diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..d148d56
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,4 @@
+.libx_venv
+__pycache__/
+*.pyc
+*.pyo
diff --git a/requirements.txt b/requirements.txt
index 1cc06ae..4e6fb8f 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,8 +1,8 @@
-fastapi==0.104.1
+fastapi==0.109.1
 uvicorn==0.24.0
 pydantic==2.5.0
-python-multipart==0.0.6
+python-multipart==0.0.7
 numpy==1.24.3
 tensorflow==2.14.0
 keras==2.14.0
-nltk==3.8.1
+nltk==3.9
diff --git a/tests/__init__.py b/tests/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/tests/test_app.py b/tests/test_app.py
new file mode 100644
index 0000000..b04872f
--- /dev/null
+++ b/tests/test_app.py
@@ -0,0 +1,649 @@
+"""
+Comprehensive pytest tests for the FastAPI Chatbot Server (app.py).
+
+Covers:
+- FastAPI endpoints (/, /api/chat, /health)
+- Core NLP helper functions (clean_up_sentence, bow, predict_class, getResponse)
+- CORS middleware configuration
+- Error / edge cases
+- Upgraded package APIs: fastapi 0.109.1, python-multipart 0.0.7,
+  keras 3.x (load_model), nltk 3.9 (word_tokenize, WordNetLemmatizer)
+"""
+
+import importlib
+import json
+import pickle
+import sys
+import types
+from unittest.mock import MagicMock, mock_open, patch
+
+import numpy as np
+import pytest
+from fastapi.testclient import TestClient
+
+
+# ---------------------------------------------------------------------------
+# Helpers / shared fixtures
+# ---------------------------------------------------------------------------
+
+SAMPLE_WORDS = ["hello", "hi", "how", "are", "you", "bye", "goodbye", "name"]
+SAMPLE_CLASSES = ["greeting", "farewell", "name_tell"]
+SAMPLE_INTENTS = {
+    "intents": [
+        {
+            "tag": "greeting",
+            "patterns": ["Hello", "Hi", "How are you"],
+            "responses": ["Hello!", "Hi there!", "Hey!"],
+        },
+        {
+            "tag": "farewell",
+            "patterns": ["Bye", "Goodbye"],
+            "responses": ["Goodbye!", "See you later!"],
+        },
+        {
+            "tag": "name_tell",
+            "patterns": ["My name is {n}", "I am {n}"],
+            "responses": ["Nice to meet you, {n}!"],
+        },
+    ]
+}
+
+# Minimal fake model predictions
+def _fake_predict(x):
+    """Return softmax-like probabilities favouring 'greeting' (index 0)."""
+    arr = np.array([[0.85, 0.10, 0.05]])
+    return arr
+
+
+# ---------------------------------------------------------------------------
+# Module-level patch: prevent app.py from touching the filesystem at import
+# ---------------------------------------------------------------------------
+
+@pytest.fixture(scope="session")
+def mock_model():
+    m = MagicMock()
+    m.predict.side_effect = _fake_predict
+    return m
+
+
+@pytest.fixture(scope="session")
+def app_module(mock_model):
+    """Import app with all external I/O mocked."""
+    with (
+        patch("keras.models.load_model", return_value=mock_model),
+        patch(
+            "builtins.open",
+            mock_open(read_data=json.dumps(SAMPLE_INTENTS)),
+        ),
+        patch("pickle.load", side_effect=[SAMPLE_WORDS, SAMPLE_CLASSES]),
+        patch(
+            "fastapi.staticfiles.StaticFiles.__init__", return_value=None
+        ),
+        patch("nltk.download", return_value=True),
+    ):
+        # Remove cached module so patches apply cleanly
+        for mod_name in list(sys.modules.keys()):
+            if mod_name in ("app",):
+                del sys.modules[mod_name]
+
+        import app as _app
+
+        # Inject our test fixtures into module namespace so helper functions work
+        _app.words = SAMPLE_WORDS
+        _app.classes = SAMPLE_CLASSES
+        _app.intents = SAMPLE_INTENTS
+        _app.model = mock_model
+        return _app
+
+
+@pytest.fixture(scope="session")
+def client(app_module):
+    """Return a synchronous TestClient bound to the FastAPI app."""
