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"""
Command-Line Interface for CoDA.
Provides a CLI for running the CoDA visualization pipeline locally.
"""
import argparse
import logging
import sys
from pathlib import Path
logging.basicConfig(
level=logging.INFO,
format="%(asctime)s - %(name)s - %(levelname)s - %(message)s"
)
logger = logging.getLogger(__name__)
def main():
"""Main entry point for the CLI."""
parser = argparse.ArgumentParser(
description="CoDA - Collaborative Data Visualization Agents",
formatter_class=argparse.RawDescriptionHelpFormatter,
epilog="""
Examples:
python main.py --query "Show sales trends" --data sales.csv
python main.py -q "Bar chart of categories" -d data.xlsx
python main.py --query "Scatter plot" --data file1.csv file2.csv
"""
)
parser.add_argument(
"-q", "--query",
type=str,
required=True,
help="Natural language visualization query"
)
parser.add_argument(
"-d", "--data",
type=str,
nargs="+",
required=True,
help="Path(s) to data file(s)"
)
parser.add_argument(
"-o", "--output",
type=str,
default="outputs",
help="Output directory for visualizations (default: outputs)"
)
parser.add_argument(
"--max-iterations",
type=int,
default=3,
help="Maximum refinement iterations (default: 3)"
)
parser.add_argument(
"--min-score",
type=float,
default=7.0,
help="Minimum quality score threshold (default: 7.0)"
)
parser.add_argument(
"-v", "--verbose",
action="store_true",
help="Enable verbose logging"
)
args = parser.parse_args()
if args.verbose:
logging.getLogger().setLevel(logging.DEBUG)
for path in args.data:
if not Path(path).exists():
logger.error(f"Data file not found: {path}")
sys.exit(1)
try:
from coda.config import Config, ExecutionConfig, QualityThresholds
from coda.orchestrator import CodaOrchestrator
except ImportError as e:
logger.error(f"Failed to import CoDA modules: {e}")
logger.error("Make sure you have installed all dependencies: pip install -r requirements.txt")
sys.exit(1)
try:
config = Config(
execution=ExecutionConfig(
max_refinement_iterations=args.max_iterations,
output_directory=args.output,
),
quality=QualityThresholds(
minimum_overall_score=args.min_score,
),
)
except ValueError as e:
logger.error(f"Configuration error: {e}")
sys.exit(1)
def progress_callback(status: str, progress: float):
bar_length = 30
filled = int(bar_length * progress)
bar = "█" * filled + "░" * (bar_length - filled)
print(f"\r[{bar}] {progress:.0%} - {status}", end="", flush=True)
if progress >= 1.0:
print()
print(f"\n{'='*60}")
print("CoDA - Collaborative Data Visualization Agents")
print(f"{'='*60}\n")
print(f"Query: {args.query}")
print(f"Data: {', '.join(args.data)}")
print(f"Output: {args.output}/")
print()
orchestrator = CodaOrchestrator(
config=config,
progress_callback=progress_callback,
)
result = orchestrator.run(
query=args.query,
data_paths=args.data,
)
print()
print(f"{'='*60}")
print("Results")
print(f"{'='*60}\n")
if result.success:
print(f"✅ Visualization generated successfully!")
print(f"📁 Output: {result.output_file}")
print(f"🔄 Iterations: {result.iterations}")
if result.scores:
print(f"\n📊 Quality Scores:")
for key, value in result.scores.items():
emoji = "🟢" if value >= 7 else "🟡" if value >= 5 else "🔴"
print(f" {key.title()}: {emoji} {value:.1f}/10")
if result.evaluation and result.evaluation.strengths:
print(f"\n💪 Strengths:")
for s in result.evaluation.strengths[:3]:
print(f" • {s}")
else:
print(f"❌ Visualization failed!")
if result.error:
print(f" Error: {result.error}")
sys.exit(1)
print()
if __name__ == "__main__":
main()
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