Task-315 Added tab for viewing the datasets used for training the models and reworked the layout of the UI
Browse files- Pipfile +3 -1
- Pipfile.lock +290 -114
- app.py +186 -67
- config.py +0 -23
- config.toml +4 -0
- scripts/__init__.py +1 -1
- scripts/config.py +76 -0
- setup.sh +32 -0
Pipfile
CHANGED
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@@ -10,13 +10,15 @@ numpy = "*"
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st-annotated-text = "*"
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transformers = "*"
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torch = "*"
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huggingface-hub = "*"
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joblib = "*"
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nltk = "*"
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htbuilder = "*"
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nest-asyncio = "*"
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mkdocs = "*"
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mkdocstrings-python = "*"
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[dev-packages]
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st-annotated-text = "*"
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transformers = "*"
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torch = "*"
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joblib = "*"
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nltk = "*"
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htbuilder = "*"
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nest-asyncio = "*"
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mkdocs = "*"
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mkdocstrings-python = "*"
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typer = "*"
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huggingface-hub = "*"
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loguru = "*"
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[dev-packages]
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Pipfile.lock
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"_meta": {
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"hash": {
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"sha256": "
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"pipfile-spec": 6,
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"requires": {
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"griffe": {
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"hashes": [
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"markers": "python_version >= '3.9'",
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"version": "==1.
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"htbuilder": {
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"hashes": [
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"markers": "python_version >= '3.9'",
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"version": "==2024.10.1"
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"markdown": {
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"sha256:2ae2471477cfd02dbbf038d5d9bc226d40def84b4fe2986e49b59b6b472bbed2",
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"markers": "python_version >= '3.8'",
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"version": "==3.7"
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"markupsafe": {
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"markers": "python_version >= '3.9'",
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"version": "==3.0.2"
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"version": "==0.9.1"
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],
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"markers": "python_version >= '3.8'",
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"version": "==2024.11.6"
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"markers": "python_version >= '3.8'",
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"version": "==2.32.3"
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"markers": "python_version >= '3.9'",
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"version": "==0.
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"safetensors": {
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"hashes": [
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"version": "==78.1.0"
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"tenacity": {
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"hashes": [
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"sha256:60d9b630c8025b9458a9d114e3af579a2c54bd32df601c4581bd054e85258143",
|
| 1022 |
+
"sha256:619ca56a5468f933d940e1bf431c6f4e13bef8e688698b067ae68eb4f9b30e3a",
|
| 1023 |
+
"sha256:630d3d8ea77eabd6cbcd2ea712e1c5cecb5b558d39547ac988351195db433f6c",
|
| 1024 |
+
"sha256:63981feca3f110ed132fd217bf7768ee8ed738a55549883628ee3da75bb9cb78",
|
| 1025 |
+
"sha256:66420986c9afff67ef0c5d1e4cdc2d0e5262f53ad11e4f90e5e22448df485bf0",
|
| 1026 |
+
"sha256:675269d407a257b8c00a6b58205b72eec8231656506c56fd429d924ca00bb350",
|
| 1027 |
+
"sha256:6a4a535013aeeef13c5532f802708cecae8d66c282babb5cd916379b72110cf7",
