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"""
Data Logger Module for DReamMachine
Handles data storage to both local JSON files and HuggingFace Datasets
"""
import os
import json
import logging
from datetime import datetime
from pathlib import Path
from typing import Dict, List, Any, Optional
import yaml
from datasets import Dataset, DatasetDict, load_dataset
from huggingface_hub import HfApi, create_repo
logger = logging.getLogger(__name__)
class DataLogger:
"""Manages logging of dream sessions to local files and HuggingFace Datasets"""
def __init__(self, config_path: str = "config.yaml", hf_token: Optional[str] = None):
"""
Initialize Data Logger
Args:
config_path: Path to configuration file
hf_token: HuggingFace API token
"""
# Load configuration
with open(config_path, 'r') as f:
self.config = yaml.safe_load(f)
# Logging settings
logging_config = self.config.get('logging', {})
self.output_format = logging_config.get('output_format', 'json')
self.chunk_size = logging_config.get('chunk_size', 100)
self.log_directory = Path(logging_config.get('log_directory', './logs'))
self.save_to_hf = logging_config.get('save_to_hf_dataset', True)
# HuggingFace settings
hf_config = self.config.get('huggingface', {})
self.dataset_name = hf_config.get('dataset_name', 'dreammachine-logs')
self.dataset_private = hf_config.get('dataset_private', True)
self.hf_token = hf_token or os.getenv('HF_TOKEN')
# Create log directory
self.log_directory.mkdir(parents=True, exist_ok=True)
# Session tracking
self.current_session_data = []
self.session_count = 0
# Initialize HuggingFace API
if self.save_to_hf and self.hf_token:
self.hf_api = HfApi(token=self.hf_token)
self.hf_username = self.hf_api.whoami()['name']
self.full_dataset_name = f"{self.hf_username}/{self.dataset_name}"
else:
self.hf_api = None
self.full_dataset_name = None
logger.info(f"DataLogger initialized. Logs will be saved to {self.log_directory}")
def initialize_hf_dataset(self) -> bool:
"""
Initialize or verify HuggingFace dataset exists
Returns:
True if successful, False otherwise
"""
if not self.save_to_hf or not self.hf_api:
logger.warning("HuggingFace dataset saving is disabled")
return False
try:
# Check if dataset already exists
try:
logger.info(f"Checking for existing dataset: {self.full_dataset_name}")
dataset = load_dataset(self.full_dataset_name, token=self.hf_token)
logger.info(f"Found existing dataset: {self.full_dataset_name}")
return True
except Exception:
# Dataset doesn't exist, create it
logger.info(f"Creating new dataset: {self.full_dataset_name}")
# Create empty initial dataset
initial_data = {
'session_id': [],
'timestamp': [],
'life_stage': [],
'dream_outputs': [],
'pitch_narrative': [],
'technical_components': [],
'feasibility_report': [],
'curator_scorecard': [],
'reforge_flag': []
}
dataset = Dataset.from_dict(initial_data)
# Push to hub
dataset.push_to_hub(
self.full_dataset_name,
private=self.dataset_private,
token=self.hf_token
)
logger.info(f"Successfully created dataset: {self.full_dataset_name}")
return True
except Exception as e:
logger.error(f"Failed to initialize HuggingFace dataset: {str(e)}")
return False
def log_session_data(self, session_data: Dict[str, Any]) -> str:
"""
Log a complete dream session
Args:
session_data: Dictionary containing all session information
Returns:
Session ID
"""
# Add timestamp and session ID
session_id = f"session_{datetime.now().strftime('%Y%m%d_%H%M%S')}_{self.session_count}"
session_data['session_id'] = session_id
session_data['timestamp'] = datetime.now().isoformat()
# Save to local JSON
self._save_to_local_json(session_data)
# Save to HuggingFace dataset
if self.save_to_hf:
self._save_to_hf_dataset(session_data)
# Add to current session data
self.current_session_data.append(session_data)
self.session_count += 1
# Check if we need to chunk
if len(self.current_session_data) >= self.chunk_size:
self._save_chunk()
logger.info(f"Logged session: {session_id}")
return session_id
def _save_to_local_json(self, session_data: Dict[str, Any]) -> None:
"""Save session data to local JSON file"""
try:
session_id = session_data.get('session_id', 'unknown')
filename = self.log_directory / f"{session_id}.json"
with open(filename, 'w', encoding='utf-8') as f:
json.dump(session_data, f, indent=2, ensure_ascii=False)
logger.debug(f"Saved session to {filename}")
except Exception as e:
logger.error(f"Failed to save to local JSON: {str(e)}")
