
+	<body
+		class="no-select items-top flex h-full w-full justify-center bg-zinc-300 dark:bg-zinc-900"
+	>
+		<div class="no-select relative w-[80vw]" id="wrapper">${body}</div>
+		<div
+			id="prompt"
+			class="no-select absolute z-50 origin-top scale-0 transform rounded-md border border-zinc-200 bg-zinc-100 text-zinc-700 shadow-lg transition-all duration-300 dark:border-zinc-700 dark:bg-zinc-800 dark:text-zinc-300"
+		>
+			<div class="p-4 text-xs">
+				<h3 class="text-base font-semibold">How do you want this to change?</h3>
+				<input
+					type="text"
+					class="mt-2 w-[300px] rounded-md border border-zinc-200 px-4 py-2 text-zinc-600 focus:outline-none focus:ring-2 focus:ring-blue-500 dark:focus:ring-blue-300"
+				/>
+				<button class="ml-2 mt-4 rounded-md bg-blue-500 p-2 text-white">
+					Submit
+				</button>
+			</div>
+		</div>
+	</body>
+	<script src="/openui/extra.min.js"></script>
+	<script type="text/javascript">
+		// Setup the room
+		window.theRoom.configure({
+			blockRedirection: false,
+			createInspector: true,
+			excludes: ['.no-select']
+		})
+		function clearInspector() {
+			const inspector = document.querySelector('.inspector-element')
+			if (inspector) {
+				inspector.style.top = '50%'
+				inspector.style.left = '50%'
+				inspector.style.width = ''
+				inspector.style.height = ''
+				inspector.style.border = 'none'
+				inspector.style.boxShadow = 'none'
+			}
+		}
+		let inspectorEnabled = false
+		// State TODO: maybe hydrate this from the parent
+		let commentIdx = 0
+		let selectedElements = []
+		// Reset the inspector every 5 seconds
+		let interval = null
+		// TODO: support more
+		let colors = ['blue', 'green', 'orange', 'yellow', 'red', 'purple']
+		let moved = false
+		document.addEventListener('mousemove', () => (moved = true))
+		document
+			.getElementById('wrapper')
+			.addEventListener('mouseleave', clearInspector)
+		function reset() {
+			interval = setInterval(() => {
+				if (!moved) {
+					clearInspector()
+				} else {
+					moved = false
+				}
+			}, 3000)
+		}
+		reset()
+
+		// TODO: might not need this if we just refactor the click handler
+		function reinitInspector(target, inspector) {
+			var pos = target.getBoundingClientRect()
+			var scrollTop = window.scrollY || document.documentElement.scrollTop
+			var scrollLeft = window.scrollX || document.documentElement.scrollLeft
+			var width = pos.width + 8
+			var height = pos.height + 8
+			var top = Math.max(-4, pos.top + scrollTop - 4)
+			var left = Math.max(-4, pos.left + scrollLeft - 4)
+
+			inspector.style.top = top + 'px'
+			inspector.style.left = left + 'px'
+			inspector.style.width = width + 'px'
+			inspector.style.height = height + 'px'
+		}
+
+		// Reset state on escape, submit on enter
+		document.addEventListener('keydown', e => {
+			if (e.key === 'Escape') {
+				clearInspector()
+				let p = document.getElementById('prompt')
+				if (p.classList.contains('scale-100')) {
+					p.querySelector('input').value = ''
+					let idx = p.dataset.commentIdx
+					document
+						.querySelectorAll('.selected-' + idx + ', .fix-legend-' + idx)
+						.forEach(selected => {
+							selected.parentNode.removeChild(selected)
+						})
+					p.classList.remove('scale-100')
+					if (inspectorEnabled) {
+						window.theRoom.start()
+					}
+				}
+			} else if (e.key === 'Enter') {
+				let sub = document.getElementById('prompt').querySelector('button')
+				sub.click()
+			}
+		})
+
+		window.addEventListener('resize', () => {
+			document.querySelectorAll('.selected').forEach(selected => {
+				let selIdx = parseInt(selected.dataset.commentIdx)
+				let el = selectedElements[selIdx]
+				reinitInspector(el, selected)
+				let fix = document.querySelector(`.fix-legend-${selIdx + 1}`)
+				fix.style.top = Math.max(3, parseFloat(selected.style.top) - 10) + 'px'
+				fix.style.left = parseFloat(selected.style.left) + 5 + 'px'
+			})
+		})
+
+		window.theRoom.on('mouseover', function () {
+			const inspector = document.querySelector('.inspector-element')
+			if (inspector) {
+				inspector.style.border = ''
+				inspector.style.boxShadow = ''
+			}
+		})
+		window.theRoom.on('click', function (element, event) {
+			event.preventDefault()
+			event.stopPropagation()
+			clearInterval(interval)
+			// TODO: reinit inspector here if it's gone
+			let inspector = document.querySelector('.inspector-element')
+			if (inspector.style.border === 'none') {
+				reinitInspector(element, inspector)
+			}
+			let selected = inspector.cloneNode()
+			let color = colors[commentIdx]
+			selected.classList.add('selected')
+			selected.classList.add('no-select')
+			selected.classList.add('selected-' + commentIdx)
+			selected.classList.remove('inspector-element')
+			selected.dataset.commentIdx = commentIdx
+			selected.style.borderColor = color
+			if (element.parentNode.id === 'wrapper') {
+				// TODO: think about this one more
+				selected.style.zIndex = -1
+			}
+			let prompt = document.getElementById('prompt')
+			prompt.dataset.commentIdx = commentIdx