|
| 1028 |
+
"sha256:6a727fd083009bc83eb83d6950f0c32b3c94c8b80a9b667c87f4bd1274ca30ba",
|
| 1029 |
+
"sha256:6e1daf5bf6c2be39654beae83ee6b9a12347cb5aced9a29eecf12a2d25fff664",
|
| 1030 |
+
"sha256:6eea559077d29486c68218178ea946263b87f1c41ae7f996b1f30a983c476a5a",
|
| 1031 |
+
"sha256:75a810b7664c17f24bf2ffd7f92416c00ec84b49bb68e6a0d93e542406336b56",
|
| 1032 |
+
"sha256:772cc1b2cd963e7e17e6cc55fe0371fb9c704d63e44cacec7b9b7f523b78919e",
|
| 1033 |
+
"sha256:78884d155fd15d9f64f5d6124b486f3d3f7fd7cd71a78e9670a0f6f6ca06fb2d",
|
| 1034 |
+
"sha256:79e8d804c2ccd618417e96720ad5cd076a86fa3f8cb310ea386a3e6229bae7d1",
|
| 1035 |
+
"sha256:7e80d375134ddb04231a53800503752093dbb65dad8dabacce2c84cccc78e964",
|
| 1036 |
+
"sha256:8097b3422d020ff1c44effc40ae58e67d93e60d540a65649d2cdaf9466030791",
|
| 1037 |
+
"sha256:8205ee14463248d3349131bb8099efe15cd3ce83b8ef3ace63c7e976998e7124",
|
| 1038 |
+
"sha256:8212ff58ac6dfde49946bea57474a386cca3f7706fc72c25b772b9ca4af6b79e",
|
| 1039 |
+
"sha256:823e74ab6fbaa028ec89615ff6acb409e90ff45580c45920d4dfdddb069f2120",
|
| 1040 |
+
"sha256:84e0566f15cf4d769dade9b366b7b87c959be472c92dffb70462dd0844d7cbad",
|
| 1041 |
+
"sha256:896c41007931217a343eff197c34513c154267636c8056fb409eafd494c3dcdc",
|
| 1042 |
+
"sha256:8aa362811ccdc1f8dadcc916c6d47e554169ab79559319ae9fae7d7752d0d60c",
|
| 1043 |
+
"sha256:8b3b397eefecec8e8e39fa65c630ef70a24b09141a6f9fc17b3c3a50bed6b50e",
|
| 1044 |
+
"sha256:8ebc7e65ca4b111d928b669713865f021b7773350eeac4a31d3e70144297baba",
|
| 1045 |
+
"sha256:9168764133fd919f8dcca2ead66de0105f4ef5659cbb4fa044f7014bed9a1797",
|
| 1046 |
+
"sha256:921ae54f9ecba3b6325df425cf72c074cd469dea843fb5743a26ca7fb2ccb149",
|
| 1047 |
+
"sha256:92558d37d872e808944c3c96d0423b8604879a3d1c86fdad508d7ed91ea547d5",
|
| 1048 |
+
"sha256:951cc481c0c395c4a08639a469d53b7d4afa252529a085418b82a6b43c45c240",
|
| 1049 |
+
"sha256:998c01b8e71cf051c28f5d6f1187abbdf5cf45fc0efce5da6c06447cba997034",
|
| 1050 |
+
"sha256:9abc80fe8c1f87218db116016de575a7998ab1629078c90840e8d11ab423ee25",
|
| 1051 |
+
"sha256:9be4f99bee42ac107870c61dfdb294d912bf81c3c6d45538aad7aecab468b6b7",
|
| 1052 |
+
"sha256:9c39438c55983d48f4bb3487734d040e22dad200dab22c41e331cee145e7a50d",
|
| 1053 |
+
"sha256:9d7e8ce990ae17dda686f7e82fd41a055c668e13ddcf058e7fb5e9da20b57793",
|
| 1054 |
+
"sha256:9ea7f4174d2e4194289cb0c4e172d83e79a6404297ff95f2875cf9ac9bced8ba",
|
| 1055 |
+
"sha256:a18fc371e900a21d7392517c6f60fe859e802547309e94313cd8181ad9db004d",
|
| 1056 |
+
"sha256:a36b452abbf29f68527cf52e181fced56685731c86b52e852053e38d8b60bc8d",
|
| 1057 |
+
"sha256:a5b66d1b201cc71bc3081bc2f1fc36b0c1f268b773e03bbc39066651b9e18391",
|
| 1058 |
+
"sha256:a824d2c7a703ba6daaca848f9c3d5cb93af0505be505de70e7e66829affd676e",
|
| 1059 |
+
"sha256:a88c0d17d039333a41d9bf4616bd062f0bd7aa0edeb6cafe00a2fc2a804e944f",
|
| 1060 |
+
"sha256:aa6800adc8204ce898c8a424303969b7aa6a5e4ad2789c13f8648739830323b7",
|
| 1061 |
+
"sha256:aad911555286884be1e427ef0dc0ba3929e6821cbeca2194b13dc415a462c7fd",
|
| 1062 |
+
"sha256:afc6e35f344490faa8276b5f2f7cbf71f88bc2cda4328e00553bd451728c571f",
|
| 1063 |
+
"sha256:b9a4df06c35465ef4d81799999bba810c68d29972bf1c31db61bfdb81dd9d5bb",
|
| 1064 |
+
"sha256:bb2954155bb8f63bb19d56d80e5e5320b61d71084617ed89efedb861a684baea",
|
| 1065 |
+
"sha256:bbc4362e06f950c62cad3d4abf1191021b2ffaf0b31ac230fbf0526453eee75e",
|
| 1066 |
+
"sha256:c0145295ca415668420ad142ee42189f78d27af806fcf1f32a18e51d47dd2052",
|
| 1067 |
+
"sha256:c30ff468163a48535ee7e9bf21bd14c7a81147c0e58a36c1078289a8ca7af0bd",
|
| 1068 |
+
"sha256:c347a20d79cedc0a7bd51c4d4b7dbc613ca4e65a756b5c3e57ec84bd43505b47",
|
| 1069 |
+
"sha256:c43583ea8517ed2e780a345dd9960896afc1327e8cf3ac8239c167530397440d",
|
| 1070 |
+
"sha256:c61a2cb0085c8783906b2f8b1f16a7e65777823c7f4d0a6aaffe26dc0d358dd9",
|
| 1071 |
+
"sha256:c9ca89938dff18828a328af41ffdf3902405a19f4131c88e22e776a8e228c5a8",
|
| 1072 |
+
"sha256:cc31e13ce212e14a539d430428cd365e74f8b2d534f8bc22dd4c9c55b277b875",
|
| 1073 |
+
"sha256:cdabcd3beb2a6dca7027007473d8ef1c3b053347c76f685f5f060a00327b8b65",
|
| 1074 |
+
"sha256:cf86f72d705fc2ef776bb7dd9e5fbba79d7e1f3e258bf9377f8204ad0fc1c51e",
|
| 1075 |
+
"sha256:d09dc82af2d3c17e7dd17120b202a79b578d79f2b5424bda209d9966efeed114",
|
| 1076 |
+
"sha256:d3aa13bdf38630da298f2e0d77aca967b200b8cc1473ea05248f6c5e9c9bdb44",
|
| 1077 |
+
"sha256:d69d003296df4840bd445a5d15fa5b6ff6ac40496f956a221c4d1f6f7b4bc4d9",
|
| 1078 |
+
"sha256:d6e109a454412ab82979c5b1b3aee0604eca4bbf9a02693bb9df027af2bfa91a",
|
| 1079 |
+
"sha256:d8551e733626afec514b5d15befabea0dd70a343a9f23322860c4f16a9430205",
|
| 1080 |
+
"sha256:d8754d872a5dfc3c5bf9c0e059e8107451364a30d9fd50f1f1a85c4fb9481164",
|
| 1081 |
+
"sha256:d8f9a6e7fd5434817526815f09ea27f2746c4a51ee11bb3439065f5fc754db58",
|
| 1082 |
+
"sha256:dbcbb6db5582ea33ce46a5d20a5793134b5365110d84df4e30b9d37c6fd40ad3",
|
| 1083 |
+
"sha256:e0f3ef95795efcd3b2ec3fe0a5bcfb5dadf5e3996ea2117427e524d4fbf309c6",
|
| 1084 |
+
"sha256:e13ae74a8a3a0c2f22f450f773e35f893484fcfacb00bb4344a7e0f4f48e1f97",
|
| 1085 |
+
"sha256:e274f62cbd274359eff63e5c7e7274c913e8e09620f6a57aae66744b3df046d6",
|
| 1086 |
+
"sha256:e838bf2bb0b91ee67bf2b889a1a841e5ecac06dd7a2b1ef4e6151e2ce155c7ae",
|
| 1087 |
+
"sha256:e8acd55bd5b071156bae57b555f5d33697998752673b9de554dd82f5b5352727",
