initial setupt
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OwusuBlessing
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#!/usr/bin/env python3
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"""
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Classification Inference Script
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Uses YAML configurations for flexible and maintainable model inference.
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"""
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import sys
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import os
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import subprocess
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import argparse
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from pathlib import Path
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def run_with_yaml_config(config_path: str, **cli_overrides):
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"""Run inference with YAML configuration"""
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print(f"=== Running Classification Inference ===")
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print(f"Config: {config_path}")
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cmd = [
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"python", "pipelines/classification/inference.py",
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"--config", config_path
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]
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# Add CLI overrides
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for key, value in cli_overrides.items():
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if value is not None:
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cmd.extend([f"--{key.replace('_', '-')}", str(value)])
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print(f"Command: {' '.join(cmd)}")
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print()
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try:
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result = subprocess.run(cmd, check=True, capture_output=True, text=True)
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print("✅ Inference completed successfully!")
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print(result.stdout)
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return True
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except subprocess.CalledProcessError as e:
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print(f"❌ Error running inference: {e}")
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print(f"Error output: {e.stderr}")
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return False
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def run_single_text_inference():
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"""Run single text inference"""
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print("=== Single Text Inference ===")
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# Check if model exists
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model_path = "./results/emotion_model"
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if not os.path.exists(model_path):
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print(f"⚠️ Model not found: {model_path}")
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print("Please train a model first using the trainer script.")
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return False
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success = run_with_yaml_config(
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"configs/classification/emotion.yaml",
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model_path=model_path,
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input_text="I love this product! It's amazing.",
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return_top_k=3
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)
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if success:
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print("✅ Single text inference completed!")
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else:
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print("❌ Single text inference failed!")
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return success
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def run_file_inference():
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"""Run file-based inference"""
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print("\n=== File-Based Inference ===")
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# Check if model exists
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model_path = "./results/emotion_model"
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if not os.path.exists(model_path):
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print(f"⚠️ Model not found: {model_path}")
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print("Please train a model first using the trainer script.")
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return False
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# Create sample input file
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sample_texts = [
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"I love this product! It's amazing.",
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"This is terrible, I hate it.",
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"The weather is okay today.",
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"Best purchase ever made!"
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]
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input_file = "sample_texts.txt"
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with open(input_file, 'w') as f:
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for text in sample_texts:
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f.write(text + '\n')
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success = run_with_yaml_config(
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"configs/classification/emotion.yaml",
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model_path=model_path,
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input_file=input_file,
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output_file="predictions.jsonl",
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batch_size=16
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)
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if success:
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print("✅ File-based inference completed!")
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print(f"Results saved to: predictions.jsonl")
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else:
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print("❌ File-based inference failed!")
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return success
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def run_interactive_inference():
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"""Run interactive inference"""
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print("\n=== Interactive Inference ===")
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# Check if model exists
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model_path = "./results/emotion_model"
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if not os.path.exists(model_path):
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print(f"⚠️ Model not found: {model_path}")
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print("Please train a model first using the trainer script.")
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return False
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success = run_with_yaml_config(
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"configs/classification/emotion.yaml",
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model_path=model_path,
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return_top_k=3
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)
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if success:
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print("✅ Interactive inference completed!")
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else:
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print("❌ Interactive inference failed!")
