chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
@@ -0,0 +1,5 @@
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data
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tmp
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results
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.env
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@@ -0,0 +1,71 @@
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OPENAI_ENDPOINT=https://api.openai.com/v1
|
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OPENAI_API_KEY=
|
||||
|
||||
ANTHROPIC_API_KEY=
|
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ANTHROPIC_ENDPOINT=https://api.anthropic.com
|
||||
|
||||
GOOGLE_API_KEY=
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||||
|
||||
AZURE_OPENAI_ENDPOINT=
|
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AZURE_OPENAI_API_KEY=
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AZURE_OPENAI_API_VERSION=2025-01-01-preview
|
||||
|
||||
DEEPSEEK_ENDPOINT=https://api.deepseek.com
|
||||
DEEPSEEK_API_KEY=
|
||||
|
||||
MISTRAL_API_KEY=
|
||||
MISTRAL_ENDPOINT=https://api.mistral.ai/v1
|
||||
|
||||
OLLAMA_ENDPOINT=http://localhost:11434
|
||||
|
||||
ALIBABA_ENDPOINT=https://dashscope.aliyuncs.com/compatible-mode/v1
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ALIBABA_API_KEY=
|
||||
|
||||
MODELSCOPE_ENDPOINT=https://api-inference.modelscope.cn/v1
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MODELSCOPE_API_KEY=
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||||
|
||||
MOONSHOT_ENDPOINT=https://api.moonshot.cn/v1
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MOONSHOT_API_KEY=
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||||
|
||||
UNBOUND_ENDPOINT=https://api.getunbound.ai
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UNBOUND_API_KEY=
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|
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SiliconFLOW_ENDPOINT=https://api.siliconflow.cn/v1/
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SiliconFLOW_API_KEY=
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IBM_ENDPOINT=https://us-south.ml.cloud.ibm.com
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IBM_API_KEY=
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IBM_PROJECT_ID=
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GROK_ENDPOINT="https://api.x.ai/v1"
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GROK_API_KEY=
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#set default LLM
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DEFAULT_LLM=openai
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# Set to false to disable anonymized telemetry
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ANONYMIZED_TELEMETRY=false
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# LogLevel: Set to debug to enable verbose logging, set to result to get results only. Available: result | debug | info
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BROWSER_USE_LOGGING_LEVEL=info
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# Browser settings
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BROWSER_PATH=
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BROWSER_USER_DATA=
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BROWSER_DEBUGGING_PORT=9222
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BROWSER_DEBUGGING_HOST=localhost
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# Set to true to keep browser open between AI tasks
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KEEP_BROWSER_OPEN=true
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USE_OWN_BROWSER=false
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BROWSER_CDP=
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# Display settings
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# Format: WIDTHxHEIGHTxDEPTH
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RESOLUTION=1920x1080x24
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# Width in pixels
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RESOLUTION_WIDTH=1920
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# Height in pixels
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RESOLUTION_HEIGHT=1080
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# VNC settings
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VNC_PASSWORD=youvncpassword
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@@ -0,0 +1,124 @@
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name: Build Docker Image
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on:
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release:
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types: [published]
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push:
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branches: [main]
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|
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env:
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GITHUB_CR_REPO: ghcr.io/${{ github.repository }}
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jobs:
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build:
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||||
runs-on: ubuntu-latest
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strategy:
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fail-fast: false
|
||||
matrix:
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platform:
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- linux/amd64
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- linux/arm64
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steps:
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- name: Prepare
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run: |
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platform=${{ matrix.platform }}
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echo "PLATFORM_PAIR=${platform//\//-}" >> $GITHUB_ENV
|
||||
|
||||
- name: Docker meta
|
||||
id: meta
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uses: docker/metadata-action@v5
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with:
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images: |
|
||||
${{ env.GITHUB_CR_REPO }}
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|
||||
- name: Login to GHCR
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uses: docker/login-action@v3
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||||
with:
|
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registry: ghcr.io
|
||||
username: ${{ github.repository_owner }}
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||||
password: ${{ secrets.GITHUB_TOKEN }}
|
||||
|
||||
- name: Set up QEMU
|
||||
uses: docker/setup-qemu-action@v3
|
||||
|
||||
- name: Set up Docker Buildx
|
||||
uses: docker/setup-buildx-action@v3
|
||||
|
||||
- name: Build and push by digest
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||||
id: build
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||||
uses: docker/build-push-action@v6
|
||||
with:
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||||
platforms: ${{ matrix.platform }}
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||||
labels: ${{ steps.meta.outputs.labels }}
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||||
tags: |
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||||
${{ env.GITHUB_CR_REPO }}
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build-args: |
|
||||
TARGETPLATFORM=${{ matrix.platform }}
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||||
outputs: type=image,push-by-digest=true,name-canonical=true,push=true
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||||
|
||||
- name: Export digest
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||||
run: |
|
||||
mkdir -p ${{ runner.temp }}/digests
|
||||
digest="${{ steps.build.outputs.digest }}"
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||||
touch "${{ runner.temp }}/digests/${digest#sha256:}"
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|
||||
- name: Upload digest
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||||
uses: actions/upload-artifact@v4
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||||
with:
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||||
name: digests-${{ env.PLATFORM_PAIR }}
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||||
path: ${{ runner.temp }}/digests/*
|
||||
if-no-files-found: error
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||||
retention-days: 1
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merge:
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runs-on: ubuntu-latest
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needs:
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||||
- build
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||||
steps:
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||||
- name: Download digests
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uses: actions/download-artifact@v4
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||||
with:
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path: ${{ runner.temp }}/digests
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||||
pattern: digests-*
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merge-multiple: true
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|
||||
- name: Login to GHCR
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||||
uses: docker/login-action@v3
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||||
with:
|
||||
registry: ghcr.io
|
||||
username: ${{ github.repository_owner }}
|
||||
password: ${{ secrets.GITHUB_TOKEN }}
|
||||
|
||||
- name: Set up Docker Buildx
|
||||
uses: docker/setup-buildx-action@v3
|
||||
|
||||
- name: Docker meta
|
||||
id: meta
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||||
uses: docker/metadata-action@v5
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||||
with:
|
||||
images: |
|
||||
${{ env.GITHUB_CR_REPO }}
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||||
tags: |
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||||
type=ref,event=branch
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||||
type=ref,event=pr
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||||
type=semver,pattern={{version}}
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||||
type=semver,pattern={{major}}
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||||
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||||
- name: Docker tags
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||||
run: |
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||||
tags=$(jq -cr '.tags | map("-t " + .) | join(" ")' <<< "$DOCKER_METADATA_OUTPUT_JSON")
|
||||
if [ -z "$tags" ]; then
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||||
echo "DOCKER_METADATA_OUTPUT_VERSION=${{ github.ref_name }}" >> $GITHUB_ENV
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||||
tags="-t ${{ env.GITHUB_CR_REPO }}:${{ github.ref_name }}"
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||||
fi
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||||
echo "DOCKER_METADATA_TAGS=$tags" >> $GITHUB_ENV
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||||
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||||
- name: Create manifest list and push
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||||
working-directory: ${{ runner.temp }}/digests
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||||
run: |
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||||
docker buildx imagetools create ${{ env.DOCKER_METADATA_TAGS }} \
|
||||
$(printf '${{ env.GITHUB_CR_REPO }}@sha256:%s ' *)
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||||
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||||
- name: Inspect image
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||||
run: |
|
||||
docker buildx imagetools inspect ${{ env.GITHUB_CR_REPO }}:${{ env.DOCKER_METADATA_OUTPUT_VERSION }}
|
||||
+192
@@ -0,0 +1,192 @@
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||||
# Byte-compiled / optimized / DLL files
|
||||
__pycache__/
|
||||
*.py[cod]
|
||||
*$py.class
|
||||
|
||||
# C extensions
|
||||
*.so
|
||||
|
||||
# Distribution / packaging
|
||||
.Python
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||||
build/
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||||
develop-eggs/
|
||||
dist/
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||||
downloads/
|
||||
eggs/
|
||||
.eggs/
|
||||
lib/
|
||||
lib64/
|
||||
parts/
|
||||
sdist/
|
||||
var/
|
||||
wheels/
|
||||
share/python-wheels/
|
||||
*.egg-info/
|
||||
.installed.cfg
|
||||
*.egg
|
||||
MANIFEST
|
||||
|
||||
# PyInstaller
|
||||
# Usually these files are written by a python script from a template
|
||||
# before PyInstaller builds the exe, so as to inject date/other infos into it.
|
||||
*.manifest
|
||||
*.spec
|
||||
|
||||
# Installer logs
|
||||
pip-log.txt
|
||||
pip-delete-this-directory.txt
|
||||
|
||||
# Unit test / coverage reports
|
||||
htmlcov/
|
||||
.tox/
|
||||
.nox/
|
||||
.coverage
|
||||
.coverage.*
|
||||
.cache
|
||||
nosetests.xml
|
||||
coverage.xml
|
||||
*.cover
|
||||
*.py,cover
|
||||
.hypothesis/
|
||||
.pytest_cache/
|
||||
cover/
|
||||
|
||||
# Translations
|
||||
*.mo
|
||||
*.pot
|
||||
|
||||
# Django stuff:
|
||||
*.log
|
||||
local_settings.py
|
||||
db.sqlite3
|
||||
db.sqlite3-journal
|
||||
|
||||
# Flask stuff:
|
||||
instance/
|
||||
.webassets-cache
|
||||
|
||||
# Scrapy stuff:
|
||||
.scrapy
|
||||
|
||||
# Sphinx documentation
|
||||
docs/_build/
|
||||
|
||||
# PyBuilder
|
||||
.pybuilder/
|
||||
target/
|
||||
|
||||
# Jupyter Notebook
|
||||
.ipynb_checkpoints
|
||||
|
||||
# IPython
|
||||
profile_default/
|
||||
ipython_config.py
|
||||
|
||||
# pyenv
|
||||
# For a library or package, you might want to ignore these files since the code is
|
||||
# intended to run in multiple environments; otherwise, check them in:
|
||||
# .python-version
|
||||
|
||||
# pipenv
|
||||
# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
|
||||
# However, in case of collaboration, if having platform-specific dependencies or dependencies
|
||||
# having no cross-platform support, pipenv may install dependencies that don't work, or not
|
||||
# install all needed dependencies.
|
||||
#Pipfile.lock
|
||||
|
||||
# poetry
|
||||
# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
|
||||
# This is especially recommended for binary packages to ensure reproducibility, and is more
|
||||
# commonly ignored for libraries.
|
||||
# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
|
||||
#poetry.lock
|
||||
|
||||
# pdm
|
||||
# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
|
||||
#pdm.lock
|
||||
# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
|
||||
# in version control.
|
||||
# https://pdm.fming.dev/latest/usage/project/#working-with-version-control
|
||||
.pdm.toml
|
||||
.pdm-python
|
||||
.pdm-build/
|
||||
|
||||
# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
|
||||
__pypackages__/
|
||||
|
||||
# Celery stuff
|
||||
celerybeat-schedule
|
||||
celerybeat.pid
|
||||
|
||||
# SageMath parsed files
|
||||
*.sage.py
|
||||
|
||||
# Environments
|
||||
.env
|
||||
.venv
|
||||
env/
|
||||
venv/
|
||||
ENV/
|
||||
env.bak/
|
||||
venv.bak/
|
||||
test_env/
|
||||
myenv
|
||||
|
||||
|
||||
# Spyder project settings
|
||||
.spyderproject
|
||||
.spyproject
|
||||
|
||||
# Rope project settings
|
||||
.ropeproject
|
||||
|
||||
# mkdocs documentation
|
||||
/site
|
||||
|
||||
# mypy
|
||||
.mypy_cache/
|
||||
.dmypy.json
|
||||
dmypy.json
|
||||
|
||||
# Pyre type checker
|
||||
.pyre/
|
||||
|
||||
# pytype static type analyzer
|
||||
.pytype/
|
||||
|
||||
# Cython debug symbols
|
||||
cython_debug/
|
||||
|
||||
# PyCharm
|
||||
# JetBrains specific template is maintained in a separate JetBrains.gitignore that can
|
||||
# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
|
||||
# and can be added to the global gitignore or merged into this file. For a more nuclear
|
||||
# option (not recommended) you can uncomment the following to ignore the entire idea folder.
|
||||
.idea/
|
||||
temp
|
||||
tmp
|
||||
|
||||
|
||||
.DS_Store
|
||||
|
||||
private_example.py
|
||||
private_example
|
||||
|
||||
browser_cookies.json
|
||||
cookies.json
|
||||
AgentHistory.json
|
||||
cv_04_24.pdf
|
||||
AgentHistoryList.json
|
||||
*.gif
|
||||
|
||||
# For Sharing (.pem files)
|
||||
.gradio/
|
||||
|
||||
# For Docker
|
||||
data/
|
||||
|
||||
# For Config Files (Current Settings)
|
||||
.config.pkl
|
||||
*.pdf
|
||||
|
||||
workflow
|
||||
Vendored
+11
@@ -0,0 +1,11 @@
|
||||
{
|
||||
"python.analysis.typeCheckingMode": "basic",
|
||||
"[python]": {
|
||||
"editor.defaultFormatter": "charliermarsh.ruff",
|
||||
"editor.formatOnSave": true,
|
||||
"editor.codeActionsOnSave": {
|
||||
"source.fixAll.ruff": "explicit",
|
||||
"source.organizeImports.ruff": "explicit"
|
||||
}
|
||||
}
|
||||
}
|
||||
+89
@@ -0,0 +1,89 @@
|
||||
FROM python:3.11-slim-bookworm
|
||||
|
||||
# Set platform for multi-arch builds (Docker Buildx will set this)
|
||||
ARG TARGETPLATFORM
|
||||
ARG NODE_MAJOR=20
|
||||
|
||||
# Install system dependencies (removed libgconf-2-4)
|
||||
RUN apt-get update && apt-get install -y \
|
||||
wget \
|
||||
netcat-traditional \
|
||||
gnupg \
|
||||
curl \
|
||||
unzip \
|
||||
xvfb \
|
||||
libxss1 \
|
||||
libnss3 \
|
||||
libnspr4 \
|
||||
libasound2 \
|
||||
libatk1.0-0 \
|
||||
libatk-bridge2.0-0 \
|
||||
libcups2 \
|
||||
libdbus-1-3 \
|
||||
libdrm2 \
|
||||
libgbm1 \
|
||||
libgtk-3-0 \
|
||||
libxcomposite1 \
|
||||
libxdamage1 \
|
||||
libxfixes3 \
|
||||
libxrandr2 \
|
||||
xdg-utils \
|
||||
fonts-liberation \
|
||||
fonts-noto-color-emoji \
|
||||
fonts-unifont \
|
||||
dbus \
|
||||
xauth \
|
||||
x11vnc \
|
||||
tigervnc-tools \
|
||||
supervisor \
|
||||
net-tools \
|
||||
procps \
|
||||
git \
|
||||
python3-numpy \
|
||||
fontconfig \
|
||||
fonts-dejavu \
|
||||
fonts-dejavu-core \
|
||||
fonts-dejavu-extra \
|
||||
vim \
|
||||
&& rm -rf /var/lib/apt/lists/*
|
||||
|
||||
# Install noVNC
|
||||
RUN git clone https://github.com/novnc/noVNC.git /opt/novnc \
|
||||
&& git clone https://github.com/novnc/websockify /opt/novnc/utils/websockify \
|
||||
&& ln -s /opt/novnc/vnc.html /opt/novnc/index.html
|
||||
|
||||
# Install Node.js using NodeSource PPA
|
||||
RUN mkdir -p /etc/apt/keyrings \
|
||||
&& curl -fsSL https://deb.nodesource.com/gpgkey/nodesource-repo.gpg.key | gpg --dearmor -o /etc/apt/keyrings/nodesource.gpg \
|
||||
&& echo "deb [signed-by=/etc/apt/keyrings/nodesource.gpg] https://deb.nodesource.com/node_$NODE_MAJOR.x nodistro main" | tee /etc/apt/sources.list.d/nodesource.list \
|
||||
&& apt-get update \
|
||||
&& apt-get install -y nodejs \
|
||||
&& rm -rf /var/lib/apt/lists/*
|
||||
|
||||
# Verify Node.js and npm installation
|
||||
RUN node -v && npm -v && npx -v
|
||||
|
||||
# Set up working directory
|
||||
WORKDIR /app
|
||||
|
||||
# Copy requirements and install Python dependencies
|
||||
COPY requirements.txt .
|
||||
RUN pip install --no-cache-dir -r requirements.txt
|
||||
|
||||
# Playwright setup
|
||||
ENV PLAYWRIGHT_BROWSERS_PATH=/ms-browsers
|
||||
RUN mkdir -p $PLAYWRIGHT_BROWSERS_PATH
|
||||
|
||||
# Install Chromium via Playwright without --with-deps
|
||||
RUN PLAYWRIGHT_SKIP_BROWSER_DOWNLOAD=0 playwright install chromium
|
||||
|
||||
# Copy application code
|
||||
COPY . .
|
||||
|
||||
# Set up supervisor configuration
|
||||
RUN mkdir -p /var/log/supervisor
|
||||
COPY supervisord.conf /etc/supervisor/conf.d/supervisord.conf
|
||||
|
||||
EXPOSE 7788 6080 5901 9222
|
||||
|
||||
CMD ["/usr/bin/supervisord", "-c", "/etc/supervisor/conf.d/supervisord.conf"]
|
||||
@@ -0,0 +1,21 @@
|
||||
MIT License
|
||||
|
||||
Copyright (c) 2024 Browser Use Inc.
|
||||
|
||||
Permission is hereby granted, free of charge, to any person obtaining a copy
|
||||
of this software and associated documentation files (the "Software"), to deal
|
||||
in the Software without restriction, including without limitation the rights
|
||||
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
|
||||
copies of the Software, and to permit persons to whom the Software is
|
||||
furnished to do so, subject to the following conditions:
|
||||
|
||||
The above copyright notice and this permission notice shall be included in all
|
||||
copies or substantial portions of the Software.
|
||||
|
||||
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
|
||||
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
|
||||
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
||||
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
|
||||
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
|
||||
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
|
||||
SOFTWARE.
|
||||
@@ -0,0 +1,151 @@
|
||||
<img src="./assets/web-ui.png" alt="Browser Use Web UI" width="full"/>
|
||||
|
||||
<br/>
|
||||
|
||||
[](https://github.com/browser-use/web-ui/stargazers)
|
||||
[](https://link.browser-use.com/discord)
|
||||
[](https://docs.browser-use.com)
|
||||
[](https://x.com/warmshao)
|
||||
|
||||
This project builds upon the foundation of the [browser-use](https://github.com/browser-use/browser-use), which is designed to make websites accessible for AI agents.
|
||||
|
||||
We would like to officially thank [WarmShao](https://github.com/warmshao) for his contribution to this project.
|
||||
|
||||
**WebUI:** is built on Gradio and supports most of `browser-use` functionalities. This UI is designed to be user-friendly and enables easy interaction with the browser agent.
|
||||
|
||||
**Expanded LLM Support:** We've integrated support for various Large Language Models (LLMs), including: Google, OpenAI, Azure OpenAI, Anthropic, DeepSeek, Ollama etc. And we plan to add support for even more models in the future.
|
||||
|
||||
**Custom Browser Support:** You can use your own browser with our tool, eliminating the need to re-login to sites or deal with other authentication challenges. This feature also supports high-definition screen recording.
|
||||
|
||||
**Persistent Browser Sessions:** You can choose to keep the browser window open between AI tasks, allowing you to see the complete history and state of AI interactions.
|
||||
|
||||
<video src="https://github.com/user-attachments/assets/56bc7080-f2e3-4367-af22-6bf2245ff6cb" controls="controls">Your browser does not support playing this video!</video>
|
||||
|
||||
## Installation Guide
|
||||
|
||||
### Option 1: Local Installation
|
||||
|
||||
Read the [quickstart guide](https://docs.browser-use.com/quickstart#prepare-the-environment) or follow the steps below to get started.
|
||||
|
||||
#### Step 1: Clone the Repository
|
||||
```bash
|
||||
git clone https://github.com/browser-use/web-ui.git
|
||||
cd web-ui
|
||||
```
|
||||
|
||||
#### Step 2: Set Up Python Environment
|
||||
We recommend using [uv](https://docs.astral.sh/uv/) for managing the Python environment.
|
||||
|
||||
Using uv (recommended):
|
||||
```bash
|
||||
uv venv --python 3.11
|
||||
```
|
||||
|
||||
Activate the virtual environment:
|
||||
- Windows (Command Prompt):
|
||||
```cmd
|
||||
.venv\Scripts\activate
|
||||
```
|
||||
- Windows (PowerShell):
|
||||
```powershell
|
||||
.\.venv\Scripts\Activate.ps1
|
||||
```
|
||||
- macOS/Linux:
|
||||
```bash
|
||||
source .venv/bin/activate
|
||||
```
|
||||
|
||||
#### Step 3: Install Dependencies
|
||||
Install Python packages:
|
||||
```bash
|
||||
uv pip install -r requirements.txt
|
||||
```
|
||||
|
||||
Install Browsers in playwright.
