import json

from app.ai.openai_client import OpenAIClient
from app.ai.prompts import build_system_prompt
from app.ai.validator import validate_ai_response
from app.config import OPENAI_API_KEY, GEMINI_API_KEY, GEMINI_MODEL, ANTHROPIC_API_KEY, ANTHROPIC_MODEL
from app.specialties.registry import get_specialty_config


class AIOrchestrator:

    def __init__(self):
        self.openai = OpenAIClient()

    async def process(
        self,
        session,
        state,
        conversation_history,
        user_message,
    ):
        specialty = get_specialty_config(
            session.category_code
        )

        current_state = json.loads(
            state.state_json
        )

        context = {
            "patient": {
                "id": session.patient_id,
                "name": session.patient_name,
                "age": session.age,
                "gender": session.gender,
            },
            "category": {
                "type": session.category_type,
                "code": session.category_code,
                "name": session.category_name,
            },
            "specialty_config": specialty,
            "language": session.language,
            "current_stage": session.stage,
            "current_state": current_state,
            "conversation_history": conversation_history,
            "new_patient_message": user_message,
        }

        prompt = build_system_prompt(context)

        # 1. Primary: Google Gemini (High-Speed Multilingual Indic Reasoning ~0.8s)
        if GEMINI_API_KEY and GEMINI_API_KEY.strip() not in ["your_gemini_key", ""]:
            try:
                import google.generativeai as genai
                genai.configure(api_key=GEMINI_API_KEY)
                model = genai.GenerativeModel(GEMINI_MODEL or "gemini-3.6-flash")
                full_prompt = f"{prompt}\n\nPATIENT MESSAGE: {user_message}\n\nRespond strictly with JSON matching the schema above."
                res = await model.generate_content_async(full_prompt)
                raw_text = res.text.replace("```json", "").replace("```", "").strip()
                parsed = json.loads(raw_text)
                validated = validate_ai_response(parsed)
                return validated.model_dump()
            except Exception as e:
                print(f"[Gemini Orchestrator Error]: {e}")

        # 2. Secondary: OpenAI Call if valid API key is set
        if OPENAI_API_KEY and OPENAI_API_KEY.strip() not in ["your_openai_api_key", "your_openai_key", ""]:
            try:
                raw_response = await self.openai.generate_response(
                    system_prompt=prompt,
                    user_message=user_message,
                )
                parsed = json.loads(raw_response)
                validated = validate_ai_response(parsed)
                return validated.model_dump()
            except Exception as e:
                print(f"[OpenAI Orchestrator Error]: {e}")

        # 3. Tertiary: Anthropic Claude
        if ANTHROPIC_API_KEY and ANTHROPIC_API_KEY.strip() not in ["your_anthropic_key", ""]:
            try:
                from anthropic import AsyncAnthropic
                client = AsyncAnthropic(api_key=ANTHROPIC_API_KEY)
                res = await client.messages.create(
                    model=ANTHROPIC_MODEL or "claude-sonnet-4-5-20250929",
                    max_tokens=600,
                    system=prompt,
                    messages=[{"role": "user", "content": user_message}],
                )
                raw_text = res.content[0].text.replace("```json", "").replace("```", "").strip()
                parsed = json.loads(raw_text)
                validated = validate_ai_response(parsed)
                return validated.model_dump()
            except Exception as e:
                print(f"[Claude Orchestrator Error]: {e}")

        # 4. Fallback simulator for offline development with localized language support
        parsed = self._simulate_specialty_response(session, specialty, user_message, len(conversation_history))
        validated = validate_ai_response(parsed)
        return validated.model_dump()

    def _simulate_specialty_response(self, session, specialty, user_message, turn_count=0):
        stage = session.stage
        code = specialty.get("code", "general")
        lang = (session.language or "ta-IN").lower()
        msg_lower = user_message.lower().strip()
        is_negative = msg_lower in ["no", "no more", "nothing", "nothing else", "know nothing", "none", "illness none", "இல்லை", "ஒன்றுமில்லை", "नहीं"]

