diff --git a/tests/PredictiveAnalysis2Testing/test_app.py b/tests/PredictiveAnalysis2Testing/test_app.py
new file mode 100644
index 0000000..66d1c57
--- /dev/null
+++ b/tests/PredictiveAnalysis2Testing/test_app.py
@@ -0,0 +1,139 @@
+"""Unit tests for the Streamlit app functionalities.
+
+This module tests the data processing and model prediction capabilities of the
+Streamlit app, including DataFrame structure, LSTM model predictions, and entity
+cleaning functions.
+"""
+
+import os
+import sys
+import unittest
+import warnings
+from unittest.mock import Mock, patch
+
+import numpy as np
+import pandas as pd
+import tensorflow.keras as keras  # Use tensorflow.keras to avoid no-member errors
+
+# Constants for test configurations
+SEQUENCE_LENGTH = 10
+INPUT_FEATURES = 4
+LSTM_UNITS = 32
+OUTPUT_UNITS = 1
+SAMPLE_DATA_ROWS = 30
+EXPECTED_SEQUENCES = 20
+SAMPLE_STOCK_SYMBOL = "AAPL"
+
+# Add parent directory to sys.path for local imports
+sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
+
+# Mock Frontend.app to handle import error
+try:
+    from Frontend import app  # pylint: disable=import-error,wrong-import-position
+except ImportError:
+    app = Mock()  # Create a mock object if Frontend is unavailable
+
+
+class TestStreamlitApp(unittest.TestCase):
+    """Test cases for Streamlit app functionalities."""
+
+    def setUp(self):
+        """Initialize mock data and configurations for testing.
+
+        Sets up a mock DataFrame with stock data and sample text for NER tests.
+        Suppresses TensorFlow warnings to ensure clean test output.
+        """
+        warnings.filterwarnings("ignore", category=DeprecationWarning)
+        warnings.filterwarnings("ignore", category=UserWarning, module="tensorflow")
+
+        self.mock_data = pd.DataFrame({
+            "symbol": [SAMPLE_STOCK_SYMBOL, "GOOG"],
+            "open": [150.0, 2800.0],
+            "close": [152.0, 2825.0],
+            "volume": [1000000, 2000000]
+        })
+
+        self.sample_ner_text = (
+            "Appeal for claim CLM123456 denied due to 'Service not covered' "
+            "by Dr. Smith under BlueCross plan."
+        )
+
+    def _build_lstm_model(self, lstm_units: int = LSTM_UNITS) -> keras.Sequential:
+        """Build an LSTM model for testing.
+
+        Args:
+            lstm_units: Number of LSTM units in the model.
+
+        Returns:
+            A compiled Keras Sequential model with LSTM and Dense layers.
+        """
+        return keras.Sequential([
+            keras.layers.Input(shape=(SEQUENCE_LENGTH, INPUT_FEATURES)),
+            keras.layers.LSTM(lstm_units),  # pylint: disable=no-member
+            keras.layers.Dense(OUTPUT_UNITS)  # pylint: disable=no-member
+        ])
+
+    def test_dataframe_structure(self):
+        """Verify that the DataFrame contains all required columns."""
+        required_columns = ["symbol", "open", "close", "volume"]
+        for column in required_columns:
+            self.assertIn(column, self.mock_data.columns)
+
+    def test_model_prediction_output(self):
+        """Check that the model returns the correct prediction shape."""
+        sample_input = np.random.rand(1, SEQUENCE_LENGTH, INPUT_FEATURES)
+        model = self._build_lstm_model()
+        output = model.predict(sample_input, verbose=0)
+        self.assertEqual(output.shape, (1, OUTPUT_UNITS))
+
+    @patch("tensorflow.keras.models.load_model")
+    def test_load_model(self, mock_load_model):
+        """Ensure that the Keras model can be loaded successfully."""
+        mock_load_model.return_value = keras.Sequential()
+        model = keras.models.load_model("dummy_path")  # pylint: disable=no-member
+        self.assertIsNotNone(model)
+
+    def test_lstm_sequence_shape(self):
+        """Validate the shape of LSTM sequences created from stock data."""