+			prompt.classList.add('scale-100')
+			prompt.style.top = event.clientY + 'px'
+			prompt.style.left = '50%' // event.clientX + 'px'
+			prompt.style.marginLeft = `-200px`
+			let input = prompt.querySelector('input')
+			input.focus()
+			prompt.querySelector('button').addEventListener('click', () => {
+				addComment(input.value)
+				input.value = ''
+				prompt.classList.remove('scale-100')
+			})
+			// TODO: make it clear "esc" cancels
+			document.body.append(selected)
+			let fix = document.createElement('div')
+			fix.innerText = element.tagName
+			fix.addEventListener('mouseenter', () => {
+				const cmt = document.createElement('div')
+				cmt.innerText = text
+				cmt.className =
+					'p-2 rounded bg-white text-purple-500 italic absolute opacity-95 fix-comment'
+				cmt.style.top = Math.max(3, parseFloat(selected.style.top)) + 'px'
+				cmt.style.left = parseFloat(selected.style.left) + 'px'
+				cmt.style.zIndex = 50
+				document.body.append(cmt)
+			})
+			fix.addEventListener('mouseleave', () => {
+				const cmt = document.querySelector('.fix-comment')
+				if (cmt) {
+					cmt.parentNode.removeChild(cmt)
+				}
+			})
+			fix.className = `no-select fix fix-legend-${commentIdx} italic text-white text-center font-mono text-[8px] px-2 pt-0 z-50`
+			fix.classList.add('bg-' + color + '-600/[0.7]')
+			// TODO: use tailwind
+			fix.style.border = '1px dashed ' + color
+			fix.style.position = 'absolute'
+			fix.style.height = '12px'
+			fix.style.top = Math.max(3, parseFloat(selected.style.top) - 10) + 'px'
+			fix.style.left = parseFloat(selected.style.left) + 5 + 'px'
+			document.body.append(fix)
+			window.theRoom.stop()
+			function addComment(text) {
+				//let text = prompt('What would you like to fix about this?')
+				if (text) {
+					commentIdx += 1
+					let c = document.createComment('FIX (' + commentIdx + '): ' + text)
+					element.parentNode.insertBefore(c, element)
+					window.parent.parent.postMessage(
+						{
+							comment: text,
+							idx: commentIdx,
+							html: document.getElementById('wrapper').innerHTML
+						},
+						'*'
+					)
+				} else {
+					return
+				}
+				selectedElements.push(element)
+				clearInspector()
+				setTimeout(() => {
+					if (inspectorEnabled) {
+						window.theRoom.start()
+					}
+					reset()
+				}, 500)
+			}
+		})
+
+		window.addEventListener('load', () => {
+			window.parent.parent.postMessage({ action: 'ready' }, '*')
+		})
+
+		window._go = function (cb) {
+			if (document.readyState === 'complete') {
+				cb()
+			} else {
+				document.addEventListener('DOMContentLoaded', cb)
+			}
+		}
+
+		// handle events from parent
+		window.addEventListener(
+			'message',
+			function (event) {
+				if (event.data.action === 'take-screenshot') {
+					console.log('Got take screenshot event')
+					html2canvas(document.body, {
+						useCors: true,
+						foreignObjectRendering: true,
+						allowTaint: true,
+						windowWidth: 1524,
+						windowHeight: 768
+					}).then(function (canvas) {
+						const data = canvas.toDataURL('image/png')
+						window.parent.parent.postMessage(
+							{ screenshot: data, action: 'screenshot' },
+							'*'
+						)
+					})
+				} else if (event.data.action === 'hydrate') {
+					let wrapper = document.getElementById('wrapper')
+					wrapper.innerHTML = event.data.html
+					wrapper.querySelectorAll('img').forEach(img => {
+						img.crossOrigin = 'anonymous'
+					})
+
+					if (event.data.darkMode) {
+						document.documentElement.classList.add('darkMode')
+					}
+					document.querySelectorAll('.user-script').forEach(scr => {
+						scr.parentNode.removeChild(scr)
+					})
+					event.data.js.forEach(js => {
+						const script = document.createElement('script')
+						script.classList.add('user-script')
+						script.type = js.type
+						if (js.src) script.setAttribute('src', js.src)
+						// Close our JS to avoid conflicts
+						script.text = `(()=>{${js.text}})()`
+						document.body.append(script)
+					})
+					// Remove our selected elements
+					selectedElements = []
+					commentIdx = 0
+					var elements = document.querySelectorAll('.selected, .fix')
+					elements.forEach(function (element) {
+						element.parentNode.removeChild(element)
+					})
+					// TODO: maybe delay this a bit
+					window.parent.parent.postMessage(
+						{
+							preview: wrapper.hasChildNodes(),
+							height: document.body.scrollHeight,
+							action: 'loaded'
+						},
+						'*'
+					)
+					// state = event.data.state
+				} else if (event.data.action === 'toggle-dark-mode') {
+					if (document.documentElement.classList.contains('darkMode')) {
+						document.documentElement.classList.remove('darkMode')
+					} else {
+						document.documentElement.classList.add('darkMode')
+					}
+				} else if (event.data.action === 'toggle-inspector') {
+					if (inspectorEnabled) {
+						window.theRoom.stop()
+					} else {
+						window.theRoom.start()
+					}
+					inspectorEnabled = !inspectorEnabled
+				}
+			},
+			false
+		)
+	</script>
+</html>