|
| 1088 |
+
"sha256:e8e5ab32cf9eb3647450bc74eb201b27c185d3857276162c101c0f8c6374e098",
|
| 1089 |
+
"sha256:ebcb786b9ff30b994d5969213a8430cbb984cdd7ea9fd6df06663194bd3c450c",
|
| 1090 |
+
"sha256:ebea2821cdb5f9fef44933617be76185b80150632736f3d76e54829ab4a3b4d1",
|
| 1091 |
+
"sha256:ed0ef550042a8dbcd657dfb284a8ee00f0ba269d3f2286b0493b15a5694f9fe8",
|
| 1092 |
+
"sha256:eda5c1e2a715a4cbbca2d6d304988460942551e4e5e3b7457b50943cd741626d",
|
| 1093 |
+
"sha256:f5c0ed12926dec1dfe7d645333ea59cf93f4d07750986a586f511c0bc61fe103",
|
| 1094 |
+
"sha256:f6016bd950be4dcd047b7475fdf55fb1e1f59fc7403f387be0e8123e4a576d30",
|
| 1095 |
+
"sha256:f9e0057a509e096e47c87f753136c9b10d7a91842d8042c2ee6866899a717c0d",
|
| 1096 |
+
"sha256:fc1c892b1ec1f8cbd5da8de287577b455e388d9c328ad592eabbdcb6fc93bee5",
|
| 1097 |
+
"sha256:fc2c1e1b00f88317d9de6b2c2b39b012ebbfe35fe5e7bef980fd2a91f6100a07",
|
| 1098 |
+
"sha256:fd822f019ccccd75c832deb7aa040bb02d70a92eb15a2f16c7987b7ad4ee8d83"
|
| 1099 |
],
|
| 1100 |
"markers": "python_version >= '3.9'",
|
| 1101 |
+
"version": "==0.24.0"
|
| 1102 |
},
|
| 1103 |
"safetensors": {
|
| 1104 |
"hashes": [
|
|
|
|
| 1126 |
"sha256:18fd474d4a82a5f83dac888df697af65afa82dec7323d09c3e37d1f14288da54",
|
| 1127 |
"sha256:3e386e96793c8702ae83d17b853fb93d3e09ef82ec62722e61da5cd22376dcd8"
|
| 1128 |
],
|
| 1129 |
+
"markers": "python_version >= '3.9'",
|
| 1130 |
"version": "==78.1.0"
|
| 1131 |
},
|
| 1132 |
+
"shellingham": {
|
| 1133 |
+
"hashes": [
|
| 1134 |
+
"sha256:7ecfff8f2fd72616f7481040475a65b2bf8af90a56c89140852d1120324e8686",
|
| 1135 |
+
"sha256:8dbca0739d487e5bd35ab3ca4b36e11c4078f3a234bfce294b0a0291363404de"
|
| 1136 |
+
],
|
| 1137 |
+
"markers": "python_version >= '3.7'",
|
| 1138 |
+
"version": "==1.5.4"
|
| 1139 |
+
},
|
| 1140 |
"six": {
|
| 1141 |
"hashes": [
|
| 1142 |
"sha256:4721f391ed90541fddacab5acf947aa0d3dc7d27b2e1e8eda2be8970586c3274",
|
|
|
|
| 1176 |
"sha256:9cebf7e04ff162015ce31c9c6c9144daa34a93bd082f54fd8f12deca4f47515f",
|
| 1177 |
"sha256:db36cdc64bf61b9b24578b6f7bab1ecdd2452cf008f34faa33776680c26d66f8"
|
| 1178 |
],
|
| 1179 |
+
"markers": "python_version >= '3.8'",
|
| 1180 |
"version": "==1.13.1"
|
| 1181 |
},
|
| 1182 |
"tenacity": {
|
|
|
|
| 1270 |
},
|
| 1271 |
"transformers": {
|
| 1272 |
"hashes": [
|
| 1273 |
+
"sha256:99bbcddd6570f080aee81f67844f4b46c8025bbdbdb86eafb82cc7d6aaafb190",
|
| 1274 |
+
"sha256:dac6cef0e2698fb25b57edbc0b9af3732e4ca148ed008d3e3118203c4dd25055"
|
| 1275 |
],
|
| 1276 |
"index": "pypi",
|
| 1277 |
"markers": "python_full_version >= '3.9.0'",
|
| 1278 |
+
"version": "==4.50.2"
|
| 1279 |
+
},
|
| 1280 |
+
"typer": {
|
| 1281 |
+
"hashes": [
|
| 1282 |
+
"sha256:46a499c6107d645a9c13f7ee46c5d5096cae6f5fc57dd11eccbbb9ae3e44ddfc",
|
| 1283 |
+
"sha256:ab2fab47533a813c49fe1f16b1a370fd5819099c00b119e0633df65f22144ba5"
|
| 1284 |
+
],
|
| 1285 |
+
"index": "pypi",
|
| 1286 |
+
"markers": "python_version >= '3.7'",
|
| 1287 |
+
"version": "==0.15.2"
|
| 1288 |
},
|
| 1289 |
"typing-extensions": {
|
| 1290 |
"hashes": [
|
|
|
|
| 1345 |
],
|
| 1346 |
"markers": "python_version >= '3.9'",
|
| 1347 |
"version": "==6.0.0"
|
| 1348 |
+
},
|
| 1349 |
+
"win32-setctime": {
|
| 1350 |
+
"hashes": [
|
| 1351 |
+
"sha256:95d644c4e708aba81dc3704a116d8cbc974d70b3bdb8be1d150e36be6e9d1390",
|
| 1352 |
+
"sha256:ae1fdf948f5640aae05c511ade119313fb6a30d7eabe25fef9764dca5873c4c0"
|
| 1353 |
+
],
|
| 1354 |
+
"markers": "python_version >= '3.5'",
|
| 1355 |
+
"version": "==1.2.0"
|
| 1356 |
}
|
| 1357 |
},
|
| 1358 |
"develop": {}
|
app.py
CHANGED
|
@@ -1,73 +1,101 @@
|
|
| 1 |
import streamlit as st
|
| 2 |
import nest_asyncio
|
| 3 |
import pandas as pd
|
|
|
|
|
|
|
|
|
|
|
|
|
| 4 |
from annotated_text import annotation
|
| 5 |
from scripts.predict import InferenceHandler
|
| 6 |
-
from
|
| 7 |
|
| 8 |
-
|
| 9 |
|
|
|
|
|
|
|
| 10 |
rc = None
|
| 11 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 12 |
@st.cache_data
|
| 13 |
-
def load_inference_handler(api_token):
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 14 |
try:
|
| 15 |
return InferenceHandler(api_token)
|
| 16 |
except:
|
| 17 |
return None
|
| 18 |
|
| 19 |
-
|
| 20 |
-
|
| 21 |
-
row_data = []
|
| 22 |
-
|
| 23 |
-
row_data.append(json_obj['text_input'])
|
| 24 |
-
row_data.append(json_obj['text_sentiment'])
|
| 25 |
-
cat_dict = json_obj['category_sentiments']
|
| 26 |
-
for cat in cat_dict.keys():
|
| 27 |
-
raw_val = cat_dict[cat]
|
| 28 |
-
val = f'{raw_val * 100: .2f}%' if raw_val is not None else 'N/A'
|
| 29 |
-
row_data.append(val)
|
| 30 |
-
|
| 31 |
-
return row_data
|
| 32 |
-
|
| 33 |
-
def load_history(parent_elem):
|
| 34 |
-
with parent_elem:
|
| 35 |
-
for idx, result in enumerate(st.session_state.results):
|
| 36 |
-
text = result['text_input']
|
| 37 |
-
discriminatory = False
|
| 38 |
-
|
| 39 |
-
data = []
|
| 40 |
-
for sent_item in result['results']:
|
| 41 |
-
sentence = sent_item['sentence']
|
| 42 |
-
bin_class = sent_item['binary_classification']['classification']
|
| 43 |
-
pred_class = sent_item['binary_classification']['prediction_class']
|
| 44 |
-
ml_regr = sent_item['multilabel_regression']
|
| 45 |
-
|
| 46 |
-
row_data = [sentence, bin_class]
|
| 47 |
-
if pred_class == 1:
|
