import pytest
import numpy as np
from unittest.mock import patch
from intent_classification.intent_router_ml import route_intent
from intent_classification.intent_classification_ml import classify_intent


def mock_classify_ready(label="PATIENT_EVIDENCE_QUERY", confidence=0.85):
    """Returns a mock confident classification result."""
    return {"status": "READY", "label": label, "confidence": confidence}

def mock_classify_uncertain(label="PATIENT_EVIDENCE_QUERY", confidence=0.40):
    """Returns a mock uncertain classification result."""
    return {"status": "UNCERTAIN", "label": label, "confidence": confidence}



#──── route_intent ────────────────────────────────────────────────────────────────────────────────

class TestRouteIntent:

    def test_confident_classification_returns_route_status(self):
        with patch("intent_classification.intent_router_ml.classify_intent",
                   return_value=mock_classify_ready()):
            result = route_intent("patient has persistent cough", 1, 1)
            assert result["status"] == "ROUTE"

    def test_confident_classification_returns_correct_intent(self):
        with patch("intent_classification.intent_router_ml.classify_intent",
                   return_value=mock_classify_ready()):
            result = route_intent("patient has persistent cough", 1, 1)
            assert result["intent"] == "PATIENT_EVIDENCE_QUERY"

    def test_confident_classification_returns_confidence(self):
        with patch("intent_classification.intent_router_ml.classify_intent",
                   return_value=mock_classify_ready()):

            result = route_intent("patient has persistent cough", 1, 1)
            assert result["confidence"] == 0.85

    def test_uncertain_classification_returns_needs_clarification(self):
        with patch("intent_classification.intent_router_ml.classify_intent",
                   return_value=mock_classify_uncertain()):

            result = route_intent("something ambiguous", 1, 1)
            assert result["status"] == "NEEDS_CLARIFICATION"

    def test_uncertain_classification_returns_options(self):
        with patch("intent_classification.intent_router_ml.classify_intent",
                   return_value=mock_classify_uncertain()):

            result = route_intent("something ambiguous", 1, 1)
            assert "options" in result
            assert isinstance(result["options"], list)
            assert len(result["options"]) > 0

    def test_uncertain_classification_options_are_strings(self):
        with patch("intent_classification.intent_router_ml.classify_intent",
                   return_value=mock_classify_uncertain()):

            result = route_intent("something ambiguous", 1, 1)
            for opt in result["options"]:
                assert isinstance(opt, str)

    def test_uncertain_classification_returns_confidence(self):
        with patch("intent_classification.intent_router_ml.classify_intent",
                   return_value=mock_classify_uncertain()):

            result = route_intent("something ambiguous", 1, 1)
            assert result["confidence"] == 0.40

    def test_clarification_options_contain_expected_labels(self):
        with patch("intent_classification.intent_router_ml.classify_intent",
                   return_value=mock_classify_uncertain()):

            result = route_intent("something ambiguous", 1, 1)
            assert "Add new evidence" in result["options"]
            assert "Ask for explanation" in result["options"]
            assert "Request source" in result["options"]
            assert "General help" in result["options"]

    def test_all_intents_route_correctly(self):
        intents = [
            "PATIENT_EVIDENCE_QUERY",
            "FOLLOW_UP_EXPLANATION",
            "SOURCE_REQUEST",
            "HELP_OR_OTHER"
        ]
        for intent in intents:
            with patch("intent_classification.intent_router_ml.classify_intent",
                       return_value=mock_classify_ready(label=intent)):
    
                result = route_intent("some input", 1, 1)
                assert result["intent"] == intent


#────── classify_intent────────────────────────────────────────────────────────────

class TestClassifyIntent:

