Merge pull request #8 from kameshsampath/updates

Updates
Snowflake-Labs · Dec 8, 2024 · 3f51530 · 3f51530
2 parents 1e542eb + 5e746d4
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Showing 7 changed files with 57 additions and 20 deletions.
diff --git a/.vscode/settings.json b/.vscode/settings.json
@@ -20,6 +20,7 @@
     "proba",
     "scikit",
     "selectbox",
-    "sklearn"
+    "sklearn",
+    "Undeploying"
   ]
 }
diff --git a/docs/explore_dataset.md b/docs/explore_dataset.md
@@ -121,8 +121,8 @@ with st.expander("Data"):
     df
     # define and display
     st.write("**X**")
-    x = df.drop("species", axis=1)
-    x
+    X = df.drop("species", axis=1)
+    X
 
     st.write("**y**")
     y = df.species

diff --git a/docs/interactive_features.md b/docs/interactive_features.md
@@ -136,8 +136,8 @@ with st.expander("Data"):
     df
     # define and display
     st.write("**X**")
-    x = df.drop("species", axis=1)
-    x
+    X = df.drop("species", axis=1)
+    X
 
     st.write("**y**")
     y = df.species

diff --git a/docs/setup.md b/docs/setup.md
@@ -44,7 +44,7 @@ cd <directory where you want to create your project>
 Create the application folder for the rest of the demo we will call this as `st-ml-app` and for easy reference we will export to an environment variable named `$TUTORIAL_HOME`,
 
 ```shell
-export TUTORIAL_HOME='st-ml-app'
+export TUTORIAL_HOME="$PWD/st-ml-app"
 mkdir -p $TUTORIAL_HOME
 cd $TUTORIAL_HOME
 ```

diff --git a/docs/snowflake_deploy.md b/docs/snowflake_deploy.md
@@ -58,7 +58,13 @@ snow object create schema \
   --database='st_ml_app'
 ```
 
-Download and import the [notebook](https://github.com/Snowflake-Labs/streamlit-oss-to-sis-bootstrap/blob/main/notebooks/sis_setup.ipynb){:target=_blank} and follow the instructions on the notebook to prepare the environment for deployment.
+## Using Notebook
+
+Here's my suggested revision to make the text clearer and more precise:
+
+We will need a few more objects and will need to ingest the penguins data into our Snowflake database `st_ml_app`, specifically in the schema `data` and into a table named `penguins`. We will use [Snowflake Notebooks](https://www.snowflake.com/en/data-cloud/notebooks/){:target=_blank} for this purpose.
+
+Download and import this [notebook](https://github.com/Snowflake-Labs/streamlit-oss-to-sis-bootstrap/blob/main/notebooks/sis_setup.ipynb){:target=_blank} and follow its instructions to prepare the environment for deployment.
 
 ## Deploying the App
 
@@ -76,8 +82,6 @@ snow init sis --template example_streamlit
 
 ### Update the App 
 
-__TODO__: Note on Copy
-
 Edit and update the `$TUTORIAL_HOME/sis/streamlit_app.py` with,
 
 ```py title="streamlit_app.py" linenums="1" hl_lines="4-7 10-24 24 34-45"
@@ -375,6 +379,23 @@ snow streamlit deploy --replace \
 
 There you go we have seamlessly deployed the application to SiS with a very little effort.
 
+## Undeploying the Application
+
+To drop the application run:
+
+```shell
+snow streamlit drop streamlit_penguin \
+  --database='st_ml_app'  --schema='apps'
+```
+
+## Cleanup 
+
+To cleanup all resources created in this tutorial including the notebook run:
+
+```shell
+snow object drop database st_ml_app
+```
+
 ## Summary
 This chapter guided you through the process of transforming a locally running Streamlit application into a production-ready deployment within Snowflake. You learned the essential modifications needed for Snowflake compatibility, understood the configuration requirements, and mastered the deployment process. You now have a fully functional Streamlit application running in Snowflake's secure environment, accessible to your organization's users through Snowflake's interface.
 

diff --git a/docs/train_and_predict.md b/docs/train_and_predict.md
@@ -14,9 +14,22 @@ In this chapter, we will:
 As part of displaying the predictions we will use the following Streamlit components to make the output aesthetically appealing,
 
 - [Progress Column Config](https://docs.streamlit.io/develop/api-reference/data/st.column_config/st.column_config.progresscolumn)
-- [Sucess Message](https://docs.streamlit.io/develop/api-reference/status/st.success)
+- [Success Message](https://docs.streamlit.io/develop/api-reference/status/st.success)
 - [Container](https://docs.streamlit.io/develop/api-reference/layout/st.container)
 
