Merge pull request #4 from KevKibe/update-readme

update tests

.github/workflows/test-kaggle-action.yml

@@ -1,4 +1,4 @@
-name: Test Kaggle Action
+name: Test Kaggle Script Action
 
 on: [pull_request]
 
@@ -10,13 +10,14 @@ jobs:
       - name: Checkout repository
         uses: actions/checkout@v3
 
-      - name: Run Kaggle Action
+      - name: Test Kaggle Script Action
         uses: ./
         with:
           username: ${{ secrets.KAGGLE_USERNAME }}
           key: ${{ secrets.KAGGLE_KEY }}
-          title: "Kernel Test v2"
-          custom_script: "pytest tests/test.py"
+          title: "Test Kaggle Script Action"
+          custom_script: "python test.py --url "https://jsonplaceholder.typicode.com/posts/1""
+          working_subdir: "tests"
           enable_internet: true
           enable_gpu: false
           enable_tpu: false

README.md

@@ -1,6 +1,6 @@
-### Kaggle Script Runner
+### Kaggle Script Action
 
-**Kaggle Script Runner** is a GitHub Action designed for automating the execution of custom scripts on Kaggle kernels, making it ideal for continuous integration (CI) and deployment (CD) of machine learning workflows directly within your GitHub repository. 
+**Kaggle Script Action** is a GitHub Action designed for automating the execution of custom scripts on Kaggle kernels, making it ideal for continuous integration (CI) and deployment (CD) of machine learning workflows directly within your GitHub repository. 
 <br>
 This action pulls the repository on the branch it is executed from, installs dependencies from the `requirements.txt` file in the root directory by default, and supports executing Python scripts on Kaggle's infrastructure with configurable options, including GPU/TPU support, internet access, and custom dataset or kernel dependencies. 
 
@@ -11,6 +11,17 @@ This action pulls the repository on the branch it is executed from, installs dep
 - **CI/CD Integration**: Incorporate machine learning workflows seamlessly into your CI/CD pipeline, with GitHub Actions monitoring the kernel execution status and fetching output logs.
 - **Detailed Logging**: Captures and logs the complete output, including kernel execution logs and errors, and displays `data` fields for streamlined debugging.
 
+#### Usage
+Sign up for a [Kaggle Account](https://www.kaggle.com/account/login?phase=startRegisterTab) and verify the account.
+Go to your account page, then settings. Create your API Token. You'll get a file with something like this:
+```json
+{
+  "username": "USERNAME",
+  "key": "TOKEN"
+}
+```
+Add `USERNAME` and `TOKEN` to Github Actions Secrets as `KAGGLE_USERNAME` and `KAGGLE_KEY`.
+
 #### Inputs
 - **`username`** (required): Kaggle username.
 - **`key`** (required): Kaggle API token.
@@ -24,10 +35,32 @@ This action pulls the repository on the branch it is executed from, installs dep
 - **`kernel_sources`** (optional): List of other kernel sources formatted as `{username}/{kernel-slug}`.
 - **`sleep_time`** (optional): Time interval (in seconds) to wait between kernel status checks. Default: `15`.
 
+
 #### Example Usage
-This GitHub Action fetches logs and kernel output after execution, making it ideal for:
-- Running model training or inference scripts in an automated fashion on Kaggle’s infrastructure.
-- Integrating Kaggle-based tests for datasets, models, or research pipelines as part of CI/CD workflows.
-- Leveraging Kaggle’s GPUs/TPUs for testing model performance and handling large data without local resource constraints.
+```yaml
+name: Run Tests
+
+on: [pull_request]
+
+jobs:
+  test_kaggle_action:
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v3
+
+      - name: Run Kaggle Action
+        uses: Kev
+        with:
+          username: ${{ secrets.KAGGLE_USERNAME }}
+          key: ${{ secrets.KAGGLE_KEY }}
+          title: "Kernel Test v2"
+          custom_script: "pytest tests/test.py"
+          enable_internet: true
+          enable_gpu: false
+          enable_tpu: false
+          sleep_time: 10
+```
 
