diff --git a/.gitignore b/.gitignore
index c4842ffa..d2d80c5e 100644
--- a/.gitignore
+++ b/.gitignore
@@ -170,4 +170,7 @@ rescuebox/bin/
 rescuebox/lib/
 rescuebox/pyvenv.cfg
 src/rb-api/rb/api/static/index/main.js
-**/*/.DS_Store
\ No newline at end of file
+**/*/.DS_Store
+*.db
+src/age_gender_classifier/**/*.csv
+src/age_gender_classifier/**/*.onnx
\ No newline at end of file
diff --git a/poetry.lock b/poetry.lock
index 5082a95b..9a382f8c 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -19,6 +19,32 @@ opencv-python = "*"
 type = "directory"
 url = "src/age_and_gender_detection"
 
+[[package]]
+name = "age-classifier"
+version = "0.1.0"
+description = "UMass Rescue Box: Age and Gender Classification"
+optional = false
+python-versions = "*"
+groups = ["main"]
+files = []
+develop = true
+
+[package.dependencies]
+joblib = "*"
+numpy = "*"
+onnxruntime = "*"
+opencv-python = "*"
+pandas = "*"
+requests = "*"
+seaborn = "*"
+sqlalchemy = "*"
+torch = "*"
+xgboost = "*"
+
+[package.source]
+type = "directory"
+url = "src/age_gender_classifier"
+
 [[package]]
 name = "altgraph"
 version = "0.17.4"
@@ -67,19 +93,16 @@ trio = ["trio (>=0.26.1)"]
 
 [[package]]
 name = "anytree"
-version = "2.12.1"
+version = "2.13.0"
 description = "Powerful and Lightweight Python Tree Data Structure with various plugins"
 optional = false
-python-versions = ">=3.7.2,<4"
+python-versions = "<4.0,>=3.9.2"
 groups = ["main", "api"]
 files = [
-    {file = "anytree-2.12.1-py3-none-any.whl", hash = "sha256:5ea9e61caf96db1e5b3d0a914378d2cd83c269dfce1fb8242ce96589fa3382f0"},
-    {file = "anytree-2.12.1.tar.gz", hash = "sha256:244def434ccf31b668ed282954e5d315b4e066c4940b94aff4a7962d85947830"},
+    {file = "anytree-2.13.0-py3-none-any.whl", hash = "sha256:4cbcf10df36b1f1cba131b7e487ff3edafc9d6e932a3c70071b5b768bab901ff"},
+    {file = "anytree-2.13.0.tar.gz", hash = "sha256:c9d3aa6825fdd06af7ebb05b4ef291d2db63e62bb1f9b7d9b71354be9d362714"},
 ]
 
-[package.dependencies]
-six = "*"
-
 [[package]]
 name = "audio-transcription"
 version = "0.1.0"
@@ -100,14 +123,14 @@ url = "src/audio-transcription"
 
 [[package]]
 name = "beautifulsoup4"
-version = "4.13.3"
+version = "4.13.4"
 description = "Screen-scraping library"
 optional = false
 python-versions = ">=3.7.0"
 groups = ["main"]
 files = [
-    {file = "beautifulsoup4-4.13.3-py3-none-any.whl", hash = "sha256:99045d7d3f08f91f0d656bc9b7efbae189426cd913d830294a15eefa0ea4df16"},
-    {file = "beautifulsoup4-4.13.3.tar.gz", hash = "sha256:1bd32405dacc920b42b83ba01644747ed77456a65760e285fbc47633ceddaf8b"},
+    {file = "beautifulsoup4-4.13.4-py3-none-any.whl", hash = "sha256:9bbbb14bfde9d79f38b8cd5f8c7c85f4b8f2523190ebed90e950a8dea4cb1c4b"},
+    {file = "beautifulsoup4-4.13.4.tar.gz", hash = "sha256:dbb3c4e1ceae6aefebdaf2423247260cd062430a410e38c66f2baa50a8437195"},
 ]
 
 [package.dependencies]
@@ -168,14 +191,14 @@ uvloop = ["uvloop (>=0.15.2)"]
 
 [[package]]
 name = "certifi"
-version = "2025.1.31"
+version = "2025.4.26"
 description = "Python package for providing Mozilla's CA Bundle."
 optional = false
 python-versions = ">=3.6"
 groups = ["main", "api"]
 files = [
-    {file = "certifi-2025.1.31-py3-none-any.whl", hash = "sha256:ca78db4565a652026a4db2bcdf68f2fb589ea80d0be70e03929ed730746b84fe"},
-    {file = "certifi-2025.1.31.tar.gz", hash = "sha256:3d5da6925056f6f18f119200434a4780a94263f10d1c21d032a6f6b2baa20651"},
+    {file = "certifi-2025.4.26-py3-none-any.whl", hash = "sha256:30350364dfe371162649852c63336a15c70c6510c2ad5015b21c2345311805f3"},
+    {file = "certifi-2025.4.26.tar.gz", hash = "sha256:0a816057ea3cdefcef70270d2c515e4506bbc954f417fa5ade2021213bb8f0c6"},
 ]
 
 [[package]]
@@ -192,116 +215,116 @@ files = [
 
 [[package]]
 name = "charset-normalizer"
-version = "3.4.1"
+version = "3.4.2"
 description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
 optional = false
 python-versions = ">=3.7"
 groups = ["main", "api"]
 files = [
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 [[package]]
 name = "click"
-version = "8.1.8"
+version = "8.2.0"
 description = "Composable command line interface toolkit"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.10"
 groups = ["main", "api", "dev"]
 files = [
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 [package.dependencies]
@@ -309,31 +332,31 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""}
 
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+version = "4.0.2"
 description = "CMake is an open-source, cross-platform family of tools designed to build, test and package software"
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 ]
 
 [[package]]
@@ -367,6 +390,99 @@ humanfriendly = ">=9.1"
 [package.extras]
 cron = ["capturer (>=2.4)"]
 
+[[package]]
+name = "contourpy"
+version = "1.3.2"
+description = "Python library for calculating contours of 2D quadrilateral grids"
+optional = false
+python-versions = ">=3.10"
+groups = ["main"]
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+test = ["objgraph", "psutil"]
+
 [[package]]
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+version = "0.16.0"
 description = "A pure-Python, bring-your-own-I/O implementation of HTTP/1.1"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 groups = ["main", "api"]
 files = [
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 ]
 
 [[package]]
 name = "httpcore"
-version = "1.0.7"
+version = "1.0.9"
 description = "A minimal low-level HTTP client."
 optional = false
 python-versions = ">=3.8"
 groups = ["main", "api"]
 files = [
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 ]
 
 [package.dependencies]
 certifi = "*"
-h11 = ">=0.13,<0.15"
+h11 = ">=0.16"
 
 [package.extras]
 asyncio = ["anyio (>=4.0,<5.0)"]
@@ -582,14 +834,14 @@ pyreadline3 = {version = "*", markers = "sys_platform == \"win32\" and python_ve
 
 [[package]]
 name = "identify"
-version = "2.6.9"
+version = "2.6.10"
 description = "File identification library for Python"
 optional = false
 python-versions = ">=3.9"
 groups = ["dev"]
 files = [
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 ]
 
 [package.extras]
@@ -655,6 +907,108 @@ MarkupSafe = ">=2.0"
 [package.extras]
 i18n = ["Babel (>=2.7)"]
 
+[[package]]
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+version = "1.5.0"
+description = "Lightweight pipelining with Python functions"
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+python-versions = ">=3.9"
+groups = ["main"]
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+]
+
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+optional = false
+python-versions = ">=3.10"
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 markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
 files = [
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+    {file = "nvidia_cuda_runtime_cu12-12.6.77-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:6116fad3e049e04791c0256a9778c16237837c08b27ed8c8401e2e45de8d60cd"},
+    {file = "nvidia_cuda_runtime_cu12-12.6.77-py3-none-manylinux2014_aarch64.whl", hash = "sha256:d461264ecb429c84c8879a7153499ddc7b19b5f8d84c204307491989a365588e"},
+    {file = "nvidia_cuda_runtime_cu12-12.6.77-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:ba3b56a4f896141e25e19ab287cd71e52a6a0f4b29d0d31609f60e3b4d5219b7"},
+    {file = "nvidia_cuda_runtime_cu12-12.6.77-py3-none-manylinux2014_x86_64.whl", hash = "sha256:a84d15d5e1da416dd4774cb42edf5e954a3e60cc945698dc1d5be02321c44dc8"},
+    {file = "nvidia_cuda_runtime_cu12-12.6.77-py3-none-win_amd64.whl", hash = "sha256:86c58044c824bf3c173c49a2dbc7a6c8b53cb4e4dca50068be0bf64e9dab3f7f"},
 ]
 
 [[package]]
 name = "nvidia-cudnn-cu12"
-version = "9.1.0.70"
+version = "9.5.1.17"
 description = "cuDNN runtime libraries"
 optional = false
 python-versions = ">=3"
 groups = ["main"]
 markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
 files = [
-    {file = "nvidia_cudnn_cu12-9.1.0.70-py3-none-manylinux2014_x86_64.whl", hash = "sha256:165764f44ef8c61fcdfdfdbe769d687e06374059fbb388b6c89ecb0e28793a6f"},
-    {file = "nvidia_cudnn_cu12-9.1.0.70-py3-none-win_amd64.whl", hash = "sha256:6278562929433d68365a07a4a1546c237ba2849852c0d4b2262a486e805b977a"},
+    {file = "nvidia_cudnn_cu12-9.5.1.17-py3-none-manylinux_2_28_aarch64.whl", hash = "sha256:9fd4584468533c61873e5fda8ca41bac3a38bcb2d12350830c69b0a96a7e4def"},
+    {file = "nvidia_cudnn_cu12-9.5.1.17-py3-none-manylinux_2_28_x86_64.whl", hash = "sha256:30ac3869f6db17d170e0e556dd6cc5eee02647abc31ca856634d5a40f82c15b2"},
+    {file = "nvidia_cudnn_cu12-9.5.1.17-py3-none-win_amd64.whl", hash = "sha256:d7af0f8a4f3b4b9dbb3122f2ef553b45694ed9c384d5a75bab197b8eefb79ab8"},
 ]
 
 [package.dependencies]
@@ -1082,41 +1503,66 @@ nvidia-cublas-cu12 = "*"
 
 [[package]]
 name = "nvidia-cufft-cu12"
-version = "11.0.2.54"
+version = "11.3.0.4"
 description = "CUFFT native runtime libraries"
 optional = false
 python-versions = ">=3"
 groups = ["main"]
 markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
 files = [
-    {file = "nvidia_cufft_cu12-11.0.2.54-py3-none-manylinux1_x86_64.whl", hash = "sha256:794e3948a1aa71fd817c3775866943936774d1c14e7628c74f6f7417224cdf56"},
-    {file = "nvidia_cufft_cu12-11.0.2.54-py3-none-win_amd64.whl", hash = "sha256:d9ac353f78ff89951da4af698f80870b1534ed69993f10a4cf1d96f21357e253"},
+    {file = "nvidia_cufft_cu12-11.3.0.4-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:d16079550df460376455cba121db6564089176d9bac9e4f360493ca4741b22a6"},
+    {file = "nvidia_cufft_cu12-11.3.0.4-py3-none-manylinux2014_aarch64.whl", hash = "sha256:8510990de9f96c803a051822618d42bf6cb8f069ff3f48d93a8486efdacb48fb"},
+    {file = "nvidia_cufft_cu12-11.3.0.4-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:ccba62eb9cef5559abd5e0d54ceed2d9934030f51163df018532142a8ec533e5"},
+    {file = "nvidia_cufft_cu12-11.3.0.4-py3-none-manylinux2014_x86_64.whl", hash = "sha256:768160ac89f6f7b459bee747e8d175dbf53619cfe74b2a5636264163138013ca"},
+    {file = "nvidia_cufft_cu12-11.3.0.4-py3-none-win_amd64.whl", hash = "sha256:6048ebddfb90d09d2707efb1fd78d4e3a77cb3ae4dc60e19aab6be0ece2ae464"},
+]
+
+[package.dependencies]
+nvidia-nvjitlink-cu12 = "*"
+
+[[package]]
+name = "nvidia-cufile-cu12"
+version = "1.11.1.6"
+description = "cuFile GPUDirect libraries"
+optional = false
+python-versions = ">=3"
+groups = ["main"]
+markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
+files = [
+    {file = "nvidia_cufile_cu12-1.11.1.6-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:cc23469d1c7e52ce6c1d55253273d32c565dd22068647f3aa59b3c6b005bf159"},
+    {file = "nvidia_cufile_cu12-1.11.1.6-py3-none-manylinux_2_27_aarch64.whl", hash = "sha256:8f57a0051dcf2543f6dc2b98a98cb2719c37d3cee1baba8965d57f3bbc90d4db"},
 ]
 
