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#!/usr/bin/env python3
"""
BACKUP of complex ui_function_wrapper that causes schema validation issues
This is the original function from app_backup.py that triggers:
TypeError: argument of type 'bool' is not iterable
"""
def ui_function_wrapper(vehicle_type, user_description):
"""
Main UI wrapper function that yields real-time updates to multiple Gradio components
Returns tuples in the order: process_log, current_design_specs, progress_bar,
final_status, simulation_video, best_design_specs, download_json,
performance_summary, llm_rationale
"""
global designer
# Reset designer for new task
designer.reset_design_session()
designer.vehicle_type = vehicle_type.lower()
designer.user_task_description = user_description
# Initial setup - yield initial states
yield (
"🚀 Initializing Agent2Robot system...\n", # process_log_output
{}, # current_design_specs_output
0, # progress_bar_output
"", # final_status_output
None, # simulation_video_output
{}, # best_design_specs_output
None, # download_json_output
"", # performance_summary_output
"" # llm_rationale_output
)
# Parse user criteria
designer.log_process_step("🎯 Analyzing user task and success criteria...")
criteria = designer.parse_user_task_for_criteria(user_description)
designer.log_process_step(f"📋 Interpreted success criteria:")
for criterion in criteria:
designer.log_process_step(f" • {criterion}")
# Update with criteria interpretation
current_log = "\n".join(designer.process_log)
yield (
current_log, # process_log_output
{"interpreted_criteria": criteria}, # current_design_specs_output
0, # progress_bar_output
"", # final_status_output
None, # simulation_video_output
{}, # best_design_specs_output
None, # download_json_output
"", # performance_summary_output
"" # llm_rationale_output
)
# Start design process
designer.log_process_step(f"🚀 Starting {vehicle_type} design process...")
designer.log_process_step(f"🎯 Target: {user_description}")
current_log = "\n".join(designer.process_log)
yield (
current_log, # process_log_output
{"status": "Design process starting..."}, # current_design_specs_output
0, # progress_bar_output
"", # final_status_output
None, # simulation_video_output
{}, # best_design_specs_output
None, # download_json_output
"", # performance_summary_output
"" # llm_rationale_output
)
# Run iterations
for iteration in range(1, MAX_ITERATIONS + 1):
designer.log_process_step(f"\n=== Starting Iteration {iteration}/{MAX_ITERATIONS} ===")
# Update progress at start of iteration
current_log = "\n".join(designer.process_log)
progress_value = (iteration - 0.5) / MAX_ITERATIONS * 100 # Convert to percentage
yield (
current_log, # process_log_output
{"current_iteration": iteration, "max_iterations": MAX_ITERATIONS, "status": "Running..."}, # current_design_specs_output
progress_value, # progress_bar_output
"", # final_status_output
None, # simulation_video_output
{}, # best_design_specs_output
None, # download_json_output
"", # performance_summary_output
"" # llm_rationale_output
)
# Run the iteration
try:
success = designer.run_single_iteration(iteration)
# Get current design specs for display
if designer.all_attempts:
current_attempt = designer.all_attempts[-1]
current_specs = current_attempt['vehicle_specs']
design_reasoning = current_attempt.get('design_reasoning', 'No reasoning provided')
# Update with current iteration results
current_log = "\n".join(designer.process_log)
progress_value = iteration / MAX_ITERATIONS * 100
current_specs_display = {
"iteration": iteration,
"vehicle_specs": current_specs,
"design_reasoning_preview": design_reasoning[:200] + "..." if len(design_reasoning) > 200 else design_reasoning,
"status": "✅ SUCCESS" if success else "🔄 Completed - Evaluating..."
