created and imported basic GUI from flutter flow
This commit is contained in:
Ashton
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aiodns==3.0.0
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aiohttp==3.9.5
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aiosignal==1.3.1
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antlr4-python3-runtime==4.9.3
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anyio==3.7.1
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appdirs==1.4.4
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argcomplete==3.3.0
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argon2-cffi==21.3.0
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argon2-cffi-bindings==21.2.0
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arrow==1.2.3
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asttokens==2.4.1
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async-lru==2.0.4
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attrs==23.2.0
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Babel==2.15.0
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backcall==0.1.0
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Beaker==1.12.1
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beautifulsoup4==4.12.3
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bleach==6.1.0
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blivet==3.10.0
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blivet-gui==2.5.0
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blosc2==2.5.1
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boto3==1.34.99
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botocore==1.34.99
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Bottleneck==1.3.7
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Brlapi==0.8.5
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Brotli==1.1.0
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cachetools==5.3.2
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certifi==2023.5.7
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cffi==1.16.0
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chardet==5.2.0
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charset-normalizer==3.3.2
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click==8.1.7
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click-plugins==1.1.1
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colorama==0.4.6
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comm==0.2.1
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contourpy==1.2.0
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cryptography==41.0.7
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cssselect==1.1.0
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cupshelpers==1.0
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cycler==0.11.0
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dasbus==1.7
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dbus-python==1.3.2
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decorator==5.1.1
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defusedxml==0.7.1
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distro==1.9.0
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entrypoints==0.4
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et-xmlfile==1.1.0
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executing==2.0.1
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fastjsonschema==2.18.0
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file-magic==0.4.0
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filelock==3.14.0
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fonttools==4.50.0
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fqdn==1.5.1
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fros==1.1
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frozenlist==1.4.1
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fs==2.4.16
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fsspec==2024.3.1
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gcsfs==2023.6.0+1.g7cc53d9
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google-api-core==2.11.1
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google-auth==2.29.0
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google-auth-oauthlib==0.8.0
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google-cloud-core==2.3.3
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google-cloud-storage==2.14.0
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google-crc32c==1.5.0
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google-resumable-media==2.7.0
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googleapis-common-protos==1.63.0
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gpg==1.23.2
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greenlet==3.0.3
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grpcio==1.48.4
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grpcio-status==1.48.4
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h11==0.14.0
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html5lib==1.1
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httpcore==1.0.2
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httpx==0.26.0
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humanize==3.13.1
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idna==3.7
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inkex==1.3.1
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ipykernel==6.29.3
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ipython==8.23.0
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ipython-genutils==0.2.0
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iso639==0.1.4
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isoduration==20.11.0
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jdcal==1.4.1
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jedi==0.19.1
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Jinja2==3.1.4
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jmespath==1.0.1
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json5==0.9.24
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jsonpointer==2.3
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jsonschema==4.19.1
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jsonschema-specifications==2023.11.2
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jupyter-events==0.9.0
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jupyter-lsp==2.2.2
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jupyter_client==7.4.9
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jupyter_core==5.1.0
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jupyter_server==2.12.5
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jupyter_server_terminals==0.4.2
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jupyterlab==4.1.0
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jupyterlab_pygments==0.3.0
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jupyterlab_server==2.25.2
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kiwisolver==1.4.5
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langtable==0.0.66
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libcomps==0.1.20
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louis==3.28.0
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lxml==5.1.0
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Mako==1.2.3
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MarkupSafe==2.1.3
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matplotlib==3.8.4
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matplotlib-inline==0.1.6
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mistune==2.0.4
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mpmath==1.3.0
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msgpack==1.0.6
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multidict==6.0.4
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munkres==1.1.2
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nbclassic==0.5.6
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nbclient==0.9.0
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nbconvert==7.16.0
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nbformat==5.9.2
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ndindex==1.7
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nest-asyncio==1.6.0
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netifaces==0.11.0
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networkx==3.3
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nftables==0.1
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notebook==7.0.7
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notebook_shim==0.2.3
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numexpr==2.8.5
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numpy==1.26.4
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nvidia-cublas-cu12==12.1.3.1
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nvidia-cuda-cupti-cu12==12.1.105
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nvidia-cuda-nvrtc-cu12==12.1.105
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nvidia-cuda-runtime-cu12==12.1.105
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nvidia-cudnn-cu12==8.9.2.26
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nvidia-cufft-cu12==11.0.2.54
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nvidia-curand-cu12==10.3.2.106
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nvidia-cusolver-cu12==11.4.5.107
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nvidia-cusparse-cu12==12.1.0.106
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nvidia-nccl-cu12==2.20.5
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nvidia-nvjitlink-cu12==12.5.40
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nvidia-nvtx-cu12==12.1.105
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oauthlib==3.2.2
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odfpy==1.4.1
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olefile==0.47
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omegaconf==2.3.0
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openpyxl==3.1.2
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packaging==23.2
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pandas==2.2.1
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pandocfilters==1.5.1
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parso==0.8.3
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Paste==3.7.1
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pexpect==4.9.0
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pickleshare==0.7.5
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pid==2.2.3
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pillow==10.3.0
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platformdirs==3.11.0
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ply==3.11
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pooch==1.5.2
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productmd==1.38
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progressbar2==3.53.2
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prometheus-client==0.19.0
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prompt-toolkit==3.0.41
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protobuf==3.19.6
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psutil==5.9.8
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psycopg2==2.9.9
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ptyprocess==0.7.0
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pure-eval==0.2.2
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pwquality==1.4.5
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py-cpuinfo==9.0.0
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pyarrow==15.0.2
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pyasn1==0.5.1
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pyasn1-modules==0.3.0
