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39028c9 | 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 | """
Data processing utilities for loading and preparing documents
"""
import json
import os
from pathlib import Path
from typing import List, Dict, Any
import logging
logger = logging.getLogger(__name__)
class DocumentProcessor:
"""Process and prepare documents for summarization."""
def __init__(self):
"""Initialize document processor."""
self.documents = []
def load_documents(self, file_path: str) -> List[Dict[str, Any]]:
"""
Load documents from JSON or JSONL file.
Args:
file_path: Path to document file
Returns:
List of document dictionaries
"""
documents = []
try:
if file_path.endswith('.json'):
with open(file_path, 'r', encoding='utf-8') as f:
data = json.load(f)
if isinstance(data, list):
documents = data
elif isinstance(data, dict) and 'documents' in data:
documents = data['documents']
elif file_path.endswith('.jsonl'):
with open(file_path, 'r', encoding='utf-8') as f:
for line in f:
if line.strip():
documents.append(json.loads(line))
logger.info(f"Loaded {len(documents)} documents from {file_path}")
self.documents = documents
return documents
except Exception as e:
logger.error(f"Error loading documents: {str(e)}")
return []
def save_documents(self, documents: List[Dict], output_path: str) -> bool:
"""
Save documents to JSON file.
Args:
documents: List of documents
output_path: Path to save documents
Returns:
Success status
"""
try:
Path(output_path).parent.mkdir(parents=True, exist_ok=True)
with open(output_path, 'w', encoding='utf-8') as f:
json.dump(documents, f, indent=2, ensure_ascii=False)
logger.info(f"Saved {len(documents)} documents to {output_path}")
return True
except Exception as e:
logger.error(f"Error saving documents: {str(e)}")
return False
def process_batch(self, documents: List[Dict]) -> List[Dict]:
"""
Process a batch of documents.
Args:
documents: List of documents to process
Returns:
List of processed documents
"""
processed = []
for doc in documents:
processed_doc = {
'id': doc.get('id', ''),
'title': doc.get('title', ''),
'abstract': doc.get('abstract', ''),
'full_text': doc.get('full_text', ''),
'sections': doc.get('sections', {}),
'word_count': len(doc.get('full_text', '').split()),
'sentence_count': len(doc.get('full_text', '').split('.')),
}
processed.append(processed_doc)
return processed
def get_statistics(self, documents: List[Dict] = None) -> Dict[str, Any]:
"""
Get statistics about documents.
Args:
documents: Documents to analyze (uses self.documents if None)
Returns:
Dictionary of statistics
"""
docs = documents or self.documents
if not docs:
return {}
word_counts = [len(doc.get('full_text', '').split()) for doc in docs]
return {
'total_documents': len(docs),
'total_words': sum(word_counts),
'average_length': sum(word_counts) / len(docs) if docs else 0,
'min_length': min(word_counts) if word_counts else 0,
'max_length': max(word_counts) if word_counts else 0,
}
class ArxivLoader:
"""Load arXiv dataset."""
@staticmethod
def load_from_csv(csv_path: str) -> List[Dict]:
"""Load arXiv data from CSV file."""
import pandas as pd
df = pd.read_csv(csv_path)
documents = []
for _, row in df.iterrows():
doc = {
'id': row.get('id', ''),
'title': row.get('title', ''),
'authors': row.get('authors', '').split(';') if 'authors' in row else [],
'abstract': row.get('abstract', ''),
'categories': row.get('categories', '').split() if 'categories' in row else [],
'published_date': row.get('update_date', ''),
}
documents.append(doc)
return documents
class PubmedLoader:
"""Load PubMed dataset."""
@staticmethod
def fetch_from_api(query: str, max_results: int = 10) -> List[Dict]:
"""Fetch PubMed papers via API."""
import requests
base_url = "https://pubmed.ncbi.nlm.nih.gov/api/gateway/search"
params = {
'term': query,
'pageSize': max_results,
'format': 'json'
}
try:
response = requests.get(base_url, params=params, timeout=10)
response.raise_for_status()
data = response.json()
documents = []
for paper in data.get('papers', []):
doc = {
'id': paper.get('pmid', ''),
'title': paper.get('title', ''),
'abstract': paper.get('abstract', ''),
'authors': paper.get('authors', []),
'published_date': paper.get('pubdate', ''),
}
documents.append(doc)
return documents
except Exception as e:
logger.error(f"Error fetching from PubMed: {str(e)}")
return []
def load_sample_data() -> List[Dict]:
"""Load sample documents for testing."""
current_dir = Path(__file__).parent.parent
sample_file = current_dir / 'sample_documents.json'
if sample_file.exists():
processor = DocumentProcessor()
return processor.load_documents(str(sample_file))
return []
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