"""
MediGuard AI RAG-Helper
Biomarker-Disease Linker Agent - Connects biomarker values to predicted disease
"""

from src.llm_config import llm_config
from src.state import AgentOutput, GuildState, KeyDriver


class BiomarkerDiseaseLinkerAgent:
    """Agent that links specific biomarker values to the predicted disease"""

    def __init__(self, retriever):
        """
        Initialize with a retriever for biomarker-disease connections.

        Args:
            retriever: Vector store retriever for biomarker evidence
        """
        self.retriever = retriever
        self.llm = llm_config.explainer

    def link(self, state: GuildState) -> GuildState:
        """
        Link biomarkers to disease prediction.

        Args:
            state: Current guild state

        Returns:
            Updated state with biomarker-disease links
        """
        print("\n" + "=" * 70)
        print("EXECUTING: Biomarker-Disease Linker Agent (RAG)")
        print("=" * 70)

        model_prediction = state["model_prediction"]
        disease = model_prediction["disease"]
        biomarkers = state["patient_biomarkers"]

        # Get biomarker analysis from previous agent
        biomarker_analysis = state.get("biomarker_analysis") or {}

        # Identify key drivers
        print(f"\nIdentifying key drivers for {disease}...")
        key_drivers, citations_missing = self._identify_key_drivers(disease, biomarkers, biomarker_analysis, state)

        print(f"Identified {len(key_drivers)} key biomarker drivers")

        # Create agent output
        output = AgentOutput(
            agent_name="Biomarker-Disease Linker",
            findings={
                "disease": disease,
                "key_drivers": [kd.model_dump() for kd in key_drivers],
                "total_drivers": len(key_drivers),
                "feature_importance_calculated": True,
                "citations_missing": citations_missing,
            },
        )

        # Update state
        print("\nBiomarker-disease linking complete")

        return {"agent_outputs": [output]}

    def _identify_key_drivers(
        self, disease: str, biomarkers: dict[str, float], analysis: dict, state: GuildState
    ) -> tuple[list[KeyDriver], bool]:
        """Identify which biomarkers are driving the disease prediction"""

        # Get out-of-range biomarkers from analysis
        flags = analysis.get("biomarker_flags", [])
        abnormal_biomarkers = [f for f in flags if f["status"] != "NORMAL"]

        # Get disease-relevant biomarkers
        relevant = analysis.get("relevant_biomarkers", [])

        # Focus on biomarkers that are both abnormal AND disease-relevant
        key_biomarkers = [f for f in abnormal_biomarkers if f["name"] in relevant]

        # If no key biomarkers found, use top abnormal ones
        if not key_biomarkers:
            key_biomarkers = abnormal_biomarkers[:5]

        print(f"  Analyzing {len(key_biomarkers)} key biomarkers...")

        # Generate key drivers with evidence
        key_drivers: list[KeyDriver] = []
        citations_missing = False
        for biomarker_flag in key_biomarkers[:5]:  # Top 5
            driver, driver_missing = self._create_key_driver(biomarker_flag, disease, state)
            key_drivers.append(driver)
            citations_missing = citations_missing or driver_missing

        return key_drivers, citations_missing

    def _create_key_driver(self, biomarker_flag: dict, disease: str, state: GuildState) -> tuple[KeyDriver, bool]:
        """Create a KeyDriver object with evidence from RAG"""

        name = biomarker_flag["name"]
        value = biomarker_flag["value"]
        unit = biomarker_flag["unit"]
        status = biomarker_flag["status"]

        # Retrieve evidence linking this biomarker to the disease
        query = f"How does {name} relate to {disease}? What does {status} {name} indicate?"

        citations_missing = False
        try:
            docs = self.retriever.invoke(query)
            if state["sop"].require_pdf_citations and not docs:
                evidence_text = "Insufficient evidence available in the knowledge base."
                contribution = "Unknown"
                citations_missing = True
            else:
                evidence_text = self._extract_evidence(docs, name, disease)
                contribution = self._estimate_contribution(biomarker_flag, len(docs))
        except Exception as e:
            print(f"  Warning: Evidence retrieval failed for {name}: {e}")
            evidence_text = f"{status} {name} may be related to {disease}."
            contribution = "Unknown"
            citations_missing = True

        # Generate explanation using LLM
        explanation = self._generate_explanation(name, value, unit, status, disease, evidence_text)

        driver = KeyDriver(
            biomarker=name,
            value=value,
            contribution=contribution,
            explanation=explanation,
            evidence=evidence_text[:500],  # Truncate long evidence
        )

        return driver, citations_missing

    def _extract_evidence(self, docs: list, biomarker: str, disease: str) -> str:
        """Extract relevant evidence from retrieved documents"""
        if not docs:
            return f"Limited evidence available for {biomarker} in {disease}."

        # Combine relevant passages
        evidence = []
        for doc in docs[:2]:  # Top 2 docs
            content = doc.page_content
            # Extract sentences mentioning the biomarker
            sentences = content.split(".")
            relevant_sentences = [
                s.strip() for s in sentences if biomarker.lower() in s.lower() or disease.lower() in s.lower()
            ]
            evidence.extend(relevant_sentences[:2])

        return ". ".join(evidence[:3]) + "." if evidence else content[:300]

    def _estimate_contribution(self, biomarker_flag: dict, doc_count: int) -> str:
        """Estimate the contribution percentage (simplified)"""
        status = biomarker_flag["status"]

        # Simple heuristic based on severity
        if "CRITICAL" in status:
            base = 40
        elif status in ["HIGH", "LOW"]:
            base = 25
        else:
            base = 10

        # Adjust based on evidence strength
        evidence_boost = min(doc_count * 2, 15)

        total = min(base + evidence_boost, 60)
        return f"{total}%"

    def _generate_explanation(
        self, biomarker: str, value: float, unit: str, status: str, disease: str, evidence: str
    ) -> str:
        """Generate patient-friendly explanation"""

        prompt = f"""Explain in 1-2 sentences how this biomarker result relates to {disease}:

Biomarker: {biomarker}
Value: {value} {unit}
Status: {status}

Medical Evidence: {evidence}

Write in patient-friendly language, explaining what this means for the diagnosis."""

        try:
            response = self.llm.invoke(prompt)
            return response.content.strip()
        except Exception:
            return f"{biomarker} at {value} {unit} is {status}, which may be associated with {disease}."


def create_biomarker_linker_agent(retriever):
    """Factory function to create agent with retriever"""
    return BiomarkerDiseaseLinkerAgent(retriever)