 [[package]]
 name = "nvidia-curand-cu12"
-version = "10.3.2.106"
+version = "10.3.7.77"
 description = "CURAND native runtime libraries"
 optional = false
 python-versions = ">=3"
 groups = ["main"]
 markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
 files = [
-    {file = "nvidia_curand_cu12-10.3.2.106-py3-none-manylinux1_x86_64.whl", hash = "sha256:9d264c5036dde4e64f1de8c50ae753237c12e0b1348738169cd0f8a536c0e1e0"},
-    {file = "nvidia_curand_cu12-10.3.2.106-py3-none-win_amd64.whl", hash = "sha256:75b6b0c574c0037839121317e17fd01f8a69fd2ef8e25853d826fec30bdba74a"},
+    {file = "nvidia_curand_cu12-10.3.7.77-py3-none-manylinux2014_aarch64.whl", hash = "sha256:6e82df077060ea28e37f48a3ec442a8f47690c7499bff392a5938614b56c98d8"},
+    {file = "nvidia_curand_cu12-10.3.7.77-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:a42cd1344297f70b9e39a1e4f467a4e1c10f1da54ff7a85c12197f6c652c8bdf"},
+    {file = "nvidia_curand_cu12-10.3.7.77-py3-none-manylinux2014_x86_64.whl", hash = "sha256:99f1a32f1ac2bd134897fc7a203f779303261268a65762a623bf30cc9fe79117"},
+    {file = "nvidia_curand_cu12-10.3.7.77-py3-none-manylinux_2_27_aarch64.whl", hash = "sha256:7b2ed8e95595c3591d984ea3603dd66fe6ce6812b886d59049988a712ed06b6e"},
+    {file = "nvidia_curand_cu12-10.3.7.77-py3-none-win_amd64.whl", hash = "sha256:6d6d935ffba0f3d439b7cd968192ff068fafd9018dbf1b85b37261b13cfc9905"},
 ]
 
 [[package]]
 name = "nvidia-cusolver-cu12"
-version = "11.4.5.107"
+version = "11.7.1.2"
 description = "CUDA solver native runtime libraries"
 optional = false
 python-versions = ">=3"
 groups = ["main"]
 markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
 files = [
-    {file = "nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl", hash = "sha256:8a7ec542f0412294b15072fa7dab71d31334014a69f953004ea7a118206fe0dd"},
-    {file = "nvidia_cusolver_cu12-11.4.5.107-py3-none-win_amd64.whl", hash = "sha256:74e0c3a24c78612192a74fcd90dd117f1cf21dea4822e66d89e8ea80e3cd2da5"},
+    {file = "nvidia_cusolver_cu12-11.7.1.2-py3-none-manylinux2014_aarch64.whl", hash = "sha256:0ce237ef60acde1efc457335a2ddadfd7610b892d94efee7b776c64bb1cac9e0"},
+    {file = "nvidia_cusolver_cu12-11.7.1.2-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:e9e49843a7707e42022babb9bcfa33c29857a93b88020c4e4434656a655b698c"},
+    {file = "nvidia_cusolver_cu12-11.7.1.2-py3-none-manylinux2014_x86_64.whl", hash = "sha256:6cf28f17f64107a0c4d7802be5ff5537b2130bfc112f25d5a30df227058ca0e6"},
+    {file = "nvidia_cusolver_cu12-11.7.1.2-py3-none-manylinux_2_27_aarch64.whl", hash = "sha256:dbbe4fc38ec1289c7e5230e16248365e375c3673c9c8bac5796e2e20db07f56e"},
+    {file = "nvidia_cusolver_cu12-11.7.1.2-py3-none-win_amd64.whl", hash = "sha256:6813f9d8073f555444a8705f3ab0296d3e1cb37a16d694c5fc8b862a0d8706d7"},
 ]
 
 [package.dependencies]
@@ -1126,70 +1572,90 @@ nvidia-nvjitlink-cu12 = "*"
 
 [[package]]
 name = "nvidia-cusparse-cu12"
-version = "12.1.0.106"
+version = "12.5.4.2"
 description = "CUSPARSE native runtime libraries"
 optional = false
 python-versions = ">=3"
 groups = ["main"]
 markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
 files = [
-    {file = "nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl", hash = "sha256:f3b50f42cf363f86ab21f720998517a659a48131e8d538dc02f8768237bd884c"},
-    {file = "nvidia_cusparse_cu12-12.1.0.106-py3-none-win_amd64.whl", hash = "sha256:b798237e81b9719373e8fae8d4f091b70a0cf09d9d85c95a557e11df2d8e9a5a"},
+    {file = "nvidia_cusparse_cu12-12.5.4.2-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:d25b62fb18751758fe3c93a4a08eff08effedfe4edf1c6bb5afd0890fe88f887"},
+    {file = "nvidia_cusparse_cu12-12.5.4.2-py3-none-manylinux2014_aarch64.whl", hash = "sha256:7aa32fa5470cf754f72d1116c7cbc300b4e638d3ae5304cfa4a638a5b87161b1"},
+    {file = "nvidia_cusparse_cu12-12.5.4.2-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:7556d9eca156e18184b94947ade0fba5bb47d69cec46bf8660fd2c71a4b48b73"},
+    {file = "nvidia_cusparse_cu12-12.5.4.2-py3-none-manylinux2014_x86_64.whl", hash = "sha256:23749a6571191a215cb74d1cdbff4a86e7b19f1200c071b3fcf844a5bea23a2f"},
+    {file = "nvidia_cusparse_cu12-12.5.4.2-py3-none-win_amd64.whl", hash = "sha256:4acb8c08855a26d737398cba8fb6f8f5045d93f82612b4cfd84645a2332ccf20"},
 ]
 
 [package.dependencies]
 nvidia-nvjitlink-cu12 = "*"
 
+[[package]]
+name = "nvidia-cusparselt-cu12"
+version = "0.6.3"
+description = "NVIDIA cuSPARSELt"
+optional = false
+python-versions = "*"
+groups = ["main"]
+markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
+files = [
+    {file = "nvidia_cusparselt_cu12-0.6.3-py3-none-manylinux2014_aarch64.whl", hash = "sha256:8371549623ba601a06322af2133c4a44350575f5a3108fb75f3ef20b822ad5f1"},
+    {file = "nvidia_cusparselt_cu12-0.6.3-py3-none-manylinux2014_x86_64.whl", hash = "sha256:e5c8a26c36445dd2e6812f1177978a24e2d37cacce7e090f297a688d1ec44f46"},
+    {file = "nvidia_cusparselt_cu12-0.6.3-py3-none-win_amd64.whl", hash = "sha256:3b325bcbd9b754ba43df5a311488fca11a6b5dc3d11df4d190c000cf1a0765c7"},
+]
+
 [[package]]
 name = "nvidia-nccl-cu12"
-version = "2.20.5"
+version = "2.26.2"
 description = "NVIDIA Collective Communication Library (NCCL) Runtime"
 optional = false
 python-versions = ">=3"
 groups = ["main"]
-markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
+markers = "platform_system == \"Linux\" and platform_machine != \"aarch64\""
 files = [
-    {file = "nvidia_nccl_cu12-2.20.5-py3-none-manylinux2014_aarch64.whl", hash = "sha256:1fc150d5c3250b170b29410ba682384b14581db722b2531b0d8d33c595f33d01"},
-    {file = "nvidia_nccl_cu12-2.20.5-py3-none-manylinux2014_x86_64.whl", hash = "sha256:057f6bf9685f75215d0c53bf3ac4a10b3e6578351de307abad9e18a99182af56"},
+    {file = "nvidia_nccl_cu12-2.26.2-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:5c196e95e832ad30fbbb50381eb3cbd1fadd5675e587a548563993609af19522"},
+    {file = "nvidia_nccl_cu12-2.26.2-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:694cf3879a206553cc9d7dbda76b13efaf610fdb70a50cba303de1b0d1530ac6"},
 ]
 
 [[package]]
 name = "nvidia-nvjitlink-cu12"
-version = "12.8.93"
+version = "12.6.85"
 description = "Nvidia JIT LTO Library"
 optional = false
 python-versions = ">=3"
 groups = ["main"]
 markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
 files = [
-    {file = "nvidia_nvjitlink_cu12-12.8.93-py3-none-manylinux2010_x86_64.manylinux_2_12_x86_64.whl", hash = "sha256:81ff63371a7ebd6e6451970684f916be2eab07321b73c9d244dc2b4da7f73b88"},
-    {file = "nvidia_nvjitlink_cu12-12.8.93-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:adccd7161ace7261e01bb91e44e88da350895c270d23f744f0820c818b7229e7"},
-    {file = "nvidia_nvjitlink_cu12-12.8.93-py3-none-win_amd64.whl", hash = "sha256:bd93fbeeee850917903583587f4fc3a4eafa022e34572251368238ab5e6bd67f"},
+    {file = "nvidia_nvjitlink_cu12-12.6.85-py3-none-manylinux2010_x86_64.manylinux_2_12_x86_64.whl", hash = "sha256:eedc36df9e88b682efe4309aa16b5b4e78c2407eac59e8c10a6a47535164369a"},
+    {file = "nvidia_nvjitlink_cu12-12.6.85-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:cf4eaa7d4b6b543ffd69d6abfb11efdeb2db48270d94dfd3a452c24150829e41"},
+    {file = "nvidia_nvjitlink_cu12-12.6.85-py3-none-win_amd64.whl", hash = "sha256:e61120e52ed675747825cdd16febc6a0730537451d867ee58bee3853b1b13d1c"},
 ]
 
 [[package]]
 name = "nvidia-nvtx-cu12"
-version = "12.1.105"
+version = "12.6.77"
 description = "NVIDIA Tools Extension"
 optional = false
 python-versions = ">=3"
 groups = ["main"]
 markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
 files = [
-    {file = "nvidia_nvtx_cu12-12.1.105-py3-none-manylinux1_x86_64.whl", hash = "sha256:dc21cf308ca5691e7c04d962e213f8a4aa9bbfa23d95412f452254c2caeb09e5"},
-    {file = "nvidia_nvtx_cu12-12.1.105-py3-none-win_amd64.whl", hash = "sha256:65f4d98982b31b60026e0e6de73fbdfc09d08a96f4656dd3665ca616a11e1e82"},
+    {file = "nvidia_nvtx_cu12-12.6.77-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:f44f8d86bb7d5629988d61c8d3ae61dddb2015dee142740536bc7481b022fe4b"},
+    {file = "nvidia_nvtx_cu12-12.6.77-py3-none-manylinux2014_aarch64.whl", hash = "sha256:adcaabb9d436c9761fca2b13959a2d237c5f9fd406c8e4b723c695409ff88059"},
+    {file = "nvidia_nvtx_cu12-12.6.77-py3-none-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:b90bed3df379fa79afbd21be8e04a0314336b8ae16768b58f2d34cb1d04cd7d2"},
+    {file = "nvidia_nvtx_cu12-12.6.77-py3-none-manylinux2014_x86_64.whl", hash = "sha256:6574241a3ec5fdc9334353ab8c479fe75841dbe8f4532a8fc97ce63503330ba1"},
+    {file = "nvidia_nvtx_cu12-12.6.77-py3-none-win_amd64.whl", hash = "sha256:2fb11a4af04a5e6c84073e6404d26588a34afd35379f0855a99797897efa75c0"},
 ]
 
 [[package]]
 name = "ollama"
-version = "0.4.7"
+version = "0.4.8"
 description = "The official Python client for Ollama."
 optional = false
 python-versions = "<4.0,>=3.8"
 groups = ["main", "api"]
 files = [
-    {file = "ollama-0.4.7-py3-none-any.whl", hash = "sha256:85505663cca67a83707be5fb3aeff0ea72e67846cea5985529d8eca4366564a1"},
-    {file = "ollama-0.4.7.tar.gz", hash = "sha256:891dcbe54f55397d82d289c459de0ea897e103b86a3f1fad0fdb1895922a75ff"},
+    {file = "ollama-0.4.8-py3-none-any.whl", hash = "sha256:04312af2c5e72449aaebac4a2776f52ef010877c554103419d3f36066fe8af4c"},
+    {file = "ollama-0.4.8.tar.gz", hash = "sha256:1121439d49b96fa8339842965d0616eba5deb9f8c790786cdf4c0b3df4833802"},
 ]
 
 [package.dependencies]
@@ -1274,22 +1740,108 @@ files = [
 
 [package.dependencies]
 numpy = [
+    {version = ">=1.23.5", markers = "python_version >= \"3.11\""},
     {version = ">=1.26.0", markers = "python_version >= \"3.12\""},
-    {version = ">=1.23.5", markers = "python_version == \"3.11\""},
 ]
 
 [[package]]
 name = "packaging"
-version = "24.2"
+version = "25.0"
 description = "Core utilities for Python packages"
 optional = false
 python-versions = ">=3.8"
 groups = ["main", "bundling", "dev"]
 files = [
-    {file = "packaging-24.2-py3-none-any.whl", hash = "sha256:09abb1bccd265c01f4a3aa3f7a7db064b36514d2cba19a2f694fe6150451a759"},
-    {file = "packaging-24.2.tar.gz", hash = "sha256:c228a6dc5e932d346bc5739379109d49e8853dd8223571c7c5b55260edc0b97f"},
+    {file = "packaging-25.0-py3-none-any.whl", hash = "sha256:29572ef2b1f17581046b3a2227d5c611fb25ec70ca1ba8554b24b0e69331a484"},
+    {file = "packaging-25.0.tar.gz", hash = "sha256:d443872c98d677bf60f6a1f2f8c1cb748e8fe762d2bf9d3148b5599295b0fc4f"},
 ]
 
