backup/main_orchestrator.py

import os
import ssl
import time
import imageio
import numpy as np
from PIL import Image
import json
from datetime import datetime
import tempfile
import traceback
import simulation_env_enhanced as simulation_env
import llm_interface_enhanced as llm_interface
import evaluation

# SSL workaround for Gradio issues
try:
    import certifi
    os.environ['SSL_CERT_FILE'] = certifi.where()
except ImportError:
    pass

# Try to disable SSL verification as a workaround
try:
    ssl._create_default_https_context = ssl._create_unverified_context
except AttributeError:
    pass

# Try to import Gradio with error handling
GRADIO_AVAILABLE = False
try:
    import gradio as gr
    GRADIO_AVAILABLE = True
    print("✓ Gradio imported successfully")
except Exception as e:
    print(f"⚠ Gradio import failed: {e}")
    print("Will use console-based interface instead")
    GRADIO_AVAILABLE = False

# Global configuration
MAX_ITERATIONS = 5
SIMULATION_DURATION_SEC = 10
OBSTACLE_FAR_EDGE_X = 0.8

class HackathonVehicleDesigner:
    """Enhanced vehicle designer for hackathon with comprehensive tracking and feedback"""
    
    def __init__(self):
        self.reset_design_session()
    
    def reset_design_session(self):
        """Reset all session variables for new design process"""
        self.all_attempts = []
        self.best_attempt = None
        self.best_iteration = None
        self.process_log = []
        self.current_iteration = 0
        self.overall_success = False
        self.user_task_description = ""
        self.vehicle_type = "robot"
        self.llm_interpreted_criteria = []
    
    def log_process_step(self, message):
        """Add a step to the process log with timestamp"""
        timestamp = datetime.now().strftime("%H:%M:%S")
        log_entry = f"[{timestamp}] {message}"
        self.process_log.append(log_entry)
        print(log_entry)  # Also print to console
    
    def parse_user_task_for_criteria(self, task_description):
        """Extract and interpret success criteria from user task description"""
        # This is where the LLM would interpret user criteria
        # For now, we'll use a simple rule-based approach and enhance with LLM later
        
        criteria = []
        task_lower = task_description.lower()
        
        # Basic criteria that are always present
        criteria.append("Cross the obstacle completely (reach x > 0.8m)")
        criteria.append("Maintain stability throughout the process")
        criteria.append("Avoid getting stuck on or damaged by the obstacle")
        
        # Additional criteria based on task description
        if "quick" in task_lower or "fast" in task_lower:
            criteria.append("Complete the task as quickly as possible")
        
        if "stop" in task_lower or "halt" in task_lower:
            criteria.append("Come to a controlled stop after crossing")
        
        if "land" in task_lower and "drone" in self.vehicle_type:
            criteria.append("Land safely after crossing the obstacle")
        
        if "stable" in task_lower or "steady" in task_lower:
            criteria.append("Maintain steady movement without excessive oscillation")
        
        self.llm_interpreted_criteria = criteria
        return criteria
    
    def run_single_iteration(self, iteration_num):
        """Run a single design and simulation iteration"""
        self.current_iteration = iteration_num
        self.log_process_step(f"=== Starting Iteration {iteration_num} ===")
        
        try:
            # Generate prompt for LLM
            if iteration_num == 1:
                self.log_process_step("Requesting initial design from LLM agent...")
                if self.vehicle_type == "robot":
                    prompt = llm_interface.generate_initial_robot_design_prompt_with_criteria(
                        self.user_task_description, self.llm_interpreted_criteria
                    )
                else:
                    prompt = llm_interface.generate_initial_drone_design_prompt_with_criteria(
                        self.user_task_description, self.llm_interpreted_criteria
                    )
                previous_attempt = None
            else:
                self.log_process_step(f"Requesting design refinement from LLM agent (iteration {iteration_num})...")
                previous_attempt = self.all_attempts[-1]
                if self.vehicle_type == "robot":
                    prompt = llm_interface.generate_iterative_robot_design_prompt_with_criteria(
                        previous_attempt, iteration_num, self.llm_interpreted_criteria
                    )
                else:
                    prompt = llm_interface.generate_iterative_drone_design_prompt_with_criteria(
                        previous_attempt, iteration_num, self.llm_interpreted_criteria
                    )
            
            # Call LLM for design
            llm_response = llm_interface.call_llm_api(prompt)
            
            if not llm_response:
                raise Exception("Failed to get valid response from LLM")
            
