Synced repo using 'sync_with_huggingface' Github Action

Pipfile

@@ -11,6 +11,7 @@ pillow = "*"
 scikit-learn = "*"
 matplotlib = "*"
 streamlit = "*"
+bokeh = "*"
 
 [dev-packages]
 

Pipfile.lock

@@ -1,7 +1,7 @@
 {
     "_meta": {
         "hash": {
-            "sha256": "cdcafe00a39a13512a9cfde10e3d2c76f821cae327534cdebcc1a688123e9a8f"
+            "sha256": "1798ba32767e70e63866b22f70defa1d9564846e0e4a234b66fa5c137bb2ad76"
         },
         "pipfile-spec": 6,
         "requires": {
@@ -40,6 +40,14 @@
             "markers": "python_version >= '3.9'",
             "version": "==1.9.0"
         },
+        "bokeh": {
+            "hashes": [
+                "sha256:2f3043d9ecb3d5dc2e8c0ebf8ad55727617188d4e534f3e7208b36357e352396",
+                "sha256:fddc4b91f8b40178c0e3e83dfcc33886d7803a3a1f041a840834255e435a18c2"
+            ],
+            "index": "pypi",
+            "version": "==3.6.2"
+        },
         "cachetools": {
             "hashes": [
                 "sha256:02134e8439cdc2ffb62023ce1debca2944c3f289d66bb17ead3ab3dede74b292",
@@ -936,6 +944,65 @@
             ],
             "version": "==2024.2"
         },
+        "pyyaml": {
+            "hashes": [
+                "sha256:01179a4a8559ab5de078078f37e5c1a30d76bb88519906844fd7bdea1b7729ff",
+                "sha256:0833f8694549e586547b576dcfaba4a6b55b9e96098b36cdc7ebefe667dfed48",
+                "sha256:0a9a2848a5b7feac301353437eb7d5957887edbf81d56e903999a75a3d743086",
+                "sha256:0b69e4ce7a131fe56b7e4d770c67429700908fc0752af059838b1cfb41960e4e",
+                "sha256:0ffe8360bab4910ef1b9e87fb812d8bc0a308b0d0eef8c8f44e0254ab3b07133",
+                "sha256:11d8f3dd2b9c1207dcaf2ee0bbbfd5991f571186ec9cc78427ba5bd32afae4b5",
+                "sha256:17e311b6c678207928d649faa7cb0d7b4c26a0ba73d41e99c4fff6b6c3276484",
+                "sha256:1e2120ef853f59c7419231f3bf4e7021f1b936f6ebd222406c3b60212205d2ee",
+                "sha256:1f71ea527786de97d1a0cc0eacd1defc0985dcf6b3f17bb77dcfc8c34bec4dc5",
+                "sha256:23502f431948090f597378482b4812b0caae32c22213aecf3b55325e049a6c68",
+                "sha256:24471b829b3bf607e04e88d79542a9d48bb037c2267d7927a874e6c205ca7e9a",
+                "sha256:29717114e51c84ddfba879543fb232a6ed60086602313ca38cce623c1d62cfbf",
+                "sha256:2e99c6826ffa974fe6e27cdb5ed0021786b03fc98e5ee3c5bfe1fd5015f42b99",
+                "sha256:39693e1f8320ae4f43943590b49779ffb98acb81f788220ea932a6b6c51004d8",
+                "sha256:3ad2a3decf9aaba3d29c8f537ac4b243e36bef957511b4766cb0057d32b0be85",
+                "sha256:3b1fdb9dc17f5a7677423d508ab4f243a726dea51fa5e70992e59a7411c89d19",
+                "sha256:41e4e3953a79407c794916fa277a82531dd93aad34e29c2a514c2c0c5fe971cc",
+                "sha256:43fa96a3ca0d6b1812e01ced1044a003533c47f6ee8aca31724f78e93ccc089a",
+                "sha256:50187695423ffe49e2deacb8cd10510bc361faac997de9efef88badc3bb9e2d1",
+                "sha256:5ac9328ec4831237bec75defaf839f7d4564be1e6b25ac710bd1a96321cc8317",
+                "sha256:5d225db5a45f21e78dd9358e58a98702a0302f2659a3c6cd320564b75b86f47c",
+                "sha256:6395c297d42274772abc367baaa79683958044e5d3835486c16da75d2a694631",
+                "sha256:688ba32a1cffef67fd2e9398a2efebaea461578b0923624778664cc1c914db5d",
+                "sha256:68ccc6023a3400877818152ad9a1033e3db8625d899c72eacb5a668902e4d652",
+                "sha256:70b189594dbe54f75ab3a1acec5f1e3faa7e8cf2f1e08d9b561cb41b845f69d5",
+                "sha256:797b4f722ffa07cc8d62053e4cff1486fa6dc094105d13fea7b1de7d8bf71c9e",
+                "sha256:7c36280e6fb8385e520936c3cb3b8042851904eba0e58d277dca80a5cfed590b",
+                "sha256:7e7401d0de89a9a855c839bc697c079a4af81cf878373abd7dc625847d25cbd8",
+                "sha256:80bab7bfc629882493af4aa31a4cfa43a4c57c83813253626916b8c7ada83476",
+                "sha256:82d09873e40955485746739bcb8b4586983670466c23382c19cffecbf1fd8706",
+                "sha256:8388ee1976c416731879ac16da0aff3f63b286ffdd57cdeb95f3f2e085687563",
+                "sha256:8824b5a04a04a047e72eea5cec3bc266db09e35de6bdfe34c9436ac5ee27d237",
+                "sha256:8b9c7197f7cb2738065c481a0461e50ad02f18c78cd75775628afb4d7137fb3b",
+                "sha256:9056c1ecd25795207ad294bcf39f2db3d845767be0ea6e6a34d856f006006083",
+                "sha256:936d68689298c36b53b29f23c6dbb74de12b4ac12ca6cfe0e047bedceea56180",
+                "sha256:9b22676e8097e9e22e36d6b7bda33190d0d400f345f23d4065d48f4ca7ae0425",
+                "sha256:a4d3091415f010369ae4ed1fc6b79def9416358877534caf6a0fdd2146c87a3e",
+                "sha256:a8786accb172bd8afb8be14490a16625cbc387036876ab6ba70912730faf8e1f",
+                "sha256:a9f8c2e67970f13b16084e04f134610fd1d374bf477b17ec1599185cf611d725",
+                "sha256:bc2fa7c6b47d6bc618dd7fb02ef6fdedb1090ec036abab80d4681424b84c1183",
+                "sha256:c70c95198c015b85feafc136515252a261a84561b7b1d51e3384e0655ddf25ab",
+                "sha256:cc1c1159b3d456576af7a3e4d1ba7e6924cb39de8f67111c735f6fc832082774",
+                "sha256:ce826d6ef20b1bc864f0a68340c8b3287705cae2f8b4b1d932177dcc76721725",
+                "sha256:d584d9ec91ad65861cc08d42e834324ef890a082e591037abe114850ff7bbc3e",
+                "sha256:d7fded462629cfa4b685c5416b949ebad6cec74af5e2d42905d41e257e0869f5",
+                "sha256:d84a1718ee396f54f3a086ea0a66d8e552b2ab2017ef8b420e92edbc841c352d",
+                "sha256:d8e03406cac8513435335dbab54c0d385e4a49e4945d2909a581c83647ca0290",
+                "sha256:e10ce637b18caea04431ce14fabcf5c64a1c61ec9c56b071a4b7ca131ca52d44",
+                "sha256:ec031d5d2feb36d1d1a24380e4db6d43695f3748343d99434e6f5f9156aaa2ed",
+                "sha256:ef6107725bd54b262d6dedcc2af448a266975032bc85ef0172c5f059da6325b4",
+                "sha256:efdca5630322a10774e8e98e1af481aad470dd62c3170801852d752aa7a783ba",
+                "sha256:f753120cb8181e736c57ef7636e83f31b9c0d1722c516f7e86cf15b7aa57ff12",
+                "sha256:ff3824dc5261f50c9b0dfb3be22b4567a6f938ccce4587b38952d85fd9e9afe4"
+            ],
+            "markers": "python_version >= '3.8'",
+            "version": "==6.0.2"
+        },
         "referencing": {
             "hashes": [
                 "sha256:25b42124a6c8b632a425174f24087783efb348a6f1e0008e63cd4466fedf703c",
@@ -1264,6 +1331,14 @@
             ],
             "markers": "platform_system != 'Darwin'",
             "version": "==6.0.0"
+        },
+        "xyzservices": {
+            "hashes": [
+                "sha256:68fb8353c9dbba4f1ff6c0f2e5e4e596bb9e1db7f94f4f7dfbcb26e25aa66fde",
+                "sha256:776ae82b78d6e5ca63dd6a94abb054df8130887a4a308473b54a6bd364de8644"
+            ],
+            "markers": "python_version >= '3.8'",
+            "version": "==2024.9.0"
         }
     },
     "develop": {}