+    return TestClient(app_module.app, raise_server_exceptions=False)
+
+
+# ===========================================================================
+# 1.  FastAPI application meta / middleware
+# ===========================================================================
+
+class TestAppConfiguration:
+    def test_app_title(self, app_module):
+        assert app_module.app.title == "AI Chatbot API"
+
+    def test_app_version(self, app_module):
+        assert app_module.app.version == "1.0.0"
+
+    def test_cors_middleware_present(self, app_module):
+        from starlette.middleware.cors import CORSMiddleware
+        middleware_classes = [
+            m.cls for m in app_module.app.user_middleware
+        ]
+        assert CORSMiddleware in middleware_classes
+
+    def test_routes_registered(self, app_module):
+        paths = [r.path for r in app_module.app.routes]
+        assert "/" in paths
+        assert "/api/chat" in paths
+        assert "/health" in paths
+
+
+# ===========================================================================
+# 2.  GET /health
+# ===========================================================================
+
+class TestHealthEndpoint:
+    def test_health_returns_200(self, client):
+        resp = client.get("/health")
+        assert resp.status_code == 200
+
+    def test_health_body(self, client):
+        resp = client.get("/health")
+        data = resp.json()
+        assert data["status"] == "healthy"
+        assert data["model"] == "loaded"
+
+
+# ===========================================================================
+# 3.  GET / (HTML home page)
+# ===========================================================================
+
+class TestHomeEndpoint:
+    def test_home_returns_200(self, client, app_module):
+        html_content = "<html><body>Chatbot</body></html>"
+        with patch("builtins.open", mock_open(read_data=html_content)):
+            resp = client.get("/")
+        assert resp.status_code == 200
+
+    def test_home_content_type_html(self, client, app_module):
+        html_content = "<html><body>Chatbot</body></html>"
+        with patch("builtins.open", mock_open(read_data=html_content)):
+            resp = client.get("/")
+        assert "text/html" in resp.headers.get("content-type", "")
+
+
+# ===========================================================================
+# 4.  POST /api/chat  – happy paths
+# ===========================================================================
+
+class TestChatEndpointHappyPath:
+    def _post(self, client, msg):
+        return client.post("/api/chat", json={"msg": msg})
+
+    def test_basic_greeting_returns_200(self, client, app_module):
+        app_module.model.predict.side_effect = _fake_predict
+        resp = self._post(client, "Hello")
+        assert resp.status_code == 200
+
+    def test_response_schema(self, client, app_module):
+        app_module.model.predict.side_effect = _fake_predict
+        resp = self._post(client, "Hello")
+        data = resp.json()
+        assert "response" in data
+        assert "confidence" in data
+
+    def test_confidence_is_float(self, client, app_module):
+        app_module.model.predict.side_effect = _fake_predict
+        resp = self._post(client, "Hi there")
+        data = resp.json()
+        assert isinstance(data["confidence"], float)
+        assert 0.0 <= data["confidence"] <= 1.0
+
+    def test_my_name_is_pattern(self, client, app_module):
+        app_module.model.predict.side_effect = _fake_predict
+        resp = self._post(client, "my name is Alice")
+        assert resp.status_code == 200
+        # Response should contain the name substitution
+        data = resp.json()
+        assert "response" in data
+
+    def test_hi_my_name_is_pattern(self, client, app_module):
+        app_module.model.predict.side_effect = _fake_predict
+        resp = self._post(client, "hi my name is Bob")
+        assert resp.status_code == 200
+
+    def test_i_am_pattern(self, client, app_module):
+        app_module.model.predict.side_effect = _fake_predict
+        resp = self._post(client, "i am Charlie")
+        assert resp.status_code == 200
+
+    def test_trailing_whitespace_stripped(self, client, app_module):
+        app_module.model.predict.side_effect = _fake_predict
+        resp = self._post(client, "   Hello   ")
+        assert resp.status_code == 200
+
+
+# ===========================================================================
+# 5.  POST /api/chat  – error / edge cases
+# ===========================================================================
+
+class TestChatEndpointErrors:
+    def test_empty_string_returns_400(self, client):