| 48 |
-
discriminatory = True
|
| 49 |
-
for cat in ml_regr.keys():
|
| 50 |
-
perc = ml_regr[cat] * 100
|
| 51 |
-
row_data.append(f'{perc:.2f}%')
|
| 52 |
-
else:
|
| 53 |
-
for i in range(6):
|
| 54 |
-
row_data.append(None)
|
| 55 |
-
|
| 56 |
-
data.append(row_data)
|
| 57 |
-
df = pd.DataFrame(data=data, columns=['Sentence', 'Binary Classification', 'Gender', 'Race', 'Sexuality', 'Disability', 'Religion', 'Unspecified'])
|
| 58 |
-
|
| 59 |
-
with st.expander(label=f'Entry #{idx+1}', icon='🔴' if discriminatory else '🟢'):
|
| 60 |
-
st.markdown('<hr style="margin: 0.5em 0 0 0;">', unsafe_allow_html=True)
|
| 61 |
-
st.markdown(
|
| 62 |
-
f"<p style='text-align: center; font-weight: bold; font-style: italic; font-size: medium;'>\"{text}\"</p>",
|
| 63 |
-
unsafe_allow_html=True
|
| 64 |
-
)
|
| 65 |
-
st.markdown('<hr style="margin: 0 0 0.5em 0;">', unsafe_allow_html=True)
|
| 66 |
-
st.markdown('##### Sentence Breakdown:')
|
| 67 |
-
st.dataframe(df)
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def build_result_tree(parent_elem, results):
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label_dict = {
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'Gender': '#4A90E2',
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'Race': '#E67E22',
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unsafe_allow_html=True
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)
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st.markdown('<hr style="margin: 0 0 0.5em 0;">', unsafe_allow_html=True)
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-
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classification = sent['classification']
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st.markdown(f'##### Classification - {classification}')
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)
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st.markdown('\n')
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else:
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st.markdown(f"#### Classification - {sent['classification']}")
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if len(sent['annotated_categories']) > 0:
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st.markdown(
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st.markdown('\n')
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@st.cache_data
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-
def analyze_text(
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if ih:
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res = None
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with rc:
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with st.spinner("Processing...", show_time=True) as spnr:
|
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# time.sleep(5)
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-
res = ih.classify_text(
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del spnr
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if res is not None:
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st.session_state.results.append(res)
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build_result_tree(rc, res)
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st.title('NLPinitiative Text Classifier')
|
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st.sidebar.write("")
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@@ -178,9 +268,10 @@ ih = load_inference_handler(API_KEY)
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|
| 179 |
tab1 = st.empty()
|
| 180 |
tab2 = st.empty()
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tab3 = st.empty()
|
| 182 |
|
| 183 |
-
tab1, tab2, tab3 = st.tabs(['Classifier', 'Input History', 'About This App'])
|
| 184 |
|
| 185 |
if "results" not in st.session_state:
|
| 186 |
st.session_state.results = []
|
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@@ -198,16 +289,44 @@ with tab1:
|
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| 198 |
analyze_text(text_area)
|
| 199 |
|
| 200 |
with tab2:
|
| 201 |
-
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|
| 203 |
with tab3:
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| 204 |
st.markdown(
|
| 205 |
-
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-
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-
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| 208 |
-
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-
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-
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| 1 |
import streamlit as st
|
| 2 |
import nest_asyncio
|
| 3 |
import pandas as pd
|
| 4 |
+
import os
|
| 5 |
+
|
| 6 |
+
from htbuilder import span, div
|
| 7 |
+
from loguru import logger
|
| 8 |
from annotated_text import annotation
|
| 9 |
from scripts.predict import InferenceHandler
|
| 10 |
+
from huggingface_hub import snapshot_download
|
| 11 |
|
| 12 |
+
from scripts.config import DATASET_REPO
|
| 13 |
|
| 14 |
+
nest_asyncio.apply()
|
| 15 |
+
st.set_page_config(layout='wide')
|
| 16 |
rc = None
|
| 17 |
|
| 18 |
+
def load_history(parent_elem):
|
| 19 |
+
"""Loads the history of results from inference for previous inputs made by the user.