+[[package]]
+name = "pandas"
+version = "2.2.3"
+description = "Powerful data structures for data analysis, time series, and statistics"
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
+    {file = "pandas-2.2.3-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:1948ddde24197a0f7add2bdc4ca83bf2b1ef84a1bc8ccffd95eda17fd836ecb5"},
+    {file = "pandas-2.2.3-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:381175499d3802cde0eabbaf6324cce0c4f5d52ca6f8c377c29ad442f50f6348"},
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 ]
 
 [package.dependencies]
@@ -1571,20 +2223,35 @@ hook-testing = ["execnet (>=1.5.0)", "psutil", "pytest (>=2.7.3)"]
 
 [[package]]
 name = "pyinstaller-hooks-contrib"
-version = "2025.2"
+version = "2025.4"
 description = "Community maintained hooks for PyInstaller"
 optional = false
 python-versions = ">=3.8"
 groups = ["bundling"]
 files = [
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 ]
 
 [package.dependencies]
 packaging = ">=22.0"
 setuptools = ">=42.0.0"
 
+[[package]]
+name = "pyparsing"
+version = "3.2.3"
+description = "pyparsing module - Classes and methods to define and execute parsing grammars"
+optional = false
+python-versions = ">=3.9"
+groups = ["main"]
+files = [
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+]
+
+[package.extras]
+diagrams = ["jinja2", "railroad-diagrams"]
+
 [[package]]
 name = "pypdf2"
 version = "3.0.1"
@@ -1641,6 +2308,33 @@ pluggy = ">=1.5,<2"
 [package.extras]
 dev = ["argcomplete", "attrs (>=19.2)", "hypothesis (>=3.56)", "mock", "pygments (>=2.7.2)", "requests", "setuptools", "xmlschema"]
 
+[[package]]
+name = "python-dateutil"
+version = "2.9.0.post0"
+description = "Extensions to the standard Python datetime module"
+optional = false
+python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7"
+groups = ["main"]
+files = [
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+]
+
+[package.dependencies]
+six = ">=1.5"
+
+[[package]]
+name = "pytz"
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+description = "World timezone definitions, modern and historical"
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 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7"
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+    {file = "torch-2.7.0-cp39-cp39-win_amd64.whl", hash = "sha256:87b0802cab44659fcb6bcf5678d58fa4a8b48561cde8fb2d317edf0b6990e1bb"},
+    {file = "torch-2.7.0-cp39-none-macosx_11_0_arm64.whl", hash = "sha256:ccd7509141713997861b7a947ef0a717143cd7e9240addd168f38ba8fd23fd56"},
 ]
 
 [package.dependencies]
@@ -2140,25 +3021,28 @@ filelock = "*"
 fsspec = "*"
 jinja2 = "*"
 networkx = "*"
-nvidia-cublas-cu12 = {version = "12.1.3.1", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
-nvidia-cuda-cupti-cu12 = {version = "12.1.105", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
-nvidia-cuda-nvrtc-cu12 = {version = "12.1.105", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
-nvidia-cuda-runtime-cu12 = {version = "12.1.105", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
-nvidia-cudnn-cu12 = {version = "9.1.0.70", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
-nvidia-cufft-cu12 = {version = "11.0.2.54", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
-nvidia-curand-cu12 = {version = "10.3.2.106", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
-nvidia-cusolver-cu12 = {version = "11.4.5.107", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
-nvidia-cusparse-cu12 = {version = "12.1.0.106", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
-nvidia-nccl-cu12 = {version = "2.20.5", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
-nvidia-nvtx-cu12 = {version = "12.1.105", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
-setuptools = "*"
-sympy = "*"
-triton = {version = "3.0.0", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and python_version < \"3.13\""}
-typing-extensions = ">=4.8.0"
+nvidia-cublas-cu12 = {version = "12.6.4.1", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-cuda-cupti-cu12 = {version = "12.6.80", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-cuda-nvrtc-cu12 = {version = "12.6.77", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-cuda-runtime-cu12 = {version = "12.6.77", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-cudnn-cu12 = {version = "9.5.1.17", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-cufft-cu12 = {version = "11.3.0.4", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-cufile-cu12 = {version = "1.11.1.6", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-curand-cu12 = {version = "10.3.7.77", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-cusolver-cu12 = {version = "11.7.1.2", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-cusparse-cu12 = {version = "12.5.4.2", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-cusparselt-cu12 = {version = "0.6.3", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-nccl-cu12 = {version = "2.26.2", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-nvjitlink-cu12 = {version = "12.6.85", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+nvidia-nvtx-cu12 = {version = "12.6.77", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+setuptools = {version = "*", markers = "python_version >= \"3.12\""}
+sympy = ">=1.13.3"
+triton = {version = "3.3.0", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
+typing-extensions = ">=4.10.0"
 
 [package.extras]
 opt-einsum = ["opt-einsum (>=3.3)"]
-optree = ["optree (>=0.11.0)"]
+optree = ["optree (>=0.13.0)"]
 
 [[package]]
 name = "tqdm"
@@ -2184,26 +3068,27 @@ telegram = ["requests"]
 
 [[package]]
 name = "triton"
-version = "3.0.0"
+version = "3.3.0"
 description = "A language and compiler for custom Deep Learning operations"
 optional = false
 python-versions = "*"
 groups = ["main"]
-markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" or platform_machine == \"x86_64\" and sys_platform == \"linux\" or sys_platform == \"linux2\""
+markers = "(platform_machine == \"x86_64\" or sys_platform == \"linux2\") and (platform_system == \"Linux\" or sys_platform == \"linux\" or sys_platform == \"linux2\")"
 files = [
-    {file = "triton-3.0.0-1-cp310-cp310-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:e1efef76935b2febc365bfadf74bcb65a6f959a9872e5bddf44cc9e0adce1e1a"},
-    {file = "triton-3.0.0-1-cp311-cp311-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:5ce8520437c602fb633f1324cc3871c47bee3b67acf9756c1a66309b60e3216c"},
-    {file = "triton-3.0.0-1-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:34e509deb77f1c067d8640725ef00c5cbfcb2052a1a3cb6a6d343841f92624eb"},
-    {file = "triton-3.0.0-1-cp38-cp38-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:bcbf3b1c48af6a28011a5c40a5b3b9b5330530c3827716b5fbf6d7adcc1e53e9"},
-    {file = "triton-3.0.0-1-cp39-cp39-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = "sha256:6e5727202f7078c56f91ff13ad0c1abab14a0e7f2c87e91b12b6f64f3e8ae609"},
+    {file = "triton-3.3.0-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:fad99beafc860501d7fcc1fb7045d9496cbe2c882b1674640304949165a916e7"},
+    {file = "triton-3.3.0-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:3161a2bf073d6b22c4e2f33f951f3e5e3001462b2570e6df9cd57565bdec2984"},
+    {file = "triton-3.3.0-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:b68c778f6c4218403a6bd01be7484f6dc9e20fe2083d22dd8aef33e3b87a10a3"},
+    {file = "triton-3.3.0-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:47bc87ad66fa4ef17968299acacecaab71ce40a238890acc6ad197c3abe2b8f1"},
+    {file = "triton-3.3.0-cp313-cp313t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:ce4700fc14032af1e049005ae94ba908e71cd6c2df682239aed08e49bc71b742"},
+    {file = "triton-3.3.0-cp39-cp39-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:1f41403bfa0cbb3e24fd958ca7fee04e9681e55e539296db9aca30c42acae693"},
 ]
 
 [package.dependencies]
-filelock = "*"
+setuptools = ">=40.8.0"
 
 [package.extras]
 build = ["cmake (>=3.20)", "lit"]
-tests = ["autopep8", "flake8", "isort", "llnl-hatchet", "numpy", "pytest", "scipy (>=1.7.1)"]
+tests = ["autopep8", "isort", "llnl-hatchet", "numpy", "pytest", "pytest-forked", "pytest-xdist", "scipy (>=1.7.1)"]
 tutorials = ["matplotlib", "pandas", "tabulate"]
 
 [[package]]
@@ -2226,14 +3111,14 @@ typing-extensions = ">=3.7.4.3"
 
 [[package]]
 name = "typing-extensions"
-version = "4.13.1"
+version = "4.13.2"
 description = "Backported and Experimental Type Hints for Python 3.8+"
 optional = false
 python-versions = ">=3.8"
 groups = ["main", "api"]
 files = [
-    {file = "typing_extensions-4.13.1-py3-none-any.whl", hash = "sha256:4b6cf02909eb5495cfbc3f6e8fd49217e6cc7944e145cdda8caa3734777f9e69"},
-    {file = "typing_extensions-4.13.1.tar.gz", hash = "sha256:98795af00fb9640edec5b8e31fc647597b4691f099ad75f469a2616be1a76dff"},
+    {file = "typing_extensions-4.13.2-py3-none-any.whl", hash = "sha256:a439e7c04b49fec3e5d3e2beaa21755cadbbdc391694e28ccdd36ca4a1408f8c"},
+    {file = "typing_extensions-4.13.2.tar.gz", hash = "sha256:e6c81219bd689f51865d9e372991c540bda33a0379d5573cddb9a3a23f7caaef"},
 ]
 
 [[package]]
@@ -2251,16 +3136,28 @@ files = [
 [package.dependencies]
 typing-extensions = ">=4.12.0"
 
+[[package]]
+name = "tzdata"
+version = "2025.2"
+description = "Provider of IANA time zone data"
+optional = false
+python-versions = ">=2"
+groups = ["main"]
+files = [
+    {file = "tzdata-2025.2-py2.py3-none-any.whl", hash = "sha256:1a403fada01ff9221ca8044d701868fa132215d84beb92242d9acd2147f667a8"},
+    {file = "tzdata-2025.2.tar.gz", hash = "sha256:b60a638fcc0daffadf82fe0f57e53d06bdec2f36c4df66280ae79bce6bd6f2b9"},
+]
+
 [[package]]
 name = "urllib3"
-version = "2.3.0"
+version = "2.4.0"
 description = "HTTP library with thread-safe connection pooling, file post, and more."
 optional = false
 python-versions = ">=3.9"
 groups = ["main", "api"]
 files = [
-    {file = "urllib3-2.3.0-py3-none-any.whl", hash = "sha256:1cee9ad369867bfdbbb48b7dd50374c0967a0bb7710050facf0dd6911440e3df"},
-    {file = "urllib3-2.3.0.tar.gz", hash = "sha256:f8c5449b3cf0861679ce7e0503c7b44b5ec981bec0d1d3795a07f1ba96f0204d"},
+    {file = "urllib3-2.4.0-py3-none-any.whl", hash = "sha256:4e16665048960a0900c702d4a66415956a584919c03361cac9f1df5c5dd7e813"},
+    {file = "urllib3-2.4.0.tar.gz", hash = "sha256:414bc6535b787febd7567804cc015fee39daab8ad86268f1310a9250697de466"},
 ]
 
 [package.extras]
@@ -2290,14 +3187,14 @@ standard = ["colorama (>=0.4) ; sys_platform == \"win32\"", "httptools (>=0.6.3)
 
 [[package]]
 name = "virtualenv"
-version = "20.30.0"
+version = "20.31.2"
 description = "Virtual Python Environment builder"
 optional = false
 python-versions = ">=3.8"
 groups = ["dev"]
 files = [
-    {file = "virtualenv-20.30.0-py3-none-any.whl", hash = "sha256:e34302959180fca3af42d1800df014b35019490b119eba981af27f2fa486e5d6"},
-    {file = "virtualenv-20.30.0.tar.gz", hash = "sha256:800863162bcaa5450a6e4d721049730e7f2dae07720e0902b0e4040bd6f9ada8"},
+    {file = "virtualenv-20.31.2-py3-none-any.whl", hash = "sha256:36efd0d9650ee985f0cad72065001e66d49a6f24eb44d98980f630686243cf11"},
+    {file = "virtualenv-20.31.2.tar.gz", hash = "sha256:e10c0a9d02835e592521be48b332b6caee6887f332c111aa79a09b9e79efc2af"},
 ]
 
 [package.dependencies]
@@ -2325,7 +3222,37 @@ files = [
 [package.extras]
 dev = ["black (>=19.3b0) ; python_version >= \"3.6\"", "pytest (>=4.6.2)"]
 