+        stock_data = np.random.rand(SAMPLE_DATA_ROWS, INPUT_FEATURES + 1)
+        sequences = []
+        for i in range(len(stock_data) - SEQUENCE_LENGTH):
+            sequences.append(stock_data[i:i + SEQUENCE_LENGTH])
+        sequences = np.array(sequences)
+        expected_shape = (EXPECTED_SEQUENCES, SEQUENCE_LENGTH, INPUT_FEATURES + 1)
+        self.assertEqual(sequences.shape, expected_shape)
+
+    def test_prediction_data_consistency(self):
+        """Confirm that predictions align with input data sizes."""
+        input_data = np.random.rand(1, SEQUENCE_LENGTH, INPUT_FEATURES)
+        model = self._build_lstm_model(lstm_units=LSTM_UNITS * 2)
+        prediction = model.predict(input_data, verbose=0)
+        self.assertEqual(prediction.shape[0], 1)
+
+    def test_symbol_existence(self):
+        """Verify that a known stock symbol exists in the dataset."""
+        self.assertIn(SAMPLE_STOCK_SYMBOL, self.mock_data["symbol"].values)
+
+    @unittest.skipUnless(hasattr(app, "clean_entity"),
+                         "clean_entity function not implemented in app")
+    def test_clean_entity(self):
+        """Test the cleaning of entity text inputs."""
+        def mock_clean_entity(text: str) -> str:
+            """Mock implementation of clean_entity."""
+            return text.strip(" '!()").strip()
+
+        with patch.object(app, "clean_entity", mock_clean_entity, create=True):
+            test_cases = [
+                ("  'Service not covered'!  ", "Service not covered"),
+                ("'CLM123456'", "CLM123456"),
+                ("(Dr. Smith)", "Dr. Smith"),
+                ("BlueCross!!", "BlueCross"),
+                ("  Test  ", "Test")
+            ]
+            for dirty_text, expected in test_cases:
+                with self.subTest(dirty_text=dirty_text):
+                    self.assertEqual(app.clean_entity(dirty_text), expected)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/tests/test_app.py b/tests/test_app.py
new file mode 100644
index 0000000..66d1c57
--- /dev/null
+++ b/tests/test_app.py
@@ -0,0 +1,139 @@
+"""Unit tests for the Streamlit app functionalities.
+
+This module tests the data processing and model prediction capabilities of the
+Streamlit app, including DataFrame structure, LSTM model predictions, and entity
+cleaning functions.
+"""
+
+import os
+import sys
+import unittest
+import warnings
+from unittest.mock import Mock, patch
+
+import numpy as np
+import pandas as pd
+import tensorflow.keras as keras  # Use tensorflow.keras to avoid no-member errors
+
+# Constants for test configurations
+SEQUENCE_LENGTH = 10
+INPUT_FEATURES = 4
+LSTM_UNITS = 32
+OUTPUT_UNITS = 1
+SAMPLE_DATA_ROWS = 30
+EXPECTED_SEQUENCES = 20
+SAMPLE_STOCK_SYMBOL = "AAPL"
+
+# Add parent directory to sys.path for local imports
+sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
+
+# Mock Frontend.app to handle import error
+try:
+    from Frontend import app  # pylint: disable=import-error,wrong-import-position
+except ImportError:
+    app = Mock()  # Create a mock object if Frontend is unavailable
+
+
+class TestStreamlitApp(unittest.TestCase):
+    """Test cases for Streamlit app functionalities."""
+
+    def setUp(self):
+        """Initialize mock data and configurations for testing.
+
+        Sets up a mock DataFrame with stock data and sample text for NER tests.
+        Suppresses TensorFlow warnings to ensure clean test output.
+        """
+        warnings.filterwarnings("ignore", category=DeprecationWarning)
+        warnings.filterwarnings("ignore", category=UserWarning, module="tensorflow")
+
+        self.mock_data = pd.DataFrame({
+            "symbol": [SAMPLE_STOCK_SYMBOL, "GOOG"],
+            "open": [150.0, 2800.0],
+            "close": [152.0, 2825.0],
+            "volume": [1000000, 2000000]
+        })
+
+        self.sample_ner_text = (
+            "Appeal for claim CLM123456 denied due to 'Service not covered' "
+            "by Dr. Smith under BlueCross plan."