|
| 20 |
+
|
| 21 |
+
Parameters
|
| 22 |
+
----------
|
| 23 |
+
parent_elem : DeltaGenerator
|
| 24 |
+
The Streamlit UI element that contains the history data.
|
| 25 |
+
"""
|
| 26 |
+
|
| 27 |
+
with parent_elem:
|
| 28 |
+
if len(st.session_state.results) == 0:
|
| 29 |
+
st.markdown(
|
| 30 |
+
f"<p style='text-align: center; font-weight: bold; font-style: italic; font-size: 1.5vw;'>No History</p>",
|
| 31 |
+
unsafe_allow_html=True
|
| 32 |
+
)
|
| 33 |
+
else:
|
| 34 |
+
for idx, result in enumerate(st.session_state.results):
|
| 35 |
+
text = result['text_input']
|
| 36 |
+
discriminatory = False
|
| 37 |
+
|
| 38 |
+
data = []
|
| 39 |
+
for sent_item in result['results']:
|
| 40 |
+
sentence = sent_item['sentence']
|
| 41 |
+
bin_class = sent_item['binary_classification']['classification']
|
| 42 |
+
pred_class = sent_item['binary_classification']['prediction_class']
|
| 43 |
+
ml_regr = sent_item['multilabel_regression']
|
| 44 |
+
|
| 45 |
+
row_data = [sentence, bin_class]
|
| 46 |
+
if pred_class == 1:
|
| 47 |
+
discriminatory = True
|
| 48 |
+
for cat in ml_regr.keys():
|
| 49 |
+
perc = ml_regr[cat] * 100
|
| 50 |
+
row_data.append(f'{perc:.2f}%')
|
| 51 |
+
else:
|
| 52 |
+
for i in range(6):
|
| 53 |
+
row_data.append(None)
|
| 54 |
+
|
| 55 |
+
data.append(row_data)
|
| 56 |
+
df = pd.DataFrame(data=data, columns=['Sentence', 'Binary Classification', 'Gender', 'Race', 'Sexuality', 'Disability', 'Religion', 'Unspecified'])
|
| 57 |
+
|
| 58 |
+
with st.expander(label=f'Entry #{idx+1}', icon='🔴' if discriminatory else '🟢'):
|
| 59 |
+
st.markdown('<hr style="margin: 0.5em 0 0 0;">', unsafe_allow_html=True)
|
| 60 |
+
st.markdown(
|
| 61 |
+
f"<p style='text-align: center; font-weight: bold; font-style: italic; font-size: medium;'>\"{text}\"</p>",
|
| 62 |
+
unsafe_allow_html=True
|
| 63 |
+
)
|
| 64 |
+
st.markdown('<hr style="margin: 0 0 0.5em 0;">', unsafe_allow_html=True)
|
| 65 |
+
st.markdown('##### Sentence Breakdown:')
|
| 66 |
+
st.dataframe(df)
|
| 67 |
+
|
| 68 |
@st.cache_data
|
| 69 |
+
def load_inference_handler(api_token: str) -> InferenceHandler | None:
|
| 70 |
+
"""Loads an instance of the InferenceHandler class once a token is entered.
|
| 71 |
+
|
| 72 |
+
Parameters
|
| 73 |
+
----------
|
| 74 |
+
api_token: str
|
| 75 |
+
The Hugging Face read/write token used for retrieving the binary classification and multilabel regression model tensor files.
|
| 76 |
+
|
| 77 |
+
Returns
|
| 78 |
+
-------
|
| 79 |
+
InferenceHandler | None
|
| 80 |
+
Returns an instance of the InferenceHandler class if a valid token is entered, otherwise returns None.
|
| 81 |
+
"""
|
| 82 |
+
|
| 83 |
try:
|
| 84 |
return InferenceHandler(api_token)
|
| 85 |
except:
|
| 86 |
return None
|
| 87 |
|
| 88 |
+
def build_result_tree(parent_elem, results: dict):
|
| 89 |
+
"""Loads the history of results from inference for previous inputs made by the user.