openui/dist/assets/SyntaxHighlighter-qlnMxUBv.js

@@ -0,0 +1,13 @@
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openui/dist/assets/index-2-rOUq-J.js

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+  }
+  return indexes.map((i) => __vite__mapDeps.viteFileDeps[i])
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openui/dist/assets/index-3rRckdGL.js

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openui/dist/assets/index-VsdO9OoH.js

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+ *
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+ * See the LICENSE file in the root directory of this source tree.
+ */const at=Ye("ChevronDown",[["path",{d:"m6 9 6 6 6-6",key:"qrunsl"}]]);/**
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+ *
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+ * See the LICENSE file in the root directory of this source tree.
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openui/dist/assets/standalone-dIyWdECw.js

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openui/dist/index.html

@@ -0,0 +1,42 @@
+<!doctype html>
+<html
+	lang="en"
+	class="text-zinc-900 antialiased dark:bg-zinc-800 dark:text-white"
+>
+	<head>
+		<script>
+			// Only add google analytics when built for hosting
+			if ('production' === 'hosted') {
+				const script = document.createElement('script')
+				script.src = 'https://www.googletagmanager.com/gtag/js?id=G-FDHP7DEY94'
+				script.async = true
+				document.head.appendChild(script)
+			}
+			window.dataLayer = window.dataLayer || []
+			function gtag() {
+				dataLayer.push(arguments)
+			}
+			gtag('js', new Date())
+
+			gtag('config', 'G-FDHP7DEY94')
+		</script>
+		<meta charset="UTF-8" />
+		<link rel="icon" type="image/png" href="/favicon.png" />
+		<link rel="apple-touch-icon" href="/apple-touch-icon.png" />
+		<meta name="viewport" content="width=device-width, initial-scale=1.0" />
+		<meta name="description" content="OpenUI" />
+		<meta
+			name="apple-mobile-web-app-status-bar-style"
+			content="black-translucent"
+		/>
+		<meta name="theme-color" content="rgb(217,87,155)" />
+		<title>OpenUI by W&B</title>
+		<script type="module" crossorigin src="/assets/index-2-rOUq-J.js"></script>
+		<link rel="modulepreload" crossorigin href="/assets/vendor-BJpsy9o3.js">
+		<link rel="stylesheet" crossorigin href="/assets/index-OfmkY5JK.css">
+	</head>
+	<body>
+		<noscript>You need to enable JavaScript to run this app.</noscript>
+		<div id="root"></div>
+	</body>
+</html>

openui/dist/robots.txt

@@ -0,0 +1,2 @@
+User-agent: *
+Allow: /

openui/eval/.gitignore

@@ -0,0 +1,3 @@
+wandb/
+datasets/
+components/

openui/eval/dataset.py

@@ -0,0 +1,78 @@
+import asyncio
+import csv
+import weave
+from weave import Dataset
+from pathlib import Path
+import json
+import sys
+
+
+# TODO: Maybe use this for finetuning
+async def flowbite():
+    weave.init("openui-test-20")
+
+    data_dir = Path(__file__).parent / "components"
+
+    ds = []
+    for file in sorted(data_dir.glob("*.json")):
+        with open(file, "r") as f:
+            data = json.load(f)
+        abort = False
+        category = file.name.split(".")[0]
+        if category == "avatar":
+            print("Skipped ", category)
+            continue
+        for row in data:
+            if not row.get("names"):
+                abort = True
+                print("Aborting!")
+                break
+            source = row["name"].lower().replace(" ", "-")
+            for i, name in enumerate(row["names"]):
+                ds.append(
+                    {
+                        "name": name,
+                        # hack for weirdly named folders.
+                        "id": f"flowbite/{category.replace(' ', '-')}/{source}/{i}",
+                        "emoji": row["emojis"][i],
+                        "prompt": row["prompts"][i],
+                        "desktop_img": f"{category}/{source}.combined.png",
+                        "mobile_img": f"{category}/{source}.combined.mobile.png",
+                    }
+                )
+        if abort:
+            break
+
+    dataset = Dataset(ds)
+    print("Created dataset of ", len(ds))
+    dataset_ref = weave.publish(dataset, "flowbite")
+    print("Published dataset:", dataset_ref)
+
+
+async def publish(model):
+    weave.init("openui-test-20")
+
+    ds_dir = Path(__file__).parent / "datasets"
+
+    if model:
+        with open(ds_dir / f"{model}.json", "r") as f:
+            ds = json.load(f)
+    else:
+        ds = []
+        with open(ds_dir / "eval.csv", "r") as f:
+            reader = csv.DictReader(f)
+            for row in reader:
+                ds.append(row)
+
+    dataset = weaveflow.Dataset(ds)
+    print("Created dataset of ", len(ds))
+    dataset_ref = weave.publish(dataset, model.replace(":", "-") if model else "eval")
+    print("Published dataset:", dataset_ref)
+
+
+if __name__ == "__main__":
+    if len(sys.argv) > 1:
+        model = sys.argv[1]
+    else:
+        model = None
+    asyncio.run(publish(model))