}
yield (
current_log, # process_log_output
current_specs_display, # current_design_specs_output
progress_value, # progress_bar_output
"", # final_status_output
None, # simulation_video_output
{}, # best_design_specs_output
None, # download_json_output
"", # performance_summary_output
"" # llm_rationale_output
)
if success:
designer.log_process_step("🎉 SUCCESS! Design meets all criteria!")
break
except Exception as e:
designer.log_process_step(f"❌ Error in iteration {iteration}: {str(e)}")
current_log = "\n".join(designer.process_log)
progress_value = iteration / MAX_ITERATIONS * 100
yield (
current_log, # process_log_output
{"error": f"Iteration {iteration} failed", "details": str(e)}, # current_design_specs_output
progress_value, # progress_bar_output
"", # final_status_output
None, # simulation_video_output
{}, # best_design_specs_output
None, # download_json_output
"", # performance_summary_output
"" # llm_rationale_output
)
# Generate final results
designer.log_process_step("📊 Generating final results and visualizations...")
current_log = "\n".join(designer.process_log)
yield (
current_log, # process_log_output
{"status": "Generating final results..."}, # current_design_specs_output
100, # progress_bar_output - complete
"", # final_status_output
None, # simulation_video_output
{}, # best_design_specs_output
None, # download_json_output
"", # performance_summary_output
"" # llm_rationale_output
)
# Prepare final outputs
if designer.overall_success:
final_status = "## 🎉 SUCCESS!\n\nThe LLM agent successfully designed a vehicle that meets all criteria!"
status_emoji = "✅"
else:
final_status = "## ⚠️ PROCESS COMPLETED\n\nThe agent completed all iterations. Showing best attempt found."
status_emoji = "🔄"
# Get best design specs
best_specs = designer.best_attempt['vehicle_specs'] if designer.best_attempt else {}
# Create visualization
simulation_gif_path = None
try:
simulation_gif_path = designer.create_final_visualization()
except Exception as e:
designer.log_process_step(f"⚠️ Error creating visualization: {str(e)}")
# Format performance summary
if designer.best_attempt:
eval_results = designer.best_attempt['evaluation_results']
performance_summary = f"""## 📊 Performance Summary of Best Design
**Iteration Found**: {designer.best_iteration}/{len(designer.all_attempts)}
**Final Position**: {eval_results.get('final_robot_x_position', 0.0):.3f}m
**Crossed Obstacle**: {'✅ Yes' if eval_results.get('robot_crossed_obstacle', False) else '❌ No'}
**Remained Stable**: {'✅ Yes' if eval_results.get('robot_remains_upright', False) else '❌ No'}
**Clean Pass**: {'✅ Yes' if eval_results.get('no_significant_collision_with_obstacle_during_pass', False) else '❌ No'}
**Overall Success**: {'✅ ACHIEVED' if eval_results.get('overall_success', False) else '❌ NOT FULLY ACHIEVED'}
**Target Distance**: 0.8m (obstacle clearance)
**Achieved Distance**: {eval_results.get('final_robot_x_position', 0.0):.3f}m
**Success Rate**: {100 if eval_results.get('overall_success', False) else 0}%
{status_emoji} **Status**: {'Complete Success' if designer.overall_success else 'Best Effort'}
"""
else:
performance_summary = "## ❌ No successful attempts recorded\n\nThe system was unable to generate valid designs."
# Get LLM rationale
llm_rationale = designer.best_attempt['design_reasoning'] if designer.best_attempt else "No design reasoning available"
# Create downloadable specs
download_specs_path = None
try:
download_specs_path = designer.save_design_specs_json()
except Exception as e:
designer.log_process_step(f"⚠️ Error saving specs: {str(e)}")
# Final log update
designer.log_process_step(f"\n🏁 DESIGN PROCESS COMPLETED")
designer.log_process_step(f"📊 Total iterations: {len(designer.all_attempts)}")
designer.log_process_step(f"🏆 Best iteration: {designer.best_iteration}")
designer.log_process_step(f"✅ Overall success: {designer.overall_success}")
final_log = "\n".join(designer.process_log)
# Final yield with all results
yield (
final_log, # process_log_output
{"final_summary": f"Process completed. {len(designer.all_attempts)} iterations run."}, # current_design_specs_output
100, # progress_bar_output
final_status, # final_status_output
simulation_gif_path, # simulation_video_output
best_specs, # best_design_specs_output
download_specs_path, # download_json_output
performance_summary, # performance_summary_output
llm_rationale # llm_rationale_output
) |