Compare commits
10
Commits
| Author | SHA1 | Date | |
|---|---|---|---|
 Brian Bjarke Jensen
|
d3ecc50075 | ||
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Brian Bjarke Jensen
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e5a09a791d | ||
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Brian Bjarke Jensen
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7068cff2da | ||
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Brian Bjarke Jensen
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c166420909 | ||
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Brian Bjarke Jensen
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f0b3de27ed | ||
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Brian Bjarke Jensen
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8e23442345 | ||
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Brian Bjarke Jensen
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6402150301 | ||
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Brian Bjarke Jensen
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7059338ede | ||
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Brian Bjarke Jensen
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5388502f42 | ||
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Brian Bjarke Jensen
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dd5760dca0 |
+1
-1
@@ -121,7 +121,7 @@ celerybeat.pid
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*.sage.py
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# Environments
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.env
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*.env
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.venv
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env/
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venv/
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@@ -1,3 +1,4 @@
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version: '3.7'
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services:
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app:
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image: visual_critical_discourse_analysis:dev
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@@ -5,11 +6,42 @@ services:
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build:
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context: .
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dockerfile: Dockerfile
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env_file:
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- mongodb.env
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environment:
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- MONGO_HOST=mongo
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ports:
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- 8050:8050
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networks:
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- backend
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depends_on:
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- mongo
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mongo:
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image: mongo:latest
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container_name: mongo
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env_file:
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- mongodb.env
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ports:
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- "27017:27017"
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networks:
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- backend
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mongo-express:
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image: mongo-express
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ports:
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- 8081:8081
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environment:
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ME_CONFIG_MONGODB_ADMINUSERNAME: root
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ME_CONFIG_MONGODB_ADMINPASSWORD: vSH7I7RxsDvb
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ME_CONFIG_MONGODB_PORT: 27017
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ME_CONFIG_BASICAUTH_USERNAME: admin
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ME_CONFIG_BASICAUTH_PASSWORD: q
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links:
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- mongo
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networks:
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- backend
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depends_on:
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- mongo
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networks:
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backend:
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external: false
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driver: bridge
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Generated
+124
-1
@@ -268,6 +268,26 @@ requests = ">=2.28.1,<3.0.0"
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[package.extras]
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async = ["httpx (>=0.23.0,<0.24.0)"]
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[[package]]
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name = "dnspython"
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version = "2.6.1"
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description = "DNS toolkit"
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optional = false
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python-versions = ">=3.8"
