remove conda. add requirements.txt. add tests

test/.env.test

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+TRAIN_PATH=${HOME}/Dev/ml/data/mnist_train.csv
+INPUT_FEATURES=40

test/conftest.py

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+# conftest.py
+import pytest
+import os
+from dotenv import load_dotenv
+from pathlib import Path
+
+
+@pytest.fixture(autouse=True)
+def load_env():
+    # Set up your environment variables here
+    env = Path(__file__).parent / ".env.test"
+    if not load_dotenv(env):
+        raise RuntimeError(".env not loaded")
+    # os.environ['MY_ENV_VAR'] = 'some_value'
+    # You can add more setup code here if needed
+
+    yield
+
+    # Optional: Cleanup code after test (if needed)
+    # e.g., unset environment variables if they should not persist after test

test/test_pipeline.py

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+from src.model.linear import DNN
+from src.data.dataset import MnistDataset
+import os
+
+
+def test_size_of_dataset():
+    examples = 500
+    os.environ["TRAINING_EXAMPLES"] = str(examples)
+    channels = 1
+    width, height = 224, 224
+    dataset = MnistDataset(os.getenv("TRAIN_PATH"))
+    # label = dataset[0][1].item()
+    image = dataset[0][0].shape
+    assert channels == image[0]
+    assert width == image[1]
+    assert height == image[2]
+    assert len(dataset) == examples