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"""
PDF Reading and Processing Module
Comprehensive PDF ingestion capabilities for geopolitical intelligence documents,
reports, briefings, and analysis.
Supports:
- Text extraction from PDFs
- Table extraction
- Metadata extraction
- Multi-format PDF handling
- Batch processing
"""
import os
from typing import Dict, List, Optional, Any, Tuple
from pathlib import Path
import re
class PDFReader:
"""
Read and extract text from PDF documents.
Supports multiple PDF libraries for robust extraction.
"""
def __init__(self, method: str = 'auto'):
"""
Initialize PDF reader.
Parameters
----------
method : str
Extraction method ('pypdf', 'pdfplumber', 'pdfminer', 'auto')
"""
self.method = method
self._check_dependencies()
def _check_dependencies(self) -> None:
"""Check which PDF libraries are available."""
self.has_pypdf = False
self.has_pdfplumber = False
self.has_pdfminer = False
try:
import pypdf
self.has_pypdf = True
except ImportError:
pass
try:
import pdfplumber
self.has_pdfplumber = True
except ImportError:
pass
try:
from pdfminer.high_level import extract_text as pdfminer_extract
self.has_pdfminer = True
except ImportError:
pass
if not any([self.has_pypdf, self.has_pdfplumber, self.has_pdfminer]):
print("Warning: No PDF libraries available. Please install pypdf, pdfplumber, or pdfminer.six")
def read_pdf(self, pdf_path: str) -> Dict[str, Any]:
"""
Read PDF and extract all information.
Parameters
----------
pdf_path : str
Path to PDF file
Returns
-------
dict
Extracted information including text, metadata, pages
"""
if not os.path.exists(pdf_path):
raise FileNotFoundError(f"PDF not found: {pdf_path}")
method = self.method
if method == 'auto':
# Choose best available method
if self.has_pdfplumber:
method = 'pdfplumber'
elif self.has_pypdf:
method = 'pypdf'
elif self.has_pdfminer:
method = 'pdfminer'
else:
raise ImportError("No PDF library available")
if method == 'pypdf':
return self._read_with_pypdf(pdf_path)
elif method == 'pdfplumber':
return self._read_with_pdfplumber(pdf_path)
elif method == 'pdfminer':
return self._read_with_pdfminer(pdf_path)
else:
raise ValueError(f"Unknown method: {method}")
def _read_with_pypdf(self, pdf_path: str) -> Dict[str, Any]:
"""Read PDF using pypdf."""
import pypdf
result = {
'text': '',
'pages': [],
'metadata': {},
'num_pages': 0
}
with open(pdf_path, 'rb') as file:
reader = pypdf.PdfReader(file)
result['num_pages'] = len(reader.pages)
# Extract metadata
if reader.metadata:
result['metadata'] = {
'title': reader.metadata.get('/Title', ''),
'author': reader.metadata.get('/Author', ''),
'subject': reader.metadata.get('/Subject', ''),
'creator': reader.metadata.get('/Creator', ''),
}
# Extract text from each page
for page_num, page in enumerate(reader.pages):
page_text = page.extract_text()
result['pages'].append({
'page_number': page_num + 1,
'text': page_text
})
result['text'] += page_text + '\n'
return result
def _read_with_pdfplumber(self, pdf_path: str) -> Dict[str, Any]:
"""Read PDF using pdfplumber (best for tables)."""
import pdfplumber
result = {
'text': '',
'pages': [],
'tables': [],
'metadata': {},
'num_pages': 0
}
with pdfplumber.open(pdf_path) as pdf:
result['num_pages'] = len(pdf.pages)
result['metadata'] = pdf.metadata
for page_num, page in enumerate(pdf.pages):
page_text = page.extract_text()
page_tables = page.extract_tables()
result['pages'].append({
'page_number': page_num + 1,
'text': page_text,
'tables': page_tables
})
result['text'] += page_text + '\n' if page_text else ''
if page_tables:
result['tables'].extend([{
'page': page_num + 1,
'data': table
} for table in page_tables])
return result
def _read_with_pdfminer(self, pdf_path: str) -> Dict[str, Any]:
"""Read PDF using pdfminer."""
