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from pathlib import Path
import sys
from loguru import logger
import typer
from turing.modeling.models.randomForestTfIdf import RandomForestTfIdf
from turing.modeling.predict import ModelInference
# Add project root to sys.path
current_dir = Path(__file__).resolve().parent
project_root = current_dir.parent
if str(project_root) not in sys.path:
sys.path.append(str(project_root))
app = typer.Typer()
@app.command()
def main(
mlflow_run_id: str = typer.Option(
"af1fa5959dc14fa9a29a0a19c11f1b08", help="The MLflow Run ID"
),
artifact_name: str = typer.Option(
"RandomForestTfIdf_java", help="The name of the model artifact"
),
language: str = typer.Option("java", help="The target programming language"),
):
"""
Run inference using the dataset stored on disk (Standard CML/DVC workflow).
"""
logger.info("Starting CLI inference process...")
try:
# Initialize inference engine
inference_engine = ModelInference()
# Run prediction on the test dataset
results = inference_engine.predict_from_mlflow(
mlflow_run_id=mlflow_run_id,
artifact_name=artifact_name,
language=language,
model_class=RandomForestTfIdf,
)
# Output results
print("\n--- Prediction Results ---")
print(results)
print("--------------------------")
except Exception as e:
logger.error(f"CLI Prediction failed: {e}")
raise typer.Exit(code=1)
if __name__ == "__main__":
app()
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