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files = [
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{file = "dnspython-2.6.1-py3-none-any.whl", hash = "sha256:5ef3b9680161f6fa89daf8ad451b5f1a33b18ae8a1c6778cdf4b43f08c0a6e50"},
|
||||
{file = "dnspython-2.6.1.tar.gz", hash = "sha256:e8f0f9c23a7b7cb99ded64e6c3a6f3e701d78f50c55e002b839dea7225cff7cc"},
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]
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||||
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[package.extras]
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dev = ["black (>=23.1.0)", "coverage (>=7.0)", "flake8 (>=7)", "mypy (>=1.8)", "pylint (>=3)", "pytest (>=7.4)", "pytest-cov (>=4.1.0)", "sphinx (>=7.2.0)", "twine (>=4.0.0)", "wheel (>=0.42.0)"]
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dnssec = ["cryptography (>=41)"]
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doh = ["h2 (>=4.1.0)", "httpcore (>=1.0.0)", "httpx (>=0.26.0)"]
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doq = ["aioquic (>=0.9.25)"]
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idna = ["idna (>=3.6)"]
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trio = ["trio (>=0.23)"]
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wmi = ["wmi (>=1.5.1)"]
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[[package]]
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name = "flask"
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version = "3.0.2"
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@@ -669,6 +689,109 @@ files = [
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[package.dependencies]
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typing-extensions = ">=4.6.0,<4.7.0 || >4.7.0"
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[[package]]
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name = "pymongo"
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version = "4.6.1"
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description = "Python driver for MongoDB <http://www.mongodb.org>"
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optional = false
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||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "pymongo-4.6.1-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:4344c30025210b9fa80ec257b0e0aab5aa1d5cca91daa70d82ab97b482cc038e"},
|
||||
{file = "pymongo-4.6.1-cp310-cp310-manylinux1_i686.whl", hash = "sha256:1c5654bb8bb2bdb10e7a0bc3c193dd8b49a960b9eebc4381ff5a2043f4c3c441"},
|
||||
{file = "pymongo-4.6.1-cp310-cp310-manylinux2014_aarch64.whl", hash = "sha256:eaf2f65190c506def2581219572b9c70b8250615dc918b3b7c218361a51ec42e"},
|
||||
{file = "pymongo-4.6.1-cp310-cp310-manylinux2014_i686.whl", hash = "sha256:262356ea5fcb13d35fb2ab6009d3927bafb9504ef02339338634fffd8a9f1ae4"},
|
||||
{file = "pymongo-4.6.1-cp310-cp310-manylinux2014_ppc64le.whl", hash = "sha256:2dd2f6960ee3c9360bed7fb3c678be0ca2d00f877068556785ec2eb6b73d2414"},
|
||||
{file = "pymongo-4.6.1-cp310-cp310-manylinux2014_s390x.whl", hash = "sha256:ff925f1cca42e933376d09ddc254598f8c5fcd36efc5cac0118bb36c36217c41"},
|
||||
{file = "pymongo-4.6.1-cp310-cp310-manylinux2014_x86_64.whl", hash = "sha256:3cadf7f4c8e94d8a77874b54a63c80af01f4d48c4b669c8b6867f86a07ba994f"},
|
||||
{file = "pymongo-4.6.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:55dac73316e7e8c2616ba2e6f62b750918e9e0ae0b2053699d66ca27a7790105"},
|
||||
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|
||||
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|
||||
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|
||||
{file = "pymongo-4.6.1-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:e470fa4bace5f50076c32f4b3cc182b31303b4fefb9b87f990144515d572820b"},
|
||||
{file = "pymongo-4.6.1-cp310-cp310-win32.whl", hash = "sha256:da08ea09eefa6b960c2dd9a68ec47949235485c623621eb1d6c02b46765322ac"},
|
||||
{file = "pymongo-4.6.1-cp310-cp310-win_amd64.whl", hash = "sha256:13d613c866f9f07d51180f9a7da54ef491d130f169e999c27e7633abe8619ec9"},
|
||||
{file = "pymongo-4.6.1-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:6a0ae7a48a6ef82ceb98a366948874834b86c84e288dbd55600c1abfc3ac1d88"},
|
||||
{file = "pymongo-4.6.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:5bd94c503271e79917b27c6e77f7c5474da6930b3fb9e70a12e68c2dff386b9a"},
|
||||
{file = "pymongo-4.6.1-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:2d4ccac3053b84a09251da8f5350bb684cbbf8c8c01eda6b5418417d0a8ab198"},
|
||||
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|
||||
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||||
{file = "pymongo-4.6.1-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:d8e62d06e90f60ea2a3d463ae51401475568b995bafaffd81767d208d84d7bb1"},
|
||||
{file = "pymongo-4.6.1-cp311-cp311-win32.whl", hash = "sha256:5556e306713e2522e460287615d26c0af0fe5ed9d4f431dad35c6624c5d277e9"},
|
||||
{file = "pymongo-4.6.1-cp311-cp311-win_amd64.whl", hash = "sha256:b10d8cda9fc2fcdcfa4a000aa10413a2bf8b575852cd07cb8a595ed09689ca98"},
|
||||
{file = "pymongo-4.6.1-cp312-cp312-macosx_10_9_universal2.whl", hash = "sha256:b435b13bb8e36be11b75f7384a34eefe487fe87a6267172964628e2b14ecf0a7"},
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||||
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||||
{file = "pymongo-4.6.1-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:8b47ebd89e69fbf33d1c2df79759d7162fc80c7652dacfec136dae1c9b3afac7"},
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||||
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||||
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||||
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||||
{file = "pymongo-4.6.1-cp312-cp312-win32.whl", hash = "sha256:3177f783ae7e08aaf7b2802e0df4e4b13903520e8380915e6337cdc7a6ff01d8"},
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||||