app.py

@@ -2,10 +2,22 @@ import streamlit as st
 
 import pandas as pd
 
-st.write("Hello **world**")
 
-df = pd.read_csv(
-    "https://raw.githubusercontent.com/StateLibraryVictoria/public-domain-hack-2024/refs/heads/main/datasets/challenge-3-Image-Pool-2024-11-27.csv"
+st.write(
+    "Scrambled Images  from [https://www.slv.vic.gov.au/images](https://www.slv.vic.gov.au/images)"
 )
 
-print(df.head(5))
+try:
+
+    df = pd.read_csv(
+        "https://raw.githubusercontent.com/StateLibraryVictoria/public-domain-hack-2024/refs/heads/main/datasets/challenge-3-Image-Pool-2024-11-27.csv"
+    )
+
+except:
+
+    df = pd.read_csv(
+        "https://raw.githubusercontent.com/StateLibraryVictoria/public-domain-hack-2024/refs/heads/main/datasets/challenge-3-Image-Pool-2024-11-27.csv"
+    )
+
+
+st.dataframe(df.head(10))

dataset-wrangler.py

@@ -0,0 +1,23 @@
+import pandas as pd
+
+dataset = "https://raw.githubusercontent.com/StateLibraryVictoria/public-domain-hack-2024/refs/heads/main/datasets/challenge-3-Image-Pool-2024-11-27.csv"
+
+columns = [
+    "IE PID",
+    "Title (DC)",
+    "ALMA _ MMS (Object Identifier - IE)",
+    "HANDLE (Object Identifier - IE)",
+    "Creator (DC)",
+    "Genre (DCTERMS)",
+    "Created (DCTERMS)",
+]
+
+
+def clean_df(columns=columns, dataset=dataset):
+
+    df = pd.read_csv(dataset)
+
+    df = df[columns]
+    df = df.dropna()
+
+    return df

image-analysis.py

@@ -73,4 +73,6 @@ clt_1 = get_palette_clusters(img)
 
 img_palette = palette(clt_1)
 
+print(img_palette)
+
 show_img_compare(img, img_palette)