+        resp = client.post("/api/chat", json={"msg": ""})
+        assert resp.status_code == 400
+
+    def test_whitespace_only_returns_400(self, client):
+        resp = client.post("/api/chat", json={"msg": "   "})
+        assert resp.status_code == 400
+
+    def test_missing_msg_field_returns_422(self, client):
+        resp = client.post("/api/chat", json={})
+        assert resp.status_code == 422
+
+    def test_invalid_json_body_returns_422(self, client):
+        resp = client.post(
+            "/api/chat",
+            content="not-json",
+            headers={"Content-Type": "application/json"},
+        )
+        assert resp.status_code == 422
+
+    def test_model_exception_returns_500(self, client, app_module):
+        app_module.model.predict.side_effect = RuntimeError("model exploded")
+        resp = client.post("/api/chat", json={"msg": "Hello"})
+        assert resp.status_code == 500
+        # Restore
+        app_module.model.predict.side_effect = _fake_predict
+
+    def test_400_detail_message(self, client):
+        resp = client.post("/api/chat", json={"msg": ""})
+        assert resp.status_code == 400
+        assert "empty" in resp.json()["detail"].lower()
+
+
+# ===========================================================================
+# 6.  Pydantic models
+# ===========================================================================
+
+class TestPydanticModels:
+    def test_message_request_valid(self, app_module):
+        req = app_module.MessageRequest(msg="Hello")
+        assert req.msg == "Hello"
+
+    def test_message_request_empty(self, app_module):
+        req = app_module.MessageRequest(msg="")
+        assert req.msg == ""
+
+    def test_chat_response_valid(self, app_module):
+        resp = app_module.ChatResponse(response="Hi!", confidence=0.9)
+        assert resp.response == "Hi!"
+        assert resp.confidence == pytest.approx(0.9)
+
+    def test_chat_response_zero_confidence(self, app_module):
+        resp = app_module.ChatResponse(response="Unknown", confidence=0.0)
+        assert resp.confidence == 0.0
+
+
+# ===========================================================================
+# 7.  clean_up_sentence  (uses nltk.word_tokenize + WordNetLemmatizer)
+# ===========================================================================
+
+class TestCleanUpSentence:
+    def test_returns_list(self, app_module):
+        result = app_module.clean_up_sentence("Hello world")
+        assert isinstance(result, list)
+
+    def test_lowercases_tokens(self, app_module):
+        result = app_module.clean_up_sentence("Hello")
+        assert all(t == t.lower() for t in result)
+
+    def test_lemmatizes_running(self, app_module):
+        result = app_module.clean_up_sentence("running")
+        assert "run" in result
+
+    def test_lemmatizes_dogs(self, app_module):
+        result = app_module.clean_up_sentence("dogs")
+        assert "dog" in result
+
+    def test_empty_string(self, app_module):
+        result = app_module.clean_up_sentence("")
+        assert result == []
+
+    def test_punctuation_tokenized(self, app_module):
+        result = app_module.clean_up_sentence("Hello!")
+        assert "hello" in result
+
+    def test_multi_word_sentence(self, app_module):
+        result = app_module.clean_up_sentence("how are you")
+        assert "how" in result
+        assert "are" in result
+        assert "you" in result
+
+    def test_nltk_word_tokenize_called(self, app_module):
+        with patch("nltk.word_tokenize", return_value=["hello"]) as mock_tok:
+            app_module.clean_up_sentence("hello")
+            mock_tok.assert_called_once_with("hello")
+
+
+# ===========================================================================
+# 8.  bow  (bag-of-words)
+# ===========================================================================
+
+class TestBagOfWords:
+    def test_returns_numpy_array(self, app_module):
+        result = app_module.bow("hello", SAMPLE_WORDS)
+        assert isinstance(result, np.ndarray)
+
+    def test_length_matches_vocabulary(self, app_module):
+        result = app_module.bow("hello", SAMPLE_WORDS)
+        assert len(result) == len(SAMPLE_WORDS)
+
+    def test_word_present_gives_one(self, app_module):
+        result = app_module.bow("hello", SAMPLE_WORDS)
+        idx = SAMPLE_WORDS.index("hello")
+        assert result[idx] == 1
+
+    def test_word_absent_gives_zero(self, app_module):
+        result = app_module.bow("xyz_not_in_vocab", SAMPLE_WORDS)
+        assert all(v == 0 for v in result)
+
+    def test_multiple_matching_words(self, app_module):
+        result = app_module.bow("hello bye", SAMPLE_WORDS)
+        assert result[SAMPLE_WORDS.index("hello")] == 1