+[[package]]
+name = "xgboost"
+version = "3.0.0"
+description = "XGBoost Python Package"
+optional = false
+python-versions = ">=3.10"
+groups = ["main"]
+files = [
+    {file = "xgboost-3.0.0-py3-none-macosx_10_15_x86_64.whl", hash = "sha256:ed8cffd7998bd9431c3b0287a70bec8e45c09b43c9474d9dfd261627713bd890"},
+    {file = "xgboost-3.0.0-py3-none-macosx_12_0_arm64.whl", hash = "sha256:314104bd3a1426a40f0c9662eef40e9ab22eb7a8068a42a8d198ce40412db75c"},
+    {file = "xgboost-3.0.0-py3-none-manylinux2014_aarch64.whl", hash = "sha256:72c3405e8dfc37048f9fe339a058fa12b9f0f03bc31d3e56f0887eed2ed2baa1"},
+    {file = "xgboost-3.0.0-py3-none-manylinux2014_x86_64.whl", hash = "sha256:72d39e74649e9b628c4221111aa6a8caa860f2e853b25480424403ee61085126"},
+    {file = "xgboost-3.0.0-py3-none-manylinux_2_28_aarch64.whl", hash = "sha256:7bdee5787f86b83bebd75e2c96caf854760788e5f4203d063da50db5bf0efc5f"},
+    {file = "xgboost-3.0.0-py3-none-manylinux_2_28_x86_64.whl", hash = "sha256:61c7e391e373b8a5312503525c0689f83ef1912a1236377022865ab340f465a4"},
+    {file = "xgboost-3.0.0-py3-none-win_amd64.whl", hash = "sha256:0ea74e97f95b1eddfd27a46b7f22f72ec5a5322e1dc7cb41c9c23fb580763df9"},
+    {file = "xgboost-3.0.0.tar.gz", hash = "sha256:45e95416df6f6f01d9a62e60cf09fc57e5ee34697f3858337c796fac9ce3b9ed"},
+]
+
+[package.dependencies]
+numpy = "*"
+nvidia-nccl-cu12 = {version = "*", markers = "platform_system == \"Linux\" and platform_machine != \"aarch64\""}
+scipy = "*"
+
+[package.extras]
+dask = ["dask", "distributed", "pandas"]
+pandas = ["pandas (>=1.2)"]
+plotting = ["graphviz", "matplotlib"]
+pyspark = ["cloudpickle", "pyspark", "scikit-learn"]
+scikit-learn = ["scikit-learn"]
+
 [metadata]
 lock-version = "2.1"
 python-versions = ">=3.11,<3.13"
-content-hash = "cdb6d01e939e10b55aaa7b42eaa8bf59eff5ec49b6af4ec92ef11262812a48bd"
+content-hash = "49664d9978edda90824f2a3e868ca6a49f12c375702ff6f79e3740065694f818"
diff --git a/pyproject.toml b/pyproject.toml
index 62f70fa1..75dcee5e 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -22,14 +22,21 @@ onnxruntime = "1.21.0"
 opencv-python = ">=4.11.0.86,<5.0.0.0"
 ollama = ">=0.4.7,<0.5.0"
 pypdf2 = ">=3.0.1,<4.0.0"
+torch = ">=2.6.0,<3.0.0"
+pandas = ">=2.2.3,<3.0.0"
+sqlalchemy = ">=2.0.38,<3.0.0"
+seaborn = ">=0.13.2,<0.14.0"
+xgboost = { version = "3.0.0", allow-prereleases = true, source = "pypi", extras = ["cpu"] }
+joblib = "1.5.0"
+scipy = "1.15.3"
 
 rb-lib = { path = "src/rb-lib", develop = true }
-
 file-utils = { path = "src/file-utils", develop = true }
 doc-parser = { path = "src/doc-parser", develop = true }
 audio-transcription = { path = "src/audio-transcription", develop = true }
 age-and-gender-detection = { path = "src/age_and_gender_detection", develop = true }
 text-summary = {path = "src/text-summary", develop = true}
+age-classifier = { path = "src/age_gender_classifier", develop = true }
 
 # Don't add new packages here, add them appropriately in the list above
 beautifulsoup4 = "^4.13.3"
diff --git a/rescuebox/plugins/__init__.py b/rescuebox/plugins/__init__.py
index e1bba2ed..d6af2d8a 100644
--- a/rescuebox/plugins/__init__.py
+++ b/rescuebox/plugins/__init__.py
@@ -12,6 +12,10 @@
 # Import plugin modules
 from doc_parser.main import app as doc_parser_app  # type: ignore
 from file_utils.main import app as file_utils_app  # type: ignore
+from age_gender_classifier.server.server_onnx import (
+    app as age_class_app,
+    APP_NAME as AGE_CLASSIFIER_APP_NAME,
+)
 
 
 @dataclass(frozen=True)
@@ -30,6 +34,7 @@ class RescueBoxPlugin:
     ),
     RescueBoxPlugin(age_gender_app, AGE_GENDER_APP_NAME, "Age and Gender Classifier"),
     RescueBoxPlugin(text_summary_app, TEXT_SUM_APP_NAME, "Text summarization library"),
+    RescueBoxPlugin(age_class_app, AGE_CLASSIFIER_APP_NAME, "Age Classifier"),
 ]
 