{file = "pymongo-4.6.1-cp312-cp312-win_amd64.whl", hash = "sha256:00c199e1c593e2c8b033136d7a08f0c376452bac8a896c923fcd6f419e07bdd2"},
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||||
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||||
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||||
{file = "pymongo-4.6.1-cp37-cp37m-manylinux1_x86_64.whl", hash = "sha256:77e0df59b1a4994ad30c6d746992ae887f9756a43fc25dec2db515d94cf0222d"},
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||||
{file = "pymongo-4.6.1-cp37-cp37m-manylinux2014_aarch64.whl", hash = "sha256:3a7f02a58a0c2912734105e05dedbee4f7507e6f1bd132ebad520be0b11d46fd"},
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
{file = "pymongo-4.6.1-cp39-cp39-manylinux1_x86_64.whl", hash = "sha256:6a1810c2cbde714decf40f811d1edc0dae45506eb37298fd9d4247b8801509fe"},
|
||||
{file = "pymongo-4.6.1-cp39-cp39-manylinux2014_aarch64.whl", hash = "sha256:e2aced6fb2f5261b47d267cb40060b73b6527e64afe54f6497844c9affed5fd0"},
|
||||
{file = "pymongo-4.6.1-cp39-cp39-manylinux2014_i686.whl", hash = "sha256:d0355cff58a4ed6d5e5f6b9c3693f52de0784aa0c17119394e2a8e376ce489d4"},
|
||||
{file = "pymongo-4.6.1-cp39-cp39-manylinux2014_ppc64le.whl", hash = "sha256:3c74f4725485f0a7a3862cfd374cc1b740cebe4c133e0c1425984bcdcce0f4bb"},
|
||||
{file = "pymongo-4.6.1-cp39-cp39-manylinux2014_s390x.whl", hash = "sha256:9c79d597fb3a7c93d7c26924db7497eba06d58f88f58e586aa69b2ad89fee0f8"},
|
||||
{file = "pymongo-4.6.1-cp39-cp39-manylinux2014_x86_64.whl", hash = "sha256:8ec75f35f62571a43e31e7bd11749d974c1b5cd5ea4a8388725d579263c0fdf6"},
|
||||
{file = "pymongo-4.6.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a5e641f931c5cd95b376fd3c59db52770e17bec2bf86ef16cc83b3906c054845"},
|
||||
{file = "pymongo-4.6.1-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:9aafd036f6f2e5ad109aec92f8dbfcbe76cff16bad683eb6dd18013739c0b3ae"},
|
||||
{file = "pymongo-4.6.1-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:1f2b856518bfcfa316c8dae3d7b412aecacf2e8ba30b149f5eb3b63128d703b9"},
|
||||
{file = "pymongo-4.6.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:5ec31adc2e988fd7db3ab509954791bbc5a452a03c85e45b804b4bfc31fa221d"},
|
||||
{file = "pymongo-4.6.1-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:9167e735379ec43d8eafa3fd675bfbb12e2c0464f98960586e9447d2cf2c7a83"},
|
||||
{file = "pymongo-4.6.1-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:1461199b07903fc1424709efafe379205bf5f738144b1a50a08b0396357b5abf"},
|
||||
{file = "pymongo-4.6.1-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.whl", hash = "sha256:3094c7d2f820eecabadae76bfec02669567bbdd1730eabce10a5764778564f7b"},
|
||||
{file = "pymongo-4.6.1-cp39-cp39-win32.whl", hash = "sha256:c91ea3915425bd4111cb1b74511cdc56d1d16a683a48bf2a5a96b6a6c0f297f7"},
|
||||
{file = "pymongo-4.6.1-cp39-cp39-win_amd64.whl", hash = "sha256:ef102a67ede70e1721fe27f75073b5314911dbb9bc27cde0a1c402a11531e7bd"},
|
||||
{file = "pymongo-4.6.1.tar.gz", hash = "sha256:31dab1f3e1d0cdd57e8df01b645f52d43cc1b653ed3afd535d2891f4fc4f9712"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
dnspython = ">=1.16.0,<3.0.0"
|
||||
|
||||
[package.extras]
|
||||
aws = ["pymongo-auth-aws (<2.0.0)"]
|
||||
encryption = ["certifi", "pymongo[aws]", "pymongocrypt (>=1.6.0,<2.0.0)"]
|
||||
gssapi = ["pykerberos", "winkerberos (>=0.5.0)"]
|
||||
ocsp = ["certifi", "cryptography (>=2.5)", "pyopenssl (>=17.2.0)", "requests (<3.0.0)", "service-identity (>=18.1.0)"]
|
||||
snappy = ["python-snappy"]
|
||||
test = ["pytest (>=7)"]
|
||||
zstd = ["zstandard"]
|
||||
|
||||
[[package]]
|
||||
name = "python-dotenv"
|
||||
version = "1.0.1"
|
||||
@@ -839,4 +962,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p
|
||||
[metadata]
|
||||
lock-version = "2.0"
|
||||
python-versions = "^3.12"
|
||||
content-hash = "e4892a5e8db437b5c79b40f259ba6b512d1c0b18677b4f13163fee518276a28d"
|
||||
content-hash = "e4aacea5a98281d935411e0d96152d1d24680f6c1e5288e9a1be913a0536b78e"
|
||||
|
||||
@@ -18,6 +18,7 @@ dash-bootstrap-components = "^1.5.0"
|
||||
dash-mantine-components = "^0.12.1"
|
||||
pydantic = "^2.6.1"
|
||||
pillow = "^10.2.0"
|
||||
pymongo = "^4.6.1"
|
||||
|
||||
|
||||
[build-system]
|
||||
|
||||
@@ -0,0 +1,5 @@
|
||||
from .classes import (
|
||||
ModelOutputs,
|
||||
VisualCommunication
|
||||
)
|
||||
from .database import connect
|
||||
@@ -0,0 +1,69 @@
|
||||
from __future__ import annotations
|
||||
from pydantic import BaseModel, field_validator
|
||||
from PIL import Image
|
||||
from io import BytesIO
|
||||
from pathlib import Path
|
||||
|
||||
from src.model_experiential import ExperientialModelOutput
|
||||
from src.model_interpersonal import (
|
||||
ContactModelOutput,
|
||||
AngleModelOutput,
|
||||
PointOfViewModelOutput,
|
||||
DistanceModelOutput,
|
||||
ModalityLightingModelOutput,
|
||||
ModalityColorModelOutput,
|
||||
ModalityDepthModelOutput
|
||||
)
|
||||
from src.model_textual import (
|
||||
InformationValueModelOutput,
|
||||
FramingModelOutput,
|
||||
SalienceModelOutput
|
||||
)
|
||||
|
||||
class ModelOutputs(BaseModel):
|
||||
experiential: ExperientialModelOutput
|
||||
contact: ContactModelOutput
|
||||
angle: AngleModelOutput
|
||||
point_of_view: PointOfViewModelOutput
|
||||
distance: DistanceModelOutput
|
||||
modality_lighting: ModalityLightingModelOutput
|
||||
modality_color: ModalityColorModelOutput
|
||||
modality_depth: ModalityDepthModelOutput
|
||||
information_value: InformationValueModelOutput
|
||||
framing: FramingModelOutput
|
||||
salience: SalienceModelOutput
|
||||
|
||||
|
||||
class VisualCommunication(BaseModel):
|
||||
name: str
|
||||
image: bytes
|
||||
annotation: ModelOutputs | None = None
|
||||
prediction: ModelOutputs | None = None
|
||||
|
||||
@classmethod
|
||||
def classname(cls) -> str:
|
||||
"""Return classname."""
