feat: Add Streamlit dashboard with Blueprint compliance (v2.1.0)
Dashboard Features: - 8 navigation sections: Overview, Outcomes, Poor CX, FCR, Churn, Agent, Call Explorer, Export - Beyond Brand Identity styling (colors #6D84E3, Outfit font) - RCA Sankey diagram (Driver → Outcome → Churn Risk flow) - Correlation heatmaps (driver co-occurrence, driver-outcome) - Outcome Deep Dive (root causes, correlation, duration analysis) - Export functionality (Excel, HTML, JSON) Blueprint Compliance: - FCR: 4 categories (Primera Llamada/Rellamada × Sin/Con Riesgo de Fuga) - Churn: Binary view (Sin Riesgo de Fuga / En Riesgo de Fuga) - Agent: Talento Para Replicar / Oportunidades de Mejora - Fixed FCR rate calculation (only FIRST_CALL counts as success) Technical: - Streamlit + Plotly for interactive visualizations - Light theme configuration (.streamlit/config.toml) - Fixed Plotly colorbar titlefont deprecation Documentation: - Updated PROJECT_CONTEXT.md, TODO.md, CHANGELOG.md - Added 4 new technical decisions (TD-014 to TD-017) - Created TROUBLESHOOTING.md with 10 common issues Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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sujucu70
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cli.py
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cli.py
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#!/usr/bin/env python3
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"""
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CXInsights - Command Line Interface
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Main entry point for running the analysis pipeline.
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"""
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import argparse
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import logging
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import sys
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from pathlib import Path
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from dotenv import load_dotenv
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# Load environment variables from .env file
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load_dotenv()
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from src.pipeline import CXInsightsPipeline, PipelineConfig
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def setup_logging(verbose: bool = False) -> None:
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"""Configure logging."""
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level = logging.DEBUG if verbose else logging.INFO
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logging.basicConfig(
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level=level,
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format="%(asctime)s | %(levelname)-8s | %(name)s | %(message)s",
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datefmt="%Y-%m-%d %H:%M:%S",
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)
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def progress_callback(stage: str, current: int, total: int) -> None:
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"""Print progress to console."""
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if total > 0:
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pct = current / total * 100
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bar_len = 30
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filled = int(bar_len * current / total)
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bar = "█" * filled + "░" * (bar_len - filled)
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print(f"\r{stage}: [{bar}] {pct:.0f}% ({current}/{total})", end="", flush=True)
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if current == total:
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print() # New line when complete
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def cmd_run(args: argparse.Namespace) -> int:
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"""Run the analysis pipeline."""
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print("=" * 60)
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print("CXInsights - Call Analysis Pipeline")
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print("=" * 60)
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# Build config
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config = PipelineConfig(
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input_dir=Path(args.input) if args.input else Path("data/audio"),
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output_dir=Path(args.output) if args.output else Path("data/output"),
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checkpoint_dir=Path(args.checkpoint) if args.checkpoint else Path("data/.checkpoints"),
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inference_model=args.model,
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use_compression=not args.no_compression,
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export_formats=args.formats.split(",") if args.formats else ["json", "excel"],
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auto_resume=not args.no_resume,
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)
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print(f"\nConfiguration:")
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print(f" Input: {config.input_dir}")
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print(f" Output: {config.output_dir}")
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print(f" Model: {config.inference_model}")
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print(f" Compression: {'Enabled' if config.use_compression else 'Disabled'}")
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print(f" Formats: {', '.join(config.export_formats)}")
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print()
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# Check for transcripts
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transcripts_file = Path(args.transcripts) if args.transcripts else None
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if transcripts_file and transcripts_file.exists():
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print(f"Loading transcripts from: {transcripts_file}")
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# Load transcripts (placeholder - would need actual loading logic)
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print("Note: Transcript loading not fully implemented in CLI")
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return 1
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# Check for audio files
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audio_files = list(config.input_dir.glob("*.wav")) + list(config.input_dir.glob("*.mp3"))
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if not audio_files and not transcripts_file:
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print(f"Error: No audio files found in {config.input_dir}")
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print("Please provide audio files or use --transcripts option")
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return 1
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print(f"Found {len(audio_files)} audio files")
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# Run pipeline
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pipeline = CXInsightsPipeline(
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config=config,
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progress_callback=progress_callback if not args.quiet else None,
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)
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try:
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result = pipeline.run(
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batch_id=args.batch_id,
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audio_files=audio_files if audio_files else None,
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resume=not args.no_resume,
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)
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print("\n" + "=" * 60)
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print("Pipeline Complete!")
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print("=" * 60)
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print(f"\nResults:")
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print(f" Total calls: {result.total_calls_processed}")
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print(f" Successful: {result.successful_analyses}")
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print(f" Failed: {result.failed_analyses}")
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print(f" Lost sales: {len(result.lost_sales_frequencies)} drivers")
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print(f" Poor CX: {len(result.poor_cx_frequencies)} drivers")
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if result.rca_tree:
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tree = result.rca_tree
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print(f"\n Top lost sales: {', '.join(tree.top_lost_sales_drivers[:3])}")
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print(f" Top poor CX: {', '.join(tree.top_poor_cx_drivers[:3])}")
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print(f"\nOutput: {config.output_dir / args.batch_id}")
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return 0
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except Exception as e:
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logging.error(f"Pipeline failed: {e}")
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if args.verbose:
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import traceback
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traceback.print_exc()
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return 1
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def cmd_status(args: argparse.Namespace) -> int:
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"""Show pipeline status."""
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from src.pipeline.models import PipelineManifest
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checkpoint_dir = Path(args.checkpoint) if args.checkpoint else Path("data/.checkpoints")
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manifest_path = checkpoint_dir / f"pipeline_{args.batch_id}.json"
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if not manifest_path.exists():
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print(f"No pipeline found for batch: {args.batch_id}")
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return 1
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manifest = PipelineManifest.load(manifest_path)
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print(f"\nPipeline Status: {manifest.batch_id}")
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print("=" * 50)
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print(f"Status: {manifest.status.value}")
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print(f"Created: {manifest.created_at}")
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print(f"Total duration: {manifest.total_duration_sec:.1f}s")
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print()
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print("Stages:")
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for stage, stage_manifest in manifest.stages.items():
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status_icon = {
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"pending": "⏳",
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"running": "🔄",
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"completed": "✅",
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"failed": "❌",
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"skipped": "⏭️",
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}.get(stage_manifest.status.value, "?")
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duration = f"({stage_manifest.duration_sec:.1f}s)" if stage_manifest.duration_sec else ""
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print(f" {status_icon} {stage.value}: {stage_manifest.status.value} {duration}")
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if stage_manifest.processed_items > 0:
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print(f" Processed: {stage_manifest.processed_items}/{stage_manifest.total_items}")
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return 0
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def cmd_export(args: argparse.Namespace) -> int:
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"""Export results to different formats."""
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print("Export command - not yet implemented")
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print("Use the run command with --formats option instead")
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return 1
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def main() -> int:
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"""Main entry point."""
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parser = argparse.ArgumentParser(
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description="CXInsights - Call Center Analysis Pipeline",
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formatter_class=argparse.RawDescriptionHelpFormatter,
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)
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parser.add_argument("-v", "--verbose", action="store_true", help="Verbose output")
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parser.add_argument("-q", "--quiet", action="store_true", help="Quiet output (no progress)")
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subparsers = parser.add_subparsers(dest="command", help="Available commands")
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# Run command
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run_parser = subparsers.add_parser("run", help="Run the analysis pipeline")
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run_parser.add_argument("batch_id", help="Unique batch identifier")
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run_parser.add_argument("-i", "--input", help="Input directory with audio files")
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run_parser.add_argument("-o", "--output", help="Output directory")
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run_parser.add_argument("-c", "--checkpoint", help="Checkpoint directory")
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run_parser.add_argument("-t", "--transcripts", help="Pre-existing transcripts file (JSON)")
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run_parser.add_argument("-m", "--model", default="gpt-4o-mini", help="LLM model to use")
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run_parser.add_argument("-f", "--formats", default="json,excel", help="Export formats (comma-separated)")
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run_parser.add_argument("--no-compression", action="store_true", help="Disable transcript compression")
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run_parser.add_argument("--no-resume", action="store_true", help="Don't resume from checkpoint")
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run_parser.set_defaults(func=cmd_run)
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# Status command
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status_parser = subparsers.add_parser("status", help="Show pipeline status")
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status_parser.add_argument("batch_id", help="Batch ID to check")
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status_parser.add_argument("-c", "--checkpoint", help="Checkpoint directory")
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status_parser.set_defaults(func=cmd_status)
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# Export command
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export_parser = subparsers.add_parser("export", help="Export results")
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export_parser.add_argument("batch_id", help="Batch ID to export")
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export_parser.add_argument("-f", "--format", choices=["json", "excel", "pdf"], default="json")
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export_parser.add_argument("-o", "--output", help="Output directory")
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export_parser.set_defaults(func=cmd_export)
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args = parser.parse_args()
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if not args.command:
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parser.print_help()
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return 0
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setup_logging(args.verbose)
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return args.func(args)
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if __name__ == "__main__":
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sys.exit(main())
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