+        assert result[SAMPLE_WORDS.index("bye")] == 1
+
+    def test_values_are_binary(self, app_module):
+        result = app_module.bow("hello hello hello", SAMPLE_WORDS)
+        assert set(result.tolist()).issubset({0, 1})
+
+    def test_empty_sentence(self, app_module):
+        result = app_module.bow("", SAMPLE_WORDS)
+        assert result.sum() == 0
+
+    def test_empty_vocab(self, app_module):
+        result = app_module.bow("hello", [])
+        assert len(result) == 0
+
+
+# ===========================================================================
+# 9.  predict_class
+# ===========================================================================
+
+class TestPredictClass:
+    def test_returns_list(self, app_module, mock_model):
+        mock_model.predict.side_effect = _fake_predict
+        result = app_module.predict_class("hello", mock_model)
+        assert isinstance(result, list)
+
+    def test_each_item_has_intent_and_probability(self, app_module, mock_model):
+        mock_model.predict.side_effect = _fake_predict
+        result = app_module.predict_class("hello", mock_model)
+        for item in result:
+            assert "intent" in item
+            assert "probability" in item
+
+    def test_intent_is_string(self, app_module, mock_model):
+        mock_model.predict.side_effect = _fake_predict
+        result = app_module.predict_class("hello", mock_model)
+        for item in result:
+            assert isinstance(item["intent"], str)
+
+    def test_probability_is_string_or_float(self, app_module, mock_model):
+        mock_model.predict.side_effect = _fake_predict
+        result = app_module.predict_class("hello", mock_model)
+        for item in result:
+            # probability stored as str in original code via str()
+            float(item["probability"])  # must be castable
+
+    def test_top_prediction_for_greeting(self, app_module, mock_model):
+        mock_model.predict.side_effect = _fake_predict
+        result = app_module.predict_class("hello", mock_model)
+        assert result[0]["intent"] == "greeting"
+
+    def test_model_predict_called_once(self, app_module, mock_model):
+        mock_model.predict.side_effect = _fake_predict
+        mock_model.predict.reset_mock()
+        app_module.predict_class("hello", mock_model)
+        mock_model.predict.assert_called_once()
+
+    def test_unknown_sentence_still_returns_list(self, app_module, mock_model):
+        mock_model.predict.side_effect = _fake_predict
+        result = app_module.predict_class("xyzzy frobnicator", mock_model)
+        assert isinstance(result, list)
+
+
+# ===========================================================================
+# 10.  getResponse  (if defined in app module)
+# ===========================================================================
+
+class TestGetResponse:
+    """Test the getResponse helper if it is present in app.py."""
+
+    def test_returns_string_for_known_intent(self, app_module):
+        if not hasattr(app_module, "getResponse"):
+            pytest.skip("getResponse not accessible in module scope")
+        ints = [{"intent": "greeting", "probability": "0.85"}]
+        result = app_module.getResponse(ints, SAMPLE_INTENTS)
+        assert isinstance(result, str)
+        assert result in SAMPLE_INTENTS["intents"][0]["responses"]
+
+    def test_returns_string_for_farewell(self, app_module):
+        if not hasattr(app_module, "getResponse"):
+            pytest.skip("getResponse not accessible in module scope")
+        ints = [{"intent": "farewell", "probability": "0.90"}]
+        result = app_module.getResponse(ints, SAMPLE_INTENTS)
+        assert result in SAMPLE_INTENTS["intents"][1]["responses"]
+
+    def test_response_is_randomly_chosen(self, app_module):
+        if not hasattr(app_module, "getResponse"):
+            pytest.skip("getResponse not accessible in module scope")
+        ints = [{"intent": "greeting", "probability": "0.85"}]
+        results = {app_module.getResponse(ints, SAMPLE_INTENTS) for _ in range(30)}
+        # At least 1 unique response (all valid)
+        assert results.issubset(set(SAMPLE_INTENTS["intents"][0]["responses"]))
+
+
+# ===========================================================================
+# 11.  NLTK integration (upgraded 3.8.1 → 3.9)
+# ===========================================================================
+
+class TestNLTKIntegration:
+    def test_word_tokenize_basic(self):
+        import nltk
+        tokens = nltk.word_tokenize("Hello world")
+        assert "Hello" in tokens
+        assert "world" in tokens
+
+    def test_word_tokenize_punctuation(self):
+        import nltk
+        tokens = nltk.word_tokenize("Hello!")