|
||||
return cls.__name__
|
||||
|
||||
@classmethod
|
||||
def from_file(cls, path: Path) -> VisualCommunication:
|
||||
"""Instantiate from file."""
|
||||
name = path.stem
|
||||
image = Image.open(path)
|
||||
return VisualCommunication(name=name, image=image)
|
||||
|
||||
@field_validator("image", mode="before")
|
||||
@classmethod
|
||||
def convert_to_bytes(cls, raw: Image.Image | BytesIO | bytes) -> bytes:
|
||||
if isinstance(raw, Image.Image):
|
||||
raw = raw.tobytes()
|
||||
if isinstance(raw, BytesIO):
|
||||
raw = raw.read()
|
||||
return raw
|
||||
|
||||
def __repr__(self) -> str:
|
||||
return f"{self.classname()}(name='{self.name}')"
|
||||
|
||||
@property
|
||||
def image(self) -> Image.Image:
|
||||
return Image.open(BytesIO(self.image))
|
||||
@@ -0,0 +1,22 @@
|
||||
from pymongo import MongoClient
|
||||
from dotenv import load_dotenv
|
||||
import os
|
||||
|
||||
def connect():
|
||||
"""Connect to MongoDB."""
|
||||
# load env vars
|
||||
load_dotenv()
|
||||
necessary_env_vars = [
|
||||
"MONGO_HOST",
|
||||
"MONGO_DB",
|
||||
"MONGO_COLLECTION"
|
||||
]
|
||||
for env_var in necessary_env_vars:
|
||||
assert env_var in os.environ, f"{env_var} not found"
|
||||
# connect to database
|
||||
client = MongoClient(os.getenv("MONGO_HOST"))
|
||||
db = client[os.getenv("MONGO_DB")]
|
||||
collection = db[os.getenv("MONGO_COLLECTION")]
|
||||
return collection, db, client
|
||||
|
||||
|
||||
@@ -1 +1,24 @@
|
||||
from .output import model_labels
|
||||
from .classes import ExperientialModelOutput
|
||||
|
||||
|
||||
|
||||
# CLASS_NAME_LIST = Literal[
|
||||
# "non transactional action",
|
||||
# "non transactional reaction",
|
||||
# "unidirectional transactional action",
|
||||
# "unidirectional transactional reaction",
|
||||
# "bidirectional transactional action",
|
||||
# "bidirectional transactional reaction",
|
||||
# "conversion",
|
||||
# "speech process",
|
||||
# "classification overt taxonomy",
|
||||
# "analytical exhaustive",
|
||||
# "analytical disarranged",
|
||||
# "analytical temporal",
|
||||
# "analytical distributed",
|
||||
# "anaytical topological",
|
||||
# "analytical exploded",
|
||||
# "analytical inclusive",
|
||||
# "symbolic suggestive",
|
||||
# "symbolic attributive"
|
||||
# ]
|
||||
@@ -0,0 +1,68 @@
|
||||
from pydantic import BaseModel
|
||||
from typing import List
|
||||
import random
|
||||
|
||||
|
||||
class ModelOutput(BaseModel):
|
||||
|
||||
@classmethod
|
||||
def classname(cls) -> str:
|
||||
"""Return classname."""
|
||||
return cls.__name__
|
||||
|
||||
@classmethod
|
||||
def list_fields(cls) -> List[str]:
|
||||
"""List options that are stored as attributes."""
|
||||
return list(cls.model_fields.keys())
|
||||
|
||||
@classmethod
|
||||
def from_random(cls):
|
||||
"""Instantiate with random numbers."""