+Update `$TUTORIAL_HOME/requirements.txt` to be like
+
+```
+streamlit>=1.26.0
+scikit-learn
+```
+
+Update the local Python Virtual Environment to add the `scikit-learn` package,
+
+```shell
+pip install -r requirements.txt
+```
+
 Edit and update the `$TUTORIAL_HOME/streamlit_app.py` with the following code,
 
 ```py title="streamlit_app.py" linenums="1" hl_lines="5 147-198"

diff --git a/notebooks/sis_setup.ipynb b/notebooks/sis_setup.ipynb
@@ -10,7 +10,7 @@
   {
    "cell_type": "markdown",
    "metadata": {
-    "name": "cell1",
+    "name": "md_setup_intro",
     "resultHeight": 282,
     "collapsed": false
    },
@@ -21,7 +21,7 @@
    "cell_type": "markdown",
    "id": "2568d714-4541-4058-8bf1-7b0cbbdc7ab5",
    "metadata": {
-    "name": "cell3",
+    "name": "md_setup_schemas",
     "collapsed": false,
     "resultHeight": 342
    },
@@ -32,7 +32,7 @@
    "id": "b28d9a98-b375-4db9-8219-0df97eb39f14",
    "metadata": {
     "language": "sql",
-    "name": "cell2",
+    "name": "sql_create_schemas",
     "collapsed": false,
     "resultHeight": 111
    },
@@ -44,7 +44,7 @@
    "cell_type": "markdown",
    "id": "fc1af8cf-344c-46c5-9007-4b2dd0c28761",
    "metadata": {
-    "name": "cell7",
+    "name": "md_external_stage",
     "collapsed": false,
     "resultHeight": 140
    },
@@ -55,7 +55,9 @@
    "id": "579e0531-4a76-493b-9089-22e0b4a59e6f",
    "metadata": {
     "language": "sql",
-    "name": "cell8"
+    "name": "sql_create_stage",
+    "collapsed": false,
+    "resultHeight": 111
    },
    "outputs": [],
    "source": "-- add an external stage to a s3 bucket\nCREATE STAGE IF NOT EXISTS STAGES.ST_ML_APP_PENGUINS\n  URL='s3://sfquickstarts/misc';\n\n-- default CSV file format and allow values to quoted by \"\nCREATE FILE FORMAT IF NOT EXISTS FILE_FORMATS.CSV\n  TYPE='CSV'\n  SKIP_HEADER=1\n  FIELD_OPTIONALLY_ENCLOSED_BY = '\"';",
@@ -65,7 +67,7 @@
    "cell_type": "markdown",
    "id": "d7c9a1b2-01eb-4430-80dd-ad895d96a530",
    "metadata": {
-    "name": "cell4",
+    "name": "md_load_data",
     "collapsed": false,
     "resultHeight": 128
    },
@@ -82,14 +84,14 @@
     "codeCollapsed": false
    },
    "outputs": [],
-   "source": "-- Create table to hold penguins data\nCREATE OR ALTER TABLE DATA.PENGUINS(\n   SPECIES STRING NOT NULL,\n   ISLAND STRING NOT NULL,\n   BILL_LENGTH_MM NUMBER NOT NULL,\n   BILL_DEPTH_MM NUMBER NOT NULL,\n   FLIPPER_LENGTH_MM NUMBER NOT NULL,\n   BODY_MASS_G NUMBER NOT NULL,\n   SEX STRING NOT NULL\n);\n\n-- Load the data from penguins_cleaned.csv\nCOPY INTO DATA.PENGUINS\nFROM @STAGES.ST_ML_APP_PENGUINS/PENGUINS_CLEANED.CSV\nFILE_FORMAT=(FORMAT_NAME='FILE_FORMATS.CSV');",
+   "source": "-- Create table to hold penguins data\nCREATE OR ALTER TABLE DATA.PENGUINS(\n   SPECIES STRING NOT NULL,\n   ISLAND STRING NOT NULL,\n   BILL_LENGTH_MM NUMBER NOT NULL,\n   BILL_DEPTH_MM NUMBER NOT NULL,\n   FLIPPER_LENGTH_MM NUMBER NOT NULL,\n   BODY_MASS_G NUMBER NOT NULL,\n   SEX STRING NOT NULL\n);\n\n-- Load the data from penguins_cleaned.csv\nCOPY INTO DATA.PENGUINS\nFROM @stages.st_ml_app_penguins/penguins_cleaned.csv\nFILE_FORMAT=(FORMAT_NAME='FILE_FORMATS.CSV');",
    "execution_count": null
   },
   {
    "cell_type": "markdown",
    "id": "cd53f3db-b787-4343-8726-2c5e8322d106",
    "metadata": {
-    "name": "cell6",
+    "name": "md_query_data",
     "collapsed": false,
     "resultHeight": 41
    },
@@ -100,12 +102,12 @@
    "id": "13ce4fc6-d28b-463f-85cf-b8dc42ae2fc0",
    "metadata": {
     "language": "python",
-    "name": "cell5",
+    "name": "py_query_data",
     "collapsed": false,
     "resultHeight": 335
    },
    "outputs": [],
-   "source": "from snowflake.snowpark.context import get_active_session\n\nsession = get_active_session()\ndf = session.table('st_ml_app.data.penguins')\ndf.show()",
+   "source": "from snowflake.snowpark.context import get_active_session\n\nsession = get_active_session()\ndf = session.table('st_ml_app.data.penguins')\ndf.show(10)",
    "execution_count": null
   }
  ]