openui/eval/evaluate.py

@@ -0,0 +1,194 @@
+import asyncio
+import sys
+import textwrap
+from weave import Evaluation, Model
+
+# from .model import EvaluateQualityModel
+import weave
+from pathlib import Path
+import base64
+import json
+from datetime import datetime
+
+last: datetime | None = None
+
+
+def pt(*args):
+    global last
+    now = datetime.now()
+    if last:
+        delta = str(now - last).split(":")[2][:5]
+    else:
+        delta = "00.00"
+    last = now
+    print(f"{now.strftime('%M:%S')} ({delta}) -", *args)
+
+
+def data_url(file_path):
+    with open(file_path, "rb") as image_file:
+        binary_data = image_file.read()
+    base64_encoded_data = base64.b64encode(binary_data)
+    base64_string = base64_encoded_data.decode("utf-8")
+    data_url = f"data:image/png;base64,{base64_string}"
+    return data_url
+
+
+base_dir = Path(__file__).parent / "datasets"
+
+
+@weave.type()
+class EvaluateQualityModel(Model):
+    system_message: str
+    model_name: str = "gpt-4-vision-preview"
+    # "gpt-3.5-turbo-1106"
+
+    @weave.op()
+    async def predict(self, input: dict) -> dict:
+        from openai import OpenAI
+
+        pt("Actually predicting", input["emoji"], input["name"] + ":", input["prompt"])
+        pt("Desktop:", input["desktop_img"], "Mobile:", input["mobile_img"])
+        client = OpenAI()
+        user_message = f"""{input['prompt']}
+---
+name: {input['name']}
+emoji: {input['emoji']}
+"""
+
+        response = client.chat.completions.create(
+            model=self.model_name,
+            messages=[
+                {"role": "system", "content": self.system_message},
+                {
+                    "role": "user",
+                    "content": [
+                        {"type": "text", "text": user_message},
+                        {
+                            "type": "text",
+                            "text": "Screenshot of the light and dark desktop versions:",
+                        },
+                        {
+                            "type": "image_url",
+                            "image_url": {
+                                "url": data_url(base_dir / input["desktop_img"])
+                            },
+                        },
+                        {
+                            "type": "text",
+                            "text": "Screenshot of the light and dark mobile versions:",
+                        },
+                        {
+                            "type": "image_url",
+                            "image_url": {
+                                "url": data_url(base_dir / input["mobile_img"])
+                            },
+                        },
+                    ],
+                },
+                {
+                    "role": "assistant",
+                    "content": "Here's my assessment of the component in JSON format:",
+                },
+            ],
+            temperature=0.3,
+            max_tokens=128,
+            seed=42,
+        )
+        extracted = response.choices[0].message.content
+        pk = response.usage.prompt_tokens
+        pc = pk * 0.01 / 1000
+        ck = response.usage.completion_tokens
+        cc = ck * 0.03 / 1000
+        pt(f"Usage: {pk} prompt tokens, {ck} completion tokens, ${round(pc + cc, 3)}")
+        try:
+            return json.loads(extracted.replace("```json", "").replace("```", ""))
+        except json.JSONDecodeError:
+            pt("Failed to decode JSON!")
+            return extracted
+
+
+@weave.op()
+def media_score(example: dict, prediction: dict) -> dict:
+    return prediction["media"]
+
+
+@weave.op()
+def contrast_score(example: dict, prediction: dict) -> dict:
+    return prediction["contrast"]
+
+
+@weave.op()
+def overall_score(example: dict, prediction: dict) -> float:
+    return prediction["relevance"]
+
+
+@weave.op()
+def example_to_model_input(example: dict) -> str:
+    return example
+
+
+SYSTEM_MESSAGE = textwrap.dedent(
+    """
+You are an expert web developer and will be assessing the quality of web components.
+Given a prompt, emoji, name and 2 images, you will be asked to rate the quality of 
+the component on the following criteria:
+
+- Media Quality: How well the component is displayed on desktop and mobile
+- Contrast: How well the component is displayed in light and dark mode
+- Relevance: Given the users prompt, do the images, name and emoji satisfy the request
+
+Use the following scale to rate each criteria:
+
+1. Terrible - The criteria isn't met at all
+2. Poor - The criteria is somewhat met but it looks very amateur
+3. Average - The criteria is met but it could be improved
+4. Good - The criteria is met and it looks professional
+5. Excellent - The criteria is met and it looks exceptional
+
+Output a JSON object with the following structure:
+    
+    {
+        "media": 4,
+        "contrast": 2,
+        "relevance": 5
+    }
+"""
+)
+model = EvaluateQualityModel(SYSTEM_MESSAGE)
+
+
+async def run(row=0, bad=False):
+    pt("Initializing weave")
+    weave.init("openui-test-21")
+    pt("Loading dataset")
+    dataset = weave.ref("flowbite").get()
+    pt("Running predict, row:", row)
+    comp = dataset.rows[row]
+    if bad:
+        comp["prompt"] = (
+            "A slider control that uses a gradient and displays a percentage."
+        )
+    res = await model.predict(comp)
+    pt("Result:", res)
+
+
+async def eval(ds="gpt-3.5-turbo"):
+    pt("Initializing weave")
+    weave.init("openui-test-21")
+    pt("Loading dataset", ds)
+    dataset = weave.ref(ds).get()
+    evaluation = Evaluation(
+        dataset,
+        scorers=[media_score, contrast_score, overall_score],
+        preprocess_model_input=example_to_model_input,
+    )
+    pt("Running evaluation")
+    await evaluation.evaluate(model)
+
+
+if __name__ == "__main__":
+    if len(sys.argv) > 1:
+        ds = sys.argv[1].replace(":", "-")
+    else:
+        ds = "gpt-3.5-turbo"
+    asyncio.run(eval(ds))