|
||||
kwargs = {field: random.random() for field in cls.list_fields()}
|
||||
return cls(**kwargs)
|
||||
|
||||
def __repr__(self) -> str:
|
||||
model_dict = self.model_dump()
|
||||
model_repr_str = f"{self.classname()}("
|
||||
model_repr_str += ", ".join([f"{field}={value:.3f}" for field, value in model_dict.items()])
|
||||
model_repr_str += ")"
|
||||
return model_repr_str
|
||||
|
||||
def highest_score_field(self) -> str:
|
||||
"""Return name of field with highest score."""
|
||||
model_dict = self.model_dump()
|
||||
return max(model_dict, key=lambda k: model_dict[k])
|
||||
|
||||
def highest_score_value(self) -> float:
|
||||
"""Return value of field with highest score."""
|
||||
model_dict = self.model_dump()
|
||||
return max(model_dict.values())
|
||||
|
||||
|
||||
class ExperientialModelOutput(ModelOutput):
|
||||
non_transactional_action: float
|
||||
non_transactional_reaction: float
|
||||
unidirectional_transactional_action: float
|
||||
unidirectional_transactional_reaction: float
|
||||
bidirectional_transactional_action: float
|
||||
bidirectional_transactional_reaction: float
|
||||
conversion: float
|
||||
speech_process: float
|
||||
classification_overt_taxonomy: float
|
||||
analytical_exhaustive: float
|
||||
analytical_disarranged: float
|
||||
analytical_temporal: float
|
||||
analytical_distributed: float
|
||||
analytical_topological: float
|
||||
analytical_exploded: float
|
||||
analytical_inclusive: float
|
||||
symbolic_suggestive: float
|
||||
symbolic_attributive: float
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
m = ExperientialModelOutput.from_random()
|
||||
print(m)
|
||||
print(repr(m))
|
||||
print(m.highest_score_field())
|
||||
print(m.highest_score_value())
|
||||
@@ -1,5 +1,4 @@
|
||||
|
||||
model_labels = [
|
||||
CLASS_NAMES = [
|
||||
"non transactional action",
|
||||
"non transactional reaction",
|
||||
"unidirectional transactional action",
|
||||
|
||||
@@ -1 +1,9 @@
|
||||
from .output import model_labels
|
||||
from .classes import (
|
||||
ContactModelOutput,
|
||||
AngleModelOutput,
|
||||
PointOfViewModelOutput,
|
||||
DistanceModelOutput,
|
||||
ModalityLightingModelOutput,
|
||||
ModalityColorModelOutput,
|
||||
ModalityDepthModelOutput
|
||||
)
|
||||
@@ -0,0 +1,120 @@
|
||||
from pydantic import BaseModel
|
||||
from typing import List
|
||||
import random
|
||||
|
||||
|
||||
class ModelOutput(BaseModel):
|
||||
|
||||
@classmethod
|
||||
def classname(cls) -> str:
|
||||
"""Return classname."""
|
||||
return cls.__name__
|
||||
|
||||
@classmethod
|
||||
def list_fields(cls) -> List[str]:
|
||||
"""List options that are stored as attributes."""
|
||||
return list(cls.model_fields.keys())
|
||||
|
||||
@classmethod
|
||||
def from_random(cls):
|
||||
"""Instantiate with random numbers."""
|
||||
kwargs = {field: random.random() for field in cls.list_fields()}
|
||||
return cls(**kwargs)
|
||||
|
||||
def __repr__(self) -> str:
|
||||
model_dict = self.model_dump()
|
||||
model_repr_str = f"{self.classname()}("
|
||||
model_repr_str += ", ".join([f"{field}={value:.3f}" for field, value in model_dict.items()])
|
||||
model_repr_str += ")"
|
||||
return model_repr_str
|
||||
|
||||
def highest_score_field(self) -> str:
|
||||
"""Return name of field with highest score."""
|
||||
model_dict = self.model_dump()
|
||||
return max(model_dict, key=lambda k: model_dict[k])
|
||||
|
||||
def highest_score_value(self) -> float:
|
||||
"""Return value of field with highest score."""