def _save_to_hf_dataset(self, session_data: Dict[str, Any]) -> None:
"""Append session data to HuggingFace dataset"""
if not self.hf_api:
return
try:
# Load existing dataset
dataset = load_dataset(self.full_dataset_name, split='train', token=self.hf_token)
# Convert session data to dataset row format
new_row = {
'session_id': [session_data.get('session_id', '')],
'timestamp': [session_data.get('timestamp', '')],
'life_stage': [session_data.get('life_stage', '')],
'dream_outputs': [json.dumps(session_data.get('dream_outputs', []))],
'pitch_narrative': [session_data.get('pitch_narrative', '')],
'technical_components': [session_data.get('technical_components', '')],
'feasibility_report': [session_data.get('feasibility_report', '')],
'curator_scorecard': [json.dumps(session_data.get('curator_scorecard', {}))],
'reforge_flag': [session_data.get('curator_scorecard', {}).get('reforge_flag', False)]
}
# Create new dataset with appended row
new_dataset = Dataset.from_dict(new_row)
# Concatenate datasets
from datasets import concatenate_datasets
updated_dataset = concatenate_datasets([dataset, new_dataset])
# Push updated dataset
updated_dataset.push_to_hub(
self.full_dataset_name,
private=self.dataset_private,
token=self.hf_token
)
logger.debug(f"Saved session to HuggingFace dataset")
except Exception as e:
logger.error(f"Failed to save to HuggingFace dataset: {str(e)}")
def _save_chunk(self) -> None:
"""Save accumulated session data as a chunk file"""
if not self.current_session_data:
return
try:
timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')
chunk_file = self.log_directory / f"chunk_{timestamp}.json"
with open(chunk_file, 'w', encoding='utf-8') as f:
json.dump(self.current_session_data, f, indent=2, ensure_ascii=False)
logger.info(f"Saved chunk with {len(self.current_session_data)} sessions to {chunk_file}")
self.current_session_data = []
except Exception as e:
logger.error(f"Failed to save chunk: {str(e)}")
def retrieve_past_data(self, session_id: str) -> Optional[Dict[str, Any]]:
"""
Retrieve data from a past session
Args:
session_id: ID of the session to retrieve
Returns:
Session data dictionary or None if not found
"""
# Try local file first
local_file = self.log_directory / f"{session_id}.json"
if local_file.exists():
try:
with open(local_file, 'r', encoding='utf-8') as f:
data = json.load(f)
logger.info(f"Retrieved session {session_id} from local storage")
return data
except Exception as e:
logger.error(f"Failed to load local session: {str(e)}")
# Try HuggingFace dataset
if self.save_to_hf and self.hf_api:
try:
dataset = load_dataset(self.full_dataset_name, split='train', token=self.hf_token)
# Find matching session
for row in dataset:
if row['session_id'] == session_id:
logger.info(f"Retrieved session {session_id} from HuggingFace dataset")
return {
'session_id': row['session_id'],
'timestamp': row['timestamp'],
'life_stage': row['life_stage'],
'dream_outputs': json.loads(row['dream_outputs']),
'pitch_narrative': row['pitch_narrative'],
'technical_components': row['technical_components'],
'feasibility_report': row['feasibility_report'],
'curator_scorecard': json.loads(row['curator_scorecard'])
}
except Exception as e:
logger.error(f"Failed to retrieve from HuggingFace: {str(e)}")
logger.warning(f"Session {session_id} not found")
return None
def get_all_sessions(self) -> List[Dict[str, Any]]:
"""
Retrieve all logged sessions
Returns:
List of all session data
"""
sessions = []
# Load from local JSON files
for json_file in self.log_directory.glob("session_*.json"):
try:
with open(json_file, 'r', encoding='utf-8') as f:
sessions.append(json.load(f))
except Exception as e:
logger.error(f"Failed to load {json_file}: {str(e)}")
logger.info(f"Retrieved {len(sessions)} sessions from local storage")
return sessions
def get_reforge_sessions(self) -> List[Dict[str, Any]]:
"""
Get all sessions that have reforge_flag = True
Returns:
List of sessions eligible for next stage
"""
all_sessions = self.get_all_sessions()
reforge_sessions = [
s for s in all_sessions
if s.get('curator_scorecard', {}).get('reforge_flag', False)
]
logger.info(f"Found {len(reforge_sessions)} reforge-eligible sessions")
return reforge_sessions
# Convenience function
def create_logger(config_path: str = "config.yaml", hf_token: Optional[str] = None) -> DataLogger:
"""Create and return a configured DataLogger"""
return DataLogger(config_path, hf_token)
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