Issue #1: tighten config handling and typed classification payload

openai_finetuner.py

@@ -1,31 +1,35 @@
 import json
 import os
 import time
-from typing import List, Dict, Any, Tuple, Optional
+from typing import List, Dict, Any, Optional, NamedTuple
 
 import yaml
+import logging
 from openai import OpenAI
 import pandas as pd
 from dotenv import load_dotenv
 
 
-def load_config(config_path="config.yml") -> Optional[Dict[str, Any]]:
-    """Loads configuration from a YAML file."""
-    try:
-        with open(config_path, 'r') as f:
-            config = yaml.safe_load(f)
-        print(f"Configuration loaded successfully from {config_path}")
-        return config
-    except FileNotFoundError:
-        print(f"ERROR: Configuration file not found at {config_path}")
-        return None
-    except yaml.YAMLError as e:
-        print(
-            f"ERROR: Could not parse YAML configuration from {config_path}: {e}")
-        return None
-    except Exception as e:
-        print(f"An unexpected error occurred while loading configuration: {e}")
-        return None
+logger = logging.getLogger(__name__)
+
+
+def load_config(config_path="config.yml") -> Dict[str, Any]:
+    """Loads configuration from a YAML file and applies defaults."""
+    with open(config_path, 'r', encoding='utf-8') as f:
+        config = yaml.safe_load(f) or {}
+
+    if not isinstance(config, dict):
+        raise ValueError(f"Configuration in {config_path} must be a mapping.")
+
+    openai_cfg = config.setdefault('openai_settings', {})
+    openai_cfg.setdefault('classification_temperature', 0.0)
+    openai_cfg.setdefault('classification_max_tokens', 200)
+
+    prompt_cfg = config.setdefault('prompt_customization', {})
+    prompt_cfg.setdefault('output_wrapper_tag', 'category')
+
+    logger.info("Configuration loaded successfully from %s", config_path)
+    return config
 
 
 CONFIG = load_config()
@@ -47,13 +51,23 @@ def initialize_openai_client() -> Optional[OpenAI]:
         return None
 
 
+
+class ClassificationData(NamedTuple):
+    train_data: pd.DataFrame
+    test_data: pd.DataFrame
+    train_texts: List[str]
+    train_labels: List[str]
+    test_texts: List[str]
+    test_labels: List[str]
+    categories: List[str]
+
 def load_classification_data(
     train_file_path: str,
     test_file_path: str,
     sep: str = "\t",
     text_column: str = "text",
     label_column: str = "label"
-) -> Optional[Tuple[pd.DataFrame, pd.DataFrame, List[str], List[str], List[str], List[str], List[str]]]:
+) -> Optional[ClassificationData]:
     """
     Loads training and testing data from specified TSV/CSV files.
     Uses text_column and label_column from global CONFIG.
@@ -93,7 +107,7 @@ def load_classification_data(
         test_texts = test_data[text_col].astype(str).tolist()
         test_labels = test_data[label_col].astype(str).tolist()
 
-        return train_data, test_data, train_texts, train_labels, test_texts, test_labels, categories
+        return ClassificationData(train_data, test_data, train_texts, train_labels, test_texts, test_labels, categories)
 
     except FileNotFoundError as e:
         print(f"ERROR: Data file not found: {e.filename}")
@@ -378,18 +392,11 @@ def classify_items_with_model(
     Classifies a list of items using a specified OpenAI model.
     Reads prompt, temperature, and max_tokens from global CONFIG.
     """
-    if not CONFIG:
-        print("ERROR: Configuration not loaded. Using default classification settings.")
-        temperature = 0.0
-        max_tokens = 200
-        output_wrapper_tag = "category"
-    else:
-        # Assuming these might be added to config.yml under openai_settings or a new section
-        openai_cfg = CONFIG.get('openai_settings', {})
-        temperature = openai_cfg.get('classification_temperature', 0.0)
-        max_tokens = openai_cfg.get('classification_max_tokens', 200)
-        prompt_cfg = CONFIG.get('prompt_customization', {})
-        output_wrapper_tag = prompt_cfg.get('output_wrapper_tag', "category")
+    openai_cfg = CONFIG.get('openai_settings', {})
+    temperature = openai_cfg.get('classification_temperature', 0.0)
+    max_tokens = openai_cfg.get('classification_max_tokens', 200)
+    prompt_cfg = CONFIG.get('prompt_customization', {})
+    output_wrapper_tag = prompt_cfg.get('output_wrapper_tag', "category")
 
     responses = []
     stop_sequence = [f"</{output_wrapper_tag}>"]