            # Extract vehicle specs and reasoning
            vehicle_specs = llm_response.get('robot_specs', {})
            vehicle_specs["vehicle_type"] = self.vehicle_type
            design_reasoning = llm_response.get('design_reasoning', 'No reasoning provided')
            llm_success_conditions = llm_response.get('llm_interpreted_success_conditions', self.llm_interpreted_criteria)
            
            self.log_process_step(f"LLM proposed design: {vehicle_specs}")
            self.log_process_step(f"Design reasoning: {design_reasoning}")
            self.log_process_step(f"LLM's success conditions: {llm_success_conditions}")
            
            # Setup and run simulation
            self.log_process_step("Setting up PyBullet simulation environment...")
            obstacle_id, plane_id = simulation_env.setup_pybullet_environment()
            
            # Create vehicle
            self.log_process_step(f"Creating {self.vehicle_type} in simulation...")
            if self.vehicle_type == "robot":
                vehicle_id, joint_indices, v_type = simulation_env.create_robot(vehicle_specs)
                vehicle_props = None
            else:
                vehicle_id, joint_indices, v_type, vehicle_props = simulation_env.create_drone(vehicle_specs)
            
            # Run simulation
            self.log_process_step("Running physics simulation...")
            frames, final_feedback = self.run_simulation_loop(
                vehicle_id, joint_indices, vehicle_props
            )
            
            # Evaluate results
            self.log_process_step("Evaluating simulation results...")
            evaluation_results = evaluation.evaluate_simulation_outcome_with_criteria(
                final_feedback, OBSTACLE_FAR_EDGE_X, llm_success_conditions
            )
            
            # Create feedback for LLM
            llm_feedback = evaluation.format_feedback_for_llm_with_criteria(
                evaluation_results, llm_success_conditions
            )
            
            self.log_process_step(f"Simulation results: {llm_feedback}")
            
            # Store attempt data
            attempt_data = {
                "iteration": iteration_num,
                "llm_design": llm_response,
                "vehicle_specs": vehicle_specs,
                "design_reasoning": design_reasoning,
                "llm_success_conditions": llm_success_conditions,
                "evaluation_results": evaluation_results,
                "feedback_from_simulation": llm_feedback,
                "frames": frames
            }
            
            self.all_attempts.append(attempt_data)
            
            # Update best attempt
            if self.is_current_better_than_best(attempt_data):
                self.best_attempt = attempt_data
                self.best_iteration = iteration_num
                self.log_process_step(f"🏆 New best design found in iteration {iteration_num}!")
            
            # Check for overall success
            if evaluation_results.get('overall_success', False):
                self.overall_success = True
                self.log_process_step("🎉 SUCCESS! Design meets all criteria!")
                return True
            else:
                failure_reason = evaluation_results.get('specific_failure_point', 'unknown')
                self.log_process_step(f"❌ Iteration {iteration_num} failed: {failure_reason}")
                return False
            
        except Exception as e:
            error_msg = f"Error in iteration {iteration_num}: {str(e)}"
            self.log_process_step(f"🚨 {error_msg}")
            print(f"Full error traceback: {traceback.format_exc()}")
            
            # Create error attempt data
            error_attempt = {
                "iteration": iteration_num,
                "llm_design": {"error": str(e)},
                "vehicle_specs": {},
                "design_reasoning": f"Error occurred: {str(e)}",
                "llm_success_conditions": self.llm_interpreted_criteria,
                "evaluation_results": {
                    "overall_success": False,
                    "robot_crossed_obstacle": False,
                    "robot_remains_upright": False,
                    "final_robot_x_position": 0.0,
                    "specific_failure_point": "simulation_error"
                },
                "feedback_from_simulation": f"Simulation failed: {str(e)}",
                "frames": []
            }
            self.all_attempts.append(error_attempt)
            return False
        
        finally:
            # Cleanup simulation
            try:
                simulation_env.reset_simulation()
            except:
                pass
    
    def run_simulation_loop(self, vehicle_id, joint_indices, vehicle_props):
        """Run the simulation loop and capture frames"""
        frames = []
        start_time = time.time()
        simulation_steps = int(SIMULATION_DURATION_SEC * 240)
        
        for step in range(simulation_steps):
            # Run simulation step
            simulation_env.run_simulation_step(
                vehicle_id, joint_indices, {}, self.vehicle_type, vehicle_props
            )
            
            current_sim_time = time.time() - start_time
            
            # Capture frames for visualization
            if step % 24 == 0:  # 10 FPS
                try:
                    frame = simulation_env.capture_frame()
                    if frame:
                        frames.append(frame)
                except:
                    pass
            
            # Get current feedback
            obstacle_id = 1  # Assuming obstacle has ID 1
            feedback = simulation_env.get_simulation_feedback(
                vehicle_id, obstacle_id, start_time, current_sim_time, self.vehicle_type
            )
            