|
||||
model_dict = self.model_dump()
|
||||
return max(model_dict.values())
|
||||
|
||||
|
||||
class ContactModelOutput(ModelOutput):
|
||||
offer: float
|
||||
demand: float
|
||||
|
||||
|
||||
class AngleModelOutput(ModelOutput):
|
||||
high: float
|
||||
eye_level: float
|
||||
low: float
|
||||
|
||||
|
||||
class PointOfViewModelOutput(ModelOutput):
|
||||
frontal: float
|
||||
oblique: float
|
||||
|
||||
|
||||
class DistanceModelOutput(ModelOutput):
|
||||
long: float
|
||||
medium: float
|
||||
close: float
|
||||
|
||||
|
||||
class ModalityLightingModelOutput(ModelOutput):
|
||||
high: float
|
||||
medium: float
|
||||
low: float
|
||||
|
||||
|
||||
class ModalityColorModelOutput(ModelOutput):
|
||||
high: float
|
||||
medium: float
|
||||
low: float
|
||||
|
||||
|
||||
class ModalityDepthModelOutput(ModelOutput):
|
||||
high: float
|
||||
medium: float
|
||||
low: float
|
||||
|
||||
|
||||
# class InterpersonalModelOutput(BaseModel):
|
||||
# contact: ContactModelOutput
|
||||
# angle: AngleModelOutput
|
||||
# point_of_view: PointOfViewModelOutput
|
||||
# distance: DistanceModelOutput
|
||||
# modality_lighting: ModalityLightingModelOutput
|
||||
# modality_color: ModalityColorModelOutput
|
||||
# modality_depth: ModalityDepthModelOutput
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
m = ContactModelOutput.from_random()
|
||||
print(repr(m))
|
||||
print(m.highest_score_field())
|
||||
print(m.highest_score_value())
|
||||
m = AngleModelOutput.from_random()
|
||||
print(repr(m))
|
||||
print(m.highest_score_field())
|
||||
print(m.highest_score_value())
|
||||
m = PointOfViewModelOutput.from_random()
|
||||
print(repr(m))
|
||||
print(m.highest_score_field())
|
||||
print(m.highest_score_value())
|
||||
m = DistanceModelOutput.from_random()
|
||||
print(repr(m))
|
||||
print(m.highest_score_field())
|
||||
print(m.highest_score_value())
|
||||
m = ModalityLightingModelOutput.from_random()
|
||||
print(repr(m))
|
||||
print(m.highest_score_field())
|
||||
print(m.highest_score_value())
|
||||
m = ModalityColorModelOutput.from_random()
|
||||
print(repr(m))
|
||||
print(m.highest_score_field())
|
||||
print(m.highest_score_value())
|
||||
m = ModalityDepthModelOutput.from_random()
|
||||
print(repr(m))
|
||||
print(m.highest_score_field())
|
||||
print(m.highest_score_value())
|
||||
@@ -1,8 +1,8 @@
|
||||
|
||||
model_labels = {
|
||||
"contact": [
|
||||
"contact offer",
|
||||
"contact demand"
|
||||
"offer",
|
||||
"demand"
|
||||
],
|
||||
"angle": [
|
||||
"high",
|
||||
|
||||
@@ -1 +1,5 @@
|
||||
from .output import model_labels
|
||||
from .classes import (
|
||||
InformationValueModelOutput,
|
||||
FramingModelOutput,
|
||||
SalienceModelOutput
|
||||
)
|
||||
@@ -0,0 +1,75 @@
|
||||
from pydantic import BaseModel
|
||||
from typing import List
|
||||
import random
|
||||
|
||||
|
||||
class ModelOutput(BaseModel):
|
||||
|
||||
@classmethod
|
||||
def classname(cls) -> str:
|
||||
"""Return classname."""
|
||||
return cls.__name__
|
||||
|
||||
@classmethod
|
||||
def list_fields(cls) -> List[str]:
|
||||
"""List options that are stored as attributes."""
|
||||
return list(cls.model_fields.keys())
|
||||
|
||||
@classmethod
|
||||
def from_random(cls):
|
||||
"""Instantiate with random numbers."""
|
||||
kwargs = {field: random.random() for field in cls.list_fields()}
|
||||
return cls(**kwargs)
|
||||
|
||||
def __repr__(self) -> str:
|
||||
model_dict = self.model_dump()
|
||||
model_repr_str = f"{self.classname()}("
|
||||
model_repr_str += ", ".join([f"{field}={value:.3f}" for field, value in model_dict.items()])
|
||||
model_repr_str += ")"
|
||||
return model_repr_str
|
||||
|
||||
def highest_score_field(self) -> str:
|
||||
"""Return name of field with highest score."""
|
||||
model_dict = self.model_dump()
|
||||
return max(model_dict, key=lambda k: model_dict[k])
|
||||
|
||||
def highest_score_value(self) -> float:
|
||||
"""Return value of field with highest score."""