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 90 |
|
| 91 |
+
Parameters
|
| 92 |
+
----------
|
| 93 |
+
parent_elem : DeltaGenerator
|
| 94 |
+
The Streamlit UI element to post the data to.
|
| 95 |
+
results : dict
|
| 96 |
+
The resulting data from performing inference.
|
| 97 |
+
"""
|
| 98 |
|
|
|
|
| 99 |
label_dict = {
|
| 100 |
'Gender': '#4A90E2',
|
| 101 |
'Race': '#E67E22',
|
|
|
|
| 153 |
unsafe_allow_html=True
|
| 154 |
)
|
| 155 |
st.markdown('<hr style="margin: 0 0 0.5em 0;">', unsafe_allow_html=True)
|
| 156 |
+
|
| 157 |
classification = sent['classification']
|
| 158 |
st.markdown(f'##### Classification - {classification}')
|
| 159 |
|
|
|
|
| 168 |
)
|
| 169 |
st.markdown('\n')
|
| 170 |
else:
|
| 171 |
+
sent = sent_details[0]
|
| 172 |
st.markdown(f"#### Classification - {sent['classification']}")
|
| 173 |
if len(sent['annotated_categories']) > 0:
|
| 174 |
st.markdown(
|
|
|
|
| 182 |
st.markdown('\n')
|
| 183 |
|
| 184 |
@st.cache_data
|
| 185 |
+
def analyze_text(input: str):
|
| 186 |
+
"""Performs infernce on the entered text using the InferenceHandler.
|
| 187 |
+
|
| 188 |
+
Parameters
|
| 189 |
+
----------
|
| 190 |
+
input : str
|
| 191 |
+
The text to analyze.
|
| 192 |
+
"""
|
| 193 |
if ih:
|
| 194 |
res = None
|
| 195 |
with rc:
|
| 196 |
with st.spinner("Processing...", show_time=True) as spnr:
|
| 197 |
# time.sleep(5)
|
| 198 |
+
res = ih.classify_text(input)
|
| 199 |
del spnr
|
| 200 |
|
| 201 |
if res is not None:
|
| 202 |
st.session_state.results.append(res)
|
| 203 |
build_result_tree(rc, res)
|
| 204 |
|
| 205 |
+
@st.cache_data
|
| 206 |
+
def load_datasets(_parent_elem, api_token: str):
|
| 207 |
+
if api_token is None or len(api_token) == 0:
|
| 208 |
+
raise Exception()
|
| 209 |
+
|
| 210 |
+
cache_path = snapshot_download(repo_id=DATASET_REPO, repo_type='dataset', token=api_token)
|
| 211 |
+
ds_record = pd.read_csv(os.path.join(cache_path, 'dataset_record.csv'))
|
| 212 |
+
|
| 213 |
+
raw_ds_path = os.path.join(cache_path, 'raw')
|
| 214 |
+
interim_ds_path = os.path.join(cache_path, 'interim')
|
| 215 |
+
processed_ds_path = os.path.join(cache_path, 'processed')
|
| 216 |
+
|
| 217 |
+
with _parent_elem:
|
| 218 |
+
st.markdown(f'### Disclaimer')
|
| 219 |
+
st.markdown("> The datasets displayed contain content that may be highly discriminatory or offensive in nature. Viewer discretion is advised. This content is presented solely for analysis, research, or educational purposes and does not reflect the views or values of the creators or maintainers of this application.")
|
| 220 |
+
st.markdown('<hr style="margin: 0 0 0.5em 0;">', unsafe_allow_html=True)
|
| 221 |
+
|
| 222 |
+
if os.path.exists(os.path.join(processed_ds_path, 'NLPinitiative_Master_Dataset.csv')):
|
| 223 |
+
master_df = pd.read_csv(os.path.join(processed_ds_path, 'NLPinitiative_Master_Dataset.csv'))
|
| 224 |
+
|
| 225 |
+
if len(master_df) > 0:
|
| 226 |
+
st.markdown(f'### NLPinitiative Master Dataset')
|
| 227 |
+
with st.expander(label='Master Dataset'):
|
| 228 |
+
st.dataframe(master_df)
|
| 229 |
+
|
| 230 |
+
if len(ds_record) > 0:
|
| 231 |
+
for _, row in ds_record.iterrows():
|
| 232 |
+
try:
|
| 233 |
+
ds_id = row['Dataset ID']
|
| 234 |
+
ds_ref_url = row['Dataset Reference URL']
|
| 235 |
+
raw_fn = row['Raw Dataset Filename']
|
| 236 |
+
norm_fn = row['Converted Filename']
|
| 237 |
+
|
| 238 |
+
raw_df = pd.read_csv(os.path.join(raw_ds_path, raw_fn))
|
| 239 |
+
norm_df = pd.read_csv(os.path.join(interim_ds_path, norm_fn))
|
| 240 |
+
|
| 241 |
+
st.markdown('<hr style="margin: 0 0 0.5em 0;">', unsafe_allow_html=True)
|
| 242 |
+
st.markdown(f'#### {ds_id} - [Link to Dataset Source]({ds_ref_url})')
|
| 243 |
+
with st.expander(label='Dataset'):
|
| 244 |
+
st.markdown(f'###### Raw Dataset')
|
| 245 |
+
st.dataframe(raw_df)
|
| 246 |
+
st.markdown(f'###### Normalized Dataset')
|
| 247 |
+
st.dataframe(norm_df)
|
| 248 |
+
|
| 249 |
+
except Exception as e:
|
| 250 |
+
logger.error(f'{e}')
|
| 251 |
+
else:
|
| 252 |
+
st.markdown(
|
| 253 |
+
f"<p style='text-align: center; font-weight: bold; font-style: italic; font-size: 1.5vw;'>No Datasets to Display</p>",
|
| 254 |
+
unsafe_allow_html=True
|
| 255 |
+
)
|
| 256 |
+
|
| 257 |
+
#===========================================================================================================================================
|
| 258 |
+
|
| 259 |
st.title('NLPinitiative Text Classifier')
|
| 260 |
|
| 261 |
st.sidebar.write("")
|
|
|
