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#!/usr/bin/env python3
"""
Test just the classification system for the chess question to show multi-agent routing
"""
from question_classifier import QuestionClassifier
from gaia_web_loader import GAIAQuestionLoaderWeb
def test_chess_classification():
"""Test classification for chess question"""
task_id = "cca530fc-4052-43b2-b130-b30968d8aa44"
print(f"🧠 Testing Multi-Agent Classification: Chess Question")
print("=" * 60)
# Initialize components
classifier = QuestionClassifier()
loader = GAIAQuestionLoaderWeb()
# Get the question
question_data = loader.get_question_by_id(task_id)
question_text = question_data.get('question', '')
file_name = question_data.get('file_name', '')
print(f"📝 Question: {question_text}")
print(f"📄 Image file: {file_name}")
# Classify the question
print(f"\n🧠 QUESTION CLASSIFICATION:")
print("-" * 40)
classification = classifier.classify_question(question_text, file_name)
routing = classifier.get_routing_recommendation(classification)
print(f"🎯 Primary Agent: {classification['primary_agent']}")
print(f"🤝 Secondary Agents: {', '.join(classification['secondary_agents'])}")
print(f"📊 Complexity: {classification['complexity']}/5")
print(f"🎲 Confidence: {classification['confidence']:.3f}")
print(f"🔧 Tools Needed: {', '.join(classification['tools_needed'])}")
print(f"🎬 Requires Multimodal: {classification['requires_multimodal']}")
print(f"📈 Estimated Steps: {classification['estimated_steps']}")
print(f"💭 Reasoning: {classification['reasoning']}")
print(f"\n🚀 ROUTING PLAN:")
print("-" * 40)
print(f"🎯 Primary Route: {routing['primary_route']} agent")
print(f"🤝 Coordination Needed: {'YES' if routing['requires_coordination'] else 'NO'}")
print(f"⚡ Parallel Execution: {'YES' if routing['parallel_execution'] else 'NO'}")
print(f"⏱️ Estimated Duration: {routing['estimated_duration']}")
print(f"\n🔧 SPECIAL REQUIREMENTS:")
for req in routing['special_requirements']:
print(f" • {req}")
print(f"\n🎮 MULTI-AGENT WORKFLOW:")
print("-" * 40)
print(f"1. 🎬 MULTIMEDIA AGENT (Primary):")
print(f" - Load chess position image: {file_name}")
print(f" - Use Gemini Vision API for board analysis")
print(f" - Extract piece positions and current game state")
print(f" - Identify chess pieces and their locations")
print(f"\n2. 🧮 LOGIC/MATH AGENT (Secondary):")
print(f" - Receive board state from multimedia agent")
print(f" - Apply chess rules and strategy analysis")
print(f" - Calculate possible moves for black")
print(f" - Identify winning move sequences")
print(f" - Verify move guarantees a win")
print(f"\n3. 🎯 COORDINATION:")
print(f" - Multimedia agent extracts visual board state")
print(f" - Logic agent processes chess strategy")
print(f" - Combined result: algebraic notation move")
print(f"\n✅ CLASSIFICATION SUMMARY:")
print("=" * 60)
print(f"This question demonstrates perfect multi-agent classification:")
print(f"• Primary: {classification['primary_agent']} (image analysis)")
print(f"• Secondary: {', '.join(classification['secondary_agents'])} (chess strategy)")
print(f"• Complexity: {classification['complexity']}/5 (high)")
print(f"• Confidence: {classification['confidence']:.1%}")
print(f"• Multi-modal: {classification['requires_multimodal']}")
print(f"• Coordination required: {routing['requires_coordination']}")
print(f"\n🚀 This showcases the LLM classifier's ability to:")
print(f" ✅ Detect image analysis requirements")
print(f" ✅ Identify need for logical reasoning")
print(f" ✅ Recommend multi-agent coordination")
print(f" ✅ Assess high complexity correctly")
print(f" ✅ Provide detailed routing plan")
if __name__ == "__main__":
test_chess_classification() |