openui/eval/evaluate_weave.py

@@ -0,0 +1,314 @@
+import asyncio
+import sys
+import os
+import textwrap
+import yaml
+import mistletoe
+from openai import AsyncOpenAI
+from weave import Evaluation, Model
+
+# from .model import EvaluateQualityModel
+import weave
+from pathlib import Path
+import base64
+import json
+from datetime import datetime
+from openui.util import gen_screenshots
+
+
+last: datetime | None = None
+
+
+def pt(*args):
+    global last
+    now = datetime.now()
+    if last:
+        delta = str(now - last).split(":")[2][:5]
+    else:
+        delta = "00.00"
+    last = now
+    print(f"{now.strftime('%M:%S')} ({delta}) -", *args)
+
+
+base_dir = Path(__file__).parent / "datasets"
+
+SYSTEM_PROMPT = """You're a frontend web developer that specializes in tailwindcss. Given a description, generate HTML with tailwindcss. You should support both dark and light mode. It should render nicely on desktop, tablet, and mobile. Keep your responses concise and just return HTML that would appear in the <body> no need for <head>. Use placehold.co for placeholder images. If the user asks for interactivity, use modern ES6 javascript and native browser apis to handle events.
+
+Always start your response with frontmatter wrapped in ---.  Set name: with a 2 to 5 word description of the component. Set emoji: with an emoji for the component, i.e.:
+---
+name: Fancy Button
+emoji: 🎉
+---
+
+<button class="bg-blue-500 text-white p-2 rounded-lg">Click me</button>"""
+
+
+@weave.type()
+class OpenUIModel(Model):
+    system_prompt: str
+    model_name: str = "gpt-3.5-turbo"
+    take_screenshot: bool = True
+    temp: float = 0.5
+    _iter: int = 0
+    # "gpt-3.5-turbo-1106"
+
+    @property
+    def client(self):
+        if self.model_name.startswith("ollama/"):
+            return AsyncOpenAI(base_url="http://localhost:11434/v1")
+        if self.model_name.startswith("fireworks/"):
+            return AsyncOpenAI(
+                api_key=os.getenv("FIREWORKS_API_KEY"),
+                base_url="https://api.fireworks.ai/inference/v1",
+            )
+        else:
+            return AsyncOpenAI()
+
+    @property
+    def model(self):
+        if self.model_name.startswith("ollama/"):
+            return self.model_name.replace("ollama/", "")
+        if self.model_name.startswith("fireworks/"):
+            return (
+                f"accounts/fireworks/models/{self.model_name.replace('fireworks/', '')}"
+            )
+        return self.model_name
+
+    @property
+    def model_dir(self):
+        return self.model_name.split("/")[-1]
+
+    @weave.op()
+    async def predict(self, prompt: str) -> dict:
+        pt("Actually predicting", prompt)
+        completion = await self.client.chat.completions.create(
+            messages=[
+                {
+                    "role": "system",
+                    "content": self.system_prompt,
+                },
+                {
+                    "role": "user",
+                    "content": prompt,
+                },
+            ],
+            max_tokens=2048,
+            temperature=self.temp,
+            model=self.model,
+        )
+        result = completion.choices[0].message.content
+        parsed = self.extract_html(result)
+        if self.take_screenshot:
+            name = f"prompt-{self._iter}"
+            self._iter += 1
+            await self.screenshot(parsed["html"], name)
+            parsed["desktop_img"] = f"./{self.model_dir}/{name}.combined.png"
+            parsed["mobile_img"] = f"./{self.model_dir}/{name}.combined.mobile.png"
+        return parsed
+
+    async def screenshot(self, html: str, name: str):
+        screenshot_dir = base_dir / self.model_dir
+        screenshot_dir.mkdir(exist_ok=True)
+        await gen_screenshots(name, html, screenshot_dir)
+
+    def extract_html(self, result: str):
+        fm = {}
+        parts = result.split("---")
+        try:
+            if len(parts) > 2:
+                fm = yaml.safe_load(parts[1])
+                if not isinstance(fm, dict):
+                    fm = {"name": fm}
+                md = "---".join(parts[2:])
+            elif len(parts) == 2:
+                fm = yaml.safe_load(parts[0])
+                if not isinstance(fm, dict):
+                    fm = {"name": fm}
+                md = parts[1]
+            else:
+                md = result
+        except Exception as e:
+            print(f"Error parsing frontmatter: {e}")
+            print(parts)
+            fm["name"] = "Component"
+            fm["emoji"] = "🎉"
+            md = result
+        doc = mistletoe.Document(md)
+        html = ""
+        blocks = 0
+        for node in doc.children:
+            if isinstance(node, mistletoe.block_token.CodeFence):
+                blocks += 1
+                if node.language == "js" or node.language == "javascript":
+                    html += f"<script>\n{node.children[0].content}\n</script>\n"
+                else:
+                    html += f"{node.children[0].content}\n"
+        if blocks == 0:
+            html = md
+        fm["html"] = html.strip()
+        return fm
+
+
+EVAL_SYSTEM_PROMPT = textwrap.dedent(
+    """
+You are an expert web developer and will be assessing the quality of web components.
