components/generators/daily_feed.py

import os
import json
import redis
import numpy as np
from typing import List, Dict
from openai import OpenAI
from components.indexers.news_indexer import get_upstash_vector_store
from llama_index.core import StorageContext
from llama_index.core.vector_stores.types import VectorStoreQuery, MetadataFilter, MetadataFilters, FilterOperator

# 🔐 Environment variables
REDIS_URL = os.environ.get("UPSTASH_REDIS_URL", "redis://localhost:6379")
REDIS_KEY = os.environ.get("UPSTASH_REDIS_TOKEN") # Using REDIS_KEY for the cache key, assuming UPSTASH_REDIS_TOKEN is meant for the cache key here
OPENAI_API_KEY = os.environ.get("OPENAI_API_KEY")

# ✅ Redis client
try:
    redis_client = redis.Redis.from_url(REDIS_URL, decode_responses=True)
except Exception as e:
    print("❌ [Redis Init Error]", e)
    raise

# 📰 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 editorial summarizer. Read the excerpts below and extract the most important stories. "
    "Return up to 3 punchy headlines, each under 20 words, written like a premium editorial bulletin."
)

# 📥 Load documents grouped by topic from Upstash
def load_all_documents_grouped_by_topic() -> Dict[str, List[str]]:
    topic_docs = {key: [] for key in TOPIC_KEYS}

    try:
        vector_store = get_upstash_vector_store()
        print("💡 Successfully retrieved Upstash vector store.")

        # --- ADD THESE PRINT STATEMENTS ---
        print(f"DEBUG: TOPICS = {TOPICS}")
        print(f"DEBUG: TOPIC_KEYS = {TOPIC_KEYS}")
        print(f"DEBUG: Length of TOPICS = {len(TOPICS)}")
        print(f"DEBUG: Length of TOPIC_KEYS = {len(TOPIC_KEYS)}")
        # ----------------------------------

        for topic, key in zip(TOPICS, TOPIC_KEYS):
            try:
                # Upstash VectorStore expects the filter value to match the exact string
                # of the topic as it was indexed. Make sure your 'topic' metadata
                # in Upstash exactly matches the values in TOPICS (e.g., "India news").
                
                # Construct MetadataFilters object
                filters = MetadataFilters(
                    filters=[
                        MetadataFilter(key="topic", value=topic, operator=FilterOperator.EQ)
                    ]
                )

                dummy_vector = np.random.rand(384).tolist()  # Assuming MiniLM embeddings
                query = VectorStoreQuery(
                    query_embedding=dummy_vector,
                    similarity_top_k=50, # Retrieve enough documents for summarization
                    filters=filters # Apply the metadata filter
                )
                
                # Removed the problematic .to_dict() call
                print(f"🔎 Querying Upstash for topic: '{topic}'") 
                result = vector_store.query(query)
                print(f"➡️ Found {len(result.nodes)} nodes for topic: '{topic}'.")

                for node in result.nodes:
                    content = node.get_content().strip()
                    if content:
                        topic_docs[key].append(content)
                        # Optional: Print metadata to verify filtering
                        # print(f"  Node metadata: {node.metadata}") 
            except Exception as e:
                print(f"❌ [Topic Metadata Filter error for '{topic}']: {e}")
                # Optional: Log the full traceback for more detailed debugging
                # import traceback
                # traceback.print_exc()

    except Exception as e:
        print("❌ [load_all_documents_grouped_by_topic Error]", e)
        # import traceback
        # traceback.print_exc()

    return topic_docs

# 🧪 Summarize one topic at a time using OpenAI GPT-4
def summarize_topic(topic_key: str, docs: List[str]) -> List[Dict]:
    if not docs:
        print(f"⚠️ No docs found for topic: {topic_key}, skipping summarization.")
        return []

    try:
        client = OpenAI(api_key=OPENAI_API_KEY)
        # Join documents, ensuring we don't exceed typical GPT-4 context window (approx 128k tokens, 12000 chars is safe)
        content = "\n\n---\n\n".join(docs)[:12000]

        print(f"🧠 Summarizing topic via OpenAI: {topic_key} ({len(docs)} documents)")
        completion = client.chat.completions.create(
            model="gpt-4", # Or "gpt-4o" for potentially better performance
            messages=[
                {"role": "system", "content": BASE_PROMPT},
                {"role": "user", "content": content},
            ],
            max_tokens=512, # Enough tokens for 3 punchy headlines
            temperature=0.7, # A bit creative but focused
        )

        text = completion.choices[0].message.content.strip()

        summaries = []
        # Parse the headlines, assuming they might be bullet points or lines
        for line in text.splitlines():
            line = line.strip("-–• ") # Remove common bullet characters
            if line:
                summaries.append({
                    "summary": line,
                    "image_url": "https://source.unsplash.com/800x600/?news", # Generic image, could be improved
                    "article_link": f"https://google.com/search?q={topic_key}+news" # Generic search link
                })
        return summaries

    except Exception as e:
        print(f"❌ [OpenAI Summarization Error for '{topic_key}']: {e}")
        # import traceback
        # traceback.print_exc()
        return []