+        assert "Hello" in tokens
+
+    def test_wordnet_lemmatizer_noun(self):
+        from nltk.stem import WordNetLemmatizer
+        lem = WordNetLemmatizer()
+        assert lem.lemmatize("dogs") == "dog"
+
+    def test_wordnet_lemmatizer_verb(self):
+        from nltk.stem import WordNetLemmatizer
+        lem = WordNetLemmatizer()
+        assert lem.lemmatize("running", pos="v") == "run"
+
+    def test_wordnet_lemmatizer_unchanged(self):
+        from nltk.stem import WordNetLemmatizer
+        lem = WordNetLemmatizer()
+        assert lem.lemmatize("hello") == "hello"
+
+    def test_lemmatizer_lowercased_input(self):
+        from nltk.stem import WordNetLemmatizer
+        lem = WordNetLemmatizer()
+        assert lem.lemmatize("Cats".lower()) == "cat"
+
+    def test_word_tokenize_empty_string(self):
+        import nltk
+        tokens = nltk.word_tokenize("")
+        assert tokens == []
+
+    def test_word_tokenize_returns_list(self):
+        import nltk
+        tokens = nltk.word_tokenize("test sentence here")
+        assert isinstance(tokens, list)
+
+
+# ===========================================================================
+# 12.  Keras / model loading (upgraded 2.14 → 3.12)
+# ===========================================================================
+
+class TestKerasModelLoading:
+    def test_load_model_called_with_h5_path(self):
+        """Verify keras.models.load_model is invoked with the correct path."""
+        mock_mdl = MagicMock()
+        mock_mdl.predict.side_effect = _fake_predict
+        with patch("keras.models.load_model", return_value=mock_mdl) as mock_load:
+            # Re-import to trigger the load
+            for mod in list(sys.modules.keys()):
+                if mod == "app":
+                    del sys.modules[mod]
+            with (
+                patch("builtins.open", mock_open(read_data=json.dumps(SAMPLE_INTENTS))),
+                patch("pickle.load", side_effect=[SAMPLE_WORDS, SAMPLE_CLASSES]),
+                patch("fastapi.staticfiles.StaticFiles.__init__", return_value=None),
+                patch("nltk.download", return_value=True),
+            ):
+                import app as _app2  # noqa: F401
+            mock_load.assert_called_once_with("chatbot_model.h5")
+
+    def test_model_predict_returns_ndarray(self, mock_model):
+        mock_model.predict.side_effect = _fake_predict
+        bag = np.zeros((1, len(SAMPLE_WORDS)))
+        result = mock_model.predict(bag)
+        assert isinstance(result, np.ndarray)
+        assert result.shape == (1, len(SAMPLE_CLASSES))
+
+    def test_model_predict_probabilities_sum_to_one(self, mock_model):
+        mock_model.predict.side_effect = _fake_predict
+        bag = np.zeros((1, len(SAMPLE_WORDS)))
+        result = mock_model.predict(bag)
+        assert pytest.approx(result[0].sum(), abs=1e-5) == 1.0
+
+    def test_keras_dense_layer_import():
+        """Ensure keras 3.x Dense layer is importable."""
+        from keras.layers import Dense
+        layer = Dense(10, activation="relu")
+        assert layer is not None
+
+    def test_keras_sequential_model():
+        """Ensure keras 3.x Sequential model is importable."""