|
||||
```bash
|
||||
playwright install --with-deps
|
||||
```
|
||||
Or you can install specific browsers by running:
|
||||
```bash
|
||||
playwright install chromium --with-deps
|
||||
```
|
||||
|
||||
#### Step 4: Configure Environment
|
||||
1. Create a copy of the example environment file:
|
||||
- Windows (Command Prompt):
|
||||
```bash
|
||||
copy .env.example .env
|
||||
```
|
||||
- macOS/Linux/Windows (PowerShell):
|
||||
```bash
|
||||
cp .env.example .env
|
||||
```
|
||||
2. Open `.env` in your preferred text editor and add your API keys and other settings
|
||||
|
||||
#### Step 5: Enjoy the web-ui
|
||||
1. **Run the WebUI:**
|
||||
```bash
|
||||
python webui.py --ip 127.0.0.1 --port 7788
|
||||
```
|
||||
2. **Access the WebUI:** Open your web browser and navigate to `http://127.0.0.1:7788`.
|
||||
3. **Using Your Own Browser(Optional):**
|
||||
- Set `BROWSER_PATH` to the executable path of your browser and `BROWSER_USER_DATA` to the user data directory of your browser. Leave `BROWSER_USER_DATA` empty if you want to use local user data.
|
||||
- Windows
|
||||
```env
|
||||
BROWSER_PATH="C:\Program Files\Google\Chrome\Application\chrome.exe"
|
||||
BROWSER_USER_DATA="C:\Users\YourUsername\AppData\Local\Google\Chrome\User Data"
|
||||
```
|
||||
> Note: Replace `YourUsername` with your actual Windows username for Windows systems.
|
||||
- Mac
|
||||
```env
|
||||
BROWSER_PATH="/Applications/Google Chrome.app/Contents/MacOS/Google Chrome"
|
||||
BROWSER_USER_DATA="/Users/YourUsername/Library/Application Support/Google/Chrome"
|
||||
```
|
||||
- Close all Chrome windows
|
||||
- Open the WebUI in a non-Chrome browser, such as Firefox or Edge. This is important because the persistent browser context will use the Chrome data when running the agent.
|
||||
- Check the "Use Own Browser" option within the Browser Settings.
|
||||
|
||||
### Option 2: Docker Installation
|
||||
|
||||
#### Prerequisites
|
||||
- Docker and Docker Compose installed
|
||||
- [Docker Desktop](https://www.docker.com/products/docker-desktop/) (For Windows/macOS)
|
||||
- [Docker Engine](https://docs.docker.com/engine/install/) and [Docker Compose](https://docs.docker.com/compose/install/) (For Linux)
|
||||
|
||||
#### Step 1: Clone the Repository
|
||||
```bash
|
||||
git clone https://github.com/browser-use/web-ui.git
|
||||
cd web-ui
|
||||
```
|
||||
|
||||
#### Step 2: Configure Environment
|
||||
1. Create a copy of the example environment file:
|
||||
- Windows (Command Prompt):
|
||||
```bash
|
||||
copy .env.example .env
|
||||
```
|
||||
- macOS/Linux/Windows (PowerShell):
|
||||
```bash
|
||||
cp .env.example .env
|
||||
```
|
||||
2. Open `.env` in your preferred text editor and add your API keys and other settings
|
||||
|
||||
#### Step 3: Docker Build and Run
|
||||
```bash
|
||||
docker compose up --build
|
||||
```
|
||||
For ARM64 systems (e.g., Apple Silicon Macs), please run follow command:
|
||||
```bash
|
||||
TARGETPLATFORM=linux/arm64 docker compose up --build
|
||||
```
|
||||
|
||||
#### Step 4: Enjoy the web-ui and vnc
|
||||
- Web-UI: Open `http://localhost:7788` in your browser
|
||||
- VNC Viewer (for watching browser interactions): Open `http://localhost:6080/vnc.html`
|
||||
- Default VNC password: "youvncpassword"
|
||||
- Can be changed by setting `VNC_PASSWORD` in your `.env` file
|
||||
|
||||
## Changelog
|
||||
- [x] **2025/01/26:** Thanks to @vvincent1234. Now browser-use-webui can combine with DeepSeek-r1 to engage in deep thinking!
|
||||
- [x] **2025/01/10:** Thanks to @casistack. Now we have Docker Setup option and also Support keep browser open between tasks.[Video tutorial demo](https://github.com/browser-use/web-ui/issues/1#issuecomment-2582511750).
|
||||
- [x] **2025/01/06:** Thanks to @richard-devbot. A New and Well-Designed WebUI is released. [Video tutorial demo](https://github.com/warmshao/browser-use-webui/issues/1#issuecomment-2573393113).
|
||||
@@ -0,0 +1,7 @@
|
||||
# WeHub 来源说明
|
||||
|
||||
- 原始项目：`browser-use/web-ui`
|
||||
- 原始仓库：https://github.com/browser-use/web-ui
|
||||
- 导入方式：上游默认分支的最新快照
|
||||
- 原作者、版权和许可证信息以原始仓库及本仓库 LICENSE 为准
|
||||
- 本文件仅用于记录来源，不代表 WeHub 是原项目作者
|
||||
+19
@@ -0,0 +1,19 @@
|
||||
## Reporting Security Issues
|
||||
|
||||
If you believe you have found a security vulnerability in browser-use, please report it through coordinated disclosure.
|
||||
|
||||
**Please do not report security vulnerabilities through the repository issues, discussions, or pull requests.**
|
||||
|
||||
Instead, please open a new [Github security advisory](https://github.com/browser-use/web-ui/security/advisories/new).
|
||||
|
||||
Please include as much of the information listed below as you can to help me better understand and resolve the issue:
|
||||
|
||||
* The type of issue (e.g., buffer overflow, SQL injection, or cross-site scripting)
|
||||
* Full paths of source file(s) related to the manifestation of the issue
|
||||
* The location of the affected source code (tag/branch/commit or direct URL)
|
||||
* Any special configuration required to reproduce the issue
|
||||
* Step-by-step instructions to reproduce the issue
|
||||
* Proof-of-concept or exploit code (if possible)
|
||||
* Impact of the issue, including how an attacker might exploit the issue
|
||||
|
||||
This information will help me triage your report more quickly.
|
||||
Binary file not shown.
|
After Width: | Height: | Size: 413 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 24 KiB |
@@ -0,0 +1,80 @@
|
||||
services:
|
||||
# debug: docker compose run --rm -it browser-use-webui bash
|
||||
browser-use-webui:
|
||||
# image: ghcr.io/browser-use/web-ui # Using precompiled image
|
||||
build:
|
||||
context: .
|
||||
dockerfile: Dockerfile
|
||||
args:
|
||||
TARGETPLATFORM: ${TARGETPLATFORM:-linux/amd64}
|
||||
ports:
|
||||
- "7788:7788"
|
||||
- "6080:6080"
|
||||
- "5901:5901"
|
||||
- "9222:9222"
|
||||
environment:
|
||||
# LLM API Keys & Endpoints
|
||||
- OPENAI_ENDPOINT=${OPENAI_ENDPOINT:-https://api.openai.com/v1}
|
||||
- OPENAI_API_KEY=${OPENAI_API_KEY:-}
|
||||
- ANTHROPIC_ENDPOINT=${ANTHROPIC_ENDPOINT:-https://api.anthropic.com}
|
||||
- ANTHROPIC_API_KEY=${ANTHROPIC_API_KEY:-}
|
||||
- GOOGLE_API_KEY=${GOOGLE_API_KEY:-}
|
||||
- AZURE_OPENAI_ENDPOINT=${AZURE_OPENAI_ENDPOINT:-}
|
||||
- AZURE_OPENAI_API_KEY=${AZURE_OPENAI_API_KEY:-}
|
||||
- AZURE_OPENAI_API_VERSION=${AZURE_OPENAI_API_VERSION:-2025-01-01-preview}
|
||||
- DEEPSEEK_ENDPOINT=${DEEPSEEK_ENDPOINT:-https://api.deepseek.com}
|
||||
- DEEPSEEK_API_KEY=${DEEPSEEK_API_KEY:-}
|
||||
- OLLAMA_ENDPOINT=${OLLAMA_ENDPOINT:-http://localhost:11434}
|
||||
- MISTRAL_ENDPOINT=${MISTRAL_ENDPOINT:-https://api.mistral.ai/v1}
|
||||
- MISTRAL_API_KEY=${MISTRAL_API_KEY:-}
|
||||
- ALIBABA_ENDPOINT=${ALIBABA_ENDPOINT:-https://dashscope.aliyuncs.com/compatible-mode/v1}
|
||||
- ALIBABA_API_KEY=${ALIBABA_API_KEY:-}
|
||||
- MOONSHOT_ENDPOINT=${MOONSHOT_ENDPOINT:-https://api.moonshot.cn/v1}
|
||||
- MOONSHOT_API_KEY=${MOONSHOT_API_KEY:-}
|
||||
- UNBOUND_ENDPOINT=${UNBOUND_ENDPOINT:-https://api.getunbound.ai}
|
||||
- UNBOUND_API_KEY=${UNBOUND_API_KEY:-}
|
||||
- SiliconFLOW_ENDPOINT=${SiliconFLOW_ENDPOINT:-https://api.siliconflow.cn/v1/}
|
||||
- SiliconFLOW_API_KEY=${SiliconFLOW_API_KEY:-}
|
||||
- IBM_ENDPOINT=${IBM_ENDPOINT:-https://us-south.ml.cloud.ibm.com}
|
||||
- IBM_API_KEY=${IBM_API_KEY:-}
|
||||
- IBM_PROJECT_ID=${IBM_PROJECT_ID:-}
|
||||
|
||||
# Application Settings
|
||||
- ANONYMIZED_TELEMETRY=${ANONYMIZED_TELEMETRY:-false}
|
||||
- BROWSER_USE_LOGGING_LEVEL=${BROWSER_USE_LOGGING_LEVEL:-info}
|
||||
|
||||
# Browser Settings
|
||||
- BROWSER_PATH=
|
||||
- BROWSER_USER_DATA=
|
||||
- BROWSER_DEBUGGING_PORT=${BROWSER_DEBUGGING_PORT:-9222}
|
||||
- BROWSER_DEBUGGING_HOST=localhost
|
||||
- USE_OWN_BROWSER=false
|
||||
- KEEP_BROWSER_OPEN=true
|
||||
- BROWSER_CDP=${BROWSER_CDP:-} # e.g., http://localhost:9222
|
||||
|
||||
# Display Settings
|
||||
- DISPLAY=:99
|
||||
# This ENV is used by the Dockerfile during build time if playwright respects it.
|
||||
# It's not strictly needed at runtime by docker-compose unless your app or scripts also read it.
|
||||
- PLAYWRIGHT_BROWSERS_PATH=/ms-browsers # Matches Dockerfile ENV
|
||||
- RESOLUTION=${RESOLUTION:-1920x1080x24}
|
||||
- RESOLUTION_WIDTH=${RESOLUTION_WIDTH:-1920}
|
||||
- RESOLUTION_HEIGHT=${RESOLUTION_HEIGHT:-1080}
|
||||
|
||||
# VNC Settings
|
||||
- VNC_PASSWORD=${VNC_PASSWORD:-youvncpassword}
|
||||
|
||||
volumes:
|
||||
- /tmp/.X11-unix:/tmp/.X11-unix
|
||||
# - ./my_chrome_data:/app/data/chrome_data # Optional: persist browser data
|
||||
restart: unless-stopped
|
||||
shm_size: "2gb"
|
||||
cap_add:
|
||||
- SYS_ADMIN
|
||||
tmpfs:
|
||||
- /tmp
|
||||
healthcheck:
|
||||
test: ["CMD", "nc", "-z", "localhost", "5901"] # VNC port
|
||||
interval: 10s
|
||||
timeout: 5s
|
||||
retries: 3
|
||||
@@ -0,0 +1,10 @@
|
||||
browser-use==0.1.48
|
||||
pyperclip==1.9.0
|
||||
gradio==5.27.0
|
||||
json-repair
|
||||
langchain-mistralai==0.2.4
|
||||
MainContentExtractor==0.0.4
|
||||
langchain-ibm==0.3.10
|
||||
langchain_mcp_adapters==0.0.9
|
||||
langgraph==0.3.34
|
||||
langchain-community
|
||||
@@ -0,0 +1,169 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import asyncio
|
||||
import logging
|
||||
import os
|
||||
|
||||
# from lmnr.sdk.decorators import observe
|
||||
from browser_use.agent.gif import create_history_gif
|
||||
from browser_use.agent.service import Agent, AgentHookFunc
|
||||
from browser_use.agent.views import (
|
||||
ActionResult,
|
||||
AgentHistory,
|
||||
AgentHistoryList,
|
||||
AgentStepInfo,
|
||||
ToolCallingMethod,
|
||||
)
|
||||
from browser_use.browser.views import BrowserStateHistory
|
||||
from browser_use.utils import time_execution_async
|
||||
from dotenv import load_dotenv
|
||||
from browser_use.agent.message_manager.utils import is_model_without_tool_support
|
||||
|
||||
load_dotenv()
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
SKIP_LLM_API_KEY_VERIFICATION = (
|
||||
os.environ.get("SKIP_LLM_API_KEY_VERIFICATION", "false").lower()[0] in "ty1"
|
||||
)
|
||||
|
||||
|
||||
class BrowserUseAgent(Agent):
|
||||
def _set_tool_calling_method(self) -> ToolCallingMethod | None:
|
||||
tool_calling_method = self.settings.tool_calling_method
|
||||
if tool_calling_method == 'auto':
|
||||
if is_model_without_tool_support(self.model_name):
|
||||
return 'raw'
|
||||
elif self.chat_model_library == 'ChatGoogleGenerativeAI':
|
||||
return None
|
||||
elif self.chat_model_library == 'ChatOpenAI':
|
||||
return 'function_calling'
|
||||
elif self.chat_model_library == 'AzureChatOpenAI':
|
||||
return 'function_calling'
|
||||
else:
|
||||
return None
|
||||
else:
|
||||
return tool_calling_method
|
||||
|
||||
@time_execution_async("--run (agent)")
|
||||
async def run(
|
||||
self, max_steps: int = 100, on_step_start: AgentHookFunc | None = None,
|
||||
on_step_end: AgentHookFunc | None = None
|
||||
) -> AgentHistoryList:
|
||||
"""Execute the task with maximum number of steps"""
|
||||
|
||||
loop = asyncio.get_event_loop()
|
||||
|
||||
# Set up the Ctrl+C signal handler with callbacks specific to this agent
|
||||
from browser_use.utils import SignalHandler
|
||||
|
||||
signal_handler = SignalHandler(
|
||||
loop=loop,
|
||||
pause_callback=self.pause,
|
||||
resume_callback=self.resume,
|
||||
custom_exit_callback=None, # No special cleanup needed on forced exit
|
||||
exit_on_second_int=True,
|
||||
)
|
||||
signal_handler.register()
|
||||
|
||||
try:
|
||||
self._log_agent_run()
|
||||
|
||||
# Execute initial actions if provided
|
||||
if self.initial_actions:
|
||||
result = await self.multi_act(self.initial_actions, check_for_new_elements=False)
|
||||
self.state.last_result = result
|
||||
|
||||
for step in range(max_steps):
|
||||
# Check if waiting for user input after Ctrl+C
|
||||
if self.state.paused:
|
||||
signal_handler.wait_for_resume()
|
||||
signal_handler.reset()
|
||||
|
||||
# Check if we should stop due to too many failures
|
||||
if self.state.consecutive_failures >= self.settings.max_failures:
|
||||
logger.error(f'❌ Stopping due to {self.settings.max_failures} consecutive failures')
|
||||
break
|
||||
|
||||
# Check control flags before each step
|
||||
if self.state.stopped:
|
||||
logger.info('Agent stopped')
|
||||
break
|
||||
|
||||
while self.state.paused:
|
||||
await asyncio.sleep(0.2) # Small delay to prevent CPU spinning
|
||||
if self.state.stopped: # Allow stopping while paused
|
||||
break
|
||||
|
||||
if on_step_start is not None:
|
||||
await on_step_start(self)
|
||||
|
||||
step_info = AgentStepInfo(step_number=step, max_steps=max_steps)
|
||||
await self.step(step_info)
|
||||
|
||||
if on_step_end is not None:
|
||||
await on_step_end(self)
|
||||
|
||||
if self.state.history.is_done():
|
||||
if self.settings.validate_output and step < max_steps - 1:
|
||||
if not await self._validate_output():
|
||||
continue
|
||||
|
||||
await self.log_completion()
|
||||
break
|
||||
else:
|
||||
error_message = 'Failed to complete task in maximum steps'
|
||||
|
||||
self.state.history.history.append(
|
||||
AgentHistory(
|
||||
model_output=None,
|
||||
result=[ActionResult(error=error_message, include_in_memory=True)],
|
||||
state=BrowserStateHistory(
|
||||
url='',
|
||||
title='',
|
||||
tabs=[],
|
||||
interacted_element=[],
|
||||
screenshot=None,
|
||||
),
|
||||
metadata=None,
|
||||
)
|
||||
)
|
||||
|
||||
logger.info(f'❌ {error_message}')
|
||||
|
||||
return self.state.history
|
||||
|
||||
except KeyboardInterrupt:
|
||||
# Already handled by our signal handler, but catch any direct KeyboardInterrupt as well
|
||||
logger.info('Got KeyboardInterrupt during execution, returning current history')
|
||||
return self.state.history
|
||||
|
||||
finally:
|
||||
# Unregister signal handlers before cleanup
|
||||
signal_handler.unregister()
|
||||
|
||||
if self.settings.save_playwright_script_path:
|
||||
logger.info(
|
||||
f'Agent run finished. Attempting to save Playwright script to: {self.settings.save_playwright_script_path}'
|
||||
)
|
||||
try:
|
||||
# Extract sensitive data keys if sensitive_data is provided
|
||||
keys = list(self.sensitive_data.keys()) if self.sensitive_data else None
|
||||
# Pass browser and context config to the saving method
|
||||
self.state.history.save_as_playwright_script(
|
||||
self.settings.save_playwright_script_path,
|
||||
sensitive_data_keys=keys,
|
||||
browser_config=self.browser.config,
|
||||
context_config=self.browser_context.config,
|
||||
)
|
||||
except Exception as script_gen_err:
|
||||
# Log any error during script generation/saving
|
||||
logger.error(f'Failed to save Playwright script: {script_gen_err}', exc_info=True)
|
||||
|
||||
await self.close()
|
||||
|
||||
if self.settings.generate_gif:
|
||||
output_path: str = 'agent_history.gif'
|
||||
if isinstance(self.settings.generate_gif, str):
|
||||
output_path = self.settings.generate_gif
|
||||
|
||||
create_history_gif(task=self.task, history=self.state.history, output_path=output_path)
|
||||
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,109 @@
|
||||
import asyncio
|
||||
import pdb
|
||||
|
||||
from playwright.async_api import Browser as PlaywrightBrowser
|
||||
from playwright.async_api import (
|
||||
BrowserContext as PlaywrightBrowserContext,
|
||||
)
|
||||
from playwright.async_api import (
|
||||
Playwright,
|
||||
async_playwright,
|
||||
)
|
||||
from browser_use.browser.browser import Browser, IN_DOCKER
|
||||
from browser_use.browser.context import BrowserContext, BrowserContextConfig
|
||||
from playwright.async_api import BrowserContext as PlaywrightBrowserContext
|
||||
import logging
|
||||
|
||||
from browser_use.browser.chrome import (
|
||||
CHROME_ARGS,
|
||||
CHROME_DETERMINISTIC_RENDERING_ARGS,
|
||||
CHROME_DISABLE_SECURITY_ARGS,
|
||||
CHROME_DOCKER_ARGS,
|
||||
CHROME_HEADLESS_ARGS,
|
||||
)
|
||||
from browser_use.browser.context import BrowserContext, BrowserContextConfig
|
||||
from browser_use.browser.utils.screen_resolution import get_screen_resolution, get_window_adjustments
|
||||
from browser_use.utils import time_execution_async
|
||||
import socket
|
||||
|
||||
from .custom_context import CustomBrowserContext
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class CustomBrowser(Browser):
|
||||
|
||||
async def new_context(self, config: BrowserContextConfig | None = None) -> CustomBrowserContext:
|
||||
"""Create a browser context"""
|
||||
browser_config = self.config.model_dump() if self.config else {}
|
||||
context_config = config.model_dump() if config else {}
|
||||
merged_config = {**browser_config, **context_config}
|
||||
return CustomBrowserContext(config=BrowserContextConfig(**merged_config), browser=self)
|
||||
|
||||
async def _setup_builtin_browser(self, playwright: Playwright) -> PlaywrightBrowser:
|
||||
"""Sets up and returns a Playwright Browser instance with anti-detection measures."""