+Given a prompt, emoji, name and 2 images, you will be asked to rate the quality of 
+the component on the following criteria:
+
+- Media Quality: How well the component is displayed on desktop and mobile
+- Contrast: How well the component is displayed in light and dark mode
+- Relevance: Given the users prompt, do the images, name and emoji satisfy the request
+
+Use the following scale to rate each criteria:
+
+1. Terrible - The criteria isn't met at all
+2. Poor - The criteria is somewhat met but it looks very amateur
+3. Average - The criteria is met but it could be improved
+4. Good - The criteria is met and it looks professional
+5. Excellent - The criteria is met and it looks exceptional
+
+Output a JSON object with the following structure:
+    
+    {
+        "media": 4,
+        "contrast": 2,
+        "relevance": 5
+    }
+"""
+)
+
+
+@weave.type()
+class OpenUIScoringModel(Model):
+    system_prompt: str
+    model_name: str = "gpt-4-vision-preview"
+    temp: float = 0.3
+
+    def data_url(self, file_path):
+        with open(file_path, "rb") as image_file:
+            binary_data = image_file.read()
+        base64_encoded_data = base64.b64encode(binary_data)
+        base64_string = base64_encoded_data.decode("utf-8")
+        data_url = f"data:image/png;base64,{base64_string}"
+        return data_url
+
+    @weave.op()
+    async def predict(self, example: dict, prediction: dict) -> dict:
+        client = AsyncOpenAI()
+
+        user_message = f"""{example['prompt']}
+---
+name: {prediction['name']}
+emoji: {prediction['emoji']}
+"""
+        response = await client.chat.completions.create(
+            model=self.model_name,
+            messages=[
+                {"role": "system", "content": self.system_prompt},
+                {
+                    "role": "user",
+                    "content": [
+                        {"type": "text", "text": user_message},
+                        {
+                            "type": "text",
+                            "text": "Screenshot of the light and dark desktop versions:",
+                        },
+                        {
+                            "type": "image_url",
+                            "image_url": {
+                                "url": self.data_url(
+                                    base_dir / prediction["desktop_img"]
+                                )
+                            },
+                        },
+                        {
+                            "type": "text",
+                            "text": "Screenshot of the light and dark mobile versions:",
+                        },
+                        {
+                            "type": "image_url",
+                            "image_url": {
+                                "url": self.data_url(
+                                    base_dir / prediction["mobile_img"]
+                                )
+                            },
+                        },
+                    ],
+                },
+                {
+                    "role": "assistant",
+                    "content": "Here's my assessment of the component in JSON format:",
+                },
+            ],
+            temperature=self.temp,
+            max_tokens=128,
+            seed=42,
+        )
+        extracted = response.choices[0].message.content
+        pk = response.usage.prompt_tokens
+        pc = pk * 0.01 / 1000
+        ck = response.usage.completion_tokens
+        cc = ck * 0.03 / 1000
+        pt(f"Usage: {pk} prompt tokens, {ck} completion tokens, ${round(pc + cc, 3)}")
+        try:
+            return json.loads(extracted.replace("```json", "").replace("```", ""))
+        except json.JSONDecodeError:
+            pt("Failed to decode JSON!")
+            return {
+                "media": None,
+                "contrast": None,
+                "relevance": None,
+                "source": extracted,
+            }
+
+
+scoring_model = OpenUIScoringModel(EVAL_SYSTEM_PROMPT)
+
+
+@weave.op()
+async def scores(example: dict, prediction: dict) -> dict:
+    return await scoring_model.predict(example, prediction)
+
+
+@weave.op()
+def example_to_model_input(example: dict) -> str:
+    return example["prompt"]
+
+
+async def run(row=0, bad=False):
+    pt("Initializing weave")
+    weave.init("openui-test-20")
+    model = OpenUIModel(SYSTEM_PROMPT)
+    pt("Loading dataset")
+    dataset = weave.ref("flowbite").get()
+    pt("Running predict, row:", row)
+    comp = dataset.rows[row]
+    if bad:
+        comp["prompt"] = (
+            "A slider control that uses a gradient and displays a percentage."
+        )
+    res = await model.predict(comp)
+    pt("Result:", res)
+
+
+async def eval(mod="gpt-3.5-turbo"):
+    pt("Initializing weave")
+    weave.init("openui-test-20")
+    model = OpenUIModel(SYSTEM_PROMPT, mod)
+    pt("Loading dataset")
+    dataset = weave.ref("eval").get()
+    evaluation = Evaluation(
+        dataset,
+        scorers=[scores],
+        preprocess_model_input=example_to_model_input,
+    )
+    pt("Running evaluation")
+    await evaluation.evaluate(model)
+
+
+if __name__ == "__main__":
+    if len(sys.argv) > 1:
+        mod = sys.argv[1]
+    else:
+        mod = "gpt-3.5-turbo"
+    asyncio.run(eval(mod))