 # Ensure this module is importable
diff --git a/src/age_gender_classifier/README.md b/src/age_gender_classifier/README.md
new file mode 100644
index 00000000..2191f584
--- /dev/null
+++ b/src/age_gender_classifier/README.md
@@ -0,0 +1,28 @@
+## Age and Gender Classification
+
+### Additional Setup and Notes
+
+- **XGBoost dependency:** Mac OSX users must have OpenMP runtime installed for XGBoost to work. On Mac OSX run this command to install. 
+```
+brew install libomp
+```
+
+- **Database SQLite:** Using the connection ORMs defined in `src/age_gender_classifier/age_gender_classifier/utils/sqlAlchemy_manager.py`, and a default environment variable in the file that sets the connection string, our project writes model output to a SQLite database called `age_classifier.db` that is created at the project's root.
+
+- **Get the ONNX models**  
+In order to run survey_models.py you must download the onnx model files from either this GoogleDrive [link](https://drive.google.com/drive/folders/1IgG6w6lJ9cd8Qlckd7HwdBUjWCd_-gxN), or this shared GoogleDrive [link](https://drive.google.com/drive/folders/1e01jA5WXsukK_NwNnNx4iL50jRCMZQXF), then copy them in their respective directories.
+
+    cp ~/Downloads/v001_model.onnx src/onnx_models/age_classify_v001/v001_model.onnx 
+    cp ~/Downloads/vit_model.onnx src/onnx_models/vit_age_classifier/vit_model.onnx
+    cp ~/Downloads/fareface_age.onnx src/onnx_models/fareface/fareface_age.onnx  
+
+    Alternatively, you could run the `convert_to_onnx.py` files in each directory to regenerate the respective ONNX files.
+
+You are good to go!
+
+---
+
+### Run on the command line
+```
+poetry run python src/age_gender_classifier/age_gender_classifier/server/server_onnx.py /age-classifier/age_classifier src/age_gender_classifier/age_gender_classifier/onnx_models/test_images 20
+```
diff --git a/src/age_gender_classifier/age_gender_classifier/__init__.py b/src/age_gender_classifier/age_gender_classifier/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/src/age_gender_classifier/age_gender_classifier/eval/README.md b/src/age_gender_classifier/age_gender_classifier/eval/README.md
new file mode 100644
index 00000000..198f20a9
--- /dev/null
+++ b/src/age_gender_classifier/age_gender_classifier/eval/README.md
@@ -0,0 +1,13 @@
+## Evaluating Labeled Data Set
+
+**Pre-requisite**  
+Ensure the labeled data set exists in the data store. If not, follow steps in the `init_data` directory.  
+
+**Analyze Model Efficacy**
+- Analyze raw labeled data set: `analyze_labeled_data_raw.py` writes charts for age distribution and gender counts to `imgs/` directory.  
+- Score labeled data: `score_labeled_data.py` runs labeled data through our models and writes scores to the DB.  
+- Analyze scored labeled data: `analyze_labeled_data_scored.py` visualize model efficacy and writes charts to `imgs/` directory.  
+
+Process is grouped in `eval_main.py`
+
+    poetry run python src/eval/eval_main.py
diff --git a/src/age_gender_classifier/age_gender_classifier/eval/analyze_labeled_data_raw.py b/src/age_gender_classifier/age_gender_classifier/eval/analyze_labeled_data_raw.py
new file mode 100644
index 00000000..925d3f2e
--- /dev/null
+++ b/src/age_gender_classifier/age_gender_classifier/eval/analyze_labeled_data_raw.py
@@ -0,0 +1,45 @@
+from pathlib import Path
+import logging
+import pandas as pd
+import seaborn as sns
+import matplotlib.pyplot as plt
+
+from age_gender_classifier.utils.common import read_db
+
+logging.basicConfig(level=logging.INFO)
+path = Path(__file__).parent
+
+
+def age_histogram(
+    df: pd.DataFrame, saveFile: str = "imgs/age_distribution.png"
+) -> None:
+    """"""
+    sns.histplot(data=df, x="age", kde=True)
+    plt.savefig(path / saveFile)
+    logging.info(f" Saved histogram to {saveFile}")
+
+
+def gender_counts(df: pd.DataFrame, saveFile: str = "imgs/gender_counts.png") -> None:
+    """"""
+    gender_counts = {0: (df["gender"] == 0).sum(), 1: (df["gender"] == 1).sum()}
+    sns.barplot(
+        x=["Male", "Female"],
+        y=[gender_counts[0], gender_counts[1]],
+        palette=["gray", "orange"],
+        hue=["Male", "Female"],
+        width=0.5,
+    )
+    plt.ylabel("Count")
+    plt.savefig(path / saveFile)
+    logging.info(f" Saved histogram to {saveFile}")
+
+
+def main(table: str = "age_gender_labeled"):
+    """Labeled data set must exist in the database."""
+    df = read_db(table_name=table, query=f"SELECT * FROM {table}")
+    age_histogram(df)
+    gender_counts(df)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/src/age_gender_classifier/age_gender_classifier/eval/eval_main.py b/src/age_gender_classifier/age_gender_classifier/eval/eval_main.py
new file mode 100644
index 00000000..c256f180
--- /dev/null
+++ b/src/age_gender_classifier/age_gender_classifier/eval/eval_main.py
@@ -0,0 +1,31 @@
+import pandas as pd
+from age_gender_classifier.eval.analyze_labeled_data_raw import main as raw_main
+from age_gender_classifier.eval.score_labeled_data import main as run_evaluation
+from eval.transform_scores import main as transform_outputs
+
+
+def main(
+    eval_table: str = "age_gender_labeled", raw_plots: bool = False
+) -> pd.DataFrame:
+    """Orchestrate full evaluation pipeline.
+
+    - Plot raw data, if raw_plots is True
+    - Run eval, query true samples and run Survey models
+    - Transform outputs, extract predictions from json payload
+    - Plot predicted data
+    """
+    if raw_plots:
+        raw_main(eval_table)
+
+    ts, df = run_evaluation()
+
+    df = transform_outputs(t_stamp=ts)
+
+    # TODO: endpoint for visualizations here
+    # chart scores and true/predicted labels
+
+    return df
+
+
+if __name__ == "__main__":
+    main()
diff --git a/src/age_gender_classifier/age_gender_classifier/eval/evaluate_inputs.py b/src/age_gender_classifier/age_gender_classifier/eval/evaluate_inputs.py
new file mode 100644
index 00000000..42c59465
--- /dev/null
+++ b/src/age_gender_classifier/age_gender_classifier/eval/evaluate_inputs.py
@@ -0,0 +1,47 @@
+from pathlib import Path
+import logging
+import pandas as pd
+import seaborn as sns
+import matplotlib.pyplot as plt
+
+from age_gender_classifier.utils.common import read_db
+
+logging.basicConfig(level=logging.INFO)
+path = Path(__file__).parent
+
+
+def age_histogram(
+    df: pd.DataFrame, saveFile: str = "imgs/age_distribution.png"
+) -> None:
+    """"""
+    sns.histplot(data=df, x="age", kde=True)
+    plt.savefig(path / saveFile)
+    logging.info(f" Saved histogram to {saveFile}")
+
+
+def gender_counts(df: pd.DataFrame, saveFile: str = "imgs/gender_counts.png") -> None:
+    """"""
+    gender_counts = {0: (df["gender"] == 0).sum(), 1: (df["gender"] == 1).sum()}
+    sns.barplot(
+        x=["Male", "Female"],
+        y=[gender_counts[0], gender_counts[1]],
+        palette=["gray", "orange"],
+        hue=["Male", "Female"],
+        width=0.5,
+    )
+    plt.ylabel("Count")
+    plt.savefig(path / saveFile)
+    logging.info(f" Saved histogram to {saveFile}")
+
+
+def main():
+    """"""
+    df = read_db(
+        table_name="age_gender_labeled", query="SELECT * FROM age_gender_labeled"
+    )
+    age_histogram(df)
+    gender_counts(df)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/src/age_gender_classifier/age_gender_classifier/eval/imgs/age_distribution.png b/src/age_gender_classifier/age_gender_classifier/eval/imgs/age_distribution.png
new file mode 100644
index 00000000..6d3530e7
Binary files /dev/null and b/src/age_gender_classifier/age_gender_classifier/eval/imgs/age_distribution.png differ
diff --git a/src/age_gender_classifier/age_gender_classifier/eval/imgs/gender_counts.png b/src/age_gender_classifier/age_gender_classifier/eval/imgs/gender_counts.png
new file mode 100644
index 00000000..11fb236e
Binary files /dev/null and b/src/age_gender_classifier/age_gender_classifier/eval/imgs/gender_counts.png differ
diff --git a/src/age_gender_classifier/age_gender_classifier/eval/score_labeled_data.py b/src/age_gender_classifier/age_gender_classifier/eval/score_labeled_data.py
new file mode 100644
index 00000000..7de27124
--- /dev/null
+++ b/src/age_gender_classifier/age_gender_classifier/eval/score_labeled_data.py
@@ -0,0 +1,42 @@
+from pathlib import Path
+from typing import Tuple
+import logging
+import pandas as pd
+
+from age_gender_classifier.utils.common import read_db
+from age_gender_classifier.onnx_models.survey_models import SurveyModels
+
+logging.basicConfig(level=logging.INFO)
+path = Path(__file__).parent
+
+
+def main(
+    table: str = "age_gender_labeled", mod_size: int = 200
+) -> Tuple[str, pd.DataFrame]:
+    """Read subset of true data from database, preprocess, run inference against our models,
+    and return a dataframe. Labeled data set must exist in the database.
+
+    Return: tuple of timestamp as str and dataframe
+    """
+    df = read_db(
+        table_name=table,
+        query=f"SELECT id, age, img_name, pixels FROM {table} where id % {mod_size} = 0 order by age",
+    )
+
+    imgs = list(df["pixels"])
+    ids = list(df["img_name"])
+    logging.info(
+        f" About to run inference on true label subset of size = {len(df)} ..."
+    )
+
+    sm = SurveyModels()
+    df = sm.main_predict_eval(imgs, age_threshold=20, ids=ids)
+
+    logging.info(
+        " Completed prediction on true labels, returning timestamp and dataframe of predicted results"
+    )
+    return sm.now, df
+
+
+if __name__ == "__main__":
+    main()
diff --git a/src/age_gender_classifier/age_gender_classifier/eval/transform_scores.py b/src/age_gender_classifier/age_gender_classifier/eval/transform_scores.py
new file mode 100644
index 00000000..a63bba7b
--- /dev/null
+++ b/src/age_gender_classifier/age_gender_classifier/eval/transform_scores.py
@@ -0,0 +1,49 @@
+from pathlib import Path
+import logging
+import json
+import pandas as pd
+from typing import Optional
+
+from age_gender_classifier.utils.common import read_db
+
+logging.basicConfig(level=logging.INFO)
+path = Path(__file__).parent
+
+
+def flatten_json_scores(df: pd.DataFrame) -> pd.DataFrame:
+    """Expand json scores from model_output."""
+    scores_expanded = pd.json_normalize(df["scores"].apply(lambda x: json.loads(x)))
+    df = df.join(scores_expanded).drop("scores", axis=1)
+    logging.info(" Expanded JSON scores")
+    return df
+
+
+def main(t_stamp: Optional[str] = None) -> pd.DataFrame:
+    """Read values from DB where created_at = t_stamp,
+    expand json scores into separate columns, and return a dataframe.
+    """
+    if t_stamp is None:
+        df = pd.read_csv(path / "temp_output.csv", header=0, index_col=False)
+    else:
+        pred_df = read_db(
+            table_name="model_output",
+            query=f"SELECT * FROM model_output where created_at = '{t_stamp}' order by created_at",
+        )
+        df = flatten_json_scores(pred_df)
+        df.to_csv(path / "temp_output.csv", index=False)
+
+    true_df = read_db(
+        table_name="age_gender_labeled",
+        query="SELECT age AS true_label, img_name FROM age_gender_labeled",
+    )
+
+    df_merged = pd.merge(df, true_df, left_on="imageId", right_on="img_name").drop(
+        "img_name", axis=1
+    )
+    df_merged.to_csv(path / "temp_output_labeled.csv", index=False)
+
+    return df_merged
+
+
+if __name__ == "__main__":
+    main()
diff --git a/src/age_gender_classifier/age_gender_classifier/init_data/README.md b/src/age_gender_classifier/age_gender_classifier/init_data/README.md
new file mode 100644
index 00000000..b3728659
--- /dev/null
+++ b/src/age_gender_classifier/age_gender_classifier/init_data/README.md
@@ -0,0 +1,10 @@
+### How to get the evaluation data set
+
+The evaluation dataset is 199MB in size, too large to check into GitHub.  
+Download it from [here](https://www.kaggle.com/datasets/nipunarora8/age-gender-and-ethnicity-face-data-csv/data), and save the file in this directory as `age_gender.csv`.  
+
+### Load into SQLite
+
+Run the `load_db.py` file. This reads the downloaded CSV file, connects to the database, creates a new table if it does not exist, truncates the table, and writes the data.
+
+    poetry run python src/init_data/load_db.py
diff --git a/src/age_gender_classifier/age_gender_classifier/init_data/load_db.py b/src/age_gender_classifier/age_gender_classifier/init_data/load_db.py
new file mode 100644
index 00000000..275429bf
--- /dev/null
+++ b/src/age_gender_classifier/age_gender_classifier/init_data/load_db.py
@@ -0,0 +1,53 @@
+import os
+import numpy as np
+from pathlib import Path
+import logging
+import pandas as pd
+from age_gender_classifier.utils.sqlAlchemy_manager import DBManager
+from age_gender_classifier.utils.preprocess import is_valid_image, is_damaged
+
+logging.basicConfig(level=logging.INFO)
+
+
+def read_csv_and_format(fileIn: str = "age_gender.csv") -> pd.DataFrame:
+    "Read labeled data set and return DataFrame"
+    path = Path(__file__).parent
+    file = str(path / fileIn)
+    if not os.path.exists(file):
+        raise FileNotFoundError(f" File not found: {file}")
+    return pd.read_csv(file)
+
+
+def transform_raw_data(df: pd.DataFrame) -> pd.DataFrame:
+    # cleaning pixels column for image preprocessing
+    df = df[df["pixels"].apply(is_valid_image)].copy()
+    df["pixels_array"] = df["pixels"].apply(
+        lambda x: np.array([int(p) for p in x.split()], dtype=np.uint8).reshape(48, 48)
+    )
+    df = df[~df["pixels_array"].apply(is_damaged)]
+    df = df.drop(columns=["pixels_array"])
+    logging.info(" Completed raw pixel transformations")
+    return df
+
+
+def clean_load_to_db(df: pd.DataFrame, table_name: str = "age_gender_labeled") -> None:
+    """Connect to DB, create/truncate table, write df to table."""
+    db_uri = os.getenv("DB_CONN_STR")
+    db = DBManager(db_uri, table_name)
+
+    db.create_table_if_not_exists()
+    db.truncate_table()
+
+    df.to_sql(table_name, con=db.engine, if_exists="append", index=False)
+    ct = pd.read_sql(f"SELECT COUNT(*) FROM {table_name}", con=db.engine).iloc[0, 0]
+    logging.info(f" Clean loaded {ct} records into DB table {table_name}")
+
+
+def main() -> None:
+    df = read_csv_and_format()
+    df = transform_raw_data(df)
+    clean_load_to_db(df)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/src/age_gender_classifier/age_gender_classifier/onnx_models/age_classify_v001/convert_to_onnx.py b/src/age_gender_classifier/age_gender_classifier/onnx_models/age_classify_v001/convert_to_onnx.py
new file mode 100644
index 00000000..0ce0d984
--- /dev/null
+++ b/src/age_gender_classifier/age_gender_classifier/onnx_models/age_classify_v001/convert_to_onnx.py
@@ -0,0 +1,36 @@
+import torch
+from transformers import ViTImageProcessor, ViTForImageClassification
+
+
+# Load model and image processor for selected model