            # Check for early exit conditions
            vehicle_x_pos = feedback['robot_position'][0]
            is_stable = feedback['is_robot_upright']
            
            if vehicle_x_pos > OBSTACLE_FAR_EDGE_X + 0.1 or not is_stable:
                break
            
            if current_sim_time > SIMULATION_DURATION_SEC:
                break
        
        return frames, feedback
    
    def is_current_better_than_best(self, current_attempt):
        """Determine if current attempt is better than the current best"""
        if not self.best_attempt:
            return True
        
        current_eval = current_attempt['evaluation_results']
        best_eval = self.best_attempt['evaluation_results']
        
        # Priority 1: Overall success
        if current_eval.get('overall_success', False) and not best_eval.get('overall_success', False):
            return True
        elif best_eval.get('overall_success', False) and not current_eval.get('overall_success', False):
            return False
        
        # Priority 2: Obstacle crossing
        if current_eval.get('robot_crossed_obstacle', False) and not best_eval.get('robot_crossed_obstacle', False):
            return True
        elif best_eval.get('robot_crossed_obstacle', False) and not current_eval.get('robot_crossed_obstacle', False):
            return False
        
        # Priority 3: Distance traveled
        current_distance = current_eval.get('final_robot_x_position', 0.0)
        best_distance = best_eval.get('final_robot_x_position', 0.0)
        
        return current_distance > best_distance
    
    def create_final_visualization(self):
        """Create GIF from best attempt frames"""
        if not self.best_attempt or not self.best_attempt.get('frames'):
            return None
        
        try:
            # Create timestamp for unique filename
            timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
            gif_filename = f"best_{self.vehicle_type}_design_{timestamp}.gif"
            gif_path = os.path.join("outputs", gif_filename)
            
            # Ensure outputs directory exists
            os.makedirs("outputs", exist_ok=True)
            
            # Convert frames to numpy arrays
            frame_arrays = []
            for frame in self.best_attempt['frames']:
                if isinstance(frame, Image.Image):
                    frame_arrays.append(np.array(frame))
                else:
                    frame_arrays.append(frame)
            
            if frame_arrays:
                imageio.mimsave(gif_path, frame_arrays, fps=10, loop=0)
                return gif_path
            else:
                return None
                
        except Exception as e:
            print(f"Error creating visualization: {e}")
            return None
    
    def save_design_specs_json(self):
        """Save best design specifications to downloadable JSON file"""
        if not self.best_attempt:
            return None
        
        try:
            # Create comprehensive design specification
            timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
            
            design_data = {
                "hackathon_submission": {
                    "project_title": "LLM-Agent-Designed Obstacle-Passing Vehicle System",
                    "track": "Track 3: Agentic Demo Showcase",
                    "timestamp": datetime.now().isoformat(),
                    "vehicle_type": self.vehicle_type
                },
                "user_task": {
                    "description": self.user_task_description,
                    "llm_interpreted_criteria": self.llm_interpreted_criteria
                },
                "design_process": {
                    "total_iterations": len(self.all_attempts),
                    "best_iteration": self.best_iteration,
                    "overall_success": self.overall_success,
                    "max_iterations_allowed": MAX_ITERATIONS
                },
                "best_design": {
                    "vehicle_specifications": self.best_attempt['vehicle_specs'],
                    "design_reasoning": self.best_attempt['design_reasoning'],
                    "llm_success_conditions": self.best_attempt['llm_success_conditions']
                },
                "performance_results": self.best_attempt['evaluation_results'],
                "technical_details": {
                    "simulation_duration_sec": SIMULATION_DURATION_SEC,
                    "obstacle_specifications": {
                        "height_cm": 5,
                        "width_cm": 50,
                        "depth_cm": 10,
                        "position_x_m": 0.75
                    },
                    "success_threshold_x_m": OBSTACLE_FAR_EDGE_X,
                    "physics_engine": "PyBullet",
                    "llm_model": "Enhanced fallback system"
                }
            }
            
            # Create temporary file for download
            temp_file = tempfile.NamedTemporaryFile(
                mode='w', suffix='.json', delete=False, 
                prefix=f'best_{self.vehicle_type}_design_{timestamp}_'
            )
            
            json.dump(design_data, temp_file, indent=2, ensure_ascii=False)
            temp_file.close()
            
            return temp_file.name
            
        except Exception as e:
            print(f"Error saving design specs: {e}")
            return None
    
    def generate_readme_content(self):
        """Generate README content for hackathon submission"""
        readme_content = f"""# 🤖🚁 LLM-Agent-Designