import os
import sys
import json
from datetime import datetime, timedelta
from app.database import SessionLocal
from app.services.stats_service import StatsService
from app.services.narrative_service import NarrativeService
from app.models import AnalysisSnapshot

def run_analysis_pipeline(days: int = 30, model_name: str = "gemini-2.5-flash"):
    db = SessionLocal()
    try:
        end_date = datetime.utcnow()
        start_date = end_date - timedelta(days=days)

        print(f"--- Starting Analysis for period: {start_date} to {end_date} ---")

        # 1. Compute Stats
        print("Calculating metrics...")
        stats_service = StatsService(db)
        stats_json = stats_service.generate_full_report(start_date, end_date)

        # Check if we have enough data
        if stats_json["volume"]["total_plays"] == 0:
            print("No plays found in this period. Skipping LLM analysis.")
            return

        print(f"Stats computed. Total Plays: {stats_json['volume']['total_plays']}")
        print(f"Top Artist: {stats_json['volume']['top_artists'][0]['name'] if stats_json['volume']['top_artists'] else 'N/A'}")

        # 2. Generate Narrative
        print(f"Generating Narrative with {model_name}...")
        narrative_service = NarrativeService(model_name=model_name)
        narrative_json = narrative_service.generate_full_narrative(stats_json)

        if "error" in narrative_json:
            print(f"LLM Error: {narrative_json['error']}")
        else:
            print("Narrative generated successfully.")
            print(f"Persona: {narrative_json.get('persona')}")

        # 3. Save Snapshot
        print("Saving snapshot to database...")
        snapshot = AnalysisSnapshot(
            period_start=start_date,
            period_end=end_date,
            period_label=f"last_{days}_days",
            metrics_payload=stats_json,
            narrative_report=narrative_json,
            model_used=model_name
        )
        db.add(snapshot)
        db.commit()
        print(f"Snapshot saved with ID: {snapshot.id}")

        # 4. Output to file for easy inspection
        output = {
            "snapshot_id": snapshot.id,
            "metrics": stats_json,
            "narrative": narrative_json
        }
        with open("latest_analysis.json", "w") as f:
            json.dump(output, f, indent=2)
        print("Full report saved to latest_analysis.json")

    except Exception as e:
        print(f"Pipeline Failed: {e}")
        import traceback
        traceback.print_exc()
    finally:
        db.close()

if __name__ == "__main__":
    # Allow arguments?
    days = 30
    if len(sys.argv) > 1:
        try:
            days = int(sys.argv[1])
        except:
            pass

    run_analysis_pipeline(days=days)