|
| 268 |
|
| 269 |
tab1 = st.empty()
|
| 270 |
tab2 = st.empty()
|
| 271 |
+
tab4 = st.empty()
|
| 272 |
tab3 = st.empty()
|
| 273 |
|
| 274 |
+
tab1, tab2, tab3, tab4 = st.tabs(['Classifier', 'Input History', 'Datasets', 'About This App'])
|
| 275 |
|
| 276 |
if "results" not in st.session_state:
|
| 277 |
st.session_state.results = []
|
|
|
|
| 289 |
analyze_text(text_area)
|
| 290 |
|
| 291 |
with tab2:
|
| 292 |
+
hist_container = st.container(border=True)
|
| 293 |
+
try:
|
| 294 |
+
load_history(hist_container)
|
| 295 |
+
except:
|
| 296 |
+
hist_container.markdown(
|
| 297 |
+
f"<p style='text-align: center; font-weight: bold; font-style: italic; font-size: 1.5vw;'>No History</p>",
|
| 298 |
+
unsafe_allow_html=True
|
| 299 |
+
)
|
| 300 |
|
| 301 |
with tab3:
|
| 302 |
+
ds_container = st.container(border=True)
|
| 303 |
+
try:
|
| 304 |
+
load_datasets(ds_container, API_KEY)
|
| 305 |
+
except Exception as e:
|
| 306 |
+
logger.error(f'{e}')
|
| 307 |
+
ds_container.markdown(
|
| 308 |
+
f"<p style='text-align: center; font-weight: bold; font-style: italic; font-size: 1.5vw;'>No Datasets to Display</p>",
|
| 309 |
+
unsafe_allow_html=True
|
| 310 |
+
)
|
| 311 |
+
|
| 312 |
+
with tab4:
|
| 313 |
st.markdown(
|
| 314 |
+
f"""
|
| 315 |
+
## About
|
| 316 |
+
The NLPinitiative Discriminatory Text Classifier is an advanced
|
| 317 |
+
natural language processing tool designed to detect and flag potentially
|
| 318 |
+
discriminatory or harmful language. By analyzing text for biased, offensive,
|
| 319 |
+
or exclusionary content, this classifier helps promote more inclusive and
|
| 320 |
+
respectful communication. Simply enter your text below, and the model will
|
| 321 |
+
assess it based on linguistic patterns and context. While the tool provides
|
| 322 |
+
valuable insights, we encourage users to review flagged content thoughtfully
|
| 323 |
+
and consider context when interpreting results.
|
| 324 |
+
|
| 325 |
+
The application utilizes two NLP models: a fine-tuned binary classifier for classifying input as
|
| 326 |
+
Discriminatory or Non-Discriminatory and a fine-tuned multilabel regression model for assessing
|
| 327 |
+
the likelihood of specific categories of discrimination (Gender, Race, Sexuality, Disability, Religion
|
| 328 |
+
and Unspecified). The base model used for both fine-tuned models is the pretrained
|
| 329 |
+
[BERT](https://doi.org/10.48550/arXiv.1810.04805) (Bidirectional Encoder Representations from Transformers)
|
| 330 |
+
model.
|
| 331 |
+
"""
|
| 332 |
+
)
|
config.py
DELETED
|
@@ -1,23 +0,0 @@
|
|
| 1 |
-
# Used for setting some constants for the project codebase
|
| 2 |
-
|
| 3 |
-
from pathlib import Path
|
| 4 |
-
|
| 5 |
-
# Root Path
|
| 6 |
-
ROOT = Path(__file__).resolve().parents[0]
|
| 7 |
-
|
| 8 |
-
# Model Directory
|
| 9 |
-
MODELS_DIR = ROOT / 'models'
|
| 10 |
-
|
| 11 |
-
# Binary Classification Model Path
|
| 12 |
-
BIN_MODEL_PATH = MODELS_DIR / 'binary_classification'
|
| 13 |
-
|
| 14 |
-
# Multilabel Regression Model Path
|
| 15 |
-
ML_MODEL_PATH = MODELS_DIR / 'multilabel_regression'
|
| 16 |
-
|
| 17 |
-
# HF Hub Repositories
|
| 18 |
-
BIN_REPO = 'dlsmallw/NLPinitiative-Binary-Classification'
|
| 19 |
-
ML_REPO = 'dlsmallw/NLPinitiative-Multilabel-Regression'
|
| 20 |
-
DATASET_REPO = 'dlsmallw/NLPinitiative-Dataset'
|
| 21 |
-
|
| 22 |
-
BIN_API_URL = f"https://api-inference.huggingface.co/models/{BIN_REPO}"
|
| 23 |
-
ML_API_URL = f"https://api-inference.huggingface.co/models/{ML_REPO}"
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
config.toml
ADDED
|
@@ -0,0 +1,4 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
[repositories]
|
| 2 |
+
bin_repo = "dlsmallw/NLPinitiative-Binary-Classification"
|
| 3 |
+
ml_repo = "dlsmallw/NLPinitiative-Multilabel-Regression"
|
| 4 |
+
ds_repo = "dlsmallw/NLPinitiative-Dataset"
|
scripts/__init__.py
CHANGED
|
@@ -1 +1 @@
|
|
| 1 |
-
import config
|
|
|
|
| 1 |
+
import scripts.config as config
|
scripts/config.py
ADDED
|
@@ -0,0 +1,76 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
# Used for setting some constants for the project codebase
|
| 2 |
+
import toml
|
| 3 |
+
import typer
|
| 4 |
+
from pathlib import Path
|
| 5 |
+
from loguru import logger
|
| 6 |
+
from typing_extensions import Annotated
|
| 7 |
+
|
| 8 |
+
app = typer.Typer()
|
| 9 |
+
|
| 10 |
+
# Root Path
|
| 11 |
+
ROOT = Path(__file__).resolve().parents[1]
|
| 12 |
+
|
| 13 |
+
try:
|
| 14 |
+
logger.info('Loading config.toml...')