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pycairo==1.25.1
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pycares==4.3.0
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pycparser==2.20
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pycrypto==2.6.1
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pycups==2.0.4
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pycurl==7.45.2
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pydub==0.25.1
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pyenchant==3.2.2
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pygit2==1.14.0
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Pygments==2.17.2
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PyGObject==3.48.2
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pykickstart==3.52
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PyMySQL==1.1.0
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PyOpenGL==3.1.7
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PyOpenGL-accelerate==3.1.7
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pyOpenSSL==23.2.0
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pyparsing==3.1.2
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pyparted==3.13.0
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PyQt5==5.15.10
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PyQt5-sip==12.13.0
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pyqtgraph==0.13.3
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pyrsistent==0.20.0
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pyserial==3.5
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PySocks==1.7.1
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python-augeas==1.1.0
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python-dateutil==2.8.2
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python-json-logger==2.0.4
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python-meh==0.51
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python-pam==2.0.2
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python-snappy==0.6.1
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python-utils==3.7.0
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pytz==2024.1
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pyudev==0.24.1
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pyxdg==0.27
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PyYAML==6.0.1
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pyzmq==25.1.1
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QtPy==2.4.1
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referencing==0.31.1
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regex==2024.4.16
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requests==2.31.0
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requests-file==2.0.0
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requests-ftp==0.3.1
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requests-oauthlib==1.3.1
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rfc3339-validator==0.1.4
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rfc3986-validator==0.1.1
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rpds-py==0.18.0
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rpm==4.19.1.1
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rpmautospec==0.6.3
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rpmautospec-core==0.1.4
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rsa==4.9
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s3transfer==0.10.1
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SciPy==1.11.3
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scour==0.38.2
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selinux @ file:///builddir/build/BUILD/libselinux-3.6/src
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Send2Trash==1.8.2
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sepolicy @ file:///builddir/build/BUILD/selinux-3.6/python/sepolicy
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setools==4.5.1
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setuptools==69.0.3
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shtab==1.6.1
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silero==0.4.1
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simpleaudio==1.0.4
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simpleline==1.9.0
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six==1.16.0
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sniffio==1.3.0
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sos==4.7.1
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sounddevice==0.4.6
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soupsieve==2.5
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SQLAlchemy==2.0.30
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stack-data==0.6.3
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sympy==1.12
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systemd-python==235
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tables==3.9.2
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tabulate==0.9.0
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Tempita==0.5.2
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termcolor==2.3.0
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terminado==0.18.0
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thrift==0.15.0
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tinycss2==1.2.1
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tldr==3.2.0
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torch==2.3.0
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torchaudio==2.3.0
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tornado==6.3.3
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traitlets==5.14.1
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typing_extensions==4.9.0
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uri-template==1.2.0
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urllib3==1.26.18
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wavio==0.0.9
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wcwidth==0.2.13
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webcolors==1.13
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webencodings==0.5.1
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websocket-client==1.3.3
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xarray==2023.8.0
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xkbregistry==0.3
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xlrd==2.0.1
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XlsxWriter==3.1.9
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xlwt==1.3.0
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yarl==1.9.2
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zstandard==0.22.0
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@@ -0,0 +1,72 @@
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# import required libraries
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from glob import glob
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from scipy.io.wavfile import write
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from pydub import AudioSegment
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from pydub.playback import play
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import os
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import torch
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import zipfile
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import torchaudio
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import sounddevice as sd
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import wave
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import wavio as wv
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########### Record audio
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def record_audio(audio, duration=5, freq=192000):
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# Start recorder with the given values of duration and sample frequency
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recording = sd.rec(int(duration * freq), samplerate=freq, channels=2)
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print('Please say your prompt. You have ' + str(duration) + ' seconds.')
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# Record audio for the given number of seconds
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sd.wait()
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# This will convert the NumPy array to an audio file with the given sampling frequency
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write(audio, freq, recording)
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def speech_to_text(audio):
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device = torch.device('cuda') # cuda also works, but our models are fast enough for CPU
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model, decoder, utils = torch.hub.load(repo_or_dir='snakers4/silero-models', model='silero_stt', language='en', device=device)
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(read_batch, split_into_batches, read_audio, prepare_model_input) = utils # see function signature for details
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test_files = glob(audio)
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batches = split_into_batches(test_files, batch_size=100)
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input = prepare_model_input(read_batch(batches[0]), device=device)
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text = ""
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output = model(input)
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for example in output:
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text += decoder(example.cpu())
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return text
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def text_to_speech():
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language = 'en'
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model_id = 'v3_en'
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sample_rate = 8000
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speaker = 'en_1'
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device = torch.device('cuda')
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model, example_text = torch.hub.load(repo_or_dir='snakers4/silero-models', model='silero_tts', language=language, speaker=model_id)
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model.to(device) # cuda or cpu
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text = open("output.txt").read()
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audio = model.apply_tts(text, speaker=speaker, sample_rate=sample_rate)
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torchaudio.save("output.mp3", audio.unsqueeze(0), sample_rate=8000)
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song = AudioSegment.from_mp3("output.mp3")
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play(song)
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def main():
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audio = "input.wav"
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record_audio(audio)
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prompt = "in 50 words or less, " + speech_to_text(audio)
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print(prompt)
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command = "ollama run llama3 " + prompt + " > output.txt"
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os.system(command)
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text_to_speech()
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if __name__ == "__main__":
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main()
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