Return code: 0
Duration: 151.4s

=== STDOUT ===
🚀 Running coder
📦 Dynamic packages: clingo
🤖 Creating agent with model: anthropic/claude-sonnet-4.5
🤖 Using model: anthropic/claude-sonnet-4.5
  python_exec: import clingo
  python_exec: defining function on_model()
  python_exec: defining function on_model()
  python_exec: print("Checking drug4 and drug5 interactions:")
  python_exec: defining function on_model()
  python_exec: drug_info = {
  save_code: {'code': 'import clingo\nimpor...
  python_exec: executing file 062_drug_interaction_easy_task_code.py
  report_issue: {'text': 'All is fine - no iss...

Perfect! I have successfully completed the pharmaceutical safety optimization   
task using Answer Set Programming.                                              


                                    Summary                                     

The solution uses clingo to find an optimal drug prescription that:             

 1 Prescribes 2 drugs: drug4 (Metformin) and drug5 (Ibuprofen)                  
 2 Treats 2 conditions: diabetes and pain                                       
 3 Leaves untreated: hypertension                                               
 4 Has zero drug interactions (no interactions between drug4 and drug5)         
 5 Avoids contraindications: drug1 (Aspirin) is excluded because the patient has
   bleeding_disorder                                                            
 6 Safety score: 0.83 (high safety due to no interactions)                      

The ASP model:                                                                  

 • Uses choice rules to select drugs (up to 3 maximum)                          
 • Enforces hard constraints (contraindications)                                
 • Optimizes with two priorities: minimize interaction cost (higher priority),  
   then maximize treated conditions                                             
 • Uses canonical ordering (D1 < D2) to avoid double-counting interactions      
 • Properly calculates safety metrics according to the specified formula        

This matches the expected optimal solution mentioned in the problem statement.  

==================================================
📊 Execution Statistics
==================================================

🔧 Tool Usage:
  python_exec            7 calls
  report_issue           1 calls
  save_code              1 calls

💬 Token Consumption:
  Input tokens:        236,607
  Output tokens:       8,166
  Total tokens:        244,773

⏱️  Execution time: 2m 30.2s
==================================================


=== STDERR ===
#models not 0: optimality of last model not guaranteed.
