from datetime import datetime
from typing import List

from fastapi import APIRouter

from app.schemas.analysis import AnalysisRecord, AnalysisRequest, AnalysisResult, ChatRequest, SearchRecord
from app.services.agents import run_full_analysis
from app.services.llm import call_gemini_with_model
from app.services.persistence import save_to_json, load_from_json

router = APIRouter()

# Load initial history from disk
analysis_history: List[AnalysisRecord] = load_from_json(AnalysisRecord, "analysis_history.json")
search_history: List[SearchRecord] = load_from_json(SearchRecord, "search_history.json")


@router.post("/analyze", response_model=AnalysisResult)
async def analyze_opportunity(request: AnalysisRequest):
    result = await run_full_analysis(request.tender, request.company_profile, request.document_text, request.models, request.tender_details, request.amd_settings)
    record = AnalysisRecord(
        tender_code=request.tender.code,
        tender_name=request.tender.name,
        analyzed_at=datetime.utcnow(),
        analysis=result,
    )
    analysis_history.insert(0, record)
    if len(analysis_history) > 20:
        analysis_history.pop()
    
    # Persist to disk
    save_to_json(analysis_history, "analysis_history.json")
    
    return result


@router.get("/analysis-history", response_model=List[AnalysisRecord])
def get_analysis_history():
    return analysis_history


@router.post("/chat")
async def agent_chat(request: ChatRequest):
    # Construct context
    history_str = "\n".join([f"{m.role.upper()}{f' ({m.agent_name})' if m.agent_name else ''}: {m.content}" for m in request.history])
    
    prompt = (
        f"Eres {request.agent} en AndesOps AI, un consultor experto de élite. "
        f"Actualmente estás operando bajo el motor de IA: {request.model}.\n\n"
        f"CONTEXTO DE LA LICITACIÓN:\n{request.tender.model_dump_json()}\n\n"
        f"DATOS DE MI EMPRESA:\n{request.company_profile.model_dump_json()}\n\n"
        f"HISTORIAL DE CHAT:\n{history_str}\n\n"
        f"PREGUNTA DEL USUARIO: {request.message}\n\n"
        f"INSTRUCCIONES CRÍTICAS:\n"
        f"1. Responde con la personalidad de {request.agent}. Sé agudo, profesional y estratégico.\n"
        f"2. IDENTIDAD: Si el usuario pregunta qué modelo eres o quién te potencia, menciona que eres {request.agent} de AndesOps, funcionando sobre {request.model}.\n"
        f"3. ANALIZA LAS BASES: Revisa el campo 'description' para responder.\n"
        f"4. CITA EL DOCUMENTO: Menciona montos, multas o plazos explícitos si están disponibles.\n"
        f"5. CONSEJO ESTRATÉGICO: Sugiere mejoras basadas en la experiencia de la empresa ({request.company_profile.experience}).\n"
        f"RESPONDE EN ESPAÑOL."
    )
    
    if request.amd_settings:
        settings.amd_inference_url = request.amd_settings.get("url")
        settings.amd_api_key = request.amd_settings.get("key")
        print(f"!!! AMD NODE ACTIVATED FOR CHAT: {settings.amd_inference_url} !!!")

    response = await call_gemini_with_model(prompt, request.model)
    if not response:
        response = "Lo siento, tuve un problema procesando tu solicitud. ¿Podrías intentar de nuevo?"
    return {"response": response}

@router.post("/search-history")
def save_search_history(record: SearchRecord):
    search_history.insert(0, record)
    if len(search_history) > 50:
        search_history.pop()
    save_to_json(search_history, "search_history.json")
    return {"status": "ok"}

@router.get("/search-history", response_model=List[SearchRecord])
def get_search_history():
    return search_history