components/generators/daily_feed.py

import os
import json
import redis
from typing import List, Dict
from llama_index.core import VectorStoreIndex, StorageContext
from llama_index.core.query_engine import RetrieverQueryEngine
from llama_index.llms.base import LLM, LLMMetadata
from llama_index.core.llms import CompletionResponse
from llama_index.core.settings import Settings
from components.LLMs.Mistral import call_mistral
from components.indexers.news_indexer import get_upstash_vector_store

# ✅ Dummy LLM config to allow higher context window in LlamaIndex
class DummyLLM(LLM):
    def complete(self, prompt: str, **kwargs) -> CompletionResponse:
        return CompletionResponse(text="")

    @property
    def metadata(self) -> LLMMetadata:
        return LLMMetadata(
            context_window=8192,
            num_output=1024,
            is_chat_model=False
        )

Settings.llm = DummyLLM()

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

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

# 📰 Topic list
TOPICS = ["India news", "World news", "Tech news", "Finance news", "Sports news"]
TOPIC_KEYS = [t.lower().replace(" news", "") for t in TOPICS]

# 🧠 Summarization prompt
BASE_PROMPT = (
    "You are Nuse’s official news summarizer — fast, sharp, and never generic.\n"
    "Your task is to read the following **collection of news excerpts** and extract the most important stories.\n"
    "\n"
    "For each distinct news item you find, write a punchy summary — exactly one line, no more than 20 words. Aim for 15–20 words per summary.\n"
    "\n"
    "Formatting rules:\n"
    "- Each summary must begin with a dash (-)\n"
    "- Do **not** number the summaries\n"
    "- Do **not** include emojis or hashtags\n"
    "- Do **not** add the source name or publication\n"
    "\n"
    "If a person is mentioned, include their designation in brackets. Examples:\n"
    "- Jeff Bezos (Amazon founder)\n"
    "- Narendra Modi (Prime Minister of India)\n"
    "- NATO Chief Jens Stoltenberg\n"
    "\n"
    "✅ Good examples:\n"
    "- India stuns Australia in last-ball World Cup thriller, secures spot in finals\n"
    "- U.S. imposes tariffs on Chinese tech giants, shaking global investor confidence\n"
    "- Ceasefire breakthrough as Netanyahu (Israeli PM) relents under diplomatic pressure\n"
    "\n"
    "❌ Avoid:\n"
    "- Source mentions like (The New York Times), (Reuters)\n"
    "- Introductory fluff or meta comments\n"
    "- Repeating prompt instructions or context\n"
    "\n"
    "You are generating sharp, editorial-style headlines. Only output the summaries. Nothing else."
)

# 📥 Load topic-wise documents from Upstash vector store
def load_documents_by_topic() -> Dict[str, List[str]]:
    vector_store = get_upstash_vector_store()
    storage_context = StorageContext.from_defaults(vector_store=vector_store)
    index = VectorStoreIndex([], storage_context=storage_context)
    retriever = index.as_retriever(similarity_top_k=10)
    query_engine = RetrieverQueryEngine(retriever=retriever)

    topic_docs = {}
    for topic, key in zip(TOPICS, TOPIC_KEYS):
        response = query_engine.query(topic)
        doc_texts = [str(node.get_content()).strip() for node in response.source_nodes if node.get_content()]
        topic_docs[key] = doc_texts
    return topic_docs

# 🧪 Summarize one topic at a time
def summarize_topic(topic_key: str, docs: List[str]) -> List[Dict]:
    if not docs:
        return []

    merged_text = "\n\n---\n\n".join(docs)
    print(f"🧠 Summarizing topic: {topic_key}")
    summary_block = call_mistral(base_prompt=BASE_PROMPT, tail_prompt=merged_text)

    summaries = []
    if summary_block:
        for line in summary_block.splitlines():
            line = line.strip()
            if line.startswith("-"):
                clean = line.lstrip("-–").strip()
                if clean:
                    summaries.append({
                        "summary": clean,
                        "image_url": "https://source.unsplash.com/800x600/?news",
                        "article_link": f"https://google.com/search?q={topic_key}+news"
                    })
    return summaries

# 🚀 Main callable
def generate_and_cache_daily_feed():
    topic_docs = load_documents_by_topic()
    feed_map = {}

    for topic_key in TOPIC_KEYS:
        summaries = summarize_topic(topic_key, topic_docs.get(topic_key, []))
        feed_map[topic_key] = summaries

    final_feed = [{"topic": topic, "feed": feed_map[topic]} for topic in TOPIC_KEYS]
    redis_client.set(REDIS_KEY, json.dumps(final_feed, ensure_ascii=False))
    print(f"✅ Cached daily feed under key '{REDIS_KEY}'")
    return final_feed

# 📦 Get cached data
def get_cached_daily_feed():
    cached = redis_client.get(REDIS_KEY)
    return json.loads(cached) if cached else []