question.py

import os
import requests
import json
from fastapi import APIRouter
from pydantic import BaseModel
from typing import List
from redis_client import redis_client as r
from dotenv import load_dotenv
from urllib.parse import quote

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()

HF_TOKEN = os.getenv("HF_TOKEN")
HF_API_URL = "https://api-inference.huggingface.co/models/mistralai/Mistral-7B-Instruct-v0.3"
HEADERS = {
    "Authorization": f"Bearer {HF_TOKEN}",
    "Content-Type": "application/json"
}

askMe = APIRouter()

class QuestionInput(BaseModel):
    question: str


def should_extract_keywords(type_id: int) -> bool:
    return type_id in {1, 2, 3, 4, 5, 6, 7, 10}


def extract_answer_after_label(text: str) -> str:
    """
    Extracts everything after the first 'Answer:' label.
    Assumes 'Answer:' appears once and is followed by the relevant content.
    """
    if "Answer:" in text:
        return text.split("Answer:", 1)[1].strip()
    return text.strip()


def mistral_generate(prompt: str, max_new_tokens=128):
    payload = {
        "inputs": prompt,
        "parameters": {
            "max_new_tokens": max_new_tokens,
            "temperature": 0.7
        }
    }
    try:
        response = requests.post(HF_API_URL, headers=HEADERS, data=json.dumps(payload), timeout=30)
        response.raise_for_status()
        result = response.json()
        if isinstance(result, list) and len(result) > 0:
            return result[0].get("generated_text", "").strip()
        else:
            return ""
    except Exception:
        return ""


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

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

    context = ""
    sources = []

    # Step 2: Keyword extraction and news search (if needed)
    if should_extract_keywords(qid):
        keywords = keywords_extractor(question)
        print("Raw extracted keywords:", keywords)

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

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

        for r in results:
            print(r["title"], r["link"])

        # Build context from snippet/description
        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
            }

    # Step 3: Ask Mistral to answer
    answer_prompt = (
        f"You are a concise news assistant. Answer the user's question clearly using the context below if relevant. "
        f"If the context is not helpful, you may rely on your own knowledge, but do not mention the context or question again.\n\n"
        f"Context:\n{context}\n\n"
        f"Question: {question}\n\n"
        f"Answer:"
    )
    answer_raw = mistral_generate(answer_prompt, max_new_tokens=256)

    if not answer_raw:
        final_answer = "Cannot answer – model did not return a valid response."
    else:
        final_answer = extract_answer_after_label(answer_raw)

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