from fastapi import FastAPI, Query
from pydantic import BaseModel
from typing import Optional
import json
import faiss
import numpy as np
from sentence_transformers import SentenceTransformer

app = FastAPI(title="Fénix QA API")

# Cargar diccionario QA
qa_list = []
with open("qa_fenix.jsonl", encoding="utf-8") as f:
    for line in f:
        obj = json.loads(line)
        qa_list.append({"q": obj["instruction"], "a": obj["response"]})

# Preparar encoder e índice FAISS
encoder = SentenceTransformer("all-MiniLM-L6-v2")
questions = [item["q"] for item in qa_list]
embs = encoder.encode(questions, convert_to_numpy=True, normalize_embeddings=True)
dim = embs.shape[1]
index = faiss.IndexFlatIP(dim)
index.add(embs)

# Función que devuelve respuesta y score
def buscar_respuesta(user_input: str, threshold: float = 0.65):
    u_emb = encoder.encode([user_input], convert_to_numpy=True, normalize_embeddings=True)
    scores, idxs = index.search(u_emb, k=1)
    score, idx = float(scores[0][0]), int(idxs[0][0])
    if score >= threshold:
        return qa_list[idx]["a"], score
    return None, score

# Modelo de entrada
class ChatRequest(BaseModel):
    user_input: str
    threshold: Optional[float] = 0.65

# Modelo de respuesta
class ChatResponse(BaseModel):
    response: Optional[str]
    score: float
    threshold: float
    success: bool

# Endpoint principal
@app.post("/chat", response_model=ChatResponse)
def chat(req: ChatRequest):
    respuesta, score = buscar_respuesta(req.user_input, req.threshold)
    return ChatResponse(
        response=respuesta if respuesta else "Lo siento, no tengo esa información.",
        score=score,
        threshold=req.threshold,
        success=respuesta is not None
    )