|
| 15 |
+
with open(ROOT / 'config.toml', 'r') as f:
|
| 16 |
+
config = toml.load(f, dict)
|
| 17 |
+
f.close()
|
| 18 |
+
|
| 19 |
+
if 'repositories' not in config.keys() or \
|
| 20 |
+
'bin_repo' not in config['repositories'] or \
|
| 21 |
+
'ml_repo' not in config['repositories'] or \
|
| 22 |
+
'ds_repo' not in config['repositories']:
|
| 23 |
+
raise Exception('Malformed toml config file.')
|
| 24 |
+
|
| 25 |
+
logger.success('config.toml loaded successfully.')
|
| 26 |
+
except Exception as e:
|
| 27 |
+
logger.error(e)
|
| 28 |
+
config = {
|
| 29 |
+
'repositories': {
|
| 30 |
+
'bin_repo': '',
|
| 31 |
+
'ml_repo': '',
|
| 32 |
+
'ds_repo': ''
|
| 33 |
+
}
|
| 34 |
+
}
|
| 35 |
+
|
| 36 |
+
with open(ROOT / 'config.toml', 'w') as f:
|
| 37 |
+
toml.dump(config, f)
|
| 38 |
+
f.close()
|
| 39 |
+
|
| 40 |
+
|
| 41 |
+
# HF Hub Repositories
|
| 42 |
+
BIN_REPO = config['repositories']['bin_repo']
|
| 43 |
+
ML_REPO = config['repositories']['ml_repo']
|
| 44 |
+
DATASET_REPO = config['repositories']['ds_repo']
|
| 45 |
+
|
| 46 |
+
@app.command()
|
| 47 |
+
def main(
|
| 48 |
+
bin_repo: Annotated[str, typer.Option("--binary-repo", "-b")] = None,
|
| 49 |
+
ml_repo: Annotated[str, typer.Option("--multilabel-regression-repo", "-m")] = None,
|
| 50 |
+
ds_repo: Annotated[str, typer.Option("--dataset-repo", "-d")] = None
|
| 51 |
+
):
|
| 52 |
+
toml_edited = False
|
| 53 |
+
|
| 54 |
+
if bin_repo is not None and len(bin_repo) > 0:
|
| 55 |
+
config['repositories']['bin_repo'] = bin_repo
|
| 56 |
+
toml_edited = True
|
| 57 |
+
logger.success(f'Successfully updated binary repository to {bin_repo}.')
|
| 58 |
+
|
| 59 |
+
if ml_repo is not None and len(ml_repo) > 0:
|
| 60 |
+
config['repositories']['ml_repo'] = ml_repo
|
| 61 |
+
toml_edited = True
|
| 62 |
+
logger.success(f'Successfully updated binary repository to {ml_repo}.')
|
| 63 |
+
|
| 64 |
+
if ds_repo is not None and len(ds_repo) > 0:
|
| 65 |
+
config['repositories']['ds_repo'] = ds_repo
|
| 66 |
+
toml_edited = True
|
| 67 |
+
logger.success(f'Successfully updated binary repository to "{ds_repo}".')
|
| 68 |
+
|
| 69 |
+
if toml_edited:
|
| 70 |
+
with open(ROOT / 'config.toml', 'w') as f:
|
| 71 |
+
toml.dump(config, f)
|
| 72 |
+
f.close()
|
| 73 |
+
|
| 74 |
+
|
| 75 |
+
if __name__ == "__main__":
|
| 76 |
+
app()
|
setup.sh
CHANGED
|
@@ -35,4 +35,36 @@ docs() {
|
|
| 35 |
log_error "Specify 'build' or 'serve'. For example: docs build"
|
| 36 |
;;
|
| 37 |
esac
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 38 |
}
|
|
|
|
| 35 |
log_error "Specify 'build' or 'serve'. For example: docs build"
|
| 36 |
;;
|
| 37 |
esac
|
| 38 |
+
}
|
| 39 |
+
|
| 40 |
+
set() {
|
| 41 |
+
if [[ $# -lt 2 ]]; then
|
| 42 |
+
echo "Command Requires Two Arguments."
|
| 43 |
+
else
|
| 44 |
+
case $1 in
|
| 45 |
+
bin_repo)
|
| 46 |
+
python ./scripts/config.py -b "$2"
|
| 47 |
+
;;
|
| 48 |
+
ml_repo)
|
| 49 |
+
python ./scripts/config.py -m "$2"
|
| 50 |
+
;;
|
| 51 |
+
ds_repo)
|
| 52 |
+
python ./scripts/config.py -d "$2"
|
| 53 |
+
;;
|
| 54 |
+
*)
|
| 55 |
+
log_error "Specify 'bin_repo', 'ml_repo' or 'ds_repo'. For example: set bin_repo <repo id>"
|
| 56 |
+
;;
|
| 57 |
+
esac
|
| 58 |
+
fi
|
| 59 |
+
}
|
| 60 |
+
|
| 61 |
+
run() {
|
| 62 |
+
case $1 in
|
| 63 |
+
dev)
|
| 64 |
+
streamlit run app.py
|
| 65 |
+
;;
|
| 66 |
+
*)
|
| 67 |
+
log_error "Specify 'dev'. For example: run dev"
|
| 68 |
+
;;
|
| 69 |
+
esac
|
| 70 |
}
|