|
||||
assert self.config.browser_binary_path is None, 'browser_binary_path should be None if trying to use the builtin browsers'
|
||||
|
||||
# Use the configured window size from new_context_config if available
|
||||
if (
|
||||
not self.config.headless
|
||||
and hasattr(self.config, 'new_context_config')
|
||||
and hasattr(self.config.new_context_config, 'window_width')
|
||||
and hasattr(self.config.new_context_config, 'window_height')
|
||||
):
|
||||
screen_size = {
|
||||
'width': self.config.new_context_config.window_width,
|
||||
'height': self.config.new_context_config.window_height,
|
||||
}
|
||||
offset_x, offset_y = get_window_adjustments()
|
||||
elif self.config.headless:
|
||||
screen_size = {'width': 1920, 'height': 1080}
|
||||
offset_x, offset_y = 0, 0
|
||||
else:
|
||||
screen_size = get_screen_resolution()
|
||||
offset_x, offset_y = get_window_adjustments()
|
||||
|
||||
chrome_args = {
|
||||
f'--remote-debugging-port={self.config.chrome_remote_debugging_port}',
|
||||
*CHROME_ARGS,
|
||||
*(CHROME_DOCKER_ARGS if IN_DOCKER else []),
|
||||
*(CHROME_HEADLESS_ARGS if self.config.headless else []),
|
||||
*(CHROME_DISABLE_SECURITY_ARGS if self.config.disable_security else []),
|
||||
*(CHROME_DETERMINISTIC_RENDERING_ARGS if self.config.deterministic_rendering else []),
|
||||
f'--window-position={offset_x},{offset_y}',
|
||||
f'--window-size={screen_size["width"]},{screen_size["height"]}',
|
||||
*self.config.extra_browser_args,
|
||||
}
|
||||
|
||||
# check if chrome remote debugging port is already taken,
|
||||
# if so remove the remote-debugging-port arg to prevent conflicts
|
||||
with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
|
||||
if s.connect_ex(('localhost', self.config.chrome_remote_debugging_port)) == 0:
|
||||
chrome_args.remove(f'--remote-debugging-port={self.config.chrome_remote_debugging_port}')
|
||||
|
||||
browser_class = getattr(playwright, self.config.browser_class)
|
||||
args = {
|
||||
'chromium': list(chrome_args),
|
||||
'firefox': [
|
||||
*{
|
||||
'-no-remote',
|
||||
*self.config.extra_browser_args,
|
||||
}
|
||||
],
|
||||
'webkit': [
|
||||
*{
|
||||
'--no-startup-window',
|
||||
*self.config.extra_browser_args,
|
||||
}
|
||||
],
|
||||
}
|
||||
|
||||
browser = await browser_class.launch(
|
||||
channel='chromium', # https://github.com/microsoft/playwright/issues/33566
|
||||
headless=self.config.headless,
|
||||
args=args[self.config.browser_class],
|
||||
proxy=self.config.proxy.model_dump() if self.config.proxy else None,
|
||||
handle_sigterm=False,
|
||||
handle_sigint=False,
|
||||
)
|
||||
return browser
|
||||
@@ -0,0 +1,22 @@
|
||||
import json
|
||||
import logging
|
||||
import os
|
||||
|
||||
from browser_use.browser.browser import Browser, IN_DOCKER
|
||||
from browser_use.browser.context import BrowserContext, BrowserContextConfig
|
||||
from playwright.async_api import Browser as PlaywrightBrowser
|
||||
from playwright.async_api import BrowserContext as PlaywrightBrowserContext
|
||||
from typing import Optional
|
||||
from browser_use.browser.context import BrowserContextState
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class CustomBrowserContext(BrowserContext):
|
||||
def __init__(
|
||||
self,
|
||||
browser: 'Browser',
|
||||
config: BrowserContextConfig | None = None,
|
||||
state: Optional[BrowserContextState] = None,
|
||||
):
|
||||
super(CustomBrowserContext, self).__init__(browser=browser, config=config, state=state)
|
||||
@@ -0,0 +1,182 @@
|
||||
import pdb
|
||||
|
||||
import pyperclip
|
||||
from typing import Optional, Type, Callable, Dict, Any, Union, Awaitable, TypeVar
|
||||
from pydantic import BaseModel
|
||||
from browser_use.agent.views import ActionResult
|
||||
from browser_use.browser.context import BrowserContext
|
||||
from browser_use.controller.service import Controller, DoneAction
|
||||
from browser_use.controller.registry.service import Registry, RegisteredAction
|
||||
from main_content_extractor import MainContentExtractor
|
||||
from browser_use.controller.views import (
|
||||
ClickElementAction,
|
||||
DoneAction,
|
||||
ExtractPageContentAction,
|
||||
GoToUrlAction,
|
||||
InputTextAction,
|
||||
OpenTabAction,
|
||||
ScrollAction,
|
||||
SearchGoogleAction,
|
||||
SendKeysAction,
|
||||
SwitchTabAction,
|
||||
)
|
||||
import logging
|
||||
import inspect
|
||||
import asyncio
|
||||
import os
|
||||
from langchain_core.language_models.chat_models import BaseChatModel
|
||||
from browser_use.agent.views import ActionModel, ActionResult
|
||||
|
||||
from src.utils.mcp_client import create_tool_param_model, setup_mcp_client_and_tools
|
||||
|
||||
from browser_use.utils import time_execution_sync
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
Context = TypeVar('Context')
|
||||
|
||||
|
||||
class CustomController(Controller):
|
||||
def __init__(self, exclude_actions: list[str] = [],
|
||||
output_model: Optional[Type[BaseModel]] = None,
|
||||
ask_assistant_callback: Optional[Union[Callable[[str, BrowserContext], Dict[str, Any]], Callable[
|
||||
[str, BrowserContext], Awaitable[Dict[str, Any]]]]] = None,
|
||||
):
|
||||
super().__init__(exclude_actions=exclude_actions, output_model=output_model)
|
||||
self._register_custom_actions()
|
||||
self.ask_assistant_callback = ask_assistant_callback
|
||||
self.mcp_client = None
|
||||
self.mcp_server_config = None
|
||||
|
||||
def _register_custom_actions(self):
|
||||
"""Register all custom browser actions"""
|
||||
|
||||
@self.registry.action(
|
||||
"When executing tasks, prioritize autonomous completion. However, if you encounter a definitive blocker "
|
||||
"that prevents you from proceeding independently – such as needing credentials you don't possess, "
|
||||
"requiring subjective human judgment, needing a physical action performed, encountering complex CAPTCHAs, "
|
||||
"or facing limitations in your capabilities – you must request human assistance."
|
||||
)
|
||||
async def ask_for_assistant(query: str, browser: BrowserContext):
|
||||
if self.ask_assistant_callback:
|
||||
if inspect.iscoroutinefunction(self.ask_assistant_callback):
|
||||
user_response = await self.ask_assistant_callback(query, browser)
|
||||
else:
|
||||
user_response = self.ask_assistant_callback(query, browser)
|
||||
msg = f"AI ask: {query}. User response: {user_response['response']}"
|
||||
logger.info(msg)
|
||||
return ActionResult(extracted_content=msg, include_in_memory=True)
|
||||
else:
|
||||
return ActionResult(extracted_content="Human cannot help you. Please try another way.",
|
||||
include_in_memory=True)
|
||||
|
||||
@self.registry.action(
|
||||
'Upload file to interactive element with file path ',
|
||||
)
|
||||
async def upload_file(index: int, path: str, browser: BrowserContext, available_file_paths: list[str]):
|
||||
if path not in available_file_paths:
|
||||
return ActionResult(error=f'File path {path} is not available')
|
||||
|
||||
if not os.path.exists(path):
|
||||
return ActionResult(error=f'File {path} does not exist')
|
||||
|
||||
dom_el = await browser.get_dom_element_by_index(index)
|
||||
|
||||
file_upload_dom_el = dom_el.get_file_upload_element()
|
||||
|
||||
if file_upload_dom_el is None:
|
||||
msg = f'No file upload element found at index {index}'
|
||||
logger.info(msg)
|
||||
return ActionResult(error=msg)
|
||||
|
||||
file_upload_el = await browser.get_locate_element(file_upload_dom_el)
|
||||
|
||||
if file_upload_el is None:
|
||||
msg = f'No file upload element found at index {index}'
|
||||
logger.info(msg)
|
||||
return ActionResult(error=msg)
|
||||
|
||||
try:
|
||||
await file_upload_el.set_input_files(path)
|
||||
msg = f'Successfully uploaded file to index {index}'
|
||||
logger.info(msg)
|
||||
return ActionResult(extracted_content=msg, include_in_memory=True)
|
||||
except Exception as e:
|
||||
msg = f'Failed to upload file to index {index}: {str(e)}'
|
||||
logger.info(msg)
|
||||
return ActionResult(error=msg)
|
||||
|
||||
@time_execution_sync('--act')
|
||||
async def act(
|
||||
self,
|
||||
action: ActionModel,
|
||||
browser_context: Optional[BrowserContext] = None,
|
||||
#
|
||||
page_extraction_llm: Optional[BaseChatModel] = None,
|
||||
sensitive_data: Optional[Dict[str, str]] = None,
|
||||
available_file_paths: Optional[list[str]] = None,
|
||||
#
|
||||
context: Context | None = None,
|
||||
) -> ActionResult:
|
||||
"""Execute an action"""
|
||||
|
||||
try:
|
||||
for action_name, params in action.model_dump(exclude_unset=True).items():
|
||||
if params is not None:
|
||||
if action_name.startswith("mcp"):
|
||||
# this is a mcp tool
|
||||
logger.debug(f"Invoke MCP tool: {action_name}")
|
||||
mcp_tool = self.registry.registry.actions.get(action_name).function
|
||||
result = await mcp_tool.ainvoke(params)
|
||||
else:
|
||||
result = await self.registry.execute_action(
|
||||
action_name,
|
||||
params,
|
||||
browser=browser_context,
|
||||
page_extraction_llm=page_extraction_llm,
|
||||
sensitive_data=sensitive_data,
|
||||
available_file_paths=available_file_paths,
|
||||
context=context,
|
||||
)
|
||||
|
||||
if isinstance(result, str):
|
||||
return ActionResult(extracted_content=result)
|
||||
elif isinstance(result, ActionResult):
|
||||
return result
|
||||
elif result is None:
|
||||
return ActionResult()
|
||||
else:
|
||||
raise ValueError(f'Invalid action result type: {type(result)} of {result}')
|
||||
return ActionResult()
|
||||
except Exception as e:
|
||||
raise e
|
||||
|
||||
async def setup_mcp_client(self, mcp_server_config: Optional[Dict[str, Any]] = None):
|
||||
self.mcp_server_config = mcp_server_config
|
||||
if self.mcp_server_config:
|
||||
self.mcp_client = await setup_mcp_client_and_tools(self.mcp_server_config)
|
||||
self.register_mcp_tools()
|
||||
|
||||
def register_mcp_tools(self):
|
||||
"""
|
||||
Register the MCP tools used by this controller.
|
||||
"""
|
||||
if self.mcp_client:
|
||||
for server_name in self.mcp_client.server_name_to_tools:
|
||||
for tool in self.mcp_client.server_name_to_tools[server_name]:
|
||||
tool_name = f"mcp.{server_name}.{tool.name}"
|
||||
self.registry.registry.actions[tool_name] = RegisteredAction(
|
||||
name=tool_name,
|
||||
description=tool.description,
|
||||
function=tool,
|
||||
param_model=create_tool_param_model(tool),
|
||||
)
|
||||
logger.info(f"Add mcp tool: {tool_name}")
|
||||
logger.debug(
|
||||
f"Registered {len(self.mcp_client.server_name_to_tools[server_name])} mcp tools for {server_name}")
|
||||
else:
|
||||
logger.warning(f"MCP client not started.")
|
||||
|
||||
async def close_mcp_client(self):
|
||||
if self.mcp_client:
|
||||
await self.mcp_client.__aexit__(None, None, None)
|
||||
@@ -0,0 +1,100 @@
|
||||
PROVIDER_DISPLAY_NAMES = {
|
||||
"openai": "OpenAI",
|
||||
"azure_openai": "Azure OpenAI",
|
||||
"anthropic": "Anthropic",
|
||||
"deepseek": "DeepSeek",
|
||||
"google": "Google",
|
||||
"alibaba": "Alibaba",
|
||||
"moonshot": "MoonShot",
|
||||
"unbound": "Unbound AI",
|
||||
"ibm": "IBM",
|
||||
"grok": "Grok",
|
||||
}
|
||||
|
||||
# Predefined model names for common providers
|
||||
model_names = {
|
||||
"anthropic": ["claude-3-5-sonnet-20241022", "claude-3-5-sonnet-20240620", "claude-3-opus-20240229"],
|
||||
"openai": ["gpt-4o", "gpt-4", "gpt-3.5-turbo", "o3-mini"],
|
||||
"deepseek": ["deepseek-chat", "deepseek-reasoner"],
|
||||
"google": ["gemini-2.0-flash", "gemini-2.0-flash-thinking-exp", "gemini-1.5-flash-latest",
|
||||
"gemini-1.5-flash-8b-latest", "gemini-2.0-flash-thinking-exp-01-21", "gemini-2.0-pro-exp-02-05",
|
||||
"gemini-2.5-pro-preview-03-25", "gemini-2.5-flash-preview-04-17"],
|
||||
"ollama": ["qwen2.5:7b", "qwen2.5:14b", "qwen2.5:32b", "qwen2.5-coder:14b", "qwen2.5-coder:32b", "llama2:7b",
|
||||
"deepseek-r1:14b", "deepseek-r1:32b"],
|
||||
"azure_openai": ["gpt-4o", "gpt-4", "gpt-3.5-turbo"],
|
||||
"mistral": ["pixtral-large-latest", "mistral-large-latest", "mistral-small-latest", "ministral-8b-latest"],
|
||||
"alibaba": ["qwen-plus", "qwen-max", "qwen-vl-max", "qwen-vl-plus", "qwen-turbo", "qwen-long"],
|
||||
"moonshot": ["moonshot-v1-32k-vision-preview", "moonshot-v1-8k-vision-preview"],
|
||||
"unbound": ["gemini-2.0-flash", "gpt-4o-mini", "gpt-4o", "gpt-4.5-preview"],
|
||||
"grok": [
|
||||
"grok-3",
|
||||
"grok-3-fast",
|
||||
"grok-3-mini",
|
||||
"grok-3-mini-fast",
|
||||
"grok-2-vision",
|
||||
"grok-2-image",
|
||||
"grok-2",
|
||||
],
|
||||
"siliconflow": [
|
||||
"deepseek-ai/DeepSeek-R1",
|
||||
"deepseek-ai/DeepSeek-V3",
|
||||
"deepseek-ai/DeepSeek-R1-Distill-Qwen-32B",
|
||||
"deepseek-ai/DeepSeek-R1-Distill-Qwen-14B",
|
||||
"deepseek-ai/DeepSeek-R1-Distill-Qwen-7B",
|
||||
"deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B",
|
||||
"deepseek-ai/DeepSeek-V2.5",
|
||||
"deepseek-ai/deepseek-vl2",
|
||||
"Qwen/Qwen2.5-72B-Instruct-128K",
|
||||
"Qwen/Qwen2.5-72B-Instruct",
|
||||
"Qwen/Qwen2.5-32B-Instruct",
|
||||
"Qwen/Qwen2.5-14B-Instruct",
|
||||
"Qwen/Qwen2.5-7B-Instruct",
|
||||
"Qwen/Qwen2.5-Coder-32B-Instruct",
|
||||
"Qwen/Qwen2.5-Coder-7B-Instruct",
|
||||
"Qwen/Qwen2-7B-Instruct",
|
||||
"Qwen/Qwen2-1.5B-Instruct",
|
||||
"Qwen/QwQ-32B-Preview",
|
||||
"Qwen/Qwen2-VL-72B-Instruct",
|
||||
"Qwen/Qwen2.5-VL-32B-Instruct",
|
||||
"Qwen/Qwen2.5-VL-72B-Instruct",
|
||||
"TeleAI/TeleChat2",
|
||||
"THUDM/glm-4-9b-chat",
|
||||
"Vendor-A/Qwen/Qwen2.5-72B-Instruct",
|
||||
"internlm/internlm2_5-7b-chat",
|
||||
"internlm/internlm2_5-20b-chat",
|
||||
"Pro/Qwen/Qwen2.5-7B-Instruct",
|
||||
"Pro/Qwen/Qwen2-7B-Instruct",
|
||||
"Pro/Qwen/Qwen2-1.5B-Instruct",
|
||||
"Pro/THUDM/chatglm3-6b",
|
||||
"Pro/THUDM/glm-4-9b-chat",
|
||||
],
|
||||
"ibm": ["ibm/granite-vision-3.1-2b-preview", "meta-llama/llama-4-maverick-17b-128e-instruct-fp8",
|
||||
"meta-llama/llama-3-2-90b-vision-instruct"],
|
||||
"modelscope":[
|
||||
"Qwen/Qwen2.5-Coder-32B-Instruct",
|
||||
"Qwen/Qwen2.5-Coder-14B-Instruct",
|
||||
"Qwen/Qwen2.5-Coder-7B-Instruct",
|
||||
"Qwen/Qwen2.5-72B-Instruct",
|
||||
"Qwen/Qwen2.5-32B-Instruct",
|
||||
"Qwen/Qwen2.5-14B-Instruct",
|
||||
"Qwen/Qwen2.5-7B-Instruct",
|
||||
"Qwen/QwQ-32B-Preview",
|
||||
"Qwen/Qwen2.5-VL-3B-Instruct",
|
||||
"Qwen/Qwen2.5-VL-7B-Instruct",
|
||||
"Qwen/Qwen2.5-VL-32B-Instruct",
|
||||
"Qwen/Qwen2.5-VL-72B-Instruct",
|
||||
"deepseek-ai/DeepSeek-R1-Distill-Qwen-32B",
|
||||
"deepseek-ai/DeepSeek-R1-Distill-Qwen-14B",
|
||||
"deepseek-ai/DeepSeek-R1-Distill-Qwen-7B",
|
||||
"deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B",
|
||||
"deepseek-ai/DeepSeek-R1",
|
||||
"deepseek-ai/DeepSeek-V3",
|
||||
"Qwen/Qwen3-1.7B",
|
||||
"Qwen/Qwen3-4B",
|
||||
"Qwen/Qwen3-8B",
|
||||
"Qwen/Qwen3-14B",
|
||||
"Qwen/Qwen3-30B-A3B",
|
||||
"Qwen/Qwen3-32B",
|
||||
"Qwen/Qwen3-235B-A22B",
|
||||
],
|
||||
}
|
||||
@@ -0,0 +1,355 @@
|
||||
from openai import OpenAI
|
||||
import pdb
|
||||
from langchain_openai import ChatOpenAI
|
||||
from langchain_core.globals import get_llm_cache
|
||||
from langchain_core.language_models.base import (
|
||||
BaseLanguageModel,
|
||||
LangSmithParams,
|
||||
LanguageModelInput,
|
||||
)
|
||||
import os
|
||||
from langchain_core.load import dumpd, dumps
|
||||
from langchain_core.messages import (
|
||||
AIMessage,
|
||||
SystemMessage,
|
||||
AnyMessage,
|
||||
BaseMessage,
|
||||
BaseMessageChunk,
|
||||
HumanMessage,
|
||||
convert_to_messages,
|
||||
message_chunk_to_message,
|
||||
)
|
||||
from langchain_core.outputs import (
|
||||
ChatGeneration,
|
||||
ChatGenerationChunk,
|
||||
ChatResult,
|
||||
LLMResult,
|
||||
RunInfo,
|
||||
)
|
||||
from langchain_ollama import ChatOllama
|
||||
from langchain_core.output_parsers.base import OutputParserLike
|
||||
from langchain_core.runnables import Runnable, RunnableConfig
|
||||
from langchain_core.tools import BaseTool
|
||||
|
||||
from typing import (
|
||||
TYPE_CHECKING,
|
||||
Any,
|
||||
Callable,
|
||||
Literal,
|
||||
Optional,
|
||||
Union,
|
||||
cast, List,
|
||||
)
|
||||
from langchain_anthropic import ChatAnthropic
|
||||
from langchain_mistralai import ChatMistralAI
|
||||
from langchain_google_genai import ChatGoogleGenerativeAI
|
||||
from langchain_ollama import ChatOllama
|
||||
from langchain_openai import AzureChatOpenAI, ChatOpenAI
|
||||
from langchain_ibm import ChatWatsonx
|
||||
from langchain_aws import ChatBedrock
|
||||
from pydantic import SecretStr
|
||||
|
||||
from src.utils import config
|
||||
|
||||
|
||||
class DeepSeekR1ChatOpenAI(ChatOpenAI):
|
||||
|
||||
def __init__(self, *args: Any, **kwargs: Any) -> None:
|
||||
super().__init__(*args, **kwargs)
|
||||
self.client = OpenAI(
|
||||
base_url=kwargs.get("base_url"),
|
||||
api_key=kwargs.get("api_key")
|
||||
)
|
||||
|
||||
async def ainvoke(
|
||||
self,
|
||||
input: LanguageModelInput,
|
||||
config: Optional[RunnableConfig] = None,
|
||||
*,
|
||||
stop: Optional[list[str]] = None,
|
||||
**kwargs: Any,
|
||||
) -> AIMessage:
|
||||
message_history = []
|
||||
for input_ in input:
|
||||
if isinstance(input_, SystemMessage):
|
||||
message_history.append({"role": "system", "content": input_.content})
|
||||
elif isinstance(input_, AIMessage):
|
||||
message_history.append({"role": "assistant", "content": input_.content})
|
||||
else:
|
||||
message_history.append({"role": "user", "content": input_.content})
|
||||
|
||||
response = self.client.chat.completions.create(
|
||||
model=self.model_name,
|
||||
messages=message_history
|
||||
)
|
||||
|
||||
reasoning_content = response.choices[0].message.reasoning_content
|
||||
content = response.choices[0].message.content
|
||||
return AIMessage(content=content, reasoning_content=reasoning_content)
|
||||
|
||||
def invoke(
|
||||
self,
|
||||
input: LanguageModelInput,
|
||||
config: Optional[RunnableConfig] = None,
|
||||
*,
|
||||
stop: Optional[list[str]] = None,
|
||||
**kwargs: Any,
|
||||
) -> AIMessage:
|
||||
message_history = []
|
||||
for input_ in input:
|
||||
if isinstance(input_, SystemMessage):
|
||||
message_history.append({"role": "system", "content": input_.content})
|
||||
elif isinstance(input_, AIMessage):
|
||||
message_history.append({"role": "assistant", "content": input_.content})
|
||||
else:
|
||||
message_history.append({"role": "user", "content": input_.content})
|
||||
|
||||
response = self.client.chat.completions.create(
|
||||
model=self.model_name,
|
||||
messages=message_history
|
||||
)
|
||||
|
||||
reasoning_content = response.choices[0].message.reasoning_content
|
||||
content = response.choices[0].message.content
|
||||
return AIMessage(content=content, reasoning_content=reasoning_content)
|
||||
|
||||
|
||||
class DeepSeekR1ChatOllama(ChatOllama):
|
||||
|
||||
async def ainvoke(
|
||||
self,
|
||||
input: LanguageModelInput,
|
||||
config: Optional[RunnableConfig] = None,
|
||||
*,
|
||||
stop: Optional[list[str]] = None,
|
||||
**kwargs: Any,
|
||||
) -> AIMessage:
|
||||
org_ai_message = await super().ainvoke(input=input)
|
||||
org_content = org_ai_message.content
|
||||
reasoning_content = org_content.split("</think>")[0].replace("<think>", "")
|
||||
content = org_content.split("</think>")[1]
|
||||
if "**JSON Response:**" in content:
|
||||
content = content.split("**JSON Response:**")[-1]
|
||||
return AIMessage(content=content, reasoning_content=reasoning_content)
|
||||
|
||||
def invoke(
|
||||
self,
|
||||
input: LanguageModelInput,
|
||||
config: Optional[RunnableConfig] = None,
|
||||
*,
|
||||
stop: Optional[list[str]] = None,
|
||||
**kwargs: Any,
|
||||
) -> AIMessage:
|
||||
org_ai_message = super().invoke(input=input)
|
||||
org_content = org_ai_message.content
|
||||
reasoning_content = org_content.split("</think>")[0].replace("<think>", "")
|
||||
content = org_content.split("</think>")[1]
|
||||
if "**JSON Response:**" in content:
|
||||
content = content.split("**JSON Response:**")[-1]
|
||||
return AIMessage(content=content, reasoning_content=reasoning_content)
|
||||
|
||||
|
||||
def get_llm_model(provider: str, **kwargs):
|
||||
"""
|
||||
Get LLM model
|
||||
:param provider: LLM provider
|
||||
:param kwargs:
|
||||
:return:
|
||||
"""
|
||||
if provider not in ["ollama", "bedrock"]:
|
||||
env_var = f"{provider.upper()}_API_KEY"
|
||||
api_key = kwargs.get("api_key", "") or os.getenv(env_var, "")
|
||||
if not api_key:
|
||||
provider_display = config.PROVIDER_DISPLAY_NAMES.get(provider, provider.upper())
|
||||
error_msg = f"💥 {provider_display} API key not found! 🔑 Please set the `{env_var}` environment variable or provide it in the UI."
