import os
import json
import datetime
from typing import List, Dict

import requests
from fastapi import APIRouter
from pydantic import BaseModel
from dotenv import load_dotenv

from clients.redis_client import redis_client as _r
from models_initialization.mistral_registry import mistral_generate
from nuse_modules.classifier import classify_question, REVERSE_MAP
from nuse_modules.keyword_extracter import keywords_extractor
from nuse_modules.google_search import search_google_news

load_dotenv()

askMe = APIRouter()

# ──────────────────────────────────────────────────────────────
# Pydantic schema
# ──────────────────────────────────────────────────────────────
class QuestionInput(BaseModel):
    question: str


# ──────────────────────────────────────────────────────────────
# Helper functions
# ──────────────────────────────────────────────────────────────

def should_extract_keywords(type_id: int) -> bool:
    """Map the intent id to whether we need keyword extraction."""
    return type_id in {1, 2, 3, 4, 5, 6, 7, 10, 11, 12}


def extract_answer_after_label(text: str) -> str:
    """Extracts everything after the first 'Answer:' label."""
    if "Answer:" in text:
        return text.split("Answer:", 1)[1].strip()
    return text.strip()


# ──────────────────────────────────────────────────────────────
# FastAPI route
# ──────────────────────────────────────────────────────────────

@askMe.post("/ask")
async def ask_question(input: QuestionInput):
    question = input.question.strip()

    # 1️⃣ Classify intent
    qid = classify_question(question)
    print("Intent ID:", qid)
    print("Category:", REVERSE_MAP.get(qid, "unknown"))

    # Special case: ID 13 → return cached headlines
    if qid == "asking_for_headlines":
        date_str = datetime.datetime.utcnow().strftime("%Y-%m-%d")
        categories = ["world", "india", "finance", "sports", "entertainment"]
        all_headlines: List[Dict] = []

        for cat in categories:
            redis_key = f"headlines:{date_str}:{cat}"
            cached = _r.get(redis_key)
            if cached:
                try:
                    articles = json.loads(cached)
                except json.JSONDecodeError:
                    continue
                for art in articles:
                    all_headlines.append({
                        "title":   art.get("title"),
                        "summary": art.get("summary"),
                        "url":     art.get("url"),
                        "image":   art.get("image"),
                        "category": cat,
                    })

        return {
            "question": question,
            "answer": "Here are today’s top headlines:",
            "headlines": all_headlines,
        }

    # 2️⃣ Keyword‑based flow for other intents
    context = ""
    sources: List[Dict] = []

    if should_extract_keywords(qid):
        keywords = keywords_extractor(question)
        print("Raw extracted keywords:", keywords)

        if not keywords:
            return {"error": "Keyword extraction failed."}

        # Google News search
        results = search_google_news(keywords)
        print("Found articles:", results)

        context = "\n\n".join([
            r.get("snippet") or r.get("description", "") for r in results
        ])[:15000]

        sources = [{"title": r["title"], "url": r["link"]} for r in results]

        if not context.strip():
            return {
                "question": question,
                "answer": "Cannot answer – no relevant context found.",
                "sources": sources,
            }

        answer_prompt = (
            "You are a concise news assistant. Answer the user's question clearly using the provided context if relevant. "
            "If the context is not helpful, rely on your own knowledge but do not mention the context.\n\n"
            f"Context:\n{context}\n\n"
            f"Question: {question}\n\nAnswer:"
        )
        answer_raw = mistral_generate(answer_prompt, max_new_tokens=256)

    else:
        answer_prompt = (
            "You are a concise news assistant. Answer the user's question clearly and accurately.\n\n"
            f"Question: {question}\n\nAnswer:"
        )
        answer_raw = mistral_generate(answer_prompt, max_new_tokens=256)

    # 3️⃣ Post‑process model output
    final_answer = extract_answer_after_label(answer_raw or "") or (
        "Cannot answer – model did not return a valid response."
    )

    return {
        "question": question,
        "answer": final_answer.strip(),
        "sources": sources,
    }