 **Note**: To use this action, you must have a Kaggle API token with the appropriate permissions for kernels and datasets.

action.yml

@@ -45,6 +45,10 @@ inputs:
     description: Duration (in seconds) before checking the status of kernel execution completion
     required: false
     default: 15
+  working_subdir:
+    description: Subdirectory inside /kaggle/working/$REPO_NAME where dependencies are installed and the custom script is run
+    required: false
+    default: ""
 
 outputs:
   automatic_releases_tag:
@@ -110,9 +114,13 @@ runs:
         log_message "Cloning repository URL: $REPO_URL on branch: $BRANCH_NAME"
         
         CUSTOM_SCRIPT="${{ inputs.custom_script }}"
-        log_message "Adding custom script to notebook: $CUSTOM_SCRIPT"
+      
+        WORKING_SUBDIR="${{ inputs.working_subdir }}"
+        TARGET_DIR="/kaggle/working/$REPO_NAME/$WORKING_SUBDIR"
+        dependencies="!cd $TARGET_DIR/$REPO_NAME && pip install -r requirements.txt"
         
-        dependencies="!cd /kaggle/working/$REPO_NAME && pip install -r requirements.txt"
+        log_message "Adding dependency installation script to notebook: $dependencies"
+        log_message "Adding custom script to notebook: $CUSTOM_SCRIPT"
         
         cat <<EOM > "$OUTPUT_NOTEBOOK"
         {
@@ -141,7 +149,7 @@ runs:
               "metadata": {},
               "outputs": [],
               "source": [
-                "!cd /kaggle/working/$REPO_NAME && $CUSTOM_SCRIPT"
+                "!cd $TARGET_DIR && $CUSTOM_SCRIPT"
               ]
             }
           ],

tests/requirements.txt

@@ -0,0 +1 @@
+requests

tests/test.py

@@ -1,42 +1,24 @@
-import unittest
-from unittest.mock import patch
 import requests
-from fetch_data import fetch_data
-
-class TestFetchData(unittest.TestCase):
-
-	@patch('requests.get')
-	def test_fetch_data_success(self, mock_get):
-		mock_response = unittest.mock.Mock()
-		mock_response.raise_for_status.return_value = None  # Simulate no HTTP error
-		mock_response.json.return_value = {"userId": 1, "id": 1, "title": "Test Post", "body": "This is a test."}
-		mock_get.return_value = mock_response
-
-		url = "https://jsonplaceholder.typicode.com/posts/1"
-		result = fetch_data(url)
-
-		self.assertEqual(result, {"userId": 1, "id": 1, "title": "Test Post", "body": "This is a test."})
-
-	@patch('requests.get')
-	def test_fetch_data_http_error(self, mock_get):
-		mock_response = unittest.mock.Mock()
-		mock_response.raise_for_status.side_effect = requests.exceptions.HTTPError("404 Not Found")
-		mock_get.return_value = mock_response
-
-		url = "https://jsonplaceholder.typicode.com/posts/1"
-		result = fetch_data(url)
-
-		self.assertIsNone(result)
-
-	@patch('requests.get')
-	def test_fetch_data_request_exception(self, mock_get):
-		mock_get.side_effect = requests.exceptions.RequestException("Network error")
-
-		url = "https://jsonplaceholder.typicode.com/posts/1"
-		result = fetch_data(url)
-
-		self.assertIsNone(result)
-
-
-if __name__ == '__main__':
-	unittest.main()
+import argparse
+
+def fetch_data(url):
+    """
+    Fetches data from the given URL and returns it as JSON.
+    """
+    try:
+        response = requests.get(url)
+        response.raise_for_status()
+        return response.json()
+    except requests.exceptions.RequestException as e:
+        print(f"An error occurred: {e}")
+        return None
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Fetch JSON data from a specified URL.")
+    parser.add_argument('-u', '--url', type=str, required=True, help="The URL to fetch data from")
+
+    args = parser.parse_args()
+    url = args.url
+
+    data = fetch_data(url)
+    print(data)