Spaces:
Sleeping
Sleeping
File size: 8,891 Bytes
cacd4d0 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 |
"""
Data loading utilities for various file formats
"""
import json
import base64
import pandas as pd
from typing import Any, Optional, Union, List , Dict
from pathlib import Path
import logging
logger = logging.getLogger(__name__)
class DataLoader:
"""
Utility class for loading data from various sources
"""
def __init__(self):
self.supported_formats = [
'.csv', '.json', '.jsonl', '.txt', '.md', '.xlsx',
'.png', '.jpg', '.jpeg'
]
def load(self, source: Union[str, Path], format_hint: Optional[str] = None) -> Optional[Any]:
"""
Load data from any supported source
Args:
source: File path or data source
format_hint: Optional format hint to override auto-detection
Returns:
Loaded data or None if failed
"""
try:
path = Path(source)
if not path.exists():
logger.error(f"File not found: {source}")
return None
# Use format hint or detect from extension
file_format = format_hint or path.suffix.lower()
if file_format == '.csv':
return self.load_csv(path)
elif file_format == '.json':
return self.load_json(path)
elif file_format == '.jsonl':
return self.load_jsonl(path)
elif file_format in ['.txt', '.md']:
return self.load_text(path)
elif file_format == '.xlsx':
return self.load_excel(path)
elif file_format in ['.png', '.jpg', '.jpeg']:
return self.load_image_base64(path)
else:
logger.warning(f"Unsupported format: {file_format}")
return None
except Exception as e:
logger.error(f"Failed to load data from {source}: {str(e)}")
return None
def load_csv(self, path: Union[str, Path]) -> Optional[pd.DataFrame]:
"""Load CSV file as pandas DataFrame"""
try:
df = pd.read_csv(path)
logger.info(f"Loaded CSV with {len(df)} rows and {len(df.columns)} columns")
return df
except Exception as e:
logger.error(f"Failed to load CSV {path}: {str(e)}")
return None
def load_json(self, path: Union[str, Path]) -> Optional[Any]:
"""Load JSON file"""
try:
with open(path, 'r', encoding='utf-8') as f:
data = json.load(f)
if isinstance(data, list):
logger.info(f"Loaded JSON with {len(data)} items")
else:
logger.info("Loaded JSON object")
return data
except Exception as e:
logger.error(f"Failed to load JSON {path}: {str(e)}")
return None
def load_jsonl(self, path: Union[str, Path]) -> Optional[List[Dict]]:
"""Load JSONL (JSON Lines) file"""
try:
data = []
with open(path, 'r', encoding='utf-8') as f:
for line_num, line in enumerate(f, 1):
line = line.strip()
if line:
try:
data.append(json.loads(line))
except json.JSONDecodeError as e:
logger.warning(f"Invalid JSON on line {line_num}: {str(e)}")
logger.info(f"Loaded JSONL with {len(data)} items")
return data
except Exception as e:
logger.error(f"Failed to load JSONL {path}: {str(e)}")
return None
def load_text(self, path: Union[str, Path]) -> Optional[str]:
"""Load plain text file"""
try:
with open(path, 'r', encoding='utf-8') as f:
content = f.read()
logger.info(f"Loaded text file with {len(content)} characters")
return content
except Exception as e:
logger.error(f"Failed to load text {path}: {str(e)}")
return None
def load_excel(self, path: Union[str, Path]) -> Optional[pd.DataFrame]:
"""Load Excel file as pandas DataFrame"""
try:
df = pd.read_excel(path)
logger.info(f"Loaded Excel with {len(df)} rows and {len(df.columns)} columns")
return df
except Exception as e:
logger.error(f"Failed to load Excel {path}: {str(e)}")
return None
def load_image_base64(self, path: Union[str, Path]) -> Optional[str]:
"""Load image file and encode as Base64 string"""
try:
with open(path, 'rb') as f:
encoded_string = base64.b64encode(f.read()).decode('utf-8')
logger.info(f"Loaded image {path} and encoded to Base64")
return encoded_string
except Exception as e:
logger.error(f"Failed to load image {path}: {str(e)}")
return None
def is_supported_format(self, file_path: Union[str, Path]) -> bool:
"""Check if file format is supported"""
path = Path(file_path)
return path.suffix.lower() in self.supported_formats
def get_file_info(self, file_path: Union[str, Path]) -> Dict[str, Any]:
"""Get information about a file"""
path = Path(file_path)
if not path.exists():
return {'exists': False}
return {
'exists': True,
'size': path.stat().st_size,
'format': path.suffix.lower(),
'supported': self.is_supported_format(path),
'name': path.name,
'stem': path.stem,
'parent': str(path.parent)
}
def load_ui_tree_dataset(self, json_dir: str, screenshots_dir: str) -> List[Dict[str, Any]]:
"""
Load UI tree dataset by pairing JSON files with corresponding screenshots
Args:
json_dir: Directory containing JSON files (e.g., "json_tree")
screenshots_dir: Directory containing screenshot images (e.g., "screenshots")
Returns:
List of dictionaries with 'input', 'output', and 'image' keys
"""
json_path = Path(json_dir)
screenshots_path = Path(screenshots_dir)
if not json_path.exists():
raise FileNotFoundError(f"JSON directory not found: {json_dir}")
if not screenshots_path.exists():
raise FileNotFoundError(f"Screenshots directory not found: {screenshots_dir}")
dataset = []
# Get all JSON files
json_files = list(json_path.glob("*.json"))
logger.info(f"Found {len(json_files)} JSON files in {json_dir}")
for json_file in json_files:
# Extract filename without extension (e.g., "2" from "2.json")
file_stem = json_file.stem
# Look for corresponding image file
image_extensions = ['.jpg', '.jpeg', '.png']
image_file = None
for ext in image_extensions:
potential_image = screenshots_path / f"{file_stem}{ext}"
if potential_image.exists():
image_file = potential_image
break
if not image_file:
logger.warning(f"No corresponding image found for {json_file.name}")
continue
try:
# Load JSON content
json_data = self.load_json(json_file)
if not json_data:
logger.warning(f"Failed to load JSON: {json_file}")
continue
# Load image as base64
image_base64 = self.load_image_base64(image_file)
if not image_base64:
logger.warning(f"Failed to load image: {image_file}")
continue
# Create dataset entry
dataset_entry = {
'input': 'Extract UI elements from this screenshot and provide the complete UI tree structure',
'output': json.dumps(json_data, indent=2), # Convert JSON to string
'image': image_base64
}
dataset.append(dataset_entry)
logger.debug(f"Loaded pair: {json_file.name} + {image_file.name}")
except Exception as e:
logger.error(f"Error loading {json_file.name}: {str(e)}")
continue
logger.info(f"Successfully loaded {len(dataset)} image-JSON pairs")
return dataset
|