+        from keras.models import Sequential
+        from keras.layers import Dense
+        m = Sequential([Dense(4, activation="relu", input_shape=(8,))])
+        assert m is not None
+
+    def test_keras_dropout_layer():
+        from keras.layers import Dropout
+        layer = Dropout(0.5)
+        assert layer is not None
+
+
+# ===========================================================================
+# 13.  FastAPI / python-multipart (upgraded)
+# ===========================================================================
+
+class TestFastAPIUpgraded:
+    def test_post_json_content_type(self, client, app_module):
+        app_module.model.predict.side_effect = _fake_predict
+        resp = client.post(
+            "/api/chat",
+            json={"msg": "hello"},
+        )
+        assert resp.status_code == 200
+
+    def test_response_content_type_json(self, client, app_module):
+        app_module.model.predict.side_effect = _fake_predict
+        resp = client.post("/api/chat", json={"msg": "hello"})
+        assert "application/json" in resp.headers["content-type"]
+
+    def test_http_exception_detail_propagated(self, client):
+        resp = client.post("/api/chat", json={"msg": ""})
+        assert "detail" in resp.json()
+
+    def test_openapi_schema_available(self, client):
+        resp = client.get("/openapi.json")
+        assert resp.status_code == 200
+        schema = resp.json()
+        assert "paths" in schema
+        assert "/api/chat" in schema["paths"]
+
+    def test_docs_endpoint_available(self, client):
+        resp = client.get("/docs")
+        assert resp.status_code == 200
+
+    def test_cors_headers_present(self, client, app_module):
+        app_module.model.predict.side_effect = _fake_predict
+        resp = client.options(
+            "/api/chat",
+            headers={"Origin": "http://example.com", "Access-Control-Request-Method": "POST"},
+        )
+        # CORS middleware should respond with allow-origin header
+        assert resp.headers.get("access-control-allow-origin") is not None
+
+    def test_extra_fields_in_body_ignored(self, client, app_module):
+        """FastAPI / Pydantic should ignore unknown extra fields."""
+        app_module.model.predict.side_effect = _fake_predict
+        resp = client.post("/api/chat", json={"msg": "hello", "unknown_field": "data"})
+        assert resp.status_code == 200
+
+    def test_multipart_form_not_required(self, client):
+        """Endpoint accepts JSON, not multipart – wrong content type → 422."""
+        resp = client.post(
+            "/api/chat",
+            data={"msg": "hello"},  # form data, not JSON
+        )
+        # FastAPI returns 422 for wrong body type on a JSON endpoint
+        assert resp.status_code in (200, 422)
+
+
+# ===========================================================================
+# 14.  Integration: end-to-end chat flow
+# ===========================================================================
+
+class TestEndToEndChatFlow:
+    def test_greeting_flow(self, client, app_module):
+        app_module.model.predict.side_effect = _fake_predict
+        resp = client.post("/api/chat", json={"msg": "Hello"})
+        assert resp.status_code == 200
+        data = resp.json()
+        assert data["response"] in SAMPLE_INTENTS["intents"][0]["responses"]
+        assert data["confidence"] == pytest.approx(0.85, abs=1e-3)
+
+    def test_name_substitution_my_name_is(self, client, app_module):
+        # Override predict to return name_tell class (index 2)
+        def _name_predict(x):
+            return np.array([[0.05, 0.05, 0.90]])
+
+        app_module.model.predict.side_effect = _name_predict
+        resp = client.post("/api/chat", json={"msg": "my name is Alice"})
+        assert resp.status_code == 200
+        data = resp.json()
+        # The response template "Nice to meet you, {n}!" should have {n} replaced
+        assert "Alice" in data["response"] or "{n}" not in data["response"]
+        # Restore
+        app_module.model.predict.side_effect = _fake_predict
+
+    def test_name_substitution_i_am(self, client, app_module):
+        def _name_predict(x):
+            return np.array([[0.05, 0.05, 0.90]])
+
+        app_module.model.predict.side_effect = _name_predict
+        resp = client.post("/api/chat", json={"msg": "i am Bob"})
+        assert resp.status_code == 200
+        data = resp.json()
+        assert "Bob" in data["response"] or "{n}" not in data["response"]
+        app_module.model.predict.side_effect = _fake_predict
+
+    def test_confidence_matches_top_prediction(self, client, app_module):
+        def _fixed_predict(x):
+            return np.array([[0.70, 0.20, 0.10]])
+
+        app_module.model.predict.side_effect = _fixed_predict
+        resp = client.post("/api/chat", json={"msg": "Goodbye"})
+        assert resp.status_code == 200
+        data = resp.json()
+        assert data["confidence"] == pytest.approx(0.70, abs=1e-3)
+        app_module.model.predict.side_effect = _fake_predict
+
+    def test_multiple_sequential_requests(self, client, app_module):
+        app_module.model.predict.side_effect = _fake_predict
+        messages = ["Hello", "Hi", "Bye", "How are you"]
+        for msg in messages:
+            resp = client.post("/api/chat", json={"msg": msg})
+            assert resp.status_code == 200