"""End-to-end smoke test of the whole pipeline:

    Strava  ->  analyze  ->  render  ->  coach (Modal/vLLM)

Run against your real Strava data:
    uv run python scripts/run_coach.py

Run against built-in demo data (no Strava account needed):
    uv run python scripts/run_coach.py --demo

Requires in your .env:
    LLM_BASE_URL   (your Modal endpoint + /v1)
    LLM_MODEL      (e.g. Qwen/Qwen3-1.7B)
    STRAVA_CLIENT_ID / STRAVA_CLIENT_SECRET / STRAVA_REFRESH_TOKEN  (real mode only)
"""

import sys
from datetime import datetime, timedelta

from dotenv import load_dotenv

from rate_my_run.client import StravaClient
from rate_my_run.analytics import analyze
from rate_my_run.render import summary_to_text
from rate_my_run.coach import generate_report
from rate_my_run.samples import demo_activities

GOAL = "Build consistency: run 3 times per week"


def main():
    load_dotenv()

    # 1. Get activities — demo data or real Strava
    if "--demo" in sys.argv:
        activities = demo_activities()
        print(f"Using {len(activities)} demo activities.\n")
    else:
        client = StravaClient._from_env()
        after = int((datetime.now() - timedelta(weeks=8)).timestamp())
        activities = client.get_activities(after=after, per_page=200)
        print(f"Fetched {len(activities)} activities from Strava.\n")

    # 2. Analyze + 3. render
    summary = analyze(activities)
    summary_text = summary_to_text(summary, goal=GOAL)
    print("=== TRAINING SUMMARY (this is what the model sees) ===")
    print(summary_text)

    # 4. Coach
    print("\n=== COACH REPORT (Sports Scientist) ===")
    report = generate_report(summary_text, personality="Sports Scientist")
    print(report)


if __name__ == "__main__":
    main()