from pdfminer.high_level import extract_text, extract_pages
from pdfminer.layout import LTTextContainer
result = {
'text': '',
'pages': [],
'metadata': {},
'num_pages': 0
}
# Extract all text
result['text'] = extract_text(pdf_path)
# Extract page by page
pages = list(extract_pages(pdf_path))
result['num_pages'] = len(pages)
for page_num, page_layout in enumerate(pages):
page_text = ''
for element in page_layout:
if isinstance(element, LTTextContainer):
page_text += element.get_text()
result['pages'].append({
'page_number': page_num + 1,
'text': page_text
})
return result
def extract_text(self, pdf_path: str) -> str:
"""
Extract text from PDF (simple interface).
Parameters
----------
pdf_path : str
Path to PDF
Returns
-------
str
Extracted text
"""
result = self.read_pdf(pdf_path)
return result['text']
def extract_tables(self, pdf_path: str) -> List[List[List[str]]]:
"""
Extract tables from PDF.
Parameters
----------
pdf_path : str
Path to PDF
Returns
-------
list
List of tables
"""
if not self.has_pdfplumber:
print("Warning: pdfplumber required for table extraction")
return []
result = self._read_with_pdfplumber(pdf_path)
return [table['data'] for table in result.get('tables', [])]
class PDFProcessor:
"""
Process and analyze PDF documents for geopolitical intelligence.
Provides high-level processing capabilities including:
- Entity extraction
- Topic extraction
- Sentiment analysis
- Key phrase extraction
"""
def __init__(self, pdf_reader: Optional[PDFReader] = None):
"""
Initialize PDF processor.
Parameters
----------
pdf_reader : PDFReader, optional
PDF reader to use
"""
self.reader = pdf_reader or PDFReader()
def process_document(self, pdf_path: str) -> Dict[str, Any]:
"""
Process PDF document and extract intelligence.
Parameters
----------
pdf_path : str
Path to PDF
Returns
-------
dict
Processed document with analysis
"""
# Extract content
content = self.reader.read_pdf(pdf_path)
# Basic processing
processed = {
'file_path': pdf_path,
'file_name': Path(pdf_path).name,
'text': content['text'],
'num_pages': content['num_pages'],
'metadata': content.get('metadata', {}),
'word_count': len(content['text'].split()),
'char_count': len(content['text']),
}
# Extract key information
processed['entities'] = self._extract_entities(content['text'])
processed['keywords'] = self._extract_keywords(content['text'])
processed['summary'] = self._generate_summary(content['text'])
return processed
def _extract_entities(self, text: str) -> Dict[str, List[str]]:
"""
Extract named entities (countries, organizations, people).
Parameters
----------
text : str
Text to analyze
Returns
-------
dict
Extracted entities by type
"""
entities = {
'countries': [],
'organizations': [],
'people': [],
'locations': []
}
# Simple pattern-based extraction (can be enhanced with NER)
# Common country names
countries = ['United States', 'China', 'Russia', 'Iran', 'North Korea',
'India', 'Pakistan', 'Israel', 'Saudi Arabia', 'Turkey',
'France', 'Germany', 'United Kingdom', 'Japan', 'South Korea']
for country in countries:
if country in text:
entities['countries'].append(country)
# Organizations (simple patterns)
org_patterns = [r'\b([A-Z][A-Za-z]+(?:\s+[A-Z][A-Za-z]+)*)\s+(?:Organization|Agency|Ministry|Department|Council)\b']
for pattern in org_patterns:
matches = re.findall(pattern, text)
entities['organizations'].extend(matches)
return entities
def _extract_keywords(self, text: str, n_keywords: int = 10) -> List[Tuple[str, float]]:
"""
Extract keywords from text.