|
||||
raise ValueError(error_msg)
|
||||
kwargs["api_key"] = api_key
|
||||
|
||||
if provider == "anthropic":
|
||||
if not kwargs.get("base_url", ""):
|
||||
base_url = "https://api.anthropic.com"
|
||||
else:
|
||||
base_url = kwargs.get("base_url")
|
||||
|
||||
return ChatAnthropic(
|
||||
model=kwargs.get("model_name", "claude-3-5-sonnet-20241022"),
|
||||
temperature=kwargs.get("temperature", 0.0),
|
||||
base_url=base_url,
|
||||
api_key=api_key,
|
||||
)
|
||||
elif provider == 'mistral':
|
||||
if not kwargs.get("base_url", ""):
|
||||
base_url = os.getenv("MISTRAL_ENDPOINT", "https://api.mistral.ai/v1")
|
||||
else:
|
||||
base_url = kwargs.get("base_url")
|
||||
if not kwargs.get("api_key", ""):
|
||||
api_key = os.getenv("MISTRAL_API_KEY", "")
|
||||
else:
|
||||
api_key = kwargs.get("api_key")
|
||||
|
||||
return ChatMistralAI(
|
||||
model=kwargs.get("model_name", "mistral-large-latest"),
|
||||
temperature=kwargs.get("temperature", 0.0),
|
||||
base_url=base_url,
|
||||
api_key=api_key,
|
||||
)
|
||||
elif provider == "openai":
|
||||
if not kwargs.get("base_url", ""):
|
||||
base_url = os.getenv("OPENAI_ENDPOINT", "https://api.openai.com/v1")
|
||||
else:
|
||||
base_url = kwargs.get("base_url")
|
||||
|
||||
return ChatOpenAI(
|
||||
model=kwargs.get("model_name", "gpt-4o"),
|
||||
temperature=kwargs.get("temperature", 0.0),
|
||||
base_url=base_url,
|
||||
api_key=api_key,
|
||||
)
|
||||
elif provider == "grok":
|
||||
if not kwargs.get("base_url", ""):
|
||||
base_url = os.getenv("GROK_ENDPOINT", "https://api.x.ai/v1")
|
||||
else:
|
||||
base_url = kwargs.get("base_url")
|
||||
|
||||
return ChatOpenAI(
|
||||
model=kwargs.get("model_name", "grok-3"),
|
||||
temperature=kwargs.get("temperature", 0.0),
|
||||
base_url=base_url,
|
||||
api_key=api_key,
|
||||
)
|
||||
elif provider == "deepseek":
|
||||
if not kwargs.get("base_url", ""):
|
||||
base_url = os.getenv("DEEPSEEK_ENDPOINT", "")
|
||||
else:
|
||||
base_url = kwargs.get("base_url")
|
||||
|
||||
if kwargs.get("model_name", "deepseek-chat") == "deepseek-reasoner":
|
||||
return DeepSeekR1ChatOpenAI(
|
||||
model=kwargs.get("model_name", "deepseek-reasoner"),
|
||||
temperature=kwargs.get("temperature", 0.0),
|
||||
base_url=base_url,
|
||||
api_key=api_key,
|
||||
)
|
||||
else:
|
||||
return ChatOpenAI(
|
||||
model=kwargs.get("model_name", "deepseek-chat"),
|
||||
temperature=kwargs.get("temperature", 0.0),
|
||||
base_url=base_url,
|
||||
api_key=api_key,
|
||||
)
|
||||
elif provider == "google":
|
||||
return ChatGoogleGenerativeAI(
|
||||
model=kwargs.get("model_name", "gemini-2.0-flash-exp"),
|
||||
temperature=kwargs.get("temperature", 0.0),
|
||||
api_key=api_key,
|
||||
)
|
||||
elif provider == "ollama":
|
||||
if not kwargs.get("base_url", ""):
|
||||
base_url = os.getenv("OLLAMA_ENDPOINT", "http://localhost:11434")
|
||||
else:
|
||||
base_url = kwargs.get("base_url")
|
||||
|
||||
if "deepseek-r1" in kwargs.get("model_name", "qwen2.5:7b"):
|
||||
return DeepSeekR1ChatOllama(
|
||||
model=kwargs.get("model_name", "deepseek-r1:14b"),
|
||||
temperature=kwargs.get("temperature", 0.0),
|
||||
num_ctx=kwargs.get("num_ctx", 32000),
|
||||
base_url=base_url,
|
||||
)
|
||||
else:
|
||||
return ChatOllama(
|
||||
model=kwargs.get("model_name", "qwen2.5:7b"),
|
||||
temperature=kwargs.get("temperature", 0.0),
|
||||
num_ctx=kwargs.get("num_ctx", 32000),
|
||||
num_predict=kwargs.get("num_predict", 1024),
|
||||
base_url=base_url,
|
||||
)
|
||||
elif provider == "azure_openai":
|
||||
if not kwargs.get("base_url", ""):
|
||||
base_url = os.getenv("AZURE_OPENAI_ENDPOINT", "")
|
||||
else:
|
||||
base_url = kwargs.get("base_url")
|
||||
api_version = kwargs.get("api_version", "") or os.getenv("AZURE_OPENAI_API_VERSION", "2025-01-01-preview")
|
||||
return AzureChatOpenAI(
|
||||
model=kwargs.get("model_name", "gpt-4o"),
|
||||
temperature=kwargs.get("temperature", 0.0),
|
||||
api_version=api_version,
|
||||
azure_endpoint=base_url,
|
||||
api_key=api_key,
|
||||
)
|
||||
elif provider == "alibaba":
|
||||
if not kwargs.get("base_url", ""):
|
||||
base_url = os.getenv("ALIBABA_ENDPOINT", "https://dashscope.aliyuncs.com/compatible-mode/v1")
|
||||
else:
|
||||
base_url = kwargs.get("base_url")
|
||||
|
||||
return ChatOpenAI(
|
||||
model=kwargs.get("model_name", "qwen-plus"),
|
||||
temperature=kwargs.get("temperature", 0.0),
|
||||
base_url=base_url,
|
||||
api_key=api_key,
|
||||
)
|
||||
elif provider == "ibm":
|
||||
parameters = {
|
||||
"temperature": kwargs.get("temperature", 0.0),
|
||||
"max_tokens": kwargs.get("num_ctx", 32000)
|
||||
}
|
||||
if not kwargs.get("base_url", ""):
|
||||
base_url = os.getenv("IBM_ENDPOINT", "https://us-south.ml.cloud.ibm.com")
|
||||
else:
|
||||
base_url = kwargs.get("base_url")
|
||||
|
||||
return ChatWatsonx(
|
||||
model_id=kwargs.get("model_name", "ibm/granite-vision-3.1-2b-preview"),
|
||||
url=base_url,
|
||||
project_id=os.getenv("IBM_PROJECT_ID"),
|
||||
apikey=os.getenv("IBM_API_KEY"),
|
||||
params=parameters
|
||||
)
|
||||
elif provider == "moonshot":
|
||||
return ChatOpenAI(
|
||||
model=kwargs.get("model_name", "moonshot-v1-32k-vision-preview"),
|
||||
temperature=kwargs.get("temperature", 0.0),
|
||||
base_url=os.getenv("MOONSHOT_ENDPOINT"),
|
||||
api_key=os.getenv("MOONSHOT_API_KEY"),
|
||||
)
|
||||
elif provider == "unbound":
|
||||
return ChatOpenAI(
|
||||
model=kwargs.get("model_name", "gpt-4o-mini"),
|
||||
temperature=kwargs.get("temperature", 0.0),
|
||||
base_url=os.getenv("UNBOUND_ENDPOINT", "https://api.getunbound.ai"),
|
||||
api_key=api_key,
|
||||
)
|
||||
elif provider == "siliconflow":
|
||||
if not kwargs.get("api_key", ""):
|
||||
api_key = os.getenv("SiliconFLOW_API_KEY", "")
|
||||
else:
|
||||
api_key = kwargs.get("api_key")
|
||||
if not kwargs.get("base_url", ""):
|
||||
base_url = os.getenv("SiliconFLOW_ENDPOINT", "")
|
||||
else:
|
||||
base_url = kwargs.get("base_url")
|
||||
return ChatOpenAI(
|
||||
api_key=api_key,
|
||||
base_url=base_url,
|
||||
model_name=kwargs.get("model_name", "Qwen/QwQ-32B"),
|
||||
temperature=kwargs.get("temperature", 0.0),
|
||||
)
|
||||
elif provider == "modelscope":
|
||||
if not kwargs.get("api_key", ""):
|
||||
api_key = os.getenv("MODELSCOPE_API_KEY", "")
|
||||
else:
|
||||
api_key = kwargs.get("api_key")
|
||||
if not kwargs.get("base_url", ""):
|
||||
base_url = os.getenv("MODELSCOPE_ENDPOINT", "")
|
||||
else:
|
||||
base_url = kwargs.get("base_url")
|
||||
return ChatOpenAI(
|
||||
api_key=api_key,
|
||||
base_url=base_url,
|
||||
model_name=kwargs.get("model_name", "Qwen/QwQ-32B"),
|
||||
temperature=kwargs.get("temperature", 0.0),
|
||||
extra_body = {"enable_thinking": False}
|
||||
)
|
||||
else:
|
||||
raise ValueError(f"Unsupported provider: {provider}")
|
||||
@@ -0,0 +1,254 @@
|
||||
import inspect
|
||||
import logging
|
||||
import uuid
|
||||
from datetime import date, datetime, time
|
||||
from enum import Enum
|
||||
from typing import Any, Dict, List, Optional, Set, Type, Union, get_type_hints
|
||||
|
||||
from browser_use.controller.registry.views import ActionModel
|
||||
from langchain.tools import BaseTool
|
||||
from langchain_mcp_adapters.client import MultiServerMCPClient
|
||||
from pydantic import BaseModel, Field, create_model
|
||||
from pydantic.v1 import BaseModel, Field
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
async def setup_mcp_client_and_tools(mcp_server_config: Dict[str, Any]) -> Optional[MultiServerMCPClient]:
|
||||
"""
|
||||
Initializes the MultiServerMCPClient, connects to servers, fetches tools,
|
||||
filters them, and returns a flat list of usable tools and the client instance.
|
||||
|
||||
Returns:
|
||||
A tuple containing:
|
||||
- list[BaseTool]: The filtered list of usable LangChain tools.
|
||||
- MultiServerMCPClient | None: The initialized and started client instance, or None on failure.
|
||||
"""
|
||||
|
||||
logger.info("Initializing MultiServerMCPClient...")
|
||||
|
||||
if not mcp_server_config:
|
||||
logger.error("No MCP server configuration provided.")
|
||||
return None
|
||||
|
||||
try:
|
||||
if "mcpServers" in mcp_server_config:
|
||||
mcp_server_config = mcp_server_config["mcpServers"]
|
||||
client = MultiServerMCPClient(mcp_server_config)
|
||||
await client.__aenter__()
|
||||
return client
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to setup MCP client or fetch tools: {e}", exc_info=True)
|
||||
return None
|
||||
|
||||
|
||||
def create_tool_param_model(tool: BaseTool) -> Type[BaseModel]:
|
||||
"""Creates a Pydantic model from a LangChain tool's schema"""
|
||||
|
||||
# Get tool schema information
|
||||
json_schema = tool.args_schema
|
||||
tool_name = tool.name
|
||||
|
||||
# If the tool already has a schema defined, convert it to a new param_model
|
||||
if json_schema is not None:
|
||||
|
||||
# Create new parameter model
|
||||
params = {}
|
||||
|
||||
# Process properties if they exist
|
||||
if 'properties' in json_schema:
|
||||
# Find required fields
|
||||
required_fields: Set[str] = set(json_schema.get('required', []))
|
||||
|
||||
for prop_name, prop_details in json_schema['properties'].items():
|
||||
field_type = resolve_type(prop_details, f"{tool_name}_{prop_name}")
|
||||
|
||||
# Check if parameter is required
|
||||
is_required = prop_name in required_fields
|
||||
|
||||
# Get default value and description
|
||||
default_value = prop_details.get('default', ... if is_required else None)
|
||||
description = prop_details.get('description', '')
|
||||
|
||||
# Add field constraints
|
||||
field_kwargs = {'default': default_value}
|
||||
if description:
|
||||
field_kwargs['description'] = description
|
||||
|
||||
# Add additional constraints if present
|
||||
if 'minimum' in prop_details:
|
||||
field_kwargs['ge'] = prop_details['minimum']
|
||||
if 'maximum' in prop_details:
|
||||
field_kwargs['le'] = prop_details['maximum']
|
||||
if 'minLength' in prop_details:
|
||||
field_kwargs['min_length'] = prop_details['minLength']
|
||||
if 'maxLength' in prop_details:
|
||||
field_kwargs['max_length'] = prop_details['maxLength']
|
||||
if 'pattern' in prop_details:
|
||||
field_kwargs['pattern'] = prop_details['pattern']
|
||||
|
||||
# Add to parameters dictionary
|
||||
params[prop_name] = (field_type, Field(**field_kwargs))
|
||||
|
||||
return create_model(
|
||||
f'{tool_name}_parameters',
|
||||
__base__=ActionModel,
|
||||
**params, # type: ignore
|
||||
)
|
||||
|
||||
# If no schema is defined, extract parameters from the _run method
|
||||
run_method = tool._run
|
||||
sig = inspect.signature(run_method)
|
||||
|
||||
# Get type hints for better type information
|
||||
try:
|
||||
type_hints = get_type_hints(run_method)
|
||||
except Exception:
|
||||
type_hints = {}
|
||||
|
||||
params = {}
|
||||
for name, param in sig.parameters.items():
|
||||
# Skip 'self' parameter and any other parameters you want to exclude
|
||||
if name == 'self':
|
||||
continue
|
||||
|
||||
# Get annotation from type hints if available, otherwise from signature
|
||||
annotation = type_hints.get(name, param.annotation)
|
||||
if annotation == inspect.Parameter.empty:
|
||||
annotation = Any
|
||||
|
||||
# Use default value if available, otherwise make it required
|
||||
if param.default != param.empty:
|
||||
params[name] = (annotation, param.default)
|
||||
else:
|
||||
params[name] = (annotation, ...)
|
||||
|
||||
return create_model(
|
||||
f'{tool_name}_parameters',
|
||||
__base__=ActionModel,
|
||||
**params, # type: ignore
|
||||
)
|
||||
|
||||
|
||||
def resolve_type(prop_details: Dict[str, Any], prefix: str = "") -> Any:
|
||||
"""Recursively resolves JSON schema type to Python/Pydantic type"""
|
||||
|
||||
# Handle reference types
|
||||
if '$ref' in prop_details:
|
||||
# In a real application, reference resolution would be needed
|
||||
return Any
|
||||
|
||||
# Basic type mapping
|
||||
type_mapping = {
|
||||
'string': str,
|
||||
'integer': int,
|
||||
'number': float,
|
||||
'boolean': bool,
|
||||
'array': List,
|
||||
'object': Dict,
|
||||
'null': type(None),
|
||||
}
|
||||
|
||||
# Handle formatted strings
|
||||
if prop_details.get('type') == 'string' and 'format' in prop_details:
|
||||
format_mapping = {
|
||||
'date-time': datetime,
|
||||
'date': date,
|
||||
'time': time,
|
||||
'email': str,
|
||||
'uri': str,
|
||||
'url': str,
|
||||
'uuid': uuid.UUID,
|
||||
'binary': bytes,
|
||||
}
|
||||
return format_mapping.get(prop_details['format'], str)
|
||||
|
||||
# Handle enum types
|
||||
if 'enum' in prop_details:
|
||||
enum_values = prop_details['enum']
|
||||
# Create dynamic enum class with safe names
|
||||
enum_dict = {}
|
||||
for i, v in enumerate(enum_values):
|
||||
# Ensure enum names are valid Python identifiers
|
||||
if isinstance(v, str):
|
||||
key = v.upper().replace(' ', '_').replace('-', '_')
|
||||
if not key.isidentifier():
|
||||
key = f"VALUE_{i}"
|
||||
else:
|
||||
key = f"VALUE_{i}"
|
||||
enum_dict[key] = v
|
||||
|
||||
# Only create enum if we have values
|
||||
if enum_dict:
|
||||
return Enum(f"{prefix}_Enum", enum_dict)
|
||||
return str # Fallback
|
||||
|
||||
# Handle array types
|
||||
if prop_details.get('type') == 'array' and 'items' in prop_details:
|
||||
item_type = resolve_type(prop_details['items'], f"{prefix}_item")
|
||||
return List[item_type] # type: ignore
|
||||
|
||||
# Handle object types with properties
|
||||
if prop_details.get('type') == 'object' and 'properties' in prop_details:
|
||||
nested_params = {}
|
||||
for nested_name, nested_details in prop_details['properties'].items():
|
||||
nested_type = resolve_type(nested_details, f"{prefix}_{nested_name}")
|
||||
# Get required field info
|
||||
required_fields = prop_details.get('required', [])
|
||||
is_required = nested_name in required_fields
|
||||
default_value = nested_details.get('default', ... if is_required else None)
|
||||
description = nested_details.get('description', '')
|
||||
|
||||
field_kwargs = {'default': default_value}
|
||||
if description:
|
||||
field_kwargs['description'] = description
|
||||
|
||||
nested_params[nested_name] = (nested_type, Field(**field_kwargs))
|
||||
|
||||
# Create nested model
|
||||
nested_model = create_model(f"{prefix}_Model", **nested_params)
|
||||
return nested_model
|
||||
|
||||
# Handle union types (oneOf, anyOf)
|
||||
if 'oneOf' in prop_details or 'anyOf' in prop_details:
|
||||
union_schema = prop_details.get('oneOf') or prop_details.get('anyOf')
|
||||
union_types = []
|
||||
for i, t in enumerate(union_schema):
|
||||
union_types.append(resolve_type(t, f"{prefix}_{i}"))
|
||||
|
||||
if union_types:
|
||||
return Union.__getitem__(tuple(union_types)) # type: ignore
|
||||
return Any
|
||||
|
||||
# Handle allOf (intersection types)
|
||||
if 'allOf' in prop_details:
|
||||
nested_params = {}
|
||||
for i, schema_part in enumerate(prop_details['allOf']):
|
||||
if 'properties' in schema_part:
|
||||
for nested_name, nested_details in schema_part['properties'].items():
|
||||
nested_type = resolve_type(nested_details, f"{prefix}_allOf_{i}_{nested_name}")
|
||||
# Check if required
|
||||
required_fields = schema_part.get('required', [])
|
||||
is_required = nested_name in required_fields
|
||||
nested_params[nested_name] = (nested_type, ... if is_required else None)
|
||||
|
||||
# Create composite model
|
||||
if nested_params:
|
||||
composite_model = create_model(f"{prefix}_CompositeModel", **nested_params)
|
||||
return composite_model
|
||||
return Dict
|
||||
|
||||
# Default to basic types
|
||||
schema_type = prop_details.get('type', 'string')
|
||||
if isinstance(schema_type, list):
|
||||
# Handle multiple types (e.g., ["string", "null"])
|
||||
non_null_types = [t for t in schema_type if t != 'null']
|
||||
if non_null_types:
|
||||
primary_type = type_mapping.get(non_null_types[0], Any)
|
||||
if 'null' in schema_type:
|
||||
return Optional[primary_type] # type: ignore
|
||||
return primary_type
|
||||
return Any
|
||||
|
||||
return type_mapping.get(schema_type, Any)
|
||||
@@ -0,0 +1,39 @@
|
||||
import base64
|
||||
import os
|
||||
import time
|
||||
from pathlib import Path
|
||||
from typing import Dict, Optional
|
||||
import requests
|
||||
import json
|
||||
import gradio as gr
|
||||
import uuid
|
||||
|
||||
|
||||
def encode_image(img_path):
|
||||
if not img_path:
|
||||
return None
|
||||
with open(img_path, "rb") as fin:
|
||||
image_data = base64.b64encode(fin.read()).decode("utf-8")
|
||||
return image_data
|
||||
|
||||
|
||||
def get_latest_files(directory: str, file_types: list = ['.webm', '.zip']) -> Dict[str, Optional[str]]:
|
||||
"""Get the latest recording and trace files"""
|
||||
latest_files: Dict[str, Optional[str]] = {ext: None for ext in file_types}
|
||||
|
||||
if not os.path.exists(directory):
|
||||
os.makedirs(directory, exist_ok=True)
|
||||
return latest_files
|
||||
|
||||
for file_type in file_types:
|
||||
try:
|
||||
matches = list(Path(directory).rglob(f"*{file_type}"))
|
||||
if matches:
|
||||
latest = max(matches, key=lambda p: p.stat().st_mtime)
|
||||
# Only return files that are complete (not being written)
|
||||
if time.time() - latest.stat().st_mtime > 1.0:
|
||||
latest_files[file_type] = str(latest)
|
||||
except Exception as e:
|
||||
print(f"Error getting latest {file_type} file: {e}")
|
||||
|
||||
return latest_files
|
||||
@@ -0,0 +1,269 @@
|
||||
import json
|
||||
import os
|
||||
|
||||
import gradio as gr
|
||||
from gradio.components import Component
|
||||
from typing import Any, Dict, Optional
|
||||
from src.webui.webui_manager import WebuiManager
|
||||
from src.utils import config
|
||||
import logging
|
||||
from functools import partial
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def update_model_dropdown(llm_provider):
|
||||
"""
|
||||
Update the model name dropdown with predefined models for the selected provider.