+
+
+model_name = "cledoux42/Age_Classify_v001"
+output_name = "v001_model.onnx"
+
+model = ViTForImageClassification.from_pretrained(model_name)
+feature_extractor = ViTImageProcessor.from_pretrained(model_name)
+
+# Set model to evaluation mode
+model.eval()
+
+# Get correct image size
+image_size = feature_extractor.size["height"]  # Default: 224
+dummy_image = torch.randn(1, 3, image_size, image_size)
+
+# Convert to ONNX with updated opset version (14+)
+onnx_path = output_name
+torch.onnx.export(
+    model,
+    (dummy_image,),
+    onnx_path,
+    input_names=["pixel_values"],
+    output_names=["logits"],
+    dynamic_axes={"pixel_values": {0: "batch_size"}, "logits": {0: "batch_size"}},
+    opset_version=14,
+)
+
+print(f"Model successfully converted to ONNX and saved at: {onnx_path}")
+
+
+# The onnx file is too large to upload to github, you must generate it yourself by running this script
diff --git a/src/age_gender_classifier/age_gender_classifier/onnx_models/age_classify_v001/model.py b/src/age_gender_classifier/age_gender_classifier/onnx_models/age_classify_v001/model.py
new file mode 100644
index 00000000..c772df07
--- /dev/null
+++ b/src/age_gender_classifier/age_gender_classifier/onnx_models/age_classify_v001/model.py
@@ -0,0 +1,81 @@
+import onnxruntime as ort
+import os
+import numpy as np
+import cv2 as cv
+from PIL import Image
+from age_gender_classifier.utils.preprocess import enhance_image
+
+
+# Preprocessing function
+def preprocess_image(image_input, target_size=224):
+    """
+    Preprocess either:
+      - image_input as an image path(string) to a .jpg/.png file
+      - image_input as a 48*48 pixel string
+      - image_input as a 48*48 numpy array
+    """
+    if isinstance(image_input, str):
+        if os.path.exists(image_input):
+            image = Image.open(image_input).convert("L")  # convert to grayscale
+            image = image.resize((48, 48))
+            image_np = np.array(image, dtype=np.uint8)
+        elif image_input.count(" ") >= 2303:
+            pixel_vals = np.array(
+                [int(p) for p in image_input.strip().split()], dtype=np.uint8
+            )
+            image_np = pixel_vals.reshape(48, 48)
+        else:
+            raise ValueError(
+                "Invalid string input: not a valid image file or input string"
+            )
+    elif isinstance(image_input, np.ndarray):
+        image_np = image_input
+    else:
+        raise ValueError("Unsupported input type.")
+    # enhance the image
+    enhanced = enhance_image(image_np)
+    # resized to model input size
+    resized = cv.resize(enhanced, (target_size, target_size))
+    # convert to float, normalize, and reshape
+    final = resized.astype(np.float32) / 255.0
+    final = np.stack([final] * 3, axis=0)
+    final = np.expand_dims(final, axis=0)
+    return final
+
+
+# Postprocessing function
+def postprocess_output(logits):
+    """
+    Apply softmax to the raw logits and return predicted classes for the batch.
+    """
+    exp_logits = np.exp(logits - np.max(logits, axis=1, keepdims=True))
+    probabilities = exp_logits / np.sum(exp_logits, axis=1, keepdims=True)
+    predicted_classes = np.argmax(probabilities, axis=1)
+    return predicted_classes, probabilities
+
+
+# Mapping from classification ids to age ranges (updated based on model's id2label)
+id2label = {
+    0: "0-2",
+    1: "10-19",
+    2: "20-29",
+    3: "3-9",
+    4: "30-39",
+    5: "40-49",
+    6: "50-59",
+    7: "60-69",
+    8: "70-79",
+}
+
+
+# Run inference
+def predict(image_input, onnx_path):
+    # Load ONNX model
+    session = ort.InferenceSession(onnx_path, providers=["CPUExecutionProvider"])
+    input_tensor = preprocess_image(image_input)
+    outputs = session.run(["logits"], {"pixel_values": input_tensor})[0]
+    predicted_classes, probabilities = postprocess_output(outputs)
+    predicted_class = predicted_classes[0]
+    predicted_label = id2label.get(predicted_class, "Unknown")
+    confidence = probabilities[0][predicted_class]
+    return predicted_label, confidence
diff --git a/src/age_gender_classifier/age_gender_classifier/onnx_models/fairface/convert_to_onnx.py b/src/age_gender_classifier/age_gender_classifier/onnx_models/fairface/convert_to_onnx.py
new file mode 100644
index 00000000..60be01ce
--- /dev/null
+++ b/src/age_gender_classifier/age_gender_classifier/onnx_models/fairface/convert_to_onnx.py
@@ -0,0 +1,34 @@
+from transformers import ViTForImageClassification
+from transformers import ViTImageProcessor
+import torch
+from pathlib import Path
+
+# Load model and processor from HuggingFace
+model_name = "dima806/fairface_age_image_detection"
+model = ViTForImageClassification.from_pretrained(model_name)
+processor = ViTImageProcessor.from_pretrained(model_name)
+
+# Set model to evaluation mode
+model.eval()
+
+# Dummy input for ONNX export
+dummy_input = torch.randn(1, 3, 224, 224)  # ViT expects (B, C, H, W)
+
+# Output path
+output_dir = Path("onnx_exports")
+output_dir.mkdir(exist_ok=True)
+onnx_model_path = output_dir / "fairface_age.onnx"
+
+
+# Export to ONNX
+torch.onnx.export(
+    model,
+    dummy_input,
+    onnx_model_path.as_posix(),
+    input_names=["pixel_values"],
+    output_names=["logits"],
+    dynamic_axes={"pixel_values": {0: "batch_size"}, "logits": {0: "batch_size"}},
+    opset_version=14,
+)
+
+print(f"Model exported to: {onnx_model_path.resolve()}")
diff --git a/src/age_gender_classifier/age_gender_classifier/onnx_models/fairface/model.py b/src/age_gender_classifier/age_gender_classifier/onnx_models/fairface/model.py
new file mode 100644
index 00000000..a77d1301
--- /dev/null
+++ b/src/age_gender_classifier/age_gender_classifier/onnx_models/fairface/model.py
@@ -0,0 +1,86 @@
+import onnxruntime as ort
+import os
+import numpy as np
+import cv2 as cv
+from PIL import Image
+from age_gender_classifier.utils.preprocess import enhance_image
+
+
+# Preprocessing function
+def preprocess_image(image_input, target_size=224):
+    """
+    Preprocess either:
+      - image_input as an image path(string) to a .jpg/.png file
+      - image_input as a 48*48 pixel string
+      - image_input as a 48*48 numpy array
+    """
+    if isinstance(image_input, str):
+        if os.path.exists(image_input):
+            image = Image.open(image_input).convert("L")  # convert to grayscale
+            image = image.resize((48, 48))
+            image_np = np.array(image, dtype=np.uint8)
+        elif image_input.count(" ") >= 2303:
+            pixel_vals = np.array(
+                [int(p) for p in image_input.strip().split()], dtype=np.uint8
+            )
+            image_np = pixel_vals.reshape(48, 48)
+        else:
+            raise ValueError(
+                "Invalid string input: not a valid image file or input string"
+            )
+    elif isinstance(image_input, np.ndarray):
+        image_np = image_input
+    else:
+        raise ValueError("Unsupported input type.")
+    # enhance the image
+    enhanced = enhance_image(image_np)
+    # resized to model input size
+    resized = cv.resize(enhanced, (target_size, target_size))
+    # convert to float, normalize, and reshape
+    final = resized.astype(np.float32) / 255.0
+    final = np.stack([final] * 3, axis=0)
+    final = np.expand_dims(final, axis=0)
+    return final
+
+
+# Postprocessing function
+def postprocess_output(logits):
+    """
+    Apply softmax to the raw logits and return predicted classes for the batch.
+    """
+    exp_logits = np.exp(logits - np.max(logits, axis=1, keepdims=True))
+    probabilities = exp_logits / np.sum(exp_logits, axis=1, keepdims=True)
+    predicted_classes = np.argmax(probabilities, axis=1)
+    return predicted_classes, probabilities
+
+
+# Mapping from classification ids to age ranges (based on fairface config)
+id2label = {
+    0: "0-2",
+    1: "3-9",
+    2: "10-19",
+    3: "20-29",
+    4: "30-39",
+    5: "40-49",
+    6: "50-59",
+    7: "60-69",
+    8: "more than 70",
+}
+
+
+# Run inference
+def predict(image_input, onnx_path):
+    """
+    Load ONNX model and predict age group from an image.
+
+    Returns:
+        (label: str, confidence: float)
+    """
+    session = ort.InferenceSession(onnx_path, providers=["CPUExecutionProvider"])
+    input_tensor = preprocess_image(image_input)
+    outputs = session.run(["logits"], {"pixel_values": input_tensor})[0]
+    predicted_classes, probabilities = postprocess_output(outputs)
+    predicted_class = predicted_classes[0]
+    predicted_label = id2label.get(predicted_class, "Unknown")
+    confidence = probabilities[0][predicted_class]
+    return predicted_label, confidence
diff --git a/src/age_gender_classifier/age_gender_classifier/onnx_models/survey_models.py b/src/age_gender_classifier/age_gender_classifier/onnx_models/survey_models.py
new file mode 100644
index 00000000..2adcfa27
--- /dev/null
+++ b/src/age_gender_classifier/age_gender_classifier/onnx_models/survey_models.py
@@ -0,0 +1,352 @@
+# survey_models.py
+
+import sys
+import os
+import json
+from pathlib import Path
+from datetime import datetime
+from typing import List, Optional
+import logging
+import pandas as pd
+from age_gender_classifier.onnx_models.age_classify_v001.model import (
+    predict as predict_age_classifier,
+)
+from age_gender_classifier.onnx_models.vit_age_classifier.model import (
+    predict as predict_vit_age_classifier,
+)
+from age_gender_classifier.onnx_models.fairface.model import (
+    predict as predict_fairface_classifier,
+)
+import joblib
+from age_gender_classifier.utils.common import write_db, read_db
+
+logging.basicConfig(level=logging.INFO)
+
+# integer codes XGBoost model was trained on
+_LABEL_MAP = {
+    "0-2": 0,
+    "3-9": 1,
+    "10-19": 2,
+    "20-29": 3,
+    "30-39": 4,
+    "40-49": 5,
+    "50-59": 6,
+    "60-69": 7,
+    "more than 70": 8,
+    "70-79": 8,
+}
+_INT_TO_LABEL = {v: k for k, v in _LABEL_MAP.items()}
+
+
+def _fix(label: str) -> str:
+    return "more than 70" if label == "70-79" else label
+
+
+# _XGB = joblib.load("src/age_gender_classifier/age_gender_classifier/onnx_models/xgboost_model.pkl")
+
+
+def label_to_binary(label, threshold):
+    """
+    Convert the age range label to a binary value based on a threshold.
+
+    Args:
+        label (str): Age range label (e.g., "10-19", "20-29", "more than 70").
+        threshold (int): The age threshold (e.g., 20 or 30).
+
+    Returns:
+        bool: True if the lower bound of the age range is >= threshold, else False.
+    """
+    if label.lower().startswith("more"):
+        # Treat "more than 70" as age 70 for threshold comparisons.
+        age_lower = 70
+    else:
+        try:
+            parts = label.split("-")
+            age_lower = int(parts[0])
+        except Exception:
+            age_lower = 0
+    return age_lower >= threshold
+
+
+class SurveyModels:
+    def __init__(self):
+        self.base_path = Path(__file__).resolve().parent
+        self.model1_path = str(self.base_path / "age_classify_v001/v001_model.onnx")
+        self.model2_path = str(self.base_path / "vit_age_classifier/vit_model.onnx")
+        self.model3_path = str(self.base_path / "fairface/fairface_age.onnx")
+        self.now = datetime.now().isoformat()
+        self._XGB = joblib.load(
+            "src/age_gender_classifier/age_gender_classifier/onnx_models/xgboost_model.pkl"
+        )
+
+        # Check if model files exist
+        if not os.path.exists(self.model1_path):
+            raise FileNotFoundError(f"Model file not found: {self.model1_path}")
+        if not os.path.exists(self.model2_path):
+            raise FileNotFoundError(f"Model file not found: {self.model2_path}")
+        if not os.path.exists(self.model3_path):
+            raise FileNotFoundError(f"Model file not found: {self.model3_path}")
+
+    def predict(self, image_path, imgId):
+        """
+        Run (both, third to be added) age classification models on the input image.
+
+        Args:
+            image_path (str): Path to the input image.
+
+        Returns:
+            dict: A dictionary containing the predicted label and confidence for each model.
+                  e.g., {"age_classify_v001": (label, confidence), "vit_age_classifier": (label, confidence)}
+        """
+        # Run the first model
+        model1_label, model1_confidence = predict_age_classifier(
+            image_path, self.model1_path
+        )
+
+        # Run the second model
+        model2_label, model2_confidence = predict_vit_age_classifier(
+            image_path, self.model2_path
+        )
+
+        # Run the third model
+        model3_label, model3_confidence = predict_fairface_classifier(
+            image_path, self.model3_path
+        )
+
+        return {
+            "model_name": [
+                "age_classify_v001",
+                "vit_age_classifier",
+                "fairface_classifier",
+            ],
+            "scores": [
+                {
+                    "label": model1_label,
+                    "confidence": str(model1_confidence),
+                    "imageId": imgId,
+                },
+                {
+                    "label": model2_label,
+                    "confidence": str(model2_confidence),
+                    "imageId": imgId,
+                },
+                {
+                    "label": model3_label,
+                    "confidence": str(model3_confidence),
+                    "imageId": imgId,
+                },
+            ],
+            "created_at": [self.now] * 3,
+        }
+
+    def predict_over_under(self, age, image_path, imgId):
+        """
+        Poll all 3 models and convert their age predictions into a binary over/under classification.
+        Use majority vote instead of random selection.
+
+        Returns:
+            dict with keys:
+                - model_name: ["predict_over_under"]
+                - scores: [{
+                    "majority_vote": majority_vote (bool),
+                    "binanry_results": {
+                        model_name: (bool, confidence), ...
+                    }
+                }]
+                - created_at: [timestamp]
+        """
+        allowed_ages = {3, 10, 20, 30, 40, 50, 60, 70}
+        if age not in allowed_ages:
+            raise ValueError(
+                f"Invalid age threshold: {age}. Allowed values are {sorted(allowed_ages)}."
+            )
+
+        pred = self.predict(image_path, imgId)
+        binary_results = {}
+
+        features = {
+            "age_classify_v001_label": _LABEL_MAP[_fix(pred["scores"][0]["label"])],
+            "age_classify_v001_confidence": float(pred["scores"][0]["confidence"]),
+            "vit_age_classifier_label": _LABEL_MAP[_fix(pred["scores"][1]["label"])],