|
||||
model_dict = self.model_dump()
|
||||
return max(model_dict.values())
|
||||
|
||||
|
||||
class InformationValueModelOutput(ModelOutput):
|
||||
given_new: float
|
||||
ideal_real: float
|
||||
central_marginal: float
|
||||
|
||||
|
||||
class FramingModelOutput(ModelOutput):
|
||||
frame_lines: float
|
||||
empty_space: float
|
||||
colour_contrast: float
|
||||
form_contrast: float
|
||||
|
||||
|
||||
class SalienceModelOutput(ModelOutput):
|
||||
size: float
|
||||
colour: float
|
||||
tone: float
|
||||
form: float
|
||||
positioning: float
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
m = InformationValueModelOutput.from_random()
|
||||
print(repr(m))
|
||||
print(m.highest_score_field())
|
||||
print(m.highest_score_value())
|
||||
m = FramingModelOutput.from_random()
|
||||
print(repr(m))
|
||||
print(m.highest_score_field())
|
||||
print(m.highest_score_value())
|
||||
m = SalienceModelOutput.from_random()
|
||||
print(repr(m))
|
||||
print(m.highest_score_field())
|
||||
print(m.highest_score_value())
|
||||
+6
-11
@@ -4,9 +4,6 @@ import dash_mantine_components as dmc
|
||||
|
||||
from .header import generate_header
|
||||
from .body import generate_body
|
||||
from model_experiential import model_labels as experiential_labels
|
||||
from model_interpersonal import model_labels as interpersonal_labels
|
||||
from model_textual import model_labels as textual_labels
|
||||
|
||||
|
||||
app = Dash(__name__, external_stylesheets=[dbc.themes.BOOTSTRAP])
|
||||
@@ -20,14 +17,12 @@ app.layout = dmc.MantineProvider(
|
||||
},
|
||||
},
|
||||
children=[
|
||||
dmc.Container([
|
||||
generate_header(),
|
||||
generate_body(
|
||||
experiential_labels,
|
||||
interpersonal_labels,
|
||||
textual_labels
|
||||
),
|
||||
]),
|
||||
dmc.Container(
|
||||
[
|
||||
generate_header(),
|
||||
generate_body(),
|
||||
], fluid=True
|
||||
),
|
||||
],
|
||||
)
|
||||
|
||||
+119
-47
@@ -1,71 +1,143 @@
|
||||
import dash_mantine_components as dmc
|
||||
from dash import dcc, html
|
||||
from typing import List
|
||||
|
||||
def generate_body(
|
||||
experiential_labels,
|
||||
interpersonal_labels,
|
||||
textual_labels
|
||||
):
|
||||
from src.model_experiential import ExperientialModelOutput
|
||||
from src.model_interpersonal import (
|
||||
ContactModelOutput,
|
||||
AngleModelOutput,
|
||||
PointOfViewModelOutput,
|
||||
DistanceModelOutput,
|
||||
ModalityLightingModelOutput,
|
||||
ModalityColorModelOutput,
|
||||
ModalityDepthModelOutput
|
||||
)
|
||||
from src.model_textual import (
|
||||
InformationValueModelOutput,
|
||||
FramingModelOutput,
|
||||
SalienceModelOutput
|
||||
)
|
||||
|
||||
def generate_option_labels(model) -> List[str]:
|
||||
"""Generate presentable list of attributes from an OutputModel."""
|
||||
labels = [
|
||||
label.replace('_', ' ').title()
|
||||
for label in model.list_fields()
|
||||
]
|
||||
return labels
|
||||
|
||||
def generate_experiential_options_map():
|
||||
"""Generate map of titles and options for experiential labels."""
|
||||
options_map = {}
|
||||
# add experiential labels
|
||||
options_map["experiential".title()] = generate_option_labels(ExperientialModelOutput)
|
||||
return options_map
|
||||
|
||||
def generate_interpersonal_options_map():
|
||||
"""Generate map of titles and options for interpersonal labels."""
|
||||
options_map = {}
|
||||
# add interpersonal labels
|
||||
options_map["contact".title()] = generate_option_labels(ContactModelOutput)
|
||||
options_map["angle".title()] = generate_option_labels(AngleModelOutput)
|
||||
options_map["point of view".title()] = generate_option_labels(PointOfViewModelOutput)
|
||||
options_map["distance".title()] = generate_option_labels(DistanceModelOutput)
|
||||
options_map["modality lighting".title()] = generate_option_labels(ModalityLightingModelOutput)
|
||||
options_map["modality color".title()] = generate_option_labels(ModalityColorModelOutput)
|
||||
options_map["modality depth".title()] = generate_option_labels(ModalityDepthModelOutput)
|
||||
return options_map
|
||||
|
||||
def generate_textual_options_map():
|
||||
"""Generate map of titles and options for textual labels."""
