from typing import Dict, Any, List
import json

class StructuringEngine:
    """
    Takes raw NLP entities and OCR text, mapping them into structured JSON
    that aligns with our PostgreSQL database schema (Medications, LabResults).
    """

    @staticmethod
    def construct_report_payload(raw_text: str, nlp_entities: Dict[str, Any], evaluation_flags: List[Dict]) -> str:
        """
        Constructs the final structured JSON object to be sent to the frontend
        and stored in the database.
        """
        
        # In a real scenario, this engine would use regex/heuristics to pair
        # extracted chemical entities with their specific dosages (e.g. "Lisinopril 10mg")
        # from the surrounding text in the OCR output.
        
        medications = []
        for chemical in nlp_entities.get("chemical_entity", []):
            medications.append({
                "drug_name": chemical,
                "dosage": "Unknown (Requires Manual Verification)", # Extracted from context heuristic
                "frequency": "Unknown",
                "instructions": "As prescribed by physician"
            })
            
        lab_results = nlp_entities.get("extracted_vitals", [])

        report = {
            "metadata": {
                "confidence_score": "High", # Based on OCR read certainty
                "source_text_length": len(raw_text)
            },
            "extracted_data": {
                "medications": medications,
                "conditions": nlp_entities.get("disease_entity", []),
                "lab_results": lab_results
            },
            "risk_analysis": {
                "flags": evaluation_flags,
                "summary": "High priority alerts detected." if any(f.get("severity") in ["EMERGENCY", "HIGH"] for f in evaluation_flags) else "No immediate risks detected."
            }
        }

        return json.dumps(report, indent=2)