+            "vit_age_classifier_confidence": float(pred["scores"][1]["confidence"]),
+            "fairface_classifier_label": _LABEL_MAP[_fix(pred["scores"][2]["label"])],
+            "fairface_classifier_confidence": float(pred["scores"][2]["confidence"]),
+        }
+        ens_idx = self._XGB.predict(pd.DataFrame([features]))[0]
+        ens_label = _INT_TO_LABEL[ens_idx]
+        ensemble_over = label_to_binary(ens_label, age)
+
+        # binary_results["xgb_ensemble"] = (ensemble_over, "1.0")
+
+        majority_vote = ensemble_over
+
+        return {
+            "model_name": ["predict_over_under"],
+            "scores": [
+                {
+                    "majority_vote": majority_vote,
+                    "binary_results": binary_results,
+                    "imageId": imgId,
+                }
+            ],
+            "created_at": [self.now],
+        }
+
+    def _predict_eval_results(self, age: int, image_path: str, imgId) -> dict:
+        """
+        Run the age prediction models once and return both the raw predictions (individual labels)
+        and the binary over/under vote result (using majority vote) for the specified age threshold.
+
+        Args:
+            age (int): The age threshold to convert the predictions to binary.
+            image_path (str): Path to the input image.
+            imgId: An identifier for the image.
+
+        Returns:
+            dict: A dictionary with keys:
+                - 'raw_predictions': Output of the original predict() method.
+                - 'binary_vote': A dictionary with:
+                      * 'majority_vote': The overall binary decision (True if at least 2 models are "over").
+                      * 'binary_results': A dict mapping each model name to a tuple (is_over, confidence).
+                      * 'imageId': The provided image identifier.
+                - 'created_at': The timestamp (from self.now).
+        """
+        # Run predictions only once
+        raw_predictions = self.predict(image_path, imgId)
+
+        # Initialize binary conversion and vote counting
+        binary_results = {}
+        vote_counts = {True: 0, False: 0}
+
+        for i, model_name in enumerate(raw_predictions["model_name"]):
+            # Get the raw label and confidence from predictions
+            label = raw_predictions["scores"][i]["label"]
+            confidence = raw_predictions["scores"][i]["confidence"]
+            # Convert to binary using the helper function
+            is_over = label_to_binary(label, age)
+            binary_results[model_name] = (is_over, confidence)
+            vote_counts[is_over] += 1
+
+        # Majority vote for whether the picture is over the threshold
+        majority_vote = True if vote_counts[True] >= 2 else False
+
+        # Combine both results into one output dictionary
+        return {
+            "raw_predictions": raw_predictions,
+            "binary_vote": {
+                "majority_vote": majority_vote,
+                "binary_results": binary_results,
+                "imageId": imgId,
+            },
+            "created_at": raw_predictions["created_at"],
+        }
+
+    def main_predict_eval(
+        self, images: List, age_threshold: int = 40, ids: Optional[List] = None
+    ) -> pd.DataFrame:
+        """
+        Loop over a list of images and, for each, run the combined evaluation (raw predictions plus binary vote)
+        in one pass. Returns a DataFrame with one row per image containing columns:
+          - imageId
+          - raw_predictions (as a JSON string)
+          - binary_vote (as a JSON string)
+          - created_at timestamp
+
+        This avoids running separate/redundant predict and predict_over_under calls
+        """
+        ids = list(range(len(images))) if ids is None else ids
+        results = []
+        for imgId, img in zip(ids, images):
+            combined_result = self._predict_eval_results(age_threshold, img, imgId)
+            row = {
+                "imageId": imgId,
+                "raw_predictions": json.dumps(combined_result["raw_predictions"]),
+                "binary_vote": json.dumps(combined_result["binary_vote"]),
+                "created_at": (
+                    combined_result["created_at"][0]
+                    if isinstance(combined_result["created_at"], list)
+                    else combined_result["created_at"]
+                ),
+            }
+            results.append(row)
+
+        df = pd.DataFrame(results)
+        write_db(df, "model_output")
+        logging.info(
+            f"Successfully completed combined evaluation for {len(images)} images."
+        )
+        return df
+
+    def main_predict(
+        self, images: List, age_threshold: int = 40, ids: Optional[List] = None
+    ) -> pd.DataFrame:
+        """Loop list of images, for each, run prediction and write results to db."""
+        ids = list(range(len(images))) if ids is None else ids
+        ct = 0
+        df = None
+        for id, img in zip(ids, images):
+            df1 = pd.DataFrame(self.predict(img, id))
+            df2 = pd.DataFrame(self.predict_over_under(age_threshold, img, id))
+            dfs = [df1, df2]
+            if ct > 0:
+                dfs = [df] + dfs
+            df = pd.concat(dfs, axis=0).reset_index(drop=True)
+            ct += 1
+
+        df["scores"] = df["scores"].apply(lambda x: json.dumps(x))
+        write_db(df, "model_output")
+        logging.info(
+            f" Successfully completed {len(df)} predictions for {len(images)} images."
+        )
+        return df
+
+
+# Command Line Execution
+if __name__ == "__main__":
+    if len(sys.argv) < 3:
+        print("Usage: python survey_models.py <image_path> <age_threshold>")
+        print("Allowed age thresholds: 3, 10, 20, 30, 40, 50, 60, 70")
+        sys.exit(1)
+
+    image_path = sys.argv[1]
+
+    try:
+        age_threshold = int(sys.argv[2])
+    except ValueError:
+        print("Error: Age threshold must be an integer.")
+        sys.exit(1)
+
+    allowed_ages = {3, 10, 20, 30, 40, 50, 60, 70}
+    if age_threshold not in allowed_ages:
+        print(
+            f"Error: Invalid age threshold {age_threshold}. Allowed values: {sorted(allowed_ages)}"
+        )
+        sys.exit(1)
+
+    try:
+        survey = SurveyModels()
+    except FileNotFoundError as e:
+        print(e)
+        sys.exit(1)
+
+    # Full Model Predictions
+    # predictions = survey.predict(image_path)
+
+    # print("\nModel Predictions:")
+    # print(predictions)
+    # for x in predictions.items():
+    #     print(x)
+    # for model_name, (label, confidence) in predictions.items():
+    #     print(f"{model_name}: {label} (confidence: {confidence:.2f})")
+
+    # Over/Under Prediction for the specified age
+    # binary_result, conf_result = survey.predict_over_under(age_threshold, image_path)
+    # chose_model = survey.predict_over_under(age_threshold, image_path)
+    # print(chose_model)
+    # print(f"\nOver/Under {age_threshold}: {'Over' if binary_result else 'Under'} {age_threshold} (confidence: {conf_result:.2f})")
+    df = survey.main_predict([image_path], age_threshold=40)
+    df2 = read_db("model_output", "SELECT * FROM model_output")
+
+    # logging.info("Current run", df)
+    # logging.info("Read DB", df2.head())
+    # python src/onnx_models/survey_models.py src/onnx_models/test_images/lebron.jpg 40
diff --git a/src/age_gender_classifier/age_gender_classifier/onnx_models/test_images/baby.jpg b/src/age_gender_classifier/age_gender_classifier/onnx_models/test_images/baby.jpg
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diff --git a/src/age_gender_classifier/age_gender_classifier/onnx_models/test_images/morganFreeman.jpg b/src/age_gender_classifier/age_gender_classifier/onnx_models/test_images/morganFreeman.jpg
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diff --git a/src/age_gender_classifier/age_gender_classifier/onnx_models/vit_age_classifier/convert_to_onnx.py b/src/age_gender_classifier/age_gender_classifier/onnx_models/vit_age_classifier/convert_to_onnx.py
new file mode 100644
index 00000000..1d8cabeb
--- /dev/null
+++ b/src/age_gender_classifier/age_gender_classifier/onnx_models/vit_age_classifier/convert_to_onnx.py
@@ -0,0 +1,39 @@
+import torch
+from transformers import ViTImageProcessor, ViTForImageClassification
+
+
+# Load model and image processor for selected model
+
+# model_name = "nateraw/vit-age-classifier"
+# output_name = "vit_age_classifier.onnx"
+
+
+model_name = "cledoux42/Age_Classify_v001"
+output_name = "vit_model.onnx"
+
+model = ViTForImageClassification.from_pretrained(model_name)
+feature_extractor = ViTImageProcessor.from_pretrained(model_name)
+
+# Set model to evaluation mode
+model.eval()
+
+# Get correct image size
+image_size = feature_extractor.size["height"]  # Default: 224
+dummy_image = torch.randn(1, 3, image_size, image_size)
+
+# Convert to ONNX with updated opset version (14+)
+onnx_path = output_name
+torch.onnx.export(
+    model,
+    (dummy_image,),
+    onnx_path,
+    input_names=["pixel_values"],
+    output_names=["logits"],
+    dynamic_axes={"pixel_values": {0: "batch_size"}, "logits": {0: "batch_size"}},
+    opset_version=14,
+)
+
+print(f"Model successfully converted to ONNX and saved at: {onnx_path}")
+
+
+# The onnx file is too large to upload to github, you must generate it yourself by running this script
diff --git a/src/age_gender_classifier/age_gender_classifier/onnx_models/vit_age_classifier/model.py b/src/age_gender_classifier/age_gender_classifier/onnx_models/vit_age_classifier/model.py
new file mode 100644
index 00000000..ea97527d
--- /dev/null
+++ b/src/age_gender_classifier/age_gender_classifier/onnx_models/vit_age_classifier/model.py
@@ -0,0 +1,81 @@
+import onnxruntime as ort
+import os
+import numpy as np
+import cv2 as cv
+from PIL import Image
+from age_gender_classifier.utils.preprocess import enhance_image
+
+
+# Preprocessing function
+def preprocess_image(image_input, target_size=224):
+    """
+    Preprocess either:
+      - image_input as an image path(string) to a .jpg/.png file
+      - image_input as a 48*48 pixel string
+      - image_input as a 48*48 numpy array
+    """
+    if isinstance(image_input, str):
+        if os.path.exists(image_input):
+            image = Image.open(image_input).convert("L")  # convert to grayscale
+            image = image.resize((48, 48))
+            image_np = np.array(image, dtype=np.uint8)
+        elif image_input.count(" ") >= 2303:
+            pixel_vals = np.array(
+                [int(p) for p in image_input.strip().split()], dtype=np.uint8
+            )
+            image_np = pixel_vals.reshape(48, 48)
+        else:
+            raise ValueError(
+                "Invalid string input: not a valid image file or input string"
+            )
+    elif isinstance(image_input, np.ndarray):
+        image_np = image_input
+    else:
+        raise ValueError("Unsupported input type.")
+    # enhance the image
+    enhanced = enhance_image(image_np)
+    # resized to model input size
+    resized = cv.resize(enhanced, (target_size, target_size))
+    # convert to float, normalize, and reshape
+    final = resized.astype(np.float32) / 255.0
+    final = np.stack([final] * 3, axis=0)
+    final = np.expand_dims(final, axis=0)
+    return final
+
+
+# Postprocessing function
+def postprocess_output(logits):
+    """
+    Apply softmax to the raw logits and return predicted classes for the batch.
+    """
+    exp_logits = np.exp(logits - np.max(logits, axis=1, keepdims=True))
+    probabilities = exp_logits / np.sum(exp_logits, axis=1, keepdims=True)
+    predicted_classes = np.argmax(probabilities, axis=1)
+    return predicted_classes, probabilities
+
+
+# Mapping from classification ids to age ranges
+id2label = {
+    0: "0-2",
+    1: "3-9",
+    2: "10-19",
+    3: "20-29",
+    4: "30-39",
+    5: "40-49",
+    6: "50-59",
+    7: "60-69",
+    8: "more than 70",
+}
+
+
+# Run inference
+def predict(image_input, onnx_path):
+    # Load ONNX model
+    session = ort.InferenceSession(onnx_path, providers=["CPUExecutionProvider"])
+    input_tensor = preprocess_image(image_input)
+    outputs = session.run(["logits"], {"pixel_values": input_tensor})[0]
+    predicted_classes, probabilities = postprocess_output(outputs)
+    predicted_class = predicted_classes[0]
+    predicted_label = id2label.get(predicted_class, "Unknown")
+    confidence = probabilities[0][predicted_class]
+    return predicted_label, confidence
diff --git a/src/age_gender_classifier/age_gender_classifier/onnx_models/xgboost_model.pkl b/src/age_gender_classifier/age_gender_classifier/onnx_models/xgboost_model.pkl
new file mode 100644
index 00000000..d195d0a3
Binary files /dev/null and b/src/age_gender_classifier/age_gender_classifier/onnx_models/xgboost_model.pkl differ
diff --git a/src/age_gender_classifier/age_gender_classifier/server/__init__.py b/src/age_gender_classifier/age_gender_classifier/server/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/src/age_gender_classifier/age_gender_classifier/server/server_onnx.py b/src/age_gender_classifier/age_gender_classifier/server/server_onnx.py
new file mode 100644
index 00000000..04302d4f
--- /dev/null
+++ b/src/age_gender_classifier/age_gender_classifier/server/server_onnx.py
@@ -0,0 +1,123 @@
+from typing import TypedDict
+from pathlib import Path
+import json
+import logging
+
+from age_gender_classifier.onnx_models.survey_models import SurveyModels
+
+from rb.lib.ml_service import MLService
+from rb.api.models import (
+    DirectoryInput,
+    TextResponse,
+    InputSchema,
+    InputType,
+    ResponseBody,
+    TaskSchema,
+    ParameterSchema,
+    IntParameterDescriptor,
+)
+import typer
+
+logging.basicConfig(level=logging.INFO)
+
+
+def create_transform_case_task_schema() -> TaskSchema:
+    """
+    Configure UI Elements in RescueBox Desktop
+    InputType.DIRECTORY: a single directory path
+    """
+    input_schema = InputSchema(
+        key="input_dataset",
+        label="Path to the directory containing images or videos",
+        input_type=InputType.DIRECTORY,
+    )
+    param_schema = ParameterSchema(
+        key="age_threshold",
+        label="Age Threshold for Over/Under Prediction",
+        value=IntParameterDescriptor(
+            default=20,
+        ),
+    )
+    return TaskSchema(inputs=[input_schema], parameters=[param_schema])
+
+
+class Inputs(TypedDict):
+    """Specify the input and output types for the task"""
+
+    input_dataset: DirectoryInput
+
+
+class Parameters(TypedDict):
+    age_threshold: int
+
+
+APP_NAME = "age-classifier"
+server = MLService(APP_NAME)
+
+server.add_app_metadata(
+    name=APP_NAME,
+    author="UMass Rescue",