|
||||
"""
|
||||
# Use predefined models for the selected provider
|
||||
if llm_provider in config.model_names:
|
||||
return gr.Dropdown(choices=config.model_names[llm_provider], value=config.model_names[llm_provider][0],
|
||||
interactive=True)
|
||||
else:
|
||||
return gr.Dropdown(choices=[], value="", interactive=True, allow_custom_value=True)
|
||||
|
||||
|
||||
async def update_mcp_server(mcp_file: str, webui_manager: WebuiManager):
|
||||
"""
|
||||
Update the MCP server.
|
||||
"""
|
||||
if hasattr(webui_manager, "bu_controller") and webui_manager.bu_controller:
|
||||
logger.warning("⚠️ Close controller because mcp file has changed!")
|
||||
await webui_manager.bu_controller.close_mcp_client()
|
||||
webui_manager.bu_controller = None
|
||||
|
||||
if not mcp_file or not os.path.exists(mcp_file) or not mcp_file.endswith('.json'):
|
||||
logger.warning(f"{mcp_file} is not a valid MCP file.")
|
||||
return None, gr.update(visible=False)
|
||||
|
||||
with open(mcp_file, 'r') as f:
|
||||
mcp_server = json.load(f)
|
||||
|
||||
return json.dumps(mcp_server, indent=2), gr.update(visible=True)
|
||||
|
||||
|
||||
def create_agent_settings_tab(webui_manager: WebuiManager):
|
||||
"""
|
||||
Creates an agent settings tab.
|
||||
"""
|
||||
input_components = set(webui_manager.get_components())
|
||||
tab_components = {}
|
||||
|
||||
with gr.Group():
|
||||
with gr.Column():
|
||||
override_system_prompt = gr.Textbox(label="Override system prompt", lines=4, interactive=True)
|
||||
extend_system_prompt = gr.Textbox(label="Extend system prompt", lines=4, interactive=True)
|
||||
|
||||
with gr.Group():
|
||||
mcp_json_file = gr.File(label="MCP server json", interactive=True, file_types=[".json"])
|
||||
mcp_server_config = gr.Textbox(label="MCP server", lines=6, interactive=True, visible=False)
|
||||
|
||||
with gr.Group():
|
||||
with gr.Row():
|
||||
llm_provider = gr.Dropdown(
|
||||
choices=[provider for provider, model in config.model_names.items()],
|
||||
label="LLM Provider",
|
||||
value=os.getenv("DEFAULT_LLM", "openai"),
|
||||
info="Select LLM provider for LLM",
|
||||
interactive=True
|
||||
)
|
||||
llm_model_name = gr.Dropdown(
|
||||
label="LLM Model Name",
|
||||
choices=config.model_names[os.getenv("DEFAULT_LLM", "openai")],
|
||||
value=config.model_names[os.getenv("DEFAULT_LLM", "openai")][0],
|
||||
interactive=True,
|
||||
allow_custom_value=True,
|
||||
info="Select a model in the dropdown options or directly type a custom model name"
|
||||
)
|
||||
with gr.Row():
|
||||
llm_temperature = gr.Slider(
|
||||
minimum=0.0,
|
||||
maximum=2.0,
|
||||
value=0.6,
|
||||
step=0.1,
|
||||
label="LLM Temperature",
|
||||
info="Controls randomness in model outputs",
|
||||
interactive=True
|
||||
)
|
||||
|
||||
use_vision = gr.Checkbox(
|
||||
label="Use Vision",
|
||||
value=True,
|
||||
info="Enable Vision(Input highlighted screenshot into LLM)",
|
||||
interactive=True
|
||||
)
|
||||
|
||||
ollama_num_ctx = gr.Slider(
|
||||
minimum=2 ** 8,
|
||||
maximum=2 ** 16,
|
||||
value=16000,
|
||||
step=1,
|
||||
label="Ollama Context Length",
|
||||
info="Controls max context length model needs to handle (less = faster)",
|
||||
visible=False,
|
||||
interactive=True
|
||||
)
|
||||
|
||||
with gr.Row():
|
||||
llm_base_url = gr.Textbox(
|
||||
label="Base URL",
|
||||
value="",
|
||||
info="API endpoint URL (if required)"
|
||||
)
|
||||
llm_api_key = gr.Textbox(
|
||||
label="API Key",
|
||||
type="password",
|
||||
value="",
|
||||
info="Your API key (leave blank to use .env)"
|
||||
)
|
||||
|
||||
with gr.Group():
|
||||
with gr.Row():
|
||||
planner_llm_provider = gr.Dropdown(
|
||||
choices=[provider for provider, model in config.model_names.items()],
|
||||
label="Planner LLM Provider",
|
||||
info="Select LLM provider for LLM",
|
||||
value=None,
|
||||
interactive=True
|
||||
)
|
||||
planner_llm_model_name = gr.Dropdown(
|
||||
label="Planner LLM Model Name",
|
||||
interactive=True,
|
||||
allow_custom_value=True,
|
||||
info="Select a model in the dropdown options or directly type a custom model name"
|
||||
)
|
||||
with gr.Row():
|
||||
planner_llm_temperature = gr.Slider(
|
||||
minimum=0.0,
|
||||
maximum=2.0,
|
||||
value=0.6,
|
||||
step=0.1,
|
||||
label="Planner LLM Temperature",
|
||||
info="Controls randomness in model outputs",
|
||||
interactive=True
|
||||
)
|
||||
|
||||
planner_use_vision = gr.Checkbox(
|
||||
label="Use Vision(Planner LLM)",
|
||||
value=False,
|
||||
info="Enable Vision(Input highlighted screenshot into LLM)",
|
||||
interactive=True
|
||||
)
|
||||
|
||||
planner_ollama_num_ctx = gr.Slider(
|
||||
minimum=2 ** 8,
|
||||
maximum=2 ** 16,
|
||||
value=16000,
|
||||
step=1,
|
||||
label="Ollama Context Length",
|
||||
info="Controls max context length model needs to handle (less = faster)",
|
||||
visible=False,
|
||||
interactive=True
|
||||
)
|
||||
|
||||
with gr.Row():
|
||||
planner_llm_base_url = gr.Textbox(
|
||||
label="Base URL",
|
||||
value="",
|
||||
info="API endpoint URL (if required)"
|
||||
)
|
||||
planner_llm_api_key = gr.Textbox(
|
||||
label="API Key",
|
||||
type="password",
|
||||
value="",
|
||||
info="Your API key (leave blank to use .env)"
|
||||
)
|
||||
|
||||
with gr.Row():
|
||||
max_steps = gr.Slider(
|
||||
minimum=1,
|
||||
maximum=1000,
|
||||
value=100,
|
||||
step=1,
|
||||
label="Max Run Steps",
|
||||
info="Maximum number of steps the agent will take",
|
||||
interactive=True
|
||||
)
|
||||
max_actions = gr.Slider(
|
||||
minimum=1,
|
||||
maximum=100,
|
||||
value=10,
|
||||
step=1,
|
||||
label="Max Number of Actions",
|
||||
info="Maximum number of actions the agent will take per step",
|
||||
interactive=True
|
||||
)
|
||||
|
||||
with gr.Row():
|
||||
max_input_tokens = gr.Number(
|
||||
label="Max Input Tokens",
|
||||
value=128000,
|
||||
precision=0,
|
||||
interactive=True
|
||||
)
|
||||
tool_calling_method = gr.Dropdown(
|
||||
label="Tool Calling Method",
|
||||
value="auto",
|
||||
interactive=True,
|
||||
allow_custom_value=True,
|
||||
choices=['function_calling', 'json_mode', 'raw', 'auto', 'tools', "None"],
|
||||
visible=True
|
||||
)
|
||||
tab_components.update(dict(
|
||||
override_system_prompt=override_system_prompt,
|
||||
extend_system_prompt=extend_system_prompt,
|
||||
llm_provider=llm_provider,
|
||||
llm_model_name=llm_model_name,
|
||||
llm_temperature=llm_temperature,
|
||||
use_vision=use_vision,
|
||||
ollama_num_ctx=ollama_num_ctx,
|
||||
llm_base_url=llm_base_url,
|
||||
llm_api_key=llm_api_key,
|
||||
planner_llm_provider=planner_llm_provider,
|
||||
planner_llm_model_name=planner_llm_model_name,
|
||||
planner_llm_temperature=planner_llm_temperature,
|
||||
planner_use_vision=planner_use_vision,
|
||||
planner_ollama_num_ctx=planner_ollama_num_ctx,
|
||||
planner_llm_base_url=planner_llm_base_url,
|
||||
planner_llm_api_key=planner_llm_api_key,
|
||||
max_steps=max_steps,
|
||||
max_actions=max_actions,
|
||||
max_input_tokens=max_input_tokens,
|
||||
tool_calling_method=tool_calling_method,
|
||||
mcp_json_file=mcp_json_file,
|
||||
mcp_server_config=mcp_server_config,
|
||||
))
|
||||
webui_manager.add_components("agent_settings", tab_components)
|
||||
|
||||
llm_provider.change(
|
||||
fn=lambda x: gr.update(visible=x == "ollama"),
|
||||
inputs=llm_provider,
|
||||
outputs=ollama_num_ctx
|
||||
)
|
||||
llm_provider.change(
|
||||
lambda provider: update_model_dropdown(provider),
|
||||
inputs=[llm_provider],
|
||||
outputs=[llm_model_name]
|
||||
)
|
||||
planner_llm_provider.change(
|
||||
fn=lambda x: gr.update(visible=x == "ollama"),
|
||||
inputs=[planner_llm_provider],
|
||||
outputs=[planner_ollama_num_ctx]
|
||||
)
|
||||
planner_llm_provider.change(
|
||||
lambda provider: update_model_dropdown(provider),
|
||||
inputs=[planner_llm_provider],
|
||||
outputs=[planner_llm_model_name]
|
||||
)
|
||||
|
||||
async def update_wrapper(mcp_file):
|
||||
"""Wrapper for handle_pause_resume."""
|
||||
update_dict = await update_mcp_server(mcp_file, webui_manager)
|
||||
yield update_dict
|
||||
|
||||
mcp_json_file.change(
|
||||
update_wrapper,
|
||||
inputs=[mcp_json_file],
|
||||
outputs=[mcp_server_config, mcp_server_config]
|
||||
)
|
||||
@@ -0,0 +1,161 @@
|
||||
import os
|
||||
from distutils.util import strtobool
|
||||
import gradio as gr
|
||||
import logging
|
||||
from gradio.components import Component
|
||||
|
||||
from src.webui.webui_manager import WebuiManager
|
||||
from src.utils import config
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
async def close_browser(webui_manager: WebuiManager):
|
||||
"""
|
||||
Close browser
|
||||
"""
|
||||
if webui_manager.bu_current_task and not webui_manager.bu_current_task.done():
|
||||
webui_manager.bu_current_task.cancel()
|
||||
webui_manager.bu_current_task = None
|
||||
|
||||
if webui_manager.bu_browser_context:
|
||||
logger.info("⚠️ Closing browser context when changing browser config.")
|
||||
await webui_manager.bu_browser_context.close()
|
||||
webui_manager.bu_browser_context = None
|
||||
|
||||
if webui_manager.bu_browser:
|
||||
logger.info("⚠️ Closing browser when changing browser config.")
|
||||
await webui_manager.bu_browser.close()
|
||||
webui_manager.bu_browser = None
|
||||
|
||||
def create_browser_settings_tab(webui_manager: WebuiManager):
|
||||
"""
|
||||
Creates a browser settings tab.
|
||||
"""
|
||||
input_components = set(webui_manager.get_components())
|
||||
tab_components = {}
|
||||
|
||||
with gr.Group():
|
||||
with gr.Row():
|
||||
browser_binary_path = gr.Textbox(
|
||||
label="Browser Binary Path",
|
||||
lines=1,
|
||||
interactive=True,
|
||||
placeholder="e.g. '/Applications/Google\\ Chrome.app/Contents/MacOS/Google\\ Chrome'"
|
||||
)
|
||||
browser_user_data_dir = gr.Textbox(
|
||||
label="Browser User Data Dir",
|
||||
lines=1,
|
||||
interactive=True,
|
||||
placeholder="Leave it empty if you use your default user data",
|
||||
)
|
||||
with gr.Group():
|
||||
with gr.Row():
|
||||
use_own_browser = gr.Checkbox(
|
||||
label="Use Own Browser",
|
||||
value=bool(strtobool(os.getenv("USE_OWN_BROWSER", "false"))),
|
||||
info="Use your existing browser instance",
|
||||
interactive=True
|
||||
)
|
||||
keep_browser_open = gr.Checkbox(
|
||||
label="Keep Browser Open",
|
||||
value=bool(strtobool(os.getenv("KEEP_BROWSER_OPEN", "true"))),
|
||||
info="Keep Browser Open between Tasks",
|
||||
interactive=True
|
||||
)
|
||||
headless = gr.Checkbox(
|
||||
label="Headless Mode",
|
||||
value=False,
|
||||
info="Run browser without GUI",
|
||||
interactive=True
|
||||
)
|
||||
disable_security = gr.Checkbox(
|
||||
label="Disable Security",
|
||||
value=False,
|
||||
info="Disable browser security",
|
||||
interactive=True
|
||||
)
|
||||
|
||||
with gr.Group():
|
||||
with gr.Row():
|
||||
window_w = gr.Number(
|
||||
label="Window Width",
|
||||
value=1280,
|
||||
info="Browser window width",
|
||||
interactive=True
|
||||
)
|
||||
window_h = gr.Number(
|
||||
label="Window Height",
|
||||
value=1100,
|
||||
info="Browser window height",
|
||||
interactive=True
|
||||
)
|
||||
with gr.Group():
|
||||
with gr.Row():
|
||||
cdp_url = gr.Textbox(
|
||||
label="CDP URL",
|
||||
value=os.getenv("BROWSER_CDP", None),
|
||||
info="CDP URL for browser remote debugging",
|
||||
interactive=True,
|
||||
)
|
||||
wss_url = gr.Textbox(
|
||||
label="WSS URL",
|
||||
info="WSS URL for browser remote debugging",
|
||||
interactive=True,
|
||||
)
|
||||
with gr.Group():
|
||||
with gr.Row():
|
||||
save_recording_path = gr.Textbox(
|
||||
label="Recording Path",
|
||||
placeholder="e.g. ./tmp/record_videos",
|
||||
info="Path to save browser recordings",
|
||||
interactive=True,
|
||||
)
|
||||
|
||||
save_trace_path = gr.Textbox(
|
||||
label="Trace Path",
|
||||
placeholder="e.g. ./tmp/traces",
|
||||
info="Path to save Agent traces",
|
||||
interactive=True,
|
||||
)
|
||||
|
||||
with gr.Row():
|
||||
save_agent_history_path = gr.Textbox(
|
||||
label="Agent History Save Path",
|
||||
value="./tmp/agent_history",
|
||||
info="Specify the directory where agent history should be saved.",
|
||||
interactive=True,
|
||||
)
|
||||
save_download_path = gr.Textbox(
|
||||
label="Save Directory for browser downloads",
|
||||
value="./tmp/downloads",
|
||||
info="Specify the directory where downloaded files should be saved.",
|
||||
interactive=True,
|
||||
)
|
||||
tab_components.update(
|
||||
dict(
|
||||
browser_binary_path=browser_binary_path,
|
||||
browser_user_data_dir=browser_user_data_dir,
|
||||
use_own_browser=use_own_browser,
|
||||
keep_browser_open=keep_browser_open,
|
||||
headless=headless,
|
||||
disable_security=disable_security,
|
||||
save_recording_path=save_recording_path,
|
||||
save_trace_path=save_trace_path,
|
||||
save_agent_history_path=save_agent_history_path,
|
||||
save_download_path=save_download_path,
|
||||
cdp_url=cdp_url,
|
||||
wss_url=wss_url,
|
||||
window_h=window_h,
|
||||
window_w=window_w,
|
||||
)
|
||||
)
|
||||
webui_manager.add_components("browser_settings", tab_components)
|
||||
|
||||
async def close_wrapper():
|
||||
"""Wrapper for handle_clear."""
|
||||
await close_browser(webui_manager)
|
||||
|
||||
headless.change(close_wrapper)
|
||||
keep_browser_open.change(close_wrapper)
|
||||
disable_security.change(close_wrapper)
|
||||
use_own_browser.change(close_wrapper)
|
||||
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,457 @@
|
||||
import gradio as gr
|
||||
from gradio.components import Component
|
||||
from functools import partial
|
||||
|
||||
from src.webui.webui_manager import WebuiManager
|
||||
from src.utils import config
|
||||
import logging
|
||||
import os
|
||||
from typing import Any, Dict, AsyncGenerator, Optional, Tuple, Union
|
||||
import asyncio
|
||||
import json
|
||||
from src.agent.deep_research.deep_research_agent import DeepResearchAgent
|
||||
from src.utils import llm_provider
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
async def _initialize_llm(provider: Optional[str], model_name: Optional[str], temperature: float,
|
||||
base_url: Optional[str], api_key: Optional[str], num_ctx: Optional[int] = None):
|
||||
"""Initializes the LLM based on settings. Returns None if provider/model is missing."""
|
||||
if not provider or not model_name:
|
||||
logger.info("LLM Provider or Model Name not specified, LLM will be None.")
|
||||
return None
|
||||
try:
|
||||
logger.info(f"Initializing LLM: Provider={provider}, Model={model_name}, Temp={temperature}")
|
||||
# Use your actual LLM provider logic here
|
||||
llm = llm_provider.get_llm_model(
|
||||
provider=provider,
|
||||
model_name=model_name,
|
||||
temperature=temperature,
|
||||
base_url=base_url or None,
|
||||
api_key=api_key or None,
|
||||
num_ctx=num_ctx if provider == "ollama" else None
|
||||
)
|
||||
return llm
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to initialize LLM: {e}", exc_info=True)
|
||||
gr.Warning(
|
||||
f"Failed to initialize LLM '{model_name}' for provider '{provider}'. Please check settings. Error: {e}")
|
||||
return None
|
||||
|
||||
|
||||
def _read_file_safe(file_path: str) -> Optional[str]:
|
||||
"""Safely read a file, returning None if it doesn't exist or on error."""