openui/eval/model.py

@@ -0,0 +1,68 @@
+from weave import Model
+import weave
+import json
+from pathlib import Path
+
+import base64
+
+
+def data_url(file_path):
+    with open(file_path, "rb") as image_file:
+        binary_data = image_file.read()
+    base64_encoded_data = base64.b64encode(binary_data)
+    base64_string = base64_encoded_data.decode("utf-8")
+    data_url = f"data:image/png;base64,{base64_string}"
+    return data_url
+
+
+base_dir = Path(__file__).parent / "components"
+
+
+@weave.type()
+class EvaluateQualityModel(Model):
+    system_message: str
+    model_name: str = "gpt-4-vision-preview"
+    # "gpt-3.5-turbo-1106"
+
+    @weave.op()
+    async def predict(self, input: dict) -> dict:
+        from openai import OpenAI
+
+        client = OpenAI()
+        user_message = f"""prompt: {input['prompt']}
+name: {input['name']}
+emoji: {input['emoji']}
+"""
+
+        response = client.chat.completions.create(
+            model=self.model_name,
+            messages=[
+                {"role": "system", "content": self.system_message},
+                {"role": "user", "content": user_message},
+                {
+                    "role": "user",
+                    "content": [
+                        {
+                            "type": "text",
+                            "text": "Screenshot of the light and dark desktop versions:",
+                        },
+                        {
+                            "type": "image_url",
+                            "image_url": data_url(base_dir / input["desktop_img"]),
+                        },
+                        {
+                            "type": "text",
+                            "text": "Screenshot of the light and dark mobile versions:",
+                        },
+                        {
+                            "type": "image_url",
+                            "image_url": data_url(base_dir / input["mobile_img"]),
+                        },
+                    ],
+                },
+            ],
+            temperature=0.7,
+            response_format={"type": "json_object"},
+        )
+        extracted = response.choices[0].message.content
+        return json.loads(extracted)

openui/eval/prompt_to_img.py

@@ -0,0 +1,125 @@
+from pathlib import Path
+import csv
+import json
+import mistletoe
+import yaml
+import asyncio
+import sys
+from openui.util import gen_screenshots
+
+from openai import AsyncOpenAI
+
+SYSTEM_PROMPT = """You're a frontend web developer that specializes in tailwindcss. Given a description, generate HTML with tailwindcss. You should support both dark and light mode. It should render nicely on desktop, tablet, and mobile. Keep your responses concise and just return HTML that would appear in the <body> no need for <head>. Use placehold.co for placeholder images. If the user asks for interactivity, use modern ES6 javascript and native browser apis to handle events.
+
+Always start your response with frontmatter wrapped in ---.  Set name: with a 2 to 5 word description of the component. Set emoji: with an emoji for the component, i.e.:
+---
+name: Fancy Button
+emoji: 🎉
+---
+
+<button class="bg-blue-500 text-white p-2 rounded-lg">Click me</button>"""
+
+
+def extract_html(result: str):
+    fm = {}
+    parts = result.split("---")
+    try:
+        if len(parts) > 2:
+            fm = yaml.safe_load(parts[1])
+            if not isinstance(fm, dict):
+                fm = {"name": fm}
+            md = "---".join(parts[2:])
+        elif len(parts) == 2:
+            fm = yaml.safe_load(parts[0])
+            if not isinstance(fm, dict):
+                fm = {"name": fm}
+            md = parts[1]
+        else:
+            md = result
+    except Exception as e:
+        print(f"Error parsing frontmatter: {e}")
+        print(parts)
+        fm["name"] = "Component"
+        fm["emoji"] = "🎉"
+        md = result
+    doc = mistletoe.Document(md)
+    html = ""
+    blocks = 0
+    for node in doc.children:
+        if isinstance(node, mistletoe.block_token.CodeFence):
+            blocks += 1
+            if node.language == "js" or node.language == "javascript":
+                html += f"<script>\n{node.children[0].content}\n</script>\n"
+            else:
+                html += f"{node.children[0].content}\n"
+    if blocks == 0:
+        html = md
+    fm["html"] = html.strip()
+    return fm
+
+
+async def synth(prompt, model="gpt-3.5-turbo"):
+    print(f"Generating HTML for: {prompt}")
+    completion = await openai.chat.completions.create(
+        messages=[
+            {
+                "role": "system",
+                "content": SYSTEM_PROMPT,
+            },
+            {
+                "role": "user",
+                "content": prompt,
+            },
+        ],
+        max_tokens=2048,
+        temperature=0.5,
+        model=model,
+    )
+    result = completion.choices[0].message.content
+    parsed = extract_html(result)
+    parsed["prompt"] = prompt
+    return parsed
+
+
+async def main(model="gpt-3.5-turbo"):
+    eval_csv = Path(__file__).parent / "datasets" / "eval.csv"
+    gen_json = Path(__file__).parent / "datasets" / f"{model}.json"
+    screenshot_dir = Path(__file__).parent / "datasets" / model
+    screenshot_dir.mkdir(exist_ok=True)
+    # Regenerate screenshots only for existing generations
+    if gen_json.exists():
+        with open(gen_json, "r") as f:
+            results = json.load(f)
+        for i, row in enumerate(results):
+            await gen_screenshots(f"prompt-{i}", row["html"], screenshot_dir)
+            row["desktop_img"] = f"./{model}/prompt-{i}.combined.png"
+            row["mobile_img"] = f"./{model}/prompt-{i}.combined.mobile.png"
+        with open(gen_json, "w") as f:
+            f.write(json.dumps(results, indent=4))
+        return
+
+    with open(eval_csv, "r") as f:
+        reader = csv.DictReader(f)
+        tasks = [synth(row["prompt"], model) for i, row in enumerate(reader)]
+    results = await asyncio.gather(*tasks)
+    for i, row in enumerate(results):
+        await gen_screenshots(f"prompt-{i}", row["html"], screenshot_dir)
+        row["desktop_img"] = f"./{model}/prompt-{i}.combined.png"
+        row["mobile_img"] = f"./{model}/prompt-{i}.combined.mobile.png"
+
+    with open(gen_json, "w") as f:
+        f.write(json.dumps(results, indent=4))
+
+
+if __name__ == "__main__":
+    if len(sys.argv) > 1:
+        model = sys.argv[1]
+    else:
+        model = "gpt-3.5-turbo"
+    if model.startswith("ollama/"):
+        model = model.replace("ollama/", "")
+        openai = AsyncOpenAI(base_url="http://localhost:11434/v1")
+    else:
+        openai = AsyncOpenAI()
+
+    asyncio.run(main(model))

