Cml test

.github/workflows/cml.yaml

@@ -0,0 +1,46 @@
+name: model-training
+on: [push]
+jobs:
+  run:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+      - uses: actions/setup-python@v4
+      - uses: iterative/setup-cml@v1
+
+      - name: Set up Python environment
+        run: |
+          python -m venv venv
+          source venv/bin/activate
+          pip install --upgrade pip
+          pip install -r Streamlit/requirements.txt
+
+      - name: Train model
+        run: |
+          source venv/bin/activate
+          python Streamlit/train.py
+
+      # Improve this .md
+      - name: Create report.md
+        run: |
+          cat Streamlit/results.txt > Streamlit/report.md
+          echo "![](feature_importance.png)" >> Streamlit/report.md
+          echo "![](residuals.png)" >> Streamlit/report.md
+
+      - name: Commit results and report
+        run: |
+          git config --global user.name "github-actions[bot]"
+          git config --global user.email "github-actions[bot]@users.noreply.github.com"
+          git add Streamlit/results.txt 
+          git add Streamlit/report.md 
+          git add Streamlit/feature_importance.png 
+          git add Streamlit/residuals.png
+          git commit -m "Add model results and report"
+          git push
+
+      - name: Create CML Report
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+        run: |
+          source venv/bin/activate
+          cml comment create Streamlit/report.md --token ${{ secrets.GITHUB_TOKEN }}

Streamlit/report.md

@@ -1 +1,4 @@
-
+Training variance explained: 33.0%
+Test variance explained: 32.0%
+![](feature_importance.png)
+![](residuals.png)

Streamlit/results.txt

@@ -1 +1,2 @@
-
+Training variance explained: 33.0%
+Test variance explained: 32.0%

Streamlit/train.py

@@ -13,6 +13,7 @@
 
 # Load in the data
 df = pd.read_csv("Streamlit/wine_quality.csv")
+#connect to supabase with github secret keys
 
 # Split into train and test sections
 y = df.pop("quality")
@@ -32,10 +33,17 @@
 test_score = regr.score(X_test, y_test) * 100
 
 # Write scores to a file
-#results was metrics
 with open("Streamlit/results.txt", 'w') as outfile:
         outfile.write("Training variance explained: %2.1f%%\n" % train_score)
         outfile.write("Test variance explained: %2.1f%%\n" % test_score)
+    print("Successfully wrote to results.txt")
+except Exception as e:
+    print(f"Failed to write to results.txt: {e}")
+
+with open("Streamlit/results.txt", 'r') as outfile:
+    contents = outfile.read()
+    print("Contents of results.txt:")
+    print(contents)
 
 
 ##########################################