components/generators/daily_feed.py

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).\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"
        "If you are mentioning a person, make sure you include who that person is in brackets next to their name. For example: Jeff Bezos (Amazon CEO), Narendra Modi (Prime minister of India)"
        "If you don't find anything useful, don't return anything for that news item"
        "Skim through the news item and form the summary in a way to make it hookable, add essentials data points and meat, in short, the summary should be a hook line."
        "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 []