+    version="0.1.0",
+    info="Model to classify ages from images.",
+    plugin_name=APP_NAME,
+)
+
+
+def age_classifier(inputs: Inputs, parameters: Parameters) -> ResponseBody:
+    """
+    In Flask-ML, an inference function takes two arguments: inputs and parameters.
+    The types of inputs and parameters must be Python TypedDict types.
+    """
+    input_path = Path(inputs["input_dataset"].path)
+    files = [str(fpath) for fpath in input_path.iterdir() if fpath.is_file()]
+    ids = [fpath.stem for fpath in input_path.iterdir() if fpath.is_file()]
+
+    models = SurveyModels()
+    df_results = models.main_predict(
+        files, age_threshold=parameters["age_threshold"], ids=ids
+    )
+
+    return ResponseBody(
+        TextResponse(
+            value=json.dumps(df_results.T.to_dict(), indent=4),
+            title="Output for Age Classifier Models",
+        )
+    )
+
+
+def cli_parser(path: str):
+    image_directory = path
+    try:
+        logging.info(f"Parsing CLI input path: {image_directory}")
+        image_directory = Path(image_directory)
+        if not image_directory.exists():
+            raise ValueError(f"Directory {image_directory} does not exist.")
+        if not image_directory.is_dir():
+            raise ValueError(f"Path {image_directory} is not a directory.")
+        inputs = Inputs(input_dataset=DirectoryInput(path=image_directory))
+        return inputs
+    except Exception as e:
+        logging.error(f"Error parsing CLI input: {e}")
+        return typer.Abort()
+
+
+def parameters_cli_parse(age_threshold: str) -> Parameters:
+    return Parameters(age_threshold=int(age_threshold))
+
+
+server.add_ml_service(
+    rule="/age_classifier",
+    ml_function=age_classifier,
+    inputs_cli_parser=typer.Argument(parser=cli_parser, help="Image directory path"),
+    parameters_cli_parser=typer.Argument(
+        parser=parameters_cli_parse, help="Age threshhold"
+    ),
+    short_title="Age and Gender Classifier",
+    order=0,
+    task_schema_func=create_transform_case_task_schema,
+)
+
+
+app = server.app
+if __name__ == "__main__":
+    app()
diff --git a/src/age_gender_classifier/age_gender_classifier/utils/common.py b/src/age_gender_classifier/age_gender_classifier/utils/common.py
new file mode 100644
index 00000000..1a2cf986
--- /dev/null
+++ b/src/age_gender_classifier/age_gender_classifier/utils/common.py
@@ -0,0 +1,31 @@
+import os
+from typing import Optional
+import logging
+import pandas as pd
+from age_gender_classifier.utils.sqlAlchemy_manager import DBManager
+
+logging.basicConfig(level=logging.INFO)
+db_uri = os.getenv("DB_CONN_STR", "sqlite:///age_classifier.db")
+
+
+def read_db(
+    table_name: str = "age_gender_labeled", query: Optional[str] = None
+) -> pd.DataFrame:
+    """Read records from custom query into pandas DataFrame."""
+    if query is None:
+        logging.warning("No query provided. Returning None.")
+        return None
+
+    db = DBManager(db_uri, table_name)
+    df = pd.read_sql(query, con=db.engine)
+    logging.info(f" Retrieved {len(df)} records from DB table {table_name}")
+    return df
+
+
+def write_db(df: pd.DataFrame, table_name: str = "model_output") -> None:
+    """Write pandas DataFrame of model output into DB."""
+
+    db = DBManager(db_uri, table_name)
+    db.create_table_if_not_exists()
+    df.to_sql(table_name, con=db.engine, if_exists="append", index=False)
+    logging.info(f" Wrote {len(df)} records to DB table {table_name}")
diff --git a/src/age_gender_classifier/age_gender_classifier/utils/preprocess.py b/src/age_gender_classifier/age_gender_classifier/utils/preprocess.py
new file mode 100644
index 00000000..fb2a4f22
--- /dev/null
+++ b/src/age_gender_classifier/age_gender_classifier/utils/preprocess.py
@@ -0,0 +1,37 @@
+import cv2 as cv
+import logging
+import numpy as np
+
+logging.basicConfig(level=logging.INFO)  # shows logs in the system
+
+
+def is_valid_image(pixel_string):
+    """checks if pixel string exactly contains 48*48 = 2304 values"""
+    pixel = pixel_string.strip().split()
+    return len(pixel) == 48 * 48
+
+
+def is_damaged(image, threshold_low=10, threshold_high=255, min_variance=10):
+    """
+    Detects damaged images based on pixel intensity distribution.
+    - threshold_low: If pixels are below this, the image may be too dark.
+    - threshold_high: If pixels are above this, the image may be too bright.
+    - min_variance: If the variance is too low, image may lack details
+    """
+    if np.mean(image) < threshold_low or np.mean(image) > threshold_high:
+        return True  # likely corrupted
+    # Check if the image lacks details (variance too low)
+    if np.mean(image) < min_variance:
+        return True  # likely corrupted
+    return False
+
+
+def enhance_image(image):
+    """kernel, sharpening, denoising, and bilateral filtering techniques for image preprocessing"""
+    kernel = np.array([[0, -0.5, 0], [-0.5, 3, -0.5], [0, -0.5, 0]])  # Sharpening
+    sharpened = cv.filter2D(image, -1, kernel)
+    smoothed = cv.bilateralFilter(sharpened, d=3, sigmaColor=55, sigmaSpace=55)
+    denoised = cv.fastNlMeansDenoising(
+        smoothed, h=5, templateWindowSize=7, searchWindowSize=21
+    )
+    return denoised
diff --git a/src/age_gender_classifier/age_gender_classifier/utils/sqlAlchemy_manager.py b/src/age_gender_classifier/age_gender_classifier/utils/sqlAlchemy_manager.py
new file mode 100644
index 00000000..c79bba75
--- /dev/null
+++ b/src/age_gender_classifier/age_gender_classifier/utils/sqlAlchemy_manager.py
@@ -0,0 +1,138 @@
+from sqlalchemy import (
+    create_engine,
+    Column,
+    Integer,
+    String,
+    Boolean,
+    DateTime,
+    JSON,
+    func,
+)
+from sqlalchemy.ext.declarative import declarative_base
+from sqlalchemy import text
+from sqlalchemy.orm import sessionmaker
+import logging
+
+
+Base = declarative_base()
+
+
+class LabeledAges(Base):
+    __tablename__ = "age_gender_labeled"
+    id = Column(Integer, primary_key=True, autoincrement=True)
+    age = Column(Integer, nullable=True)
+    ethnicity = Column(Integer, nullable=True)
+    gender = Column(Boolean, nullable=True)
+    img_name = Column(String, nullable=True)
+    pixels = Column(String, nullable=True)
+
+
+class ModelOutput(Base):
+    __tablename__ = "model_output"
+    id = Column(Integer, primary_key=True, autoincrement=True)
+    model_name = Column(String, nullable=False)
+    scores = Column(JSON, nullable=False)
+    created_at = Column(DateTime, nullable=False, default=func.now())
+
+
+defined_tables = {"age_gender_labeled": LabeledAges, "model_output": ModelOutput}
+
+
+class DBManager:
+    def __init__(self, db_url: str, table_name: str) -> None:
+        """Initialize the database connection.
+
+        Args:
+            db_url (str): The database connection URL.
+            table_name (str): The table name to operate on.
+        """
+        self.engine = create_engine(db_url)
+        self.Session = sessionmaker(bind=self.engine)
+
+        if table_name not in defined_tables:
+            raise ValueError(f"Table '{table_name}' is not recognized.")
+        self.table = defined_tables[table_name]
+
+    def create_table_if_not_exists(self) -> None:
+        """Create the users table if it does not exist."""
+        Base.metadata.create_all(self.engine)
+
+    def insert_record(self, **kwargs) -> None:
+        """Insert a new record into the specified table.
+
+        Args:
+            kwargs: Column values for the table.
+        """
+        session = self.Session()
+        try:
+            new_rec = self.table(**kwargs)
+            session.add(new_rec)
+            session.commit()
+        except Exception as e:
+            session.rollback()
+            logging.error(f"Error inserting record: {e}")
+        finally:
+            session.close()
+
+    def update_record(self, record_id: int, **kwargs) -> None:
+        """Update an existing record in the specified table.
+
+        Args:
+            record_id (int): The ID of the record to update.
+            kwargs: Fields to update with new values.
+        """
+        session = self.Session()
+        try:
+            rec = session.query(self.table).filter_by(id=record_id).first()
+            if rec:
+                for key, value in kwargs.items():
+                    if hasattr(rec, key):
+                        setattr(rec, key, value)
+                session.commit()
+        except Exception as e:
+            session.rollback()
+            logging.error(f"Error updating record: {e}")
+        finally:
+            session.close()
+
+    def delete_user(self, record_id: int) -> None:
+        """Delete a record from the specified table by ID.
+
+        Args:
+            record_id (int): The ID of the record to delete.
+        """
+        session = self.Session()
+        try:
+            rec = session.query(self.table).filter_by(id=record_id).first()
+            if rec:
+                session.delete(rec)
+                session.commit()
+        except Exception as e:
+            session.rollback()
+            logging.error(f"Error deleting record: {e}")
+        finally:
+            session.close()
+
+    def truncate_table(self) -> None:
+        """Truncate the specified table."""
+        session = self.Session()
+        try:
+            session.execute(text(f"DELETE FROM {self.table.__tablename__}"))
+            session.commit()
+        except Exception as e:
+            session.rollback()
+            logging.error(f"Error truncating table {self.table.__tablename__}: {e}")
+        finally:
+            session.close()
+
+    def drop_table(self, table: str) -> None:
+        """Drop specified table."""
+        session = self.Session()
+        try:
+            session.execute(text(f"DROP TABLE IF EXISTS {table}"))
+            session.commit()
+        except Exception as e:
+            session.rollback()
+            logging.error(f"Error dropping table {table}: {e}")
+        finally:
+            session.close()
diff --git a/src/age_gender_classifier/pyproject.toml b/src/age_gender_classifier/pyproject.toml
new file mode 100644
index 00000000..7a261d25
--- /dev/null
+++ b/src/age_gender_classifier/pyproject.toml
@@ -0,0 +1,32 @@
+[project]
+name = "age-classifier"
+version = "0.1.0"
+description = "UMass Rescue Box: Age and Gender Classification"
+authors = [
+    {name = "James Bardowski",email = "jbardowski@umass.edu"},
+    {name = "Juhi Manish Jain",email = "juhimanishja@umass.edu"},
+    {name = "Jacob Sweet",email = "jdsweet@umass.edu"}
+]
+readme = "README.md"
+
+dependencies = [
+    "torch",
+    "numpy",
+    "pandas",
+    "requests",
+    "opencv-python",
+    "sqlalchemy",
+    "seaborn",
+    "onnxruntime",
+    "joblib",
+    "xgboost"
+]
+
+[tool.poetry]
+packages = [
+    { include = "age_gender_classifier"}
+]
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"
diff --git a/src/age_gender_classifier/tests/test_age_classifier.py b/src/age_gender_classifier/tests/test_age_classifier.py
new file mode 100644
index 00000000..ff5738b7
--- /dev/null
+++ b/src/age_gender_classifier/tests/test_age_classifier.py
@@ -0,0 +1,110 @@
+from age_gender_classifier.server.server_onnx import (
+    app,
+    APP_NAME,
+    create_transform_case_task_schema,
+)
+from rb.lib.common_tests import RBAppTest
+from rb.api.models import AppMetadata
+from pathlib import Path
+from unittest.mock import patch
+import json
+import pandas as pd
+
+
+class TestAgeClassifier(RBAppTest):
+    def setup_method(self):
+        self.set_app(app, APP_NAME)
+
+    def get_metadata(self):
+        return AppMetadata(
+            name=APP_NAME,
+            author="UMass Rescue",
+            version="0.1.0",
+            info="Model to classify ages from images.",
+            plugin_name=APP_NAME,
+        )
+
+    def get_all_ml_services(self):
+        return [
+            (
+                0,
+                "age_classifier",
+                "Age and Gender Classifier",
+                create_transform_case_task_schema(),
+            ),
+        ]
+
+    @patch(
+        "age_gender_classifier.onnx_models.survey_models.SurveyModels.__init__",
+        return_value=None,
+    )
+    @patch("age_gender_classifier.onnx_models.survey_models.SurveyModels.main_predict")
+    def test_api_age_classifier(self, age_class_mock, init_mock):
+        age_class_mock.return_value = pd.DataFrame()
+
+        age_class_api = f"/{APP_NAME}/age_classifier"
+        full_path = (
+            Path.cwd()
+            / "src"
+            / "age_gender_classifier"
+            / "age_gender_classifier"
+            / "onnx_models"
+            / "test_images"
+        )
+        input_json = {
+            "inputs": {
+                "input_dataset": {"path": str(full_path)},
+            },
+            "parameters": {"age_threshold": 20},
+        }
+        response = self.client.post(age_class_api, json=input_json)
+        body = response.json()
+        results = json.loads(body["value"])
+
+        assert response.status_code == 200
+        assert results is not None
+        assert isinstance(results, dict)
+
+    @patch(
+        "age_gender_classifier.onnx_models.survey_models.SurveyModels.__init__",
+        return_value=None,
+    )
+    @patch("age_gender_classifier.onnx_models.survey_models.SurveyModels.main_predict")
+    def test_age_class_command(self, age_class_mock, init_mock):
+        age_class_mock.return_value = pd.DataFrame()
+        age_class_api = f"/{APP_NAME}/age_classifier"
+        full_path = (
+            Path.cwd()
+            / "src"
+            / "age_gender_classifier"
+            / "age_gender_classifier"
+            / "onnx_models"
+            / "test_images"
+        )
+        parameter_str = "20"
+        result = self.runner.invoke(
+            self.cli_app, [age_class_api, str(full_path), parameter_str]
+        )
+        assert result.exit_code == 0, f"Error: {result.output}"
+
+    @patch(
+        "age_gender_classifier.onnx_models.survey_models.SurveyModels.__init__",
+        return_value=None,
+    )
+    @patch("age_gender_classifier.onnx_models.survey_models.SurveyModels.main_predict")
+    def test_bad_path(self, age_class_mock, init_mock):
+        age_class_mock.return_value = pd.DataFrame()
+        age_class_api = f"/{APP_NAME}/age_classifier"
+        full_path = (
+            Path.cwd()
+            / "src"
+            / "age_gender_classifier"
+            / "age_gender_classifier"
+            / "onnx_models"
+            / "no_dir"
+        )
+        parameter_str = "20"
+        result = self.runner.invoke(
+            self.cli_app, [age_class_api, str(full_path), parameter_str]
+        )
+        assert result.exit_code != 0