# This script gathers upcoming events, recent emails, and journal entries; 
# then it searches your flat-file KB for related notes based on tags; 
# finally, it compiles all the information into a briefing. 
#
# (Optionally, you can send the briefing text to an LLM API for enhancement.)


import os, glob, yaml
from datetime import datetime

# Placeholder function for the LLM API call
def generate_briefing(text):
    # Replace with actual API call if needed
    return f"Enhanced Briefing:\n\n{text}"

def get_upcoming_events():
    # In a real scenario, fetch events from a calendar source.
    events = [
        {"title": "Project Meeting", "datetime": "2025-02-17 10:00", "tags": ["meeting", "projectX"]},
        {"title": "Code Review", "datetime": "2025-02-17 14:00", "tags": ["review", "code"]},
    ]
    return events

def get_recent_emails():
    # Simulate fetching recent emails (could be replaced with a file or API call)
    emails = [
        {"subject": "Follow-up on project", "content": "Let's discuss the new project approach", "tags": ["projectX"]},
    ]
    return emails

def get_journal_entries():
    # Simulate fetching journal entries (for example, from text files)
    journals = [
        {"date": "2025-02-15", "content": "Had a breakthrough with the complex feature."},
    ]
    return journals

def search_kb_for_tags(tags, kb_dir="path/to/kb"):
    notes = []
    # Scan all Markdown files in the KB directory
    for md_file in glob.glob(os.path.join(kb_dir, "*.md")):
        with open(md_file, "r", encoding="utf-8") as f:
            content = f.read()
            if content.startswith("---"):
                # Extract YAML front matter (assumes two '---' delimiters)
                end = content.find("---", 3)
                if end != -1:
                    frontmatter_str = content[3:end]
                    try:
                        metadata = yaml.safe_load(frontmatter_str)
                        if "tags" in metadata and any(tag in metadata["tags"] for tag in tags):
                            notes.append({"id": metadata.get("id", md_file), "tags": metadata["tags"], "file": md_file})
                    except Exception as e:
                        print(f"Error parsing {md_file}: {e}")
    return notes

def build_briefing():
    events = get_upcoming_events()
    emails = get_recent_emails()
    journals = get_journal_entries()

    # Aggregate tags from events and emails to search the KB
    tags = set()
    for item in events + emails:
        if "tags" in item:
            tags.update(item["tags"])

    kb_notes = search_kb_for_tags(tags)

    # Build the briefing text
    briefing_text = "Upcoming Briefing:\n\n"
    briefing_text += "Events:\n"
    for event in events:
        briefing_text += f"- {event['datetime']}: {event['title']}\n"

    briefing_text += "\nEmails:\n"
    for email in emails:
        briefing_text += f"- {email['subject']}: {email['content']}\n"

    briefing_text += "\nJournal Entries:\n"
    for journal in journals:
        briefing_text += f"- {journal['date']}: {journal['content']}\n"

    briefing_text += "\nRelated KB Notes:\n"
    for note in kb_notes:
        briefing_text += f"- Note {note['id']} with tags {', '.join(note['tags'])}\n"

    # Optionally, send the briefing text to an LLM API to enhance it
    enhanced_briefing = generate_briefing(briefing_text)
    return enhanced_briefing

def main():
    briefing = build_briefing()
    with open("briefing.txt", "w", encoding="utf-8") as f:
        f.write(briefing)
    print("Briefing generated and saved to briefing.txt")

if __name__ == "__main__":
    main()