    @patch("intent_classification.intent_classification_ml.embedder")
    @patch("intent_classification.intent_classification_ml.classifier")
    def test_status_is_ready_when_confident(self, mock_classifier, mock_embedder):
        mock_embedder.encode.return_value = np.zeros((1, 384))
        mock_probs = np.array([0.05, 0.85, 0.05, 0.05])
        mock_classifier.predict_proba.return_value = [mock_probs]

        mock_id_to_label = {0: "HELP_OR_OTHER", 1: "PATIENT_EVIDENCE_QUERY",
                            2: "FOLLOW_UP_EXPLANATION", 3: "SOURCE_REQUEST"}

        with patch("intent_classification.intent_classification_ml.id_to_label", mock_id_to_label):
            result = classify_intent("patient has a fever")
            assert result["status"] == "READY"

    @patch("intent_classification.intent_classification_ml.embedder")
    @patch("intent_classification.intent_classification_ml.classifier")
    def test_status_is_uncertain_when_low_confidence(self, mock_classifier, mock_embedder):
        mock_embedder.encode.return_value = np.zeros((1, 384))
        mock_probs = np.array([0.30, 0.35, 0.20, 0.15])
        mock_classifier.predict_proba.return_value = [mock_probs]

        mock_id_to_label = {0: "HELP_OR_OTHER", 1: "PATIENT_EVIDENCE_QUERY",
                            2: "FOLLOW_UP_EXPLANATION", 3: "SOURCE_REQUEST"}

        with patch("intent_classification.intent_classification_ml.id_to_label", mock_id_to_label):

            result = classify_intent("patient has a cough")
            assert result["status"] == "UNCERTAIN"

    @patch("intent_classification.intent_classification_ml.embedder")
    @patch("intent_classification.intent_classification_ml.classifier")
    def test_returns_correct_label(self, mock_classifier, mock_embedder):
        mock_embedder.encode.return_value = np.zeros((1, 384))
        mock_probs = np.array([0.05, 0.05, 0.85, 0.05])
        mock_classifier.predict_proba.return_value = [mock_probs]

        mock_id_to_label = {0: "HELP_OR_OTHER", 1: "PATIENT_EVIDENCE_QUERY",
                            2: "FOLLOW_UP_EXPLANATION", 3: "SOURCE_REQUEST"}

        with patch("intent_classification.intent_classification_ml.id_to_label", mock_id_to_label):

            result = classify_intent("why does smoking cause cancer?")
            assert result["label"] == "FOLLOW_UP_EXPLANATION"


    @patch("intent_classification.intent_classification_ml.embedder")
    @patch("intent_classification.intent_classification_ml.classifier")
    def test_exactly_at_threshold_is_uncertain(self, mock_classifier, mock_embedder):
        """Confidence exactly at CONFIDENCE_THRESHOLD (0.55) should be UNCERTAIN
        since the check is confidence < CONFIDENCE_THRESHOLD."""
        mock_embedder.encode.return_value = np.zeros((1, 384))
        mock_probs = np.array([0.15, 0.55, 0.15, 0.15])
        mock_classifier.predict_proba.return_value = [mock_probs]

        mock_id_to_label = {0: "HELP_OR_OTHER", 1: "PATIENT_EVIDENCE_QUERY",
                            2: "FOLLOW_UP_EXPLANATION", 3: "SOURCE_REQUEST"}

        with patch("intent_classification.intent_classification_ml.id_to_label", mock_id_to_label):

            result = classify_intent("some borderline threshold input")
            assert result["status"] == "READY"

    @patch("intent_classification.intent_classification_ml.embedder")
    @patch("intent_classification.intent_classification_ml.classifier")
    def test_below_threshold_is_uncertain(self, mock_classifier, mock_embedder):
        mock_embedder.encode.return_value = np.zeros((1, 384))
        mock_probs = np.array([0.18, 0.54, 0.16, 0.12])
        mock_classifier.predict_proba.return_value = [mock_probs]

        mock_id_to_label = {0: "HELP_OR_OTHER", 1: "PATIENT_EVIDENCE_QUERY",
                            2: "FOLLOW_UP_EXPLANATION", 3: "SOURCE_REQUEST"}

        with patch("intent_classification.intent_classification_ml.id_to_label", mock_id_to_label):

            result = classify_intent("borderline input")
            assert result["status"] == "UNCERTAIN"