openui/eval/scrape.py

@@ -0,0 +1,139 @@
+from bs4 import BeautifulSoup
+from urllib.request import urlopen
+from pathlib import Path
+import json
+
+comps = [
+    "Accordion",
+    "Alerts",
+    "Avatar",
+    "Badge",
+    "Banner",
+    "Bottom Navigation",
+    "Breadcrumb",
+    "Buttons",
+    "Button Group",
+    "Card",
+    "Carousel",
+    "Chat Bubble",
+    "Device Mockups",
+    "Drawer",
+    "Dropdowns",
+    "Footer",
+    "Forms",
+    "Gallery",
+    "Indicators",
+    "Jumbotron",
+    "List Group",
+    "Mega Menu",
+    "Modal",
+    "Navbar",
+    "Pagination",
+    "Popover",
+    "Progress",
+    "Rating",
+    "Sidebar",
+    "Skeleton",
+    "Speed Dial",
+    "Spinner",
+    "Stepper",
+    "Tables",
+    "Tabs",
+    "Timeline",
+    "Toast",
+    "Tooltips",
+    "Typography",
+    "Video",
+    "FORMS",
+    "Input Field",
+    "File Input",
+    "Search Input",
+    "Number Input",
+    "Phone Input",
+    "Select",
+    "Textarea",
+    "Checkbox",
+    "Radio",
+    "Toggle",
+    "Range",
+    "Floating Label",
+    "TYPOGRAPHY",
+    "Headings",
+    "Paragraphs",
+    "Blockquote",
+    "Images",
+    "Lists",
+    "Links",
+    "Text",
+    "PLUGINS",
+    "Charts",
+    "Datepicker",
+]
+
+base_url = "https://flowbite.com/docs/components/"
+total_examples = 0
+for comp in comps:
+    if comp == "FORMS":
+        base_url = "https://flowbite.com/docs/forms/"
+        continue
+    elif comp == "TYPOGRAPHY":
+        base_url = "https://flowbite.com/docs/typography/"
+        continue
+    elif comp == "PLUGINS":
+        base_url = "https://flowbite.com/docs/plugins/"
+        continue
+    clean_comp = comp.lower().replace(" ", "-")
+    print(f"Loading examples for: {clean_comp}, ({total_examples} total examples)")
+
+    cached_html = Path(__file__).parent / "components" / f"{clean_comp}.html"
+    cached_dataset = Path(__file__).parent / "components" / f"{comp.lower()}.json"
+    if cached_dataset.exists():
+        print(f"Skipping:{comp} as we parsed it back in the day")
+        ds = json.load(cached_dataset.open("r"))
+        total_examples += len(ds)
+        continue
+    if not cached_html.exists():
+        response = urlopen(base_url + clean_comp)
+        html_content = response.read()
+        with open(cached_html, "wb") as f:
+            f.write(html_content)
+    else:
+        with cached_html.open("rb") as f:
+            html_content = f.read()
+
+    soup = BeautifulSoup(html_content, "html.parser")
+
+    dataset = []
+    flavors = soup.find_all("h2")
+    for flavor in flavors:
+        row = {}
+        row["name"] = flavor.text.replace("\n #", "").strip()
+        row["description"] = flavor.next_sibling.text
+        code_parent = flavor.find_next_sibling("div")
+        if not code_parent:
+            print(f"Something's fucked up with {row['name']}, skipping")
+            continue
+        iframe = code_parent.find("iframe")
+        if not iframe or not iframe.has_attr("srcdoc"):
+            print("Skipped, no iframe: ", row["name"])
+            continue
+        srcdoc_soup = BeautifulSoup(iframe["srcdoc"], "html.parser")
+        row["html"] = srcdoc_soup.find("div", id="exampleWrapper").prettify()
+        dataset.append(row)
+
+    print(f"Collected {len(dataset)} examples for {clean_comp} like a boss\n=====\n")
+    total_examples += len(dataset)
+
+    with open(cached_dataset, "w") as f:
+        json.dump(dataset, f, indent=2)
+
+    """
+    for row in dataset:
+        print("======================================")
+        print(row["name"], "-", row["description"])
+        print("======================================\n")
+        print(row["html"])
+        print("\n\n")
+    """
+print("!!!!!!!!!!!!!")
+print(f"Collected {total_examples} total examples")

openui/eval/screenshots.py

@@ -0,0 +1,20 @@
+import os
+from pathlib import Path
+import json
+from openui.util import gen_screenshots
+
+data_dir = Path(__file__).parent / "components"
+
+if __name__ == "__main__":
+    i = 0
+    for file in sorted(data_dir.glob("*.json")):
+        with open(file, "r") as f:
+            data = json.load(f)
+        i += 1
+        if i > 10:
+            continue
+        img_dir = data_dir / file.name.split(".")[0]
+        os.makedirs(img_dir, exist_ok=True)
+        for row in data:
+            root_name = row["name"].lower().replace(" ", "-")
+            gen_screenshots(root_name, row["html"], img_dir)