from typing import Dict, Any

class ClinicalAssessmentEngine:
    """
    Ithu thaan namba main Local AI Engine. Neenga kudutha ella list-aiyum 
    (Depression types, Alleviation states, Attrition reasons, Patient Intent)
    ithulla exact-a train panna porom.
    """
    def __init__(self):
        # Placeholder for loading actual BERT / LLM pipeline
        # e.g., self.model = pipeline('text-classification', model='medical-bert')
        
        # --- THE MASTER CLINICAL TAXONOMY (As strictly defined by you) ---
        self.master_categories = {
            "Main_Clinical_Types": [
                "Major Depressive Disorder (MDD)", "Persistent Depressive Disorder (PDD / Dysthymia)",
                "Bipolar Disorder (Manic Depression)", "Postpartum (Perinatal) Depression",
                "Seasonal Affective Disorder (SAD)", "Psychotic Depression",
                "Premenstrual Dysphoric Disorder (PMDD)", "Atypical Depression"
            ],
            
            "Clinical_Sub_Types": [
                "Disruptive Mood Dysregulation Disorder (DMDD)", "Treatment-Resistant Depression (TRD)",
                "Substance/Medication-Induced Depression", "Depression Due to Another Medical Condition",
                "Smiling Depression (High-Functioning)", "Melancholic Depression",
                "Agitated Depression", "Double Depression", "Minor Depression"
            ],
            
            "Rare_and_Specific_Origins": [
                "Catatonic Depression", "Endogenous Depression", "Reactive (Exogenous) Depression",
                "Existential Depression", "Masked Depression", "Geriatric Depression",
                "Recurrent Brief Depression", "Unipolar Depression", "Vascular Depression"
            ],
            
            "Niche_Types": [
                "Cyclothymia", "Mixed Anxiety-Depressive Disorder", "Perimenopausal Depression",
                "Post-Schizophrenic Depression", "Adjustment Disorder", "Antenatal (Prenatal) Depression",
                "Prolonged Grief Disorder", "Male Depressive Syndrome", "Burnout-Induced Depression",
                "Anergic Depression", "D-MER (Dysphoric Milk Ejection Reflex)", "Post-coital Dysphoria (PCD)"
            ],
            
            "Treatment_Alleviation_States": [
                "Partial Response", "Residual Symptoms", "Tachyphylaxis (Poop-Out)",
                "Breakthrough Depression", "Treatment-Resistant (TRD)", "Full Remission",
                "Sustained Recovery", "Spontaneous Remission", "Placebo Effect",
                "Palliative Alleviation", "Nocebo Effect", "The Honeymoon Effect",
                "Relapse", "Recurrence", "Pseudo-Resistance", "Emotional Blunting (Apathy)",
                "Treatment-Emergent Affective Switch (TEAS)", "Discontinuation Syndrome (Withdrawal)"
            ],
            
            "Attrition_and_Loss_Of_FollowUp": [
                # Attrition Domains
                "Corporate/HR Attrition", "Customer Churn", "Academic Attrition (Dropouts)",
                "Therapeutic/Clinical Trial Attrition", 
                # Patient Intent Loss
                "Voluntary Withdrawal", "Intentional Non-compliance", "Cured Perception", "Unintentional Loss",
                # Reachability
                "Silent Loss", "Passive Dropout", "Active Dropout",
                # Clinical Outcomes
                "Mortality (Death)", "Severe Morbidity",
                # Barriers
                "Geographic Attrition", "Economic Attrition", "Social Stigma",
                # System Failures
                "Administrative Loss", "Provider Relocation", "Protocol Burden",
                # Timelines
                "Intermittent Loss", "Permanent Loss"
            ]
        }

    def evaluate_patient_state(self, patient_text: str) -> Dict[str, Any]:
        """
        Patient chat pannumbothu intha function than antha Text-a read panni, 
        mela irukkura antha categories-la etha match aaguthu nu theliva analyze pannum.
        """
        # ==========================================
        # REAL AI LOGIC WILL BE IMPLEMENTED HERE
        # (Tokenizing, running through BERT, extracting exact category)
        # ==========================================
        
        # MOCK RETURN FOR NOW based on rule definitions
        return {
            "mapped_condition": "Smiling Depression (High-Functioning)",
            "mapped_treatment_state": "Incomplete Relief",
            "attrition_risk": "High (Possible Passive Dropout)",
            "requires_doctor": False,
            "extracted_feeling": "Hiding deep sadness while working normally."
        }

# Initialize a global instance to be used by the FastAPI router
analyzer_engine = ClinicalAssessmentEngine()