FedMCP - Federal Parliamentary Information

#!/usr/bin/env python3 """ Test script to demonstrate enhanced Hansard XML metadata extraction. This script fetches a recent Hansard sitting and displays the rich metadata that is now being captured from the XML, including: - Person database IDs (Affiliation@DbId) - Role type codes (1=PM, 2=MP, 15=Speaker, etc.) - Paragraph IDs for citations - Structured timestamps - Floor language - Intervention types """ import sys from pathlib import Path # Add src to path sys.path.insert(0, str(Path(__file__).parent / "src")) from fedmcp.clients.ourcommons import OurCommonsHansardClient def main(): print("=" * 70) print("Enhanced Hansard XML Metadata Extraction Test") print("=" * 70) print() client = OurCommonsHansardClient() print("Fetching latest Hansard sitting...") try: sitting = client.get_sitting("latest/hansard", parse=True) except Exception as e: print(f"❌ Error fetching Hansard: {e}") return print(f"✅ Successfully parsed Hansard sitting") print() # Display document-level metadata print("📄 Document Metadata") print("-" * 70) print(f"Date: {sitting.date}") print(f"Number: {sitting.number}") print(f"Language: {sitting.language}") print(f"Source URL: {sitting.source_xml_url}") print() print("🆕 Enhanced Document Metadata:") print(f" Creation Timestamp: {sitting.creation_timestamp}") print(f" Speaker of the Day: {sitting.speaker_of_day}") print(f" Hansard Document ID: {sitting.hansard_document_id}") print(f" Parliament #: {sitting.parliament_number}") print(f" Session #: {sitting.session_number}") print(f" Volume: {sitting.volume}") print() # Display enhanced statement metadata print("💬 Sample Statements with Enhanced Metadata") print("-" * 70) total_statements = sum(len(section.speeches) for section in sitting.sections) print(f"Total sections: {len(sitting.sections)}") print(f"Total statements: {total_statements}") print() # Show first 3 statements from first section if sitting.sections and sitting.sections[0].speeches: section = sitting.sections[0] print(f"Section: {section.title}") print() for i, speech in enumerate(section.speeches[:3], 1): print(f"Statement {i}:") print(f" Speaker: {speech.speaker_name}") print(f" Party: {speech.party}") print(f" Riding: {speech.riding}") print(f" Time: {speech.timecode}") print() print(" 🆕 Enhanced Metadata:") print(f" Person DB ID: {speech.person_db_id} {'✅' if speech.person_db_id else '❌'}") print(f" Role Type Code: {speech.role_type_code} {'✅' if speech.role_type_code else '❌'}") # Decode role type if speech.role_type_code: role_names = { 1: "Prime Minister", 2: "MP", 9: "Leader of Opposition", 15: "Speaker", 60107: "Presiding Officer", 96: "Clerk" } role_name = role_names.get(speech.role_type_code, f"Unknown ({speech.role_type_code})") print(f" → Role: {role_name}") print(f" Intervention ID: {speech.intervention_id} {'✅' if speech.intervention_id else '❌'}") print(f" Paragraph IDs: {len(speech.paragraph_ids)} paragraphs {'✅' if speech.paragraph_ids else '❌'}") if speech.paragraph_ids: print(f" → First 3: {speech.paragraph_ids[:3]}") if speech.timestamp_hour is not None and speech.timestamp_minute is not None: print(f" Structured Time: {speech.timestamp_hour}:{speech.timestamp_minute:02d} ✅") else: print(f" Structured Time: None ❌") print(f" Floor Language: {speech.floor_language} {'✅' if speech.floor_language else '❌'}") print(f" Intervention Type: {speech.intervention_type} {'✅' if speech.intervention_type else '❌'}") print() print(f" Content Preview: {speech.text[:150]}...") print() print("-" * 70) # Statistics print() print("📊 Metadata Capture Statistics") print("-" * 70) all_speeches = [speech for section in sitting.sections for speech in section.speeches] stats = { "person_db_id": sum(1 for s in all_speeches if s.person_db_id), "role_type_code": sum(1 for s in all_speeches if s.role_type_code), "paragraph_ids": sum(1 for s in all_speeches if s.paragraph_ids), "structured_time": sum(1 for s in all_speeches if s.timestamp_hour is not None), "floor_language": sum(1 for s in all_speeches if s.floor_language), "intervention_type": sum(1 for s in all_speeches if s.intervention_type), } for field, count in stats.items(): percentage = (count / len(all_speeches) * 100) if all_speeches else 0 print(f"{field:20s}: {count:4d}/{len(all_speeches)} ({percentage:5.1f}%) {'✅' if percentage > 50 else '⚠️ '}") print() print("=" * 70) print("✅ Enhanced metadata extraction successful!") print("=" * 70) if __name__ == "__main__": main()