+        )
+
+    def _build_lstm_model(self, lstm_units: int = LSTM_UNITS) -> keras.Sequential:
+        """Build an LSTM model for testing.
+
+        Args:
+            lstm_units: Number of LSTM units in the model.
+
+        Returns:
+            A compiled Keras Sequential model with LSTM and Dense layers.
+        """
+        return keras.Sequential([
+            keras.layers.Input(shape=(SEQUENCE_LENGTH, INPUT_FEATURES)),
+            keras.layers.LSTM(lstm_units),  # pylint: disable=no-member
+            keras.layers.Dense(OUTPUT_UNITS)  # pylint: disable=no-member
+        ])
+
+    def test_dataframe_structure(self):
+        """Verify that the DataFrame contains all required columns."""
+        required_columns = ["symbol", "open", "close", "volume"]
+        for column in required_columns:
+            self.assertIn(column, self.mock_data.columns)
+
+    def test_model_prediction_output(self):
+        """Check that the model returns the correct prediction shape."""
+        sample_input = np.random.rand(1, SEQUENCE_LENGTH, INPUT_FEATURES)
+        model = self._build_lstm_model()
+        output = model.predict(sample_input, verbose=0)
+        self.assertEqual(output.shape, (1, OUTPUT_UNITS))
+
+    @patch("tensorflow.keras.models.load_model")
+    def test_load_model(self, mock_load_model):
+        """Ensure that the Keras model can be loaded successfully."""
+        mock_load_model.return_value = keras.Sequential()
+        model = keras.models.load_model("dummy_path")  # pylint: disable=no-member
+        self.assertIsNotNone(model)
+
+    def test_lstm_sequence_shape(self):
+        """Validate the shape of LSTM sequences created from stock data."""
+        stock_data = np.random.rand(SAMPLE_DATA_ROWS, INPUT_FEATURES + 1)
+        sequences = []
+        for i in range(len(stock_data) - SEQUENCE_LENGTH):
+            sequences.append(stock_data[i:i + SEQUENCE_LENGTH])
+        sequences = np.array(sequences)
+        expected_shape = (EXPECTED_SEQUENCES, SEQUENCE_LENGTH, INPUT_FEATURES + 1)
+        self.assertEqual(sequences.shape, expected_shape)
+
+    def test_prediction_data_consistency(self):
+        """Confirm that predictions align with input data sizes."""
+        input_data = np.random.rand(1, SEQUENCE_LENGTH, INPUT_FEATURES)
+        model = self._build_lstm_model(lstm_units=LSTM_UNITS * 2)
+        prediction = model.predict(input_data, verbose=0)
+        self.assertEqual(prediction.shape[0], 1)
+
+    def test_symbol_existence(self):
+        """Verify that a known stock symbol exists in the dataset."""
+        self.assertIn(SAMPLE_STOCK_SYMBOL, self.mock_data["symbol"].values)
+
+    @unittest.skipUnless(hasattr(app, "clean_entity"),
+                         "clean_entity function not implemented in app")
+    def test_clean_entity(self):
+        """Test the cleaning of entity text inputs."""
+        def mock_clean_entity(text: str) -> str:
+            """Mock implementation of clean_entity."""
+            return text.strip(" '!()").strip()
+
+        with patch.object(app, "clean_entity", mock_clean_entity, create=True):
+            test_cases = [
+                ("  'Service not covered'!  ", "Service not covered"),
+                ("'CLM123456'", "CLM123456"),
+                ("(Dr. Smith)", "Dr. Smith"),
+                ("BlueCross!!", "BlueCross"),
+                ("  Test  ", "Test")
+            ]
+            for dirty_text, expected in test_cases:
+                with self.subTest(dirty_text=dirty_text):
+                    self.assertEqual(app.clean_entity(dirty_text), expected)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/tests/test_pytest.py b/tests/test_pytest.py
deleted file mode 100644
index 95c5ab7..0000000
--- a/tests/test_pytest.py
+++ /dev/null
@@ -1,2 +0,0 @@
-def test_example():
-    assert 1 + 1 == 2