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return success
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def create_inference_config():
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"""Create an inference configuration file"""
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inference_config = """model_path: "./results/emotion_model"
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device: "auto"
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batch_size: 32
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max_length: 512
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return_probabilities: true
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return_top_k: 3
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"""
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config_path = "configs/classification/inference.yaml"
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with open(config_path, 'w') as f:
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f.write(inference_config)
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print(f"✅ Created inference config: {config_path}")
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def show_usage():
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"""Show usage examples"""
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print("=== Classification Inference Usage ===")
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print()
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print("1. Use YAML config only:")
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print(" python scripts/classification/inference.py --config configs/classification/inference.yaml")
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print()
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print("2. Override YAML values:")
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print(" python scripts/classification/inference.py --config configs/classification/inference.yaml --input-text 'Your text here'")
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print()
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print("3. Use CLI only (backward compatibility):")
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print(" python scripts/classification/inference.py --model-path ./results/emotion_model --input-text 'Your text here'")
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print()
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print("4. Run examples:")
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print(" python scripts/classification/inference.py examples")
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print()
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print("5. Create inference config:")
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print(" python scripts/classification/inference.py create-config")
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def handle_direct_args():
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"""Handle direct command-line arguments by passing them to the pipeline"""
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parser = argparse.ArgumentParser(description="Classification Inference")
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# Add all the same arguments as the pipeline
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parser.add_argument("--config", type=str, help="Path to YAML configuration file")
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parser.add_argument("--model-path", type=str, help="Path to saved model directory")
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parser.add_argument("--device", choices=["auto", "cuda", "cpu"], help="Device to run inference on")
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parser.add_argument("--batch-size", type=int, help="Batch size for inference")
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parser.add_argument("--max-length", type=int, help="Maximum sequence length for tokenization")
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parser.add_argument("--return-probabilities", action="store_true", help="Return all class probabilities")
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parser.add_argument("--return-top-k", type=int, help="Return top K predictions")
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parser.add_argument("--input-text", type=str, help="Single text for prediction")
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parser.add_argument("--input-file", type=str, help="Input file path (txt or jsonl)")
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parser.add_argument("--output-file", type=str, help="Output file path for results")
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parser.add_argument("--chunk-size", type=int, help="Chunk size for large file processing")
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parser.add_argument("--log-level", choices=["DEBUG", "INFO", "WARNING", "ERROR"], default="INFO", help="Logging level")
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args = parser.parse_args()
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# Build command to call the pipeline
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cmd = ["python", "pipelines/classification/inference.py"]
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# Add all arguments that were provided
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for arg_name, arg_value in vars(args).items():
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if arg_value is not None:
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if isinstance(arg_value, bool):
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if arg_value: # Only add flag if True
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cmd.append(f"--{arg_name.replace('_', '-')}")
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else:
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cmd.extend([f"--{arg_name.replace('_', '-')}", str(arg_value)])
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print(f"Running: {' '.join(cmd)}")
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print()
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try:
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result = subprocess.run(cmd, check=True, capture_output=True, text=True)
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print("✅ Inference completed successfully!")
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print(result.stdout)
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return True
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except subprocess.CalledProcessError as e:
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print(f"❌ Error running inference: {e}")
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print(f"Error output: {e.stderr}")
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return False
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def main():
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"""Main function"""
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# Check if any command-line arguments were provided
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if len(sys.argv) > 1:
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# Check if it's a subcommand
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if sys.argv[1] in ["examples", "single", "file", "interactive", "create-config", "help"]:
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# Handle subcommands
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if sys.argv[1] == "examples":
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run_single_text_inference()
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run_file_inference()
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run_interactive_inference()
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elif sys.argv[1] == "single":
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run_single_text_inference()
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elif sys.argv[1] == "file":
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run_file_inference()
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elif sys.argv[1] == "interactive":
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run_interactive_inference()
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elif sys.argv[1] == "create-config":
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create_inference_config()
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elif sys.argv[1] == "help":
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show_usage()
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else:
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# Handle direct arguments (pass through to pipeline)
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handle_direct_args()
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else:
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print("Classification Inference")
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print("=======================")
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print()
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print("This script performs inference using trained classification models.")
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print()
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print("Usage:")
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print(" python scripts/classification/inference.py examples # Run examples")
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print(" python scripts/classification/inference.py single # Single text inference")
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print(" python scripts/classification/inference.py file # File-based inference")
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print(" python scripts/classification/inference.py interactive # Interactive inference")
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print(" python scripts/classification/inference.py create-config # Create inference config")
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print(" python scripts/classification/inference.py help # Show usage")
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print()
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print("Direct pipeline usage:")
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print(" python scripts/classification/inference.py --config configs/classification/inference.yaml")
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print(" python scripts/classification/inference.py --model-path ./results/emotion_model --input-text 'Your text here'")
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print()
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print("Benefits of YAML configurations:")
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print(" ✅ Easier to manage complex configurations")
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print(" ✅ Version control friendly")
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print(" ✅ Self-documenting")
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print(" ✅ Can still override with CLI args")
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print(" ✅ Better for team collaboration")
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if __name__ == "__main__":
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main()
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