Parameters
----------
text : str
Text to analyze
n_keywords : int
Number of keywords to extract
Returns
-------
list
List of (keyword, score) tuples
"""
# Simple frequency-based extraction
words = text.lower().split()
# Remove common words
stopwords = {'the', 'a', 'an', 'and', 'or', 'but', 'in', 'on', 'at',
'to', 'for', 'of', 'with', 'by', 'from', 'as', 'is', 'was',
'are', 'were', 'been', 'be', 'have', 'has', 'had', 'do',
'does', 'did', 'will', 'would', 'should', 'could', 'may',
'might', 'can', 'this', 'that', 'these', 'those'}
words = [w for w in words if w not in stopwords and len(w) > 3]
# Count frequencies
from collections import Counter
word_freq = Counter(words)
# Return top keywords
return word_freq.most_common(n_keywords)
def _generate_summary(self, text: str, num_sentences: int = 3) -> str:
"""
Generate simple extractive summary.
Parameters
----------
text : str
Text to summarize
num_sentences : int
Number of sentences in summary
Returns
-------
str
Summary
"""
# Split into sentences
sentences = re.split(r'[.!?]+', text)
sentences = [s.strip() for s in sentences if len(s.strip()) > 20]
# Take first few sentences as summary (simple approach)
summary_sentences = sentences[:num_sentences]
return '. '.join(summary_sentences) + '.'
def batch_process(self, pdf_directory: str, pattern: str = '*.pdf') -> List[Dict[str, Any]]:
"""
Process multiple PDFs in a directory.
Parameters
----------
pdf_directory : str
Directory containing PDFs
pattern : str
File pattern to match
Returns
-------
list
List of processed documents
"""
pdf_dir = Path(pdf_directory)
pdf_files = list(pdf_dir.glob(pattern))
results = []
for pdf_file in pdf_files:
try:
processed = self.process_document(str(pdf_file))
results.append(processed)
except Exception as e:
print(f"Error processing {pdf_file}: {e}")
return results
def extract_intelligence(self, pdf_path: str) -> Dict[str, Any]:
"""
Extract geopolitical intelligence from PDF.
Parameters
----------
pdf_path : str
Path to PDF
Returns
-------
dict
Intelligence summary
"""
processed = self.process_document(pdf_path)
# Analyze for geopolitical indicators
text = processed['text'].lower()
indicators = {
'conflict_indicators': self._detect_conflict_indicators(text),
'risk_level': self._assess_risk_level(text),
'mentioned_countries': processed['entities'].get('countries', []),
'key_topics': [kw[0] for kw in processed['keywords'][:5]],
'document_type': self._classify_document_type(text)
}
return {**processed, 'intelligence': indicators}
def _detect_conflict_indicators(self, text: str) -> List[str]:
"""Detect conflict-related keywords."""
conflict_keywords = ['war', 'conflict', 'military', 'attack', 'invasion',
'sanctions', 'escalation', 'tension', 'threat', 'crisis']
detected = [kw for kw in conflict_keywords if kw in text]
return detected
def _assess_risk_level(self, text: str) -> str:
"""Simple risk level assessment."""
high_risk_terms = ['imminent', 'urgent', 'critical', 'severe', 'escalating']
medium_risk_terms = ['concern', 'monitoring', 'potential', 'emerging']
high_count = sum(1 for term in high_risk_terms if term in text)
medium_count = sum(1 for term in medium_risk_terms if term in text)
if high_count > 2:
return 'HIGH'
elif medium_count > 2:
return 'MEDIUM'
else:
return 'LOW'
def _classify_document_type(self, text: str) -> str:
"""Classify document type."""
if 'intelligence report' in text or 'classified' in text:
return 'Intelligence Report'
elif 'analysis' in text or 'assessment' in text:
return 'Analysis'
elif 'briefing' in text:
return 'Briefing'
else:
return 'General Document'
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