|
||||
if not os.path.exists(file_path):
|
||||
return None
|
||||
try:
|
||||
with open(file_path, 'r', encoding='utf-8') as f:
|
||||
return f.read()
|
||||
except Exception as e:
|
||||
logger.error(f"Error reading file {file_path}: {e}")
|
||||
return None
|
||||
|
||||
|
||||
# --- Deep Research Agent Specific Logic ---
|
||||
|
||||
async def run_deep_research(webui_manager: WebuiManager, components: Dict[Component, Any]) -> AsyncGenerator[
|
||||
Dict[Component, Any], None]:
|
||||
"""Handles initializing and running the DeepResearchAgent."""
|
||||
|
||||
# --- Get Components ---
|
||||
research_task_comp = webui_manager.get_component_by_id("deep_research_agent.research_task")
|
||||
resume_task_id_comp = webui_manager.get_component_by_id("deep_research_agent.resume_task_id")
|
||||
parallel_num_comp = webui_manager.get_component_by_id("deep_research_agent.parallel_num")
|
||||
save_dir_comp = webui_manager.get_component_by_id(
|
||||
"deep_research_agent.max_query") # Note: component ID seems misnamed in original code
|
||||
start_button_comp = webui_manager.get_component_by_id("deep_research_agent.start_button")
|
||||
stop_button_comp = webui_manager.get_component_by_id("deep_research_agent.stop_button")
|
||||
markdown_display_comp = webui_manager.get_component_by_id("deep_research_agent.markdown_display")
|
||||
markdown_download_comp = webui_manager.get_component_by_id("deep_research_agent.markdown_download")
|
||||
mcp_server_config_comp = webui_manager.get_component_by_id("deep_research_agent.mcp_server_config")
|
||||
|
||||
# --- 1. Get Task and Settings ---
|
||||
task_topic = components.get(research_task_comp, "").strip()
|
||||
task_id_to_resume = components.get(resume_task_id_comp, "").strip() or None
|
||||
max_parallel_agents = int(components.get(parallel_num_comp, 1))
|
||||
base_save_dir = components.get(save_dir_comp, "./tmp/deep_research").strip()
|
||||
safe_root_dir = "./tmp/deep_research"
|
||||
normalized_base_save_dir = os.path.abspath(os.path.normpath(base_save_dir))
|
||||
if os.path.commonpath([normalized_base_save_dir, os.path.abspath(safe_root_dir)]) != os.path.abspath(safe_root_dir):
|
||||
logger.warning(f"Unsafe base_save_dir detected: {base_save_dir}. Using default directory.")
|
||||
normalized_base_save_dir = os.path.abspath(safe_root_dir)
|
||||
base_save_dir = normalized_base_save_dir
|
||||
mcp_server_config_str = components.get(mcp_server_config_comp)
|
||||
mcp_config = json.loads(mcp_server_config_str) if mcp_server_config_str else None
|
||||
|
||||
if not task_topic:
|
||||
gr.Warning("Please enter a research task.")
|
||||
yield {start_button_comp: gr.update(interactive=True)} # Re-enable start button
|
||||
return
|
||||
|
||||
# Store base save dir for stop handler
|
||||
webui_manager.dr_save_dir = base_save_dir
|
||||
os.makedirs(base_save_dir, exist_ok=True)
|
||||
|
||||
# --- 2. Initial UI Update ---
|
||||
yield {
|
||||
start_button_comp: gr.update(value="⏳ Running...", interactive=False),
|
||||
stop_button_comp: gr.update(interactive=True),
|
||||
research_task_comp: gr.update(interactive=False),
|
||||
resume_task_id_comp: gr.update(interactive=False),
|
||||
parallel_num_comp: gr.update(interactive=False),
|
||||
save_dir_comp: gr.update(interactive=False),
|
||||
markdown_display_comp: gr.update(value="Starting research..."),
|
||||
markdown_download_comp: gr.update(value=None, interactive=False)
|
||||
}
|
||||
|
||||
agent_task = None
|
||||
running_task_id = None
|
||||
plan_file_path = None
|
||||
report_file_path = None
|
||||
last_plan_content = None
|
||||
last_plan_mtime = 0
|
||||
|
||||
try:
|
||||
# --- 3. Get LLM and Browser Config from other tabs ---
|
||||
# Access settings values via components dict, getting IDs from webui_manager
|
||||
def get_setting(tab: str, key: str, default: Any = None):
|
||||
comp = webui_manager.id_to_component.get(f"{tab}.{key}")
|
||||
return components.get(comp, default) if comp else default
|
||||
|
||||
# LLM Config (from agent_settings tab)
|
||||
llm_provider_name = get_setting("agent_settings", "llm_provider")
|
||||
llm_model_name = get_setting("agent_settings", "llm_model_name")
|
||||
llm_temperature = max(get_setting("agent_settings", "llm_temperature", 0.5), 0.5)
|
||||
llm_base_url = get_setting("agent_settings", "llm_base_url")
|
||||
llm_api_key = get_setting("agent_settings", "llm_api_key")
|
||||
ollama_num_ctx = get_setting("agent_settings", "ollama_num_ctx")
|
||||
|
||||
llm = await _initialize_llm(
|
||||
llm_provider_name, llm_model_name, llm_temperature, llm_base_url, llm_api_key,
|
||||
ollama_num_ctx if llm_provider_name == "ollama" else None
|
||||
)
|
||||
if not llm:
|
||||
raise ValueError("LLM Initialization failed. Please check Agent Settings.")
|
||||
|
||||
# Browser Config (from browser_settings tab)
|
||||
# Note: DeepResearchAgent constructor takes a dict, not full Browser/Context objects
|
||||
browser_config_dict = {
|
||||
"headless": get_setting("browser_settings", "headless", False),
|
||||
"disable_security": get_setting("browser_settings", "disable_security", False),
|
||||
"browser_binary_path": get_setting("browser_settings", "browser_binary_path"),
|
||||
"user_data_dir": get_setting("browser_settings", "browser_user_data_dir"),
|
||||
"window_width": int(get_setting("browser_settings", "window_w", 1280)),
|
||||
"window_height": int(get_setting("browser_settings", "window_h", 1100)),
|
||||
# Add other relevant fields if DeepResearchAgent accepts them
|
||||
}
|
||||
|
||||
# --- 4. Initialize or Get Agent ---
|
||||
if not webui_manager.dr_agent:
|
||||
webui_manager.dr_agent = DeepResearchAgent(
|
||||
llm=llm,
|
||||
browser_config=browser_config_dict,
|
||||
mcp_server_config=mcp_config
|
||||
)
|
||||
logger.info("DeepResearchAgent initialized.")
|
||||
|
||||
# --- 5. Start Agent Run ---
|
||||
agent_run_coro = webui_manager.dr_agent.run(
|
||||
topic=task_topic,
|
||||
task_id=task_id_to_resume,
|
||||
save_dir=base_save_dir,
|
||||
max_parallel_browsers=max_parallel_agents
|
||||
)
|
||||
agent_task = asyncio.create_task(agent_run_coro)
|
||||
webui_manager.dr_current_task = agent_task
|
||||
|
||||
# Wait briefly for the agent to start and potentially create the task ID/folder
|
||||
await asyncio.sleep(1.0)
|
||||
|
||||
# Determine the actual task ID being used (agent sets this)
|
||||
running_task_id = webui_manager.dr_agent.current_task_id
|
||||
if not running_task_id:
|
||||
# Agent might not have set it yet, try to get from result later? Risky.
|
||||
# Or derive from resume_task_id if provided?
|
||||
running_task_id = task_id_to_resume
|
||||
if not running_task_id:
|
||||
logger.warning("Could not determine running task ID immediately.")
|
||||
# We can still monitor, but might miss initial plan if ID needed for path
|
||||
else:
|
||||
logger.info(f"Assuming task ID based on resume ID: {running_task_id}")
|
||||
else:
|
||||
logger.info(f"Agent started with Task ID: {running_task_id}")
|
||||
|
||||
webui_manager.dr_task_id = running_task_id # Store for stop handler
|
||||
|
||||
# --- 6. Monitor Progress via research_plan.md ---
|
||||
if running_task_id:
|
||||
task_specific_dir = os.path.join(base_save_dir, str(running_task_id))
|
||||
plan_file_path = os.path.join(task_specific_dir, "research_plan.md")
|
||||
report_file_path = os.path.join(task_specific_dir, "report.md")
|
||||
logger.info(f"Monitoring plan file: {plan_file_path}")
|
||||
else:
|
||||
logger.warning("Cannot monitor plan file: Task ID unknown.")
|
||||
plan_file_path = None
|
||||
last_plan_content = None
|
||||
while not agent_task.done():
|
||||
update_dict = {}
|
||||
update_dict[resume_task_id_comp] = gr.update(value=running_task_id)
|
||||
agent_stopped = getattr(webui_manager.dr_agent, 'stopped', False)
|
||||
if agent_stopped:
|
||||
logger.info("Stop signal detected from agent state.")
|
||||
break # Exit monitoring loop
|
||||
|
||||
# Check and update research plan display
|
||||
if plan_file_path:
|
||||
try:
|
||||
current_mtime = os.path.getmtime(plan_file_path) if os.path.exists(plan_file_path) else 0
|
||||
if current_mtime > last_plan_mtime:
|
||||
logger.info(f"Detected change in {plan_file_path}")
|
||||
plan_content = _read_file_safe(plan_file_path)
|
||||
if last_plan_content is None or (
|
||||
plan_content is not None and plan_content != last_plan_content):
|
||||
update_dict[markdown_display_comp] = gr.update(value=plan_content)
|
||||
last_plan_content = plan_content
|
||||
last_plan_mtime = current_mtime
|
||||
elif plan_content is None:
|
||||
# File might have been deleted or became unreadable
|
||||
last_plan_mtime = 0 # Reset to force re-read attempt later
|
||||
except Exception as e:
|
||||
logger.warning(f"Error checking/reading plan file {plan_file_path}: {e}")
|
||||
# Avoid continuous logging for the same error
|
||||
await asyncio.sleep(2.0)
|
||||
|
||||
# Yield updates if any
|
||||
if update_dict:
|
||||
yield update_dict
|
||||
|
||||
await asyncio.sleep(1.0) # Check file changes every second
|
||||
|
||||
# --- 7. Task Finalization ---
|
||||
logger.info("Agent task processing finished. Awaiting final result...")
|
||||
final_result_dict = await agent_task # Get result or raise exception
|
||||
logger.info(f"Agent run completed. Result keys: {final_result_dict.keys() if final_result_dict else 'None'}")
|
||||
|
||||
# Try to get task ID from result if not known before
|
||||
if not running_task_id and final_result_dict and 'task_id' in final_result_dict:
|
||||
running_task_id = final_result_dict['task_id']
|
||||
webui_manager.dr_task_id = running_task_id
|
||||
task_specific_dir = os.path.join(base_save_dir, str(running_task_id))
|
||||
report_file_path = os.path.join(task_specific_dir, "report.md")
|
||||
logger.info(f"Task ID confirmed from result: {running_task_id}")
|
||||
|
||||
final_ui_update = {}
|
||||
if report_file_path and os.path.exists(report_file_path):
|
||||
logger.info(f"Loading final report from: {report_file_path}")
|
||||
report_content = _read_file_safe(report_file_path)
|
||||
if report_content:
|
||||
final_ui_update[markdown_display_comp] = gr.update(value=report_content)
|
||||
final_ui_update[markdown_download_comp] = gr.File(value=report_file_path,
|
||||
label=f"Report ({running_task_id}.md)",
|
||||
interactive=True)
|
||||
else:
|
||||
final_ui_update[markdown_display_comp] = gr.update(
|
||||
value="# Research Complete\n\n*Error reading final report file.*")
|
||||
elif final_result_dict and 'report' in final_result_dict:
|
||||
logger.info("Using report content directly from agent result.")
|
||||
# If agent directly returns report content
|
||||
final_ui_update[markdown_display_comp] = gr.update(value=final_result_dict['report'])
|
||||
# Cannot offer download if only content is available
|
||||
final_ui_update[markdown_download_comp] = gr.update(value=None, label="Download Research Report",
|
||||
interactive=False)
|
||||
else:
|
||||
logger.warning("Final report file not found and not in result dict.")
|
||||
final_ui_update[markdown_display_comp] = gr.update(value="# Research Complete\n\n*Final report not found.*")
|
||||
|
||||
yield final_ui_update
|
||||
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error during Deep Research Agent execution: {e}", exc_info=True)
|
||||
gr.Error(f"Research failed: {e}")
|
||||
yield {markdown_display_comp: gr.update(value=f"# Research Failed\n\n**Error:**\n```\n{e}\n```")}
|
||||
|
||||
finally:
|
||||
# --- 8. Final UI Reset ---
|
||||
webui_manager.dr_current_task = None # Clear task reference
|
||||
webui_manager.dr_task_id = None # Clear running task ID
|
||||
|
||||
yield {
|
||||
start_button_comp: gr.update(value="▶️ Run", interactive=True),
|
||||
stop_button_comp: gr.update(interactive=False),
|
||||
research_task_comp: gr.update(interactive=True),
|
||||
resume_task_id_comp: gr.update(value="", interactive=True),
|
||||
parallel_num_comp: gr.update(interactive=True),
|
||||
save_dir_comp: gr.update(interactive=True),
|
||||
# Keep download button enabled if file exists
|
||||
markdown_download_comp: gr.update() if report_file_path and os.path.exists(report_file_path) else gr.update(
|
||||
interactive=False)
|
||||
}
|
||||
|
||||
|
||||
async def stop_deep_research(webui_manager: WebuiManager) -> Dict[Component, Any]:
|
||||
"""Handles the Stop button click."""
|
||||
logger.info("Stop button clicked for Deep Research.")
|
||||
agent = webui_manager.dr_agent
|
||||
task = webui_manager.dr_current_task
|
||||
task_id = webui_manager.dr_task_id
|
||||
base_save_dir = webui_manager.dr_save_dir
|
||||
|
||||
stop_button_comp = webui_manager.get_component_by_id("deep_research_agent.stop_button")
|
||||
start_button_comp = webui_manager.get_component_by_id("deep_research_agent.start_button")
|
||||
markdown_display_comp = webui_manager.get_component_by_id("deep_research_agent.markdown_display")
|
||||
markdown_download_comp = webui_manager.get_component_by_id("deep_research_agent.markdown_download")
|
||||
|
||||
final_update = {
|
||||
stop_button_comp: gr.update(interactive=False, value="⏹️ Stopping...")
|
||||
}
|
||||
|
||||
if agent and task and not task.done():
|
||||
logger.info("Signalling DeepResearchAgent to stop.")
|
||||
try:
|
||||
# Assuming stop is synchronous or sets a flag quickly
|
||||
await agent.stop()
|
||||
except Exception as e:
|
||||
logger.error(f"Error calling agent.stop(): {e}")
|
||||
|
||||
# The run_deep_research loop should detect the stop and exit.
|
||||
# We yield an intermediate "Stopping..." state. The final reset is done by run_deep_research.
|
||||
|
||||
# Try to show the final report if available after stopping
|
||||
await asyncio.sleep(1.5) # Give agent a moment to write final files potentially
|
||||
report_file_path = None
|
||||
if task_id and base_save_dir:
|
||||
report_file_path = os.path.join(base_save_dir, str(task_id), "report.md")
|
||||
|
||||
if report_file_path and os.path.exists(report_file_path):
|
||||
report_content = _read_file_safe(report_file_path)
|
||||
if report_content:
|
||||
final_update[markdown_display_comp] = gr.update(
|
||||
value=report_content + "\n\n---\n*Research stopped by user.*")
|
||||
final_update[markdown_download_comp] = gr.File(value=report_file_path, label=f"Report ({task_id}.md)",
|
||||
interactive=True)
|
||||
else:
|
||||
final_update[markdown_display_comp] = gr.update(
|
||||
value="# Research Stopped\n\n*Error reading final report file after stop.*")
|
||||
else:
|
||||
final_update[markdown_display_comp] = gr.update(value="# Research Stopped by User")
|
||||
|
||||
# Keep start button disabled, run_deep_research finally block will re-enable it.
|
||||
final_update[start_button_comp] = gr.update(interactive=False)
|
||||
|
||||
else:
|
||||
logger.warning("Stop clicked but no active research task found.")
|
||||
# Reset UI state just in case
|
||||
final_update = {
|
||||
start_button_comp: gr.update(interactive=True),
|
||||
stop_button_comp: gr.update(interactive=False),
|
||||
webui_manager.get_component_by_id("deep_research_agent.research_task"): gr.update(interactive=True),
|
||||
webui_manager.get_component_by_id("deep_research_agent.resume_task_id"): gr.update(interactive=True),
|
||||
webui_manager.get_component_by_id("deep_research_agent.max_iteration"): gr.update(interactive=True),
|
||||
webui_manager.get_component_by_id("deep_research_agent.max_query"): gr.update(interactive=True),
|
||||
}
|
||||
|
||||
return final_update
|
||||
|
||||
|
||||
async def update_mcp_server(mcp_file: str, webui_manager: WebuiManager):
|
||||
"""
|
||||
Update the MCP server.
|
||||
"""
|
||||
if hasattr(webui_manager, "dr_agent") and webui_manager.dr_agent:
|
||||
logger.warning("⚠️ Close controller because mcp file has changed!")
|
||||
await webui_manager.dr_agent.close_mcp_client()
|
||||
|
||||
if not mcp_file or not os.path.exists(mcp_file) or not mcp_file.endswith('.json'):
|
||||
logger.warning(f"{mcp_file} is not a valid MCP file.")
|
||||
return None, gr.update(visible=False)
|
||||
|
||||
with open(mcp_file, 'r') as f:
|
||||
mcp_server = json.load(f)
|
||||
|
||||
return json.dumps(mcp_server, indent=2), gr.update(visible=True)
|
||||
|
||||
|
||||
def create_deep_research_agent_tab(webui_manager: WebuiManager):
|
||||
"""
|
||||
Creates a deep research agent tab
|
||||
"""
|
||||
input_components = set(webui_manager.get_components())
|
||||
tab_components = {}
|
||||
|
||||
with gr.Group():
|
||||
with gr.Row():
|
||||
mcp_json_file = gr.File(label="MCP server json", interactive=True, file_types=[".json"])
|
||||
mcp_server_config = gr.Textbox(label="MCP server", lines=6, interactive=True, visible=False)
|
||||
|
||||
with gr.Group():
|
||||
research_task = gr.Textbox(label="Research Task", lines=5,
|
||||
value="Give me a detailed travel plan to Switzerland from June 1st to 10th.",
|
||||
interactive=True)
|
||||
with gr.Row():
|
||||
resume_task_id = gr.Textbox(label="Resume Task ID", value="",
|
||||
interactive=True)
|
||||
parallel_num = gr.Number(label="Parallel Agent Num", value=1,
|
||||
precision=0,
|
||||
interactive=True)
|
||||
max_query = gr.Textbox(label="Research Save Dir", value="./tmp/deep_research",
|
||||
interactive=True)
|
||||
with gr.Row():
|
||||
stop_button = gr.Button("⏹️ Stop", variant="stop", scale=2)
|
||||
start_button = gr.Button("▶️ Run", variant="primary", scale=3)
|
||||
with gr.Group():
|
||||
markdown_display = gr.Markdown(label="Research Report")
|
||||
markdown_download = gr.File(label="Download Research Report", interactive=False)
|
||||
tab_components.update(
|
||||
dict(
|
||||
research_task=research_task,
|
||||
parallel_num=parallel_num,
|
||||
max_query=max_query,
|
||||
start_button=start_button,
|
||||
stop_button=stop_button,
|
||||
markdown_display=markdown_display,
|
||||
markdown_download=markdown_download,
|
||||
resume_task_id=resume_task_id,
|
||||
mcp_json_file=mcp_json_file,
|
||||
mcp_server_config=mcp_server_config,
|
||||
)
|
||||
)
|
||||
webui_manager.add_components("deep_research_agent", tab_components)
|
||||
webui_manager.init_deep_research_agent()
|
||||
|
||||
async def update_wrapper(mcp_file):
|
||||
"""Wrapper for handle_pause_resume."""
|
||||
update_dict = await update_mcp_server(mcp_file, webui_manager)
|
||||
yield update_dict
|
||||
|
||||
mcp_json_file.change(
|
||||
update_wrapper,
|
||||
inputs=[mcp_json_file],
|
||||
outputs=[mcp_server_config, mcp_server_config]
|
||||
)
|
||||
|
||||
dr_tab_outputs = list(tab_components.values())
|
||||
all_managed_inputs = set(webui_manager.get_components())
|
||||
|
||||
# --- Define Event Handler Wrappers ---
|
||||
async def start_wrapper(comps: Dict[Component, Any]) -> AsyncGenerator[Dict[Component, Any], None]:
|
||||
async for update in run_deep_research(webui_manager, comps):
|
||||
yield update
|
||||
|
||||
async def stop_wrapper() -> AsyncGenerator[Dict[Component, Any], None]:
|
||||
update_dict = await stop_deep_research(webui_manager)
|
||||
yield update_dict
|
||||
|
||||
# --- Connect Handlers ---
|
||||
start_button.click(
|
||||
fn=start_wrapper,
|
||||
inputs=all_managed_inputs,
|
||||
outputs=dr_tab_outputs
|
||||
)
|
||||
|
||||
stop_button.click(
|
||||
fn=stop_wrapper,
|
||||
inputs=None,
|
||||
outputs=dr_tab_outputs
|
||||
)
|
||||
@@ -0,0 +1,50 @@
|
||||
import gradio as gr
|
||||
from gradio.components import Component
|
||||
|
||||
from src.webui.webui_manager import WebuiManager
|
||||
from src.utils import config
|
||||
|
||||
|
||||
def create_load_save_config_tab(webui_manager: WebuiManager):
|
||||
"""
|
||||
Creates a load and save config tab.