# 🚀 Main callable
def generate_and_cache_daily_feed():
    try:
        print("🆕 Running OpenAI-powered daily feed generator....")
        topic_docs = load_all_documents_grouped_by_topic()
        feed_map = {}

        for topic_key in TOPIC_KEYS:
            try:
                summaries = summarize_topic(topic_key, topic_docs.get(topic_key, []))
                feed_map[topic_key] = summaries
            except Exception as e:
                print(f"❌ [Topic Loop Error for '{topic_key}']: {e}")
                # import traceback
                # traceback.print_exc()
                feed_map[topic_key] = []

        final_feed = [{"topic": topic, "feed": feed_map[topic_key]} for topic, topic_key in zip(TOPICS, TOPIC_KEYS)]

        try:
            # Ensure the REDIS_KEY is suitable for a key name (e.g., not an API token itself)
            # You might want a separate environment variable for the cache key, e.g., DAILY_FEED_CACHE_KEY
            cache_key_name = "daily_news_feed_cache" # A more descriptive key
            redis_client.set(cache_key_name, json.dumps(final_feed, ensure_ascii=False))
            # Set an expiry for the cache, e.g., 24 hours (86400 seconds)
            redis_client.expire(cache_key_name, 86400) 
            print(f"✅ Cached daily feed under key '{cache_key_name}' with 24-hour expiry.")
        except Exception as e:
            print("❌ [Redis Cache Error]", e)
            # import traceback
            # traceback.print_exc()

        return final_feed

    except Exception as e:
        print("❌ [generate_and_cache_daily_feed Overall Error]", e)
        # import traceback
        # traceback.print_exc()
        return []

# 📦 Get cached data
def get_cached_daily_feed():
    try:
        cache_key_name = "daily_news_feed_cache" # Use the same key name as in generate_and_cache_daily_feed
        cached = redis_client.get(cache_key_name)
        if cached:
            print(f"✅ Retrieved cached daily feed from '{cache_key_name}'.")
            return json.loads(cached)
        else:
            print(f"ℹ️ No cached data found under key '{cache_key_name}'.")
            return []
    except Exception as e:
        print("❌ [get_cached_daily_feed Error]", e)
        # import traceback
        # traceback.print_exc()
        return []

# Example of how to run it (for testing purposes, if this were the main script)
if __name__ == "__main__":
    # Ensure your environment variables are set before running
    # os.environ["UPSTASH_REDIS_URL"] = "your_upstash_redis_url"
    # os.environ["UPSTASH_REDIS_TOKEN"] = "your_upstash_redis_token" # This should ideally be a unique key for caching, not the token
    # os.environ["OPENAI_API_KEY"] = "your_openai_api_key"

    # For the UPSTASH_REDIS_TOKEN environment variable, if it's truly a Redis token
    # that shouldn't be used as a cache key, you should define a separate environment
    # variable for the cache key, or use a hardcoded string as I've done with "daily_news_feed_cache".
    # For Upstash Vector connection, ensure UPSTASH_VECTOR_REST_URL and UPSTASH_VECTOR_REST_TOKEN
    # are configured in your `components.indexers.news_indexer.py`'s `get_upstash_vector_store` function.

    # Generate and cache the feed
    generated_feed = generate_and_cache_daily_feed()
    print("\n--- Generated and Cached Feed ---")
    # for item in generated_feed:
    #     print(f"Topic: {item['topic']}")
    #     for summary in item['feed']:
    #         print(f"  - {summary['summary']}")
    # print(json.dumps(generated_feed, indent=2, ensure_ascii=False)) # For full output

    # Retrieve from cache
    cached_feed = get_cached_daily_feed()
    print("\n--- Retrieved from Cache ---")
    # for item in cached_feed:
    #     print(f"Topic: {item['topic']}")
    #     for summary in item['feed']:
    #         print(f"  - {summary['summary']}")
    # print(json.dumps(cached_feed, indent=2, ensure_ascii=False)) # For full output