from fastapi import FastAPI
from pydantic import BaseModel
from typing import List
from contextlib import asynccontextmanager
from src.engine.analysis_engine import AnalysisEngine

engine = None

@asynccontextmanager
async def lifespan(app: FastAPI):
    global engine
    engine = AnalysisEngine()
    yield

app = FastAPI(lifespan=lifespan, title="FactChecker Ultra-Ligero")

class Paragraph(BaseModel):
    type: str
    text: str

class AnalysisRequest(BaseModel):
    title: str
    paragraphs: List[Paragraph]
    user_text: str
    
class RagItem(BaseModel):
    summary: str

class RagAnalysisRequest(BaseModel):
    rag_results: List[RagItem]
    user_text: str

@app.get("/")
def home():
    return {"status": "corriendo", "modelos_cargados": "una sola vez"}

@app.post("/analyze")
async def analyze(payload: AnalysisRequest):
    result = engine.analyze(
        user_text=payload.user_text,
        document_paragraphs=[p.dict() for p in payload.paragraphs],
        title=payload.title,
    )
    return result


@app.post("/analyze-rag")
async def analyze_rag(payload: RagAnalysisRequest):
    # 1. Convertir summaries a párrafos estándar del analizador
    rag_paragraphs = [
        {
            "type": "rag_summary",
            "text": item.summary
        }
        for item in payload.rag_results
    ]

    # 2. Ejecutar el MISMO engine
    result = engine.analyze(
        user_text=payload.user_text,
        document_paragraphs=rag_paragraphs,
        title="RAG_CONTEXT"
    )

    # 3. Marcar que viene de RAG (trazabilidad futura)
    result["source"] = "rag"

    return result