import os
import sys
import json
import requests
import redis
from typing import List, Dict, Optional
from llama_index.core import VectorStoreIndex
from llama_index.core.query_engine import RetrieverQueryEngine
from llama_index.core.schema import Document
from llama_index.core.settings import Settings

# ✅ Disable implicit LLM usage
Settings.llm = None

# 🔐 Environment variables
REDIS_URL = os.environ.get("UPSTASH_REDIS_URL", "redis://localhost:6379")
REDIS_KEY = os.environ.get("UPSTASH_REDIS_TOKEN")
MISTRAL_URL = os.environ.get("MISTRAL_URL")
HF_TOKEN = os.environ.get("HF_TOKEN")

# ✅ Redis client
redis_client = redis.Redis.from_url(REDIS_URL, decode_responses=True)

# 📰 Topics
TOPICS = ["India news", "World news", "Tech news", "Finance news", "Sports news"]

# 📄 Headers for HF endpoint
HEADERS = {
    "Authorization": f"Bearer {HF_TOKEN}",
    "Content-Type": "application/json"
}

# 🧠 Build Mistral-style instruction prompt
def build_prompt(content: str, topic: str) -> str:
    base_instruction = (
        "You are Nuse’s official news summarizer — insightful, punchy, and always on point. 🧠✨\n"
        "Your job is to scan the content below and extract the key news items. For each item, craft a crisp summary (15–20 words), add 1–2 fitting emojis, and make it pop.\n"
        "List each summary on a new line starting with a dash (-) and no numbers. This is how Nuse keeps it clean and scannable.\n"
        "\n"
        "Example format:\n"
        "- India stuns Australia in a last-ball thriller at the World Cup finals 🏏🇮🇳\n (15–20 words)"
        "- U.S. imposes sweeping tariffs on Chinese tech giants, rattling global markets 📉🇺🇸\n (15–20 words)"
        "- Ceasefire breakthrough: Netanyahu bows to pressure after week-long escalation 🔥🕊️\n (15–20 words)"
        "\n"
        "Be sharp. Be brief. No fluff. No preambles. Just the summaries.\n"
        "Return only the final summary block — no extra commentary, no prompt repetition."
    )
    tail = f"Topic: {topic}\n\n{content.strip()}"
    return f"<s>[INST]{base_instruction}\n\n{tail}[/INST]</s>"


# 🔁 Call Mistral using HF Inference Endpoint
def call_mistral(prompt: str) -> Optional[str]:
    headers = {
        "Authorization": f"Bearer {HF_TOKEN}",
        "Content-Type": "application/json"
    }
    payload = {
        "inputs": prompt
    }

    try:
        response = requests.post(MISTRAL_URL, headers=headers, json=payload, timeout=20)
        response.raise_for_status()
        data = response.json()

        # Get the generated text
        if isinstance(data, list) and data:
            raw_output = data[0].get("generated_text", "")
        elif isinstance(data, dict):
            raw_output = data.get("generated_text", "")
        else:
            return None

        # ✅ Extract only the portion after the [/INST]</s> marker
        if "[/INST]</s>" in raw_output:
            return raw_output.split("[/INST]</s>")[-1].strip()
        return raw_output.strip()

    except Exception as e:
        print(f"⚠️ Mistral error: {e}")
        return None

# ✂️ Summarize top N documents
def summarize_topic(docs: List[str], topic: str) -> List[Dict]:
    feed = []
    for doc in docs[:5]:
        prompt = build_prompt(doc, topic)
        print("\n📤 Prompt sent to Mistral:\n", prompt[:300], "...\n")
        summary_block = call_mistral(prompt)

        if summary_block:
            # Split by lines that start with "- " or "– " (dash or en dash)
            for line in summary_block.splitlines():
                line = line.strip()
                if line.startswith("-") or line.startswith("–"):
                    clean_summary = line.lstrip("-–").strip()
                    if clean_summary:
                        feed.append({
                            "summary": clean_summary,
                            "image_url": "https://source.unsplash.com/800x600/?news",
                            "article_link": "https://google.com/search?q=" + topic.replace(" ", "+")
                        })

    return feed


# ⚡ Generate and cache daily feed
def generate_and_cache_daily_feed(documents: List[Document]):
    index = VectorStoreIndex.from_documents(documents)
    retriever = index.as_retriever()
    query_engine = RetrieverQueryEngine(retriever=retriever)

    final_feed = []

    for topic in TOPICS:
        print(f"\n🔍 Generating for: {topic}")
        response = query_engine.query(topic)
        docs = [str(node.get_content()) for node in response.source_nodes]

        topic_feed = summarize_topic(docs, topic)
        final_feed.append({
            "topic": topic.lower().replace(" news", ""),
            "feed": topic_feed
        })

    redis_client.set(REDIS_KEY, json.dumps(final_feed, ensure_ascii=False))
    print(f"✅ Cached daily feed under key '{REDIS_KEY}'")
    return final_feed

# 📦 For testing or API access
def get_cached_daily_feed():
    cached = redis_client.get(REDIS_KEY)
    return json.loads(cached) if cached else []