|
||||
options_map = {}
|
||||
# add textual labels
|
||||
options_map["information value".title()] = generate_option_labels(InformationValueModelOutput)
|
||||
options_map["framing".title()] = generate_option_labels(FramingModelOutput)
|
||||
options_map["salience".title()] = generate_option_labels(SalienceModelOutput)
|
||||
return options_map
|
||||
|
||||
def generate_body():
|
||||
image_container = dmc.Image(
|
||||
width=400,
|
||||
height=400,
|
||||
width=600,
|
||||
height=600,
|
||||
withPlaceholder=True,
|
||||
placeholder=[dmc.Loader(color="gray", size="md")],
|
||||
)
|
||||
|
||||
experiential_labels_container = dmc.Container(
|
||||
# prepare experiential container
|
||||
experiential_map = generate_experiential_options_map()
|
||||
experiential_container = dmc.Col(
|
||||
children=[
|
||||
html.H4("experiential labels".title()),
|
||||
dcc.RadioItems(options=list(experiential_labels)),
|
||||
]
|
||||
dmc.Container([
|
||||
html.H4(list(experiential_map.keys())[0]),
|
||||
html.B("visual syntax".title()),
|
||||
dcc.RadioItems(options=list(experiential_map.values())[0]),
|
||||
])
|
||||
], span=4
|
||||
)
|
||||
|
||||
interpersonal_labels_container = dmc.Container(
|
||||
children=[]
|
||||
)
|
||||
for category, options in interpersonal_labels.items():
|
||||
interpersonal_labels_container.children.append(html.H4(category.title()))
|
||||
interpersonal_labels_container.children.append(dcc.RadioItems(options))
|
||||
|
||||
textual_labels_container = dmc.Container(
|
||||
children=[]
|
||||
)
|
||||
for category, options in textual_labels.items():
|
||||
textual_labels_container.children.append(html.H4(category.title()))
|
||||
textual_labels_container.children.append(dcc.RadioItems(options))
|
||||
|
||||
label_container = dmc.Container(
|
||||
# prepare interpersonal container
|
||||
interpersonal_map = generate_interpersonal_options_map()
|
||||
interpersonal_container = dmc.Col(
|
||||
children=[
|
||||
experiential_labels_container,
|
||||
dmc.Divider(),
|
||||
interpersonal_labels_container,
|
||||
dmc.Divider(),
|
||||
textual_labels_container,
|
||||
dmc.Divider(),
|
||||
html.Button(
|
||||
"confirm",
|
||||
id="submit-button"
|
||||
)
|
||||
]
|
||||
html.H4("interpersonal".title()),
|
||||
], span=4
|
||||
)
|
||||
|
||||
for title, options in interpersonal_map.items():
|
||||
interpersonal_container.children.append(
|
||||
dmc.Container([
|
||||
html.B(title),
|
||||
dcc.RadioItems(options)
|
||||
])
|
||||
)
|
||||
# prepare textual container
|
||||
textual_map = generate_textual_options_map()
|
||||
textual_container = dmc.Col(
|
||||
children=[
|
||||
html.H4("textual".title()),
|
||||
], span=4
|
||||
)
|
||||
for title, options in textual_map.items():
|
||||
textual_container.children.append(
|
||||
dmc.Container([
|
||||
html.B(title),
|
||||
dcc.RadioItems(options)
|
||||
])
|
||||
)
|
||||
# prepare labels container
|
||||
label_container = dmc.Grid(
|
||||
children=[
|
||||
experiential_container,
|
||||
interpersonal_container,
|
||||
textual_container,
|
||||
],
|
||||
)
|
||||
# build the full body container
|
||||
body_container = dmc.Container(
|
||||
dmc.Grid(
|
||||
children=[
|
||||
dmc.Col(
|
||||
dmc.Center(
|
||||
image_container,
|
||||
),
|
||||
span=5,
|
||||
),
|
||||
dmc.Col(
|
||||
dmc.Divider(orientation="vertical"),
|
||||
span=1,
|
||||
),
|
||||
dmc.Col(
|
||||
# radio buttons part
|
||||
label_container,
|
||||
span=5,
|
||||
children = [
|
||||
label_container,
|
||||
dmc.Button(
|
||||
"confirm",
|
||||
id="submit-button",
|
||||
fullWidth=True,
|
||||
color="lime",
|
||||
radius="sm",
|
||||
size="md",
|
||||
style={
|
||||
"height": "50px"
|
||||
}
|
||||
),
|
||||
], span=7,
|
||||
),
|
||||
# dmc.Col(span=1),
|
||||
], grow=True
|
||||
)
|
||||
), fluid=True
|
||||
)
|
||||
return body_container
|
||||
Binary file not shown.
|
After Width: | Height: | Size: 26 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 29 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 37 KiB |
@@ -0,0 +1,26 @@
|
||||
from pathlib import Path
|
||||
from dotenv import load_dotenv
|
||||
from pymongo import MongoClient
|
||||
import os
|
||||
|
||||
from src.database import VisualCommunication, connect
|
||||
|
||||
if __name__ == "__main__":
|
||||
# get list of image paths
|
||||
test_dir = Path(__file__).parent
|
||||
img_dir = test_dir / "imgs"
|
||||
img_path_list = [path for path in img_dir.glob("*.jpeg") if path.is_file()]
|
||||
print(img_path_list)
|
||||
# instantiate data object
|
||||
vis_com_list = [VisualCommunication.from_file(path) for path in img_path_list]
|
||||
for vis_com in vis_com_list:
|
||||
print(repr(vis_com))
|
||||
# upload images to database
|
||||
env_path = test_dir.parent / "mongodb.env"
|
||||
assert env_path.exists()
|
||||
load_dotenv(env_path)
|
||||
collection, db, client = connect()
|
||||
print(client.server_info())
|
||||
for vis_com in vis_com_list:
|
||||
result = collection.insert_one(vis_com.model_dump_json())
|
||||
print(f"inserted document: {result}")
|
||||
Reference in New Issue
Block a user