        # Localized clinical phrases
        if lang.startswith("ta"):
            conclusion_text = "நன்றி. உங்கள் உடல்நல விவரங்கள் பதிவு செய்யப்பட்டுள்ளன. மருத்துவர் விரைவில் பரிசீலிப்பார்."
            symptom_q = "இந்த அறிகுறிகள் எவ்வளவு நாட்களாக இருக்கிறது, மற்றும் ஏதேனும் மாத்திரைகள் சாப்பிட்டீர்களா?"
            med_q = "நன்றி. உங்களுக்கு ஏதேனும் மருந்து ஒவ்வாமை (Allergy) அல்லது வேறு உடல்நல பிரச்சனைகள் உள்ளதா?"
        elif lang.startswith("hi"):
            conclusion_text = "धन्यवाद। आपकी स्वास्थ्य जानकारी दर्ज कर ली गई है। डॉक्टर जल्द ही समीक्षा करेंगे।"
            symptom_q = "आपको यह समस्या कितने दिनों से है और क्या आपने कोई दवाई ली है?"
            med_q = "धन्यवाद। क्या आपको किसी दवाई से एलर्जी या कोई अन्य बीमारी है?"
        elif lang.startswith("te"):
            conclusion_text = "ధన్యవాదాలు. మీ ఆరోగ్య వివరాలు నమోదు చేయబడ్డాయి."
            symptom_q = "ఈ సమస్య మీకు ఎన్ని రోజుల నుండి ఉంది మరియు ఏవైనా మందులు వాడారా?"
            med_q = "ధన్యవాదాలు. మీకు ఏవైనా మందుల పడనితనం (Allergy) ఉందా?"
        elif lang.startswith("kn"):
            conclusion_text = "ಧನ್ಯವಾದಗಳು. ನಿಮ್ಮ ಆರೋಗ್ಯದ ಮಾಹಿತಿ ದಾಖಲಾಗಿದೆ."
            symptom_q = "ಈ ಸಮಸ್ಯೆ ನಿಮಗೆ ಎಷ್ಟು ದಿನಗಳಿಂದ ಇದೆ ಮತ್ತು ಯಾವುದೇ ಮಾತ್ರೆಗಳನ್ನು ತೆಗೆದುಕೊಂಡಿದ್ದೀರಾ?"
            med_q = "ಧನ್ಯವಾದಗಳು. ನಿಮಗೆ ಯಾವುದೇ ಔಷಧಿ ಅಲರ್ಜಿ ಇದೆಯೇ?"
        else:
            conclusion_text = "Thank you. I have recorded your intake details for the doctor to review."
            symptom_q = "How long have you had these symptoms, and have you taken any medicines?"
            med_q = "Thank you. Do you have any known drug allergies or existing health conditions?"

        # Fast conclusion if patient says "No" or already had 6 turns
        if is_negative or turn_count >= 6:
            return {
                "reply": conclusion_text,
                "next_stage": "confirmation",
                "state_updates": {
                    "reason_for_visit": None,
                    "symptoms": [],
                    "medical_history": ["None reported"] if is_negative else [],
                    "medications": [],
                    "allergies": ["None"] if is_negative else []
                },
                "completed": True
            }

        if stage == "greeting":
            return {
                "reply": symptom_q,
                "next_stage": "reason_for_visit",
                "state_updates": {
                    "reason_for_visit": user_message,
                    "symptoms": [{"name": user_message, "duration": "recent"}],
                    "medical_history": [],
                    "medications": [],
                    "allergies": []
                },
                "completed": False
            }
        elif stage == "reason_for_visit":
            return {
                "reply": med_q,
                "next_stage": "medications",
                "state_updates": {
                    "reason_for_visit": user_message,
                    "symptoms": [{"name": user_message, "duration": "reported"}],
                    "medical_history": [],
                    "medications": [],
                    "allergies": []
                },
                "completed": False
            }
        elif stage == "medications":
            return {
                "reply": conclusion_text,
                "next_stage": "confirmation",
                "state_updates": {
                    "reason_for_visit": None,
                    "symptoms": [],
                    "medical_history": [],
                    "medications": [user_message] if not is_negative else [],
                    "allergies": []
                },
                "completed": False
            }
        else:
            return {
                "reply": conclusion_text,
                "next_stage": "confirmation",
                "state_updates": {
                    "reason_for_visit": None,
                    "symptoms": [],
                    "medical_history": [],
                    "medications": [],
                    "allergies": ["None"] if is_negative else [user_message]
                },
                "completed": True
            }