|
||||
"""
|
||||
input_components = set(webui_manager.get_components())
|
||||
tab_components = {}
|
||||
|
||||
config_file = gr.File(
|
||||
label="Load UI Settings from json",
|
||||
file_types=[".json"],
|
||||
interactive=True
|
||||
)
|
||||
with gr.Row():
|
||||
load_config_button = gr.Button("Load Config", variant="primary")
|
||||
save_config_button = gr.Button("Save UI Settings", variant="primary")
|
||||
|
||||
config_status = gr.Textbox(
|
||||
label="Status",
|
||||
lines=2,
|
||||
interactive=False
|
||||
)
|
||||
|
||||
tab_components.update(dict(
|
||||
load_config_button=load_config_button,
|
||||
save_config_button=save_config_button,
|
||||
config_status=config_status,
|
||||
config_file=config_file,
|
||||
))
|
||||
|
||||
webui_manager.add_components("load_save_config", tab_components)
|
||||
|
||||
save_config_button.click(
|
||||
fn=webui_manager.save_config,
|
||||
inputs=set(webui_manager.get_components()),
|
||||
outputs=[config_status]
|
||||
)
|
||||
|
||||
load_config_button.click(
|
||||
fn=webui_manager.load_config,
|
||||
inputs=[config_file],
|
||||
outputs=webui_manager.get_components(),
|
||||
)
|
||||
|
||||
@@ -0,0 +1,95 @@
|
||||
import gradio as gr
|
||||
|
||||
from src.webui.webui_manager import WebuiManager
|
||||
from src.webui.components.agent_settings_tab import create_agent_settings_tab
|
||||
from src.webui.components.browser_settings_tab import create_browser_settings_tab
|
||||
from src.webui.components.browser_use_agent_tab import create_browser_use_agent_tab
|
||||
from src.webui.components.deep_research_agent_tab import create_deep_research_agent_tab
|
||||
from src.webui.components.load_save_config_tab import create_load_save_config_tab
|
||||
|
||||
theme_map = {
|
||||
"Default": gr.themes.Default(),
|
||||
"Soft": gr.themes.Soft(),
|
||||
"Monochrome": gr.themes.Monochrome(),
|
||||
"Glass": gr.themes.Glass(),
|
||||
"Origin": gr.themes.Origin(),
|
||||
"Citrus": gr.themes.Citrus(),
|
||||
"Ocean": gr.themes.Ocean(),
|
||||
"Base": gr.themes.Base()
|
||||
}
|
||||
|
||||
|
||||
def create_ui(theme_name="Ocean"):
|
||||
css = """
|
||||
.gradio-container {
|
||||
width: 70vw !important;
|
||||
max-width: 70% !important;
|
||||
margin-left: auto !important;
|
||||
margin-right: auto !important;
|
||||
padding-top: 10px !important;
|
||||
}
|
||||
.header-text {
|
||||
text-align: center;
|
||||
margin-bottom: 20px;
|
||||
}
|
||||
.tab-header-text {
|
||||
text-align: center;
|
||||
}
|
||||
.theme-section {
|
||||
margin-bottom: 10px;
|
||||
padding: 15px;
|
||||
border-radius: 10px;
|
||||
}
|
||||
"""
|
||||
|
||||
# dark mode in default
|
||||
js_func = """
|
||||
function refresh() {
|
||||
const url = new URL(window.location);
|
||||
|
||||
if (url.searchParams.get('__theme') !== 'dark') {
|
||||
url.searchParams.set('__theme', 'dark');
|
||||
window.location.href = url.href;
|
||||
}
|
||||
}
|
||||
"""
|
||||
|
||||
ui_manager = WebuiManager()
|
||||
|
||||
with gr.Blocks(
|
||||
title="Browser Use WebUI", theme=theme_map[theme_name], css=css, js=js_func,
|
||||
) as demo:
|
||||
with gr.Row():
|
||||
gr.Markdown(
|
||||
"""
|
||||
# 🌐 Browser Use WebUI
|
||||
### Control your browser with AI assistance
|
||||
""",
|
||||
elem_classes=["header-text"],
|
||||
)
|
||||
|
||||
with gr.Tabs() as tabs:
|
||||
with gr.TabItem("⚙️ Agent Settings"):
|
||||
create_agent_settings_tab(ui_manager)
|
||||
|
||||
with gr.TabItem("🌐 Browser Settings"):
|
||||
create_browser_settings_tab(ui_manager)
|
||||
|
||||
with gr.TabItem("🤖 Run Agent"):
|
||||
create_browser_use_agent_tab(ui_manager)
|
||||
|
||||
with gr.TabItem("🎁 Agent Marketplace"):
|
||||
gr.Markdown(
|
||||
"""
|
||||
### Agents built on Browser-Use
|
||||
""",
|
||||
elem_classes=["tab-header-text"],
|
||||
)
|
||||
with gr.Tabs():
|
||||
with gr.TabItem("Deep Research"):
|
||||
create_deep_research_agent_tab(ui_manager)
|
||||
|
||||
with gr.TabItem("📁 Load & Save Config"):
|
||||
create_load_save_config_tab(ui_manager)
|
||||
|
||||
return demo
|
||||
@@ -0,0 +1,122 @@
|
||||
import json
|
||||
from collections.abc import Generator
|
||||
from typing import TYPE_CHECKING
|
||||
import os
|
||||
import gradio as gr
|
||||
from datetime import datetime
|
||||
from typing import Optional, Dict, List
|
||||
import uuid
|
||||
import asyncio
|
||||
import time
|
||||
|
||||
from gradio.components import Component
|
||||
from browser_use.browser.browser import Browser
|
||||
from browser_use.browser.context import BrowserContext
|
||||
from browser_use.agent.service import Agent
|
||||
from src.browser.custom_browser import CustomBrowser
|
||||
from src.browser.custom_context import CustomBrowserContext
|
||||
from src.controller.custom_controller import CustomController
|
||||
from src.agent.deep_research.deep_research_agent import DeepResearchAgent
|
||||
|
||||
|
||||
class WebuiManager:
|
||||
def __init__(self, settings_save_dir: str = "./tmp/webui_settings"):
|
||||
self.id_to_component: dict[str, Component] = {}
|
||||
self.component_to_id: dict[Component, str] = {}
|
||||
|
||||
self.settings_save_dir = settings_save_dir
|
||||
os.makedirs(self.settings_save_dir, exist_ok=True)
|
||||
|
||||
def init_browser_use_agent(self) -> None:
|
||||
"""
|
||||
init browser use agent
|
||||
"""
|
||||
self.bu_agent: Optional[Agent] = None
|
||||
self.bu_browser: Optional[CustomBrowser] = None
|
||||
self.bu_browser_context: Optional[CustomBrowserContext] = None
|
||||
self.bu_controller: Optional[CustomController] = None
|
||||
self.bu_chat_history: List[Dict[str, Optional[str]]] = []
|
||||
self.bu_response_event: Optional[asyncio.Event] = None
|
||||
self.bu_user_help_response: Optional[str] = None
|
||||
self.bu_current_task: Optional[asyncio.Task] = None
|
||||
self.bu_agent_task_id: Optional[str] = None
|
||||
|
||||
def init_deep_research_agent(self) -> None:
|
||||
"""
|
||||
init deep research agent
|
||||
"""
|
||||
self.dr_agent: Optional[DeepResearchAgent] = None
|
||||
self.dr_current_task = None
|
||||
self.dr_agent_task_id: Optional[str] = None
|
||||
self.dr_save_dir: Optional[str] = None
|
||||
|
||||
def add_components(self, tab_name: str, components_dict: dict[str, "Component"]) -> None:
|
||||
"""
|
||||
Add tab components
|
||||
"""
|
||||
for comp_name, component in components_dict.items():
|
||||
comp_id = f"{tab_name}.{comp_name}"
|
||||
self.id_to_component[comp_id] = component
|
||||
self.component_to_id[component] = comp_id
|
||||
|
||||
def get_components(self) -> list["Component"]:
|
||||
"""
|
||||
Get all components
|
||||
"""
|
||||
return list(self.id_to_component.values())
|
||||
|
||||
def get_component_by_id(self, comp_id: str) -> "Component":
|
||||
"""
|
||||
Get component by id
|
||||
"""
|
||||
return self.id_to_component[comp_id]
|
||||
|
||||
def get_id_by_component(self, comp: "Component") -> str:
|
||||
"""
|
||||
Get id by component
|
||||
"""
|
||||
return self.component_to_id[comp]
|
||||
|
||||
def save_config(self, components: Dict["Component", str]) -> None:
|
||||
"""
|
||||
Save config
|
||||
"""
|
||||
cur_settings = {}
|
||||
for comp in components:
|
||||
if not isinstance(comp, gr.Button) and not isinstance(comp, gr.File) and str(
|
||||
getattr(comp, "interactive", True)).lower() != "false":
|
||||
comp_id = self.get_id_by_component(comp)
|
||||
cur_settings[comp_id] = components[comp]
|
||||
|
||||
config_name = datetime.now().strftime("%Y%m%d-%H%M%S")
|
||||
with open(os.path.join(self.settings_save_dir, f"{config_name}.json"), "w") as fw:
|
||||
json.dump(cur_settings, fw, indent=4)
|
||||
|
||||
return os.path.join(self.settings_save_dir, f"{config_name}.json")
|
||||
|
||||
def load_config(self, config_path: str):
|
||||
"""
|
||||
Load config
|
||||
"""
|
||||
with open(config_path, "r") as fr:
|
||||
ui_settings = json.load(fr)
|
||||
|
||||
update_components = {}
|
||||
for comp_id, comp_val in ui_settings.items():
|
||||
if comp_id in self.id_to_component:
|
||||
comp = self.id_to_component[comp_id]
|
||||
if comp.__class__.__name__ == "Chatbot":
|
||||
update_components[comp] = comp.__class__(value=comp_val, type="messages")
|
||||
else:
|
||||
update_components[comp] = comp.__class__(value=comp_val)
|
||||
if comp_id == "agent_settings.planner_llm_provider":
|
||||
yield update_components # yield provider, let callback run
|
||||
time.sleep(0.1) # wait for Gradio UI callback
|
||||
|
||||
config_status = self.id_to_component["load_save_config.config_status"]
|
||||
update_components.update(
|
||||
{
|
||||
config_status: config_status.__class__(value=f"Successfully loaded config: {config_path}")
|
||||
}
|
||||
)
|
||||
yield update_components
|
||||
@@ -0,0 +1,80 @@
|
||||
[supervisord]
|
||||
user=root
|
||||
nodaemon=true
|
||||
logfile=/dev/stdout
|
||||
logfile_maxbytes=0
|
||||
loglevel=error
|
||||
|
||||
[program:xvfb]
|
||||
command=Xvfb :99 -screen 0 %(ENV_RESOLUTION)s -ac +extension GLX +render -noreset
|
||||
autorestart=true
|
||||
stdout_logfile=/dev/stdout
|
||||
stdout_logfile_maxbytes=0
|
||||
stderr_logfile=/dev/stderr
|
||||
stderr_logfile_maxbytes=0
|
||||
priority=100
|
||||
startsecs=3
|
||||
stopsignal=TERM
|
||||
stopwaitsecs=10
|
||||
|
||||
[program:vnc_setup]
|
||||
command=bash -c "mkdir -p ~/.vnc && echo '%(ENV_VNC_PASSWORD)s' | vncpasswd -f > ~/.vnc/passwd && chmod 600 ~/.vnc/passwd && ls -la ~/.vnc/passwd"
|
||||
autorestart=false
|
||||
startsecs=0
|
||||
priority=150
|
||||
stdout_logfile=/dev/stdout
|
||||
stdout_logfile_maxbytes=0
|
||||
stderr_logfile=/dev/stderr
|
||||
stderr_logfile_maxbytes=0
|
||||
|
||||
[program:x11vnc]
|
||||
command=bash -c "mkdir -p /var/log && touch /var/log/x11vnc.log && chmod 666 /var/log/x11vnc.log && sleep 5 && DISPLAY=:99 x11vnc -display :99 -forever -shared -rfbauth /root/.vnc/passwd -rfbport 5901 -o /var/log/x11vnc.log"
|
||||
autorestart=true
|
||||
stdout_logfile=/dev/stdout
|
||||
stdout_logfile_maxbytes=0
|
||||
stderr_logfile=/dev/stderr
|
||||
stderr_logfile_maxbytes=0
|
||||
priority=200
|
||||
startretries=10
|
||||
startsecs=10
|
||||
stopsignal=TERM
|
||||
stopwaitsecs=10
|
||||
depends_on=vnc_setup,xvfb
|
||||
|
||||
[program:x11vnc_log]
|
||||
command=bash -c "mkdir -p /var/log && touch /var/log/x11vnc.log && tail -f /var/log/x11vnc.log"
|
||||
autorestart=true
|
||||
stdout_logfile=/dev/stdout
|
||||
stdout_logfile_maxbytes=0
|
||||
stderr_logfile=/dev/stderr
|
||||
stderr_logfile_maxbytes=0
|
||||
priority=250
|
||||
stopsignal=TERM
|
||||
stopwaitsecs=5
|
||||
depends_on=x11vnc
|
||||
|
||||
[program:novnc]
|
||||
command=bash -c "sleep 5 && cd /opt/novnc && ./utils/novnc_proxy --vnc localhost:5901 --listen 0.0.0.0:6080 --web /opt/novnc"
|
||||
autorestart=true
|
||||
stdout_logfile=/dev/stdout
|
||||
stdout_logfile_maxbytes=0
|
||||
stderr_logfile=/dev/stderr
|
||||
stderr_logfile_maxbytes=0
|
||||
priority=300
|
||||
startretries=5
|
||||
startsecs=3
|
||||
depends_on=x11vnc
|
||||
|
||||
[program:webui]
|
||||
command=python webui.py --ip 0.0.0.0 --port 7788
|
||||
directory=/app
|
||||
autorestart=true
|
||||
stdout_logfile=/dev/stdout
|
||||
stdout_logfile_maxbytes=0
|
||||
stderr_logfile=/dev/stderr
|
||||
stderr_logfile_maxbytes=0
|
||||
priority=400
|
||||
startretries=3
|
||||
startsecs=3
|
||||
stopsignal=TERM
|
||||
stopwaitsecs=10
|
||||
@@ -0,0 +1,400 @@
|
||||
import pdb
|
||||
|
||||
from dotenv import load_dotenv
|
||||
|
||||
load_dotenv()
|
||||
import sys
|
||||
|
||||
sys.path.append(".")
|
||||
import asyncio
|
||||
import os
|
||||
import sys
|
||||
from pprint import pprint
|
||||
|
||||
from browser_use import Agent
|
||||
from browser_use.agent.views import AgentHistoryList
|
||||
|
||||
from src.utils import utils
|
||||
|
||||
|
||||
async def test_browser_use_agent():
|
||||
from browser_use.browser.browser import Browser, BrowserConfig
|
||||
from browser_use.browser.context import (
|
||||
BrowserContextConfig
|
||||
)
|
||||
from browser_use.agent.service import Agent
|
||||
|
||||
from src.browser.custom_browser import CustomBrowser
|
||||
from src.controller.custom_controller import CustomController
|
||||
from src.utils import llm_provider
|
||||
from src.agent.browser_use.browser_use_agent import BrowserUseAgent
|
||||
|
||||
llm = llm_provider.get_llm_model(
|
||||
provider="openai",
|
||||
model_name="gpt-4o",
|
||||
temperature=0.8,
|
||||
)
|
||||
|
||||
# llm = llm_provider.get_llm_model(
|
||||
# provider="google",
|
||||
# model_name="gemini-2.0-flash",
|
||||
# temperature=0.6,
|
||||
# api_key=os.getenv("GOOGLE_API_KEY", "")
|
||||
# )
|
||||
|
||||
# llm = utils.get_llm_model(
|
||||
# provider="deepseek",
|
||||
# model_name="deepseek-reasoner",
|
||||
# temperature=0.8
|
||||
# )
|
||||
|
||||
# llm = utils.get_llm_model(
|
||||
# provider="deepseek",
|
||||
# model_name="deepseek-chat",
|
||||
# temperature=0.8
|
||||
# )
|
||||
|
||||
# llm = utils.get_llm_model(
|
||||
# provider="ollama", model_name="qwen2.5:7b", temperature=0.5
|
||||
# )
|
||||
|
||||
# llm = utils.get_llm_model(
|
||||
# provider="ollama", model_name="deepseek-r1:14b", temperature=0.5
|
||||
# )
|
||||
|
||||
window_w, window_h = 1280, 1100
|
||||
|
||||
# llm = llm_provider.get_llm_model(
|
||||
# provider="azure_openai",
|
||||
# model_name="gpt-4o",
|
||||
# temperature=0.5,
|
||||
# base_url=os.getenv("AZURE_OPENAI_ENDPOINT", ""),
|
||||
# api_key=os.getenv("AZURE_OPENAI_API_KEY", ""),
|
||||
# )
|
||||
|
||||
mcp_server_config = {
|
||||
"mcpServers": {
|
||||
# "markitdown": {
|
||||
# "command": "docker",
|
||||
# "args": [
|
||||
# "run",
|
||||
# "--rm",
|
||||
# "-i",
|
||||
# "markitdown-mcp:latest"
|
||||
# ]
|
||||
# },
|
||||
"desktop-commander": {
|
||||
"command": "npx",
|
||||
"args": [
|
||||
"-y",
|
||||
"@wonderwhy-er/desktop-commander"
|
||||
]
|
||||
},
|
||||
}
|
||||
}
|
||||
controller = CustomController()
|
||||
await controller.setup_mcp_client(mcp_server_config)
|
||||
use_own_browser = True
|
||||
use_vision = True # Set to False when using DeepSeek
|
||||
|
||||
max_actions_per_step = 10
|
||||
browser = None
|
||||
browser_context = None
|
||||
|
||||
try:
|
||||
extra_browser_args = []
|
||||
if use_own_browser:
|
||||
browser_binary_path = os.getenv("BROWSER_PATH", None)
|
||||
if browser_binary_path == "":
|
||||
browser_binary_path = None
|
||||
browser_user_data = os.getenv("BROWSER_USER_DATA", None)
|
||||
if browser_user_data:
|
||||
extra_browser_args += [f"--user-data-dir={browser_user_data}"]
|
||||
else:
|
||||
browser_binary_path = None
|
||||
browser = CustomBrowser(
|
||||
config=BrowserConfig(
|
||||
headless=False,
|
||||
browser_binary_path=browser_binary_path,
|
||||
extra_browser_args=extra_browser_args,
|
||||
new_context_config=BrowserContextConfig(
|
||||
window_width=window_w,
|
||||
window_height=window_h,
|
||||
)
|
||||
)
|
||||
)
|
||||
browser_context = await browser.new_context(
|
||||
config=BrowserContextConfig(
|
||||
trace_path=None,
|
||||
save_recording_path=None,
|
||||
save_downloads_path="./tmp/downloads",
|
||||
window_height=window_h,
|
||||
window_width=window_w,
|
||||
)
|
||||
)
|
||||
agent = BrowserUseAgent(
|
||||
# task="download pdf from https://arxiv.org/pdf/2311.16498 and rename this pdf to 'mcp-test.pdf'",
|
||||
task="give me nvidia stock price",
|
||||
llm=llm,
|
||||
browser=browser,
|
||||
browser_context=browser_context,
|
||||
controller=controller,
|
||||
use_vision=use_vision,
|
||||
max_actions_per_step=max_actions_per_step,
|
||||
generate_gif=True
|
||||
)
|
||||
history: AgentHistoryList = await agent.run(max_steps=100)
|
||||
|
||||
print("Final Result:")
|
||||
pprint(history.final_result(), indent=4)
|
||||
|
||||
print("\nErrors:")
|
||||
pprint(history.errors(), indent=4)
|
||||
|
||||
except Exception:
|
||||
import traceback
|
||||
traceback.print_exc()
|
||||
finally:
|
||||
if browser_context:
|
||||
await browser_context.close()
|
||||
if browser:
|
||||
await browser.close()
|
||||
if controller:
|
||||
await controller.close_mcp_client()
|
||||
|
||||
|
||||
async def test_browser_use_parallel():
|
||||
from browser_use.browser.browser import Browser, BrowserConfig
|
||||
from browser_use.browser.context import (
|
||||
BrowserContextConfig,
|
||||
)
|
||||
from browser_use.agent.service import Agent
|
||||
|
||||
from src.browser.custom_browser import CustomBrowser
|
||||
from src.controller.custom_controller import CustomController
|
||||
from src.utils import llm_provider
|
||||
from src.agent.browser_use.browser_use_agent import BrowserUseAgent
|
||||
|
||||
# llm = utils.get_llm_model(
|
||||
# provider="openai",
|
||||
# model_name="gpt-4o",
|
||||
# temperature=0.8,
|
||||
# base_url=os.getenv("OPENAI_ENDPOINT", ""),
|
||||
# api_key=os.getenv("OPENAI_API_KEY", ""),
|
||||
# )
|
||||
|
||||
# llm = utils.get_llm_model(
|
||||
# provider="google",
|
||||
# model_name="gemini-2.0-flash",
|
||||
# temperature=0.6,
|
||||
# api_key=os.getenv("GOOGLE_API_KEY", "")
|
||||
# )
|
||||
|
||||
# llm = utils.get_llm_model(
|
||||
# provider="deepseek",
|
||||
# model_name="deepseek-reasoner",
|
||||
# temperature=0.8
|
||||
# )
|
||||
|
||||
# llm = utils.get_llm_model(
|
||||
# provider="deepseek",
|
||||
# model_name="deepseek-chat",
|
||||
# temperature=0.8
|
||||
# )
|
||||
|
||||
# llm = utils.get_llm_model(
|
||||
# provider="ollama", model_name="qwen2.5:7b", temperature=0.5
|
||||
# )
|
||||
|
||||
# llm = utils.get_llm_model(
|
||||
# provider="ollama", model_name="deepseek-r1:14b", temperature=0.5
|
||||
# )
|
||||
|
||||
window_w, window_h = 1280, 1100
|
||||
|
||||
llm = llm_provider.get_llm_model(
|
||||
provider="azure_openai",
|
||||
model_name="gpt-4o",
|
||||
temperature=0.5,
|
||||
base_url=os.getenv("AZURE_OPENAI_ENDPOINT", ""),
|
||||
api_key=os.getenv("AZURE_OPENAI_API_KEY", ""),
|
||||
)
|
||||
|
||||
mcp_server_config = {
|
||||
"mcpServers": {
|
||||
# "markitdown": {
|
||||
# "command": "docker",
|
||||
# "args": [
|
||||
# "run",
|
||||
# "--rm",
|
||||
# "-i",
|
||||
# "markitdown-mcp:latest"
|
||||
# ]
|
||||
# },
|
||||
"desktop-commander": {
|
||||
"command": "npx",
|
||||
"args": [
|
||||
"-y",
|
||||
"@wonderwhy-er/desktop-commander"
|
||||
]
|
||||
},
|
||||
# "filesystem": {
|
||||
# "command": "npx",
|
||||
# "args": [
|
||||
# "-y",
|
||||
# "@modelcontextprotocol/server-filesystem",
|
||||
# "/Users/xxx/ai_workspace",
|
||||
# ]
|
||||
# },
|
||||
}
|
||||
}
|
||||
controller = CustomController()
|
||||
await controller.setup_mcp_client(mcp_server_config)
|
||||
use_own_browser = True
|
||||
use_vision = True # Set to False when using DeepSeek
|
||||
|
||||
max_actions_per_step = 10
|
||||
browser = None
|
||||
browser_context = None
|
||||
|
||||
try:
|
||||
extra_browser_args = []
|
||||
if use_own_browser:
|
||||
browser_binary_path = os.getenv("BROWSER_PATH", None)
|
||||
if browser_binary_path == "":
|
||||
browser_binary_path = None
|
||||
browser_user_data = os.getenv("BROWSER_USER_DATA", None)
|
||||
if browser_user_data:
|
||||
extra_browser_args += [f"--user-data-dir={browser_user_data}"]
|
||||
else:
|
||||
browser_binary_path = None
|
||||
browser = CustomBrowser(
|
||||
config=BrowserConfig(
|
||||
headless=False,
|
||||
browser_binary_path=browser_binary_path,
|
||||
extra_browser_args=extra_browser_args,
|
||||
new_context_config=BrowserContextConfig(
|
||||
window_width=window_w,
|
||||
window_height=window_h,
|
||||
)
|
||||
)
|
||||
)
|
||||
browser_context = await browser.new_context(
|
||||
config=BrowserContextConfig(
|
||||
trace_path=None,
|
||||
save_recording_path=None,
|
||||
save_downloads_path="./tmp/downloads",
|
||||
window_height=window_h,
|
||||
window_width=window_w,
|
||||
force_new_context=True
|
||||
)
|
||||
)
|
||||
agents = [
|
||||
BrowserUseAgent(task=task, llm=llm, browser=browser, controller=controller)
|
||||
for task in [
|
||||
'Search Google for weather in Tokyo',
|
||||
# 'Check Reddit front page title',
|
||||
# 'Find NASA image of the day',
|
||||
# 'Check top story on CNN',
|
||||
# 'Search latest SpaceX launch date',
|
||||
# 'Look up population of Paris',
|
||||
'Find current time in Sydney',
|
||||
'Check who won last Super Bowl',
|
||||
# 'Search trending topics on Twitter',
|
||||
]
|
||||
]
|
||||
|
||||
history = await asyncio.gather(*[agent.run() for agent in agents])
|
||||
print("Final Result:")
|
||||
pprint(history.final_result(), indent=4)
|
||||
|
||||
print("\nErrors:")
|
||||
pprint(history.errors(), indent=4)
|
||||
|
||||
pdb.set_trace()
|
||||
|
||||
except Exception:
|
||||
import traceback
|
||||
|
||||
traceback.print_exc()
|
||||
finally:
|
||||
if browser_context:
|
||||
await browser_context.close()
|
||||
if browser:
|
||||
await browser.close()
|
||||
if controller:
|
||||
await controller.close_mcp_client()
|
||||
|
||||
|
||||
async def test_deep_research_agent():
|
||||
from src.agent.deep_research.deep_research_agent import DeepResearchAgent, PLAN_FILENAME, REPORT_FILENAME
|
||||
from src.utils import llm_provider
|
||||
|
||||
llm = llm_provider.get_llm_model(
|
||||
provider="openai",
|
||||
model_name="gpt-4o",
|
||||
temperature=0.5
|
||||
)
|
||||
|
||||
# llm = llm_provider.get_llm_model(
|
||||
# provider="bedrock",
|
||||
# )
|
||||
|
||||
mcp_server_config = {
|
||||
"mcpServers": {
|
||||
"desktop-commander": {
|
||||
"command": "npx",
|
||||
"args": [
|
||||
"-y",
|
||||
"@wonderwhy-er/desktop-commander"
|
||||
]
|
||||
},
|
||||
}
|
||||
}
|
||||
|
||||
browser_config = {"headless": False, "window_width": 1280, "window_height": 1100, "use_own_browser": False}
|
||||
agent = DeepResearchAgent(llm=llm, browser_config=browser_config, mcp_server_config=mcp_server_config)
|
||||
research_topic = "Give me investment advices of nvidia and tesla."
|
||||
task_id_to_resume = "" # Set this to resume a previous task ID
|
||||
|
||||
print(f"Starting research on: {research_topic}")
|
||||
|
||||
try:
|
||||
# Call run and wait for the final result dictionary
|
||||
result = await agent.run(research_topic,
|
||||
task_id=task_id_to_resume,
|
||||
save_dir="./tmp/deep_research",
|
||||
max_parallel_browsers=1,
|
||||
)
|
||||
|
||||
print("\n--- Research Process Ended ---")
|
||||
print(f"Status: {result.get('status')}")
|
||||
print(f"Message: {result.get('message')}")
|
||||
print(f"Task ID: {result.get('task_id')}")
|
||||
|
||||
# Check the final state for the report
|
||||
final_state = result.get('final_state', {})
|
||||
if final_state:
|
||||
print("\n--- Final State Summary ---")
|
||||
print(
|
||||
f" Plan Steps Completed: {sum(1 for item in final_state.get('research_plan', []) if item.get('status') == 'completed')}")
|
||||
print(f" Total Search Results Logged: {len(final_state.get('search_results', []))}")
|
||||
if final_state.get("final_report"):
|
||||
print(" Final Report: Generated (content omitted). You can find it in the output directory.")
|
||||
# print("\n--- Final Report ---") # Optionally print report
|
||||
# print(final_state["final_report"])
|
||||
else:
|
||||
print(" Final Report: Not generated.")
|
||||
else:
|
||||
print("Final state information not available.")
|
||||
|
||||
|
||||
except Exception as e:
|
||||
print(f"\n--- An unhandled error occurred outside the agent run ---")
|
||||
print(e)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(test_browser_use_agent())
|
||||
# asyncio.run(test_browser_use_parallel())
|
||||
# asyncio.run(test_deep_research_agent())
|
||||
@@ -0,0 +1,131 @@
|
||||
import asyncio
|
||||
import pdb
|
||||
import sys
|
||||
import time
|
||||
|
||||
sys.path.append(".")
|
||||
|
||||
from dotenv import load_dotenv
|
||||
|
||||
load_dotenv()
|
||||
|
||||
|
||||
async def test_mcp_client():
|
||||
from src.utils.mcp_client import setup_mcp_client_and_tools, create_tool_param_model
|
||||
|
||||
test_server_config = {
|
||||
"mcpServers": {
|
||||
# "markitdown": {
|
||||
# "command": "docker",
|
||||
# "args": [
|
||||
# "run",
|
||||
# "--rm",
|
||||
# "-i",
|
||||
# "markitdown-mcp:latest"
|
||||
# ]
|
||||
# },
|
||||
"desktop-commander": {
|
||||
"command": "npx",
|
||||
"args": [
|
||||
"-y",
|
||||
"@wonderwhy-er/desktop-commander"
|
||||
]
|
||||
},
|
||||
# "filesystem": {
|
||||
# "command": "npx",
|
||||
# "args": [
|
||||
# "-y",
|
||||
# "@modelcontextprotocol/server-filesystem",
|
||||
# "/Users/xxx/ai_workspace",
|
||||
# ]
|
||||
# },
|
||||
}
|
||||
}
|
||||
|
||||
mcp_tools, mcp_client = await setup_mcp_client_and_tools(test_server_config)
|
||||
|
||||
for tool in mcp_tools:
|
||||
tool_param_model = create_tool_param_model(tool)
|
||||
print(tool.name)
|
||||
print(tool.description)
|
||||
print(tool_param_model.model_json_schema())
|
||||
pdb.set_trace()
|
||||
|
||||
|
||||
async def test_controller_with_mcp():
|
||||
import os
|
||||
from src.controller.custom_controller import CustomController
|
||||
from browser_use.controller.registry.views import ActionModel
|
||||
|
||||
mcp_server_config = {
|
||||
"mcpServers": {
|
||||
# "markitdown": {
|
||||
# "command": "docker",
|
||||
# "args": [
|
||||
# "run",
|
||||
# "--rm",
|
||||
# "-i",
|
||||
# "markitdown-mcp:latest"
|
||||
# ]
|
||||
# },
|
||||
"desktop-commander": {
|
||||
"command": "npx",
|
||||
"args": [
|
||||
"-y",
|
||||
"@wonderwhy-er/desktop-commander"
|
||||
]
|
||||
},
|
||||
# "filesystem": {
|
||||
# "command": "npx",
|
||||
# "args": [
|
||||
# "-y",
|
||||
# "@modelcontextprotocol/server-filesystem",
|
||||
# "/Users/xxx/ai_workspace",
|
||||
# ]
|
||||
# },
|
||||
}
|
||||
}
|
||||
|
||||
controller = CustomController()
|
||||
await controller.setup_mcp_client(mcp_server_config)
|
||||
action_name = "mcp.desktop-commander.execute_command"
|
||||
action_info = controller.registry.registry.actions[action_name]
|
||||
param_model = action_info.param_model
|
||||
print(param_model.model_json_schema())
|
||||
params = {"command": f"python ./tmp/test.py"
|
||||
}
|
||||
validated_params = param_model(**params)
|
||||
ActionModel_ = controller.registry.create_action_model()
|
||||
# Create ActionModel instance with the validated parameters
|
||||
action_model = ActionModel_(**{action_name: validated_params})
|
||||
result = await controller.act(action_model)
|
||||
result = result.extracted_content
|
||||
print(result)
|
||||
if result and "Command is still running. Use read_output to get more output." in result and "PID" in \
|
||||
result.split("\n")[0]:
|
||||
pid = int(result.split("\n")[0].split("PID")[-1].strip())
|
||||
action_name = "mcp.desktop-commander.read_output"
|
||||
action_info = controller.registry.registry.actions[action_name]
|
||||
param_model = action_info.param_model
|
||||
print(param_model.model_json_schema())
|
||||
params = {"pid": pid}
|
||||
validated_params = param_model(**params)
|
||||
action_model = ActionModel_(**{action_name: validated_params})
|
||||
output_result = ""
|
||||
while True:
|
||||
time.sleep(1)
|
||||
result = await controller.act(action_model)
|
||||
result = result.extracted_content
|
||||
if result:
|
||||
pdb.set_trace()
|
||||
output_result = result
|
||||
break
|
||||
print(output_result)
|
||||
pdb.set_trace()
|
||||
await controller.close_mcp_client()
|
||||
pdb.set_trace()
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
# asyncio.run(test_mcp_client())
|
||||
asyncio.run(test_controller_with_mcp())
|
||||
@@ -0,0 +1,159 @@
|
||||
import os
|
||||
import pdb
|
||||
from dataclasses import dataclass
|
||||
|
||||
from dotenv import load_dotenv
|
||||
from langchain_core.messages import HumanMessage, SystemMessage
|
||||
from langchain_ollama import ChatOllama
|
||||
|
||||
load_dotenv()
|
||||
|
||||
import sys
|
||||
|
||||
sys.path.append(".")
|
||||
|
||||
|
||||
@dataclass
|
||||
class LLMConfig:
|
||||
provider: str
|
||||
model_name: str
|
||||
temperature: float = 0.8
|
||||
base_url: str = None
|
||||
api_key: str = None
|
||||
|
||||
|
||||
def create_message_content(text, image_path=None):
|
||||
content = [{"type": "text", "text": text}]
|
||||
image_format = "png" if image_path and image_path.endswith(".png") else "jpeg"
|
||||
if image_path:
|
||||
from src.utils import utils
|
||||
image_data = utils.encode_image(image_path)
|
||||
content.append({
|
||||
"type": "image_url",
|
||||
"image_url": {"url": f"data:image/{image_format};base64,{image_data}"}
|
||||
})
|
||||
return content
|
||||
|
||||
|
||||
def get_env_value(key, provider):
|
||||
env_mappings = {
|
||||
"openai": {"api_key": "OPENAI_API_KEY", "base_url": "OPENAI_ENDPOINT"},
|
||||
"azure_openai": {"api_key": "AZURE_OPENAI_API_KEY", "base_url": "AZURE_OPENAI_ENDPOINT"},
|
||||
"google": {"api_key": "GOOGLE_API_KEY"},
|
||||
"deepseek": {"api_key": "DEEPSEEK_API_KEY", "base_url": "DEEPSEEK_ENDPOINT"},
|
||||
"mistral": {"api_key": "MISTRAL_API_KEY", "base_url": "MISTRAL_ENDPOINT"},
|
||||
"alibaba": {"api_key": "ALIBABA_API_KEY", "base_url": "ALIBABA_ENDPOINT"},
|
||||
"moonshot": {"api_key": "MOONSHOT_API_KEY", "base_url": "MOONSHOT_ENDPOINT"},
|
||||
"ibm": {"api_key": "IBM_API_KEY", "base_url": "IBM_ENDPOINT"}
|
||||
}
|
||||
|
||||
if provider in env_mappings and key in env_mappings[provider]:
|
||||
return os.getenv(env_mappings[provider][key], "")
|
||||
return ""
|
||||
|
||||
|
||||
def test_llm(config, query, image_path=None, system_message=None):
|
||||
from src.utils import utils, llm_provider
|
||||
|
||||
# Special handling for Ollama-based models
|
||||
if config.provider == "ollama":
|
||||
if "deepseek-r1" in config.model_name:
|
||||
from src.utils.llm_provider import DeepSeekR1ChatOllama
|
||||
llm = DeepSeekR1ChatOllama(model=config.model_name)
|
||||
else:
|
||||
llm = ChatOllama(model=config.model_name)
|
||||
|
||||
ai_msg = llm.invoke(query)
|
||||
print(ai_msg.content)
|
||||
if "deepseek-r1" in config.model_name:
|
||||
pdb.set_trace()
|
||||
return
|
||||
|
||||
# For other providers, use the standard configuration
|
||||
llm = llm_provider.get_llm_model(
|
||||
provider=config.provider,
|
||||
model_name=config.model_name,
|
||||
temperature=config.temperature,
|
||||
base_url=config.base_url or get_env_value("base_url", config.provider),
|
||||
api_key=config.api_key or get_env_value("api_key", config.provider)
|
||||
)
|
||||
|
||||
# Prepare messages for non-Ollama models
|
||||
messages = []
|
||||
if system_message:
|
||||
messages.append(SystemMessage(content=create_message_content(system_message)))
|
||||
messages.append(HumanMessage(content=create_message_content(query, image_path)))
|
||||
ai_msg = llm.invoke(messages)
|
||||
|
||||
# Handle different response types
|
||||
if hasattr(ai_msg, "reasoning_content"):
|
||||
print(ai_msg.reasoning_content)
|
||||
print(ai_msg.content)
|
||||
|
||||
def test_openai_model():
|
||||
config = LLMConfig(provider="openai", model_name="gpt-4o")
|
||||
test_llm(config, "Describe this image", "assets/examples/test.png")
|
||||
|
||||
|
||||
def test_google_model():
|
||||
# Enable your API key first if you haven't: https://ai.google.dev/palm_docs/oauth_quickstart
|
||||
config = LLMConfig(provider="google", model_name="gemini-2.0-flash-exp")
|
||||
test_llm(config, "Describe this image", "assets/examples/test.png")
|
||||
|
||||
|
||||
def test_azure_openai_model():
|
||||
config = LLMConfig(provider="azure_openai", model_name="gpt-4o")
|
||||
test_llm(config, "Describe this image", "assets/examples/test.png")
|
||||
|
||||
|
||||
def test_deepseek_model():
|
||||
config = LLMConfig(provider="deepseek", model_name="deepseek-chat")
|
||||
test_llm(config, "Who are you?")
|
||||
|
||||
|
||||
def test_deepseek_r1_model():
|
||||
config = LLMConfig(provider="deepseek", model_name="deepseek-reasoner")
|
||||
test_llm(config, "Which is greater, 9.11 or 9.8?", system_message="You are a helpful AI assistant.")
|
||||
|
||||
|
||||
def test_ollama_model():
|
||||
config = LLMConfig(provider="ollama", model_name="qwen2.5:7b")
|
||||
test_llm(config, "Sing a ballad of LangChain.")
|
||||
|
||||
|
||||
def test_deepseek_r1_ollama_model():
|
||||
config = LLMConfig(provider="ollama", model_name="deepseek-r1:14b")
|
||||
test_llm(config, "How many 'r's are in the word 'strawberry'?")
|
||||
|
||||
|
||||
def test_mistral_model():
|
||||
config = LLMConfig(provider="mistral", model_name="pixtral-large-latest")
|
||||
test_llm(config, "Describe this image", "assets/examples/test.png")
|
||||
|
||||
|
||||
def test_moonshot_model():
|
||||
config = LLMConfig(provider="moonshot", model_name="moonshot-v1-32k-vision-preview")
|
||||
test_llm(config, "Describe this image", "assets/examples/test.png")
|
||||
|
||||
|
||||
def test_ibm_model():
|
||||
config = LLMConfig(provider="ibm", model_name="meta-llama/llama-4-maverick-17b-128e-instruct-fp8")
|
||||
test_llm(config, "Describe this image", "assets/examples/test.png")
|
||||
|
||||
|
||||
def test_qwen_model():
|
||||
config = LLMConfig(provider="alibaba", model_name="qwen-vl-max")
|
||||
test_llm(config, "How many 'r's are in the word 'strawberry'?")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
# test_openai_model()
|
||||
# test_google_model()
|
||||
test_azure_openai_model()
|
||||
# test_deepseek_model()
|
||||
# test_ollama_model()
|
||||
# test_deepseek_r1_model()
|
||||
# test_deepseek_r1_ollama_model()
|
||||
# test_mistral_model()
|
||||
# test_ibm_model()
|
||||
# test_qwen_model()
|
||||
@@ -0,0 +1,31 @@
|
||||
import pdb
|
||||
from dotenv import load_dotenv
|
||||
|
||||
load_dotenv()
|
||||
|
||||
|
||||
def test_connect_browser():
|
||||
import os
|
||||
from playwright.sync_api import sync_playwright
|
||||
|
||||
chrome_exe = os.getenv("CHROME_PATH", "")
|
||||
chrome_use_data = os.getenv("CHROME_USER_DATA", "")
|
||||
|
||||
with sync_playwright() as p:
|
||||
browser = p.chromium.launch_persistent_context(
|
||||
user_data_dir=chrome_use_data,
|
||||
executable_path=chrome_exe,
|
||||
headless=False # Keep browser window visible
|
||||
)
|
||||
|
||||
page = browser.new_page()
|
||||
page.goto("https://mail.google.com/mail/u/0/#inbox")
|
||||
page.wait_for_load_state()
|
||||
|
||||
input("Press the Enter key to close the browser...")
|
||||
|
||||
browser.close()
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
test_connect_browser()
|
||||
@@ -0,0 +1,19 @@
|
||||
from dotenv import load_dotenv
|
||||
load_dotenv()
|
||||
import argparse
|
||||
from src.webui.interface import theme_map, create_ui
|
||||
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser(description="Gradio WebUI for Browser Agent")
|
||||
parser.add_argument("--ip", type=str, default="127.0.0.1", help="IP address to bind to")
|
||||
parser.add_argument("--port", type=int, default=7788, help="Port to listen on")
|
||||
parser.add_argument("--theme", type=str, default="Ocean", choices=theme_map.keys(), help="Theme to use for the UI")
|
||||
args = parser.parse_args()
|
||||
|
||||
demo = create_ui(theme_name=args.theme)
|
||||
demo.queue().launch(server_name=args.ip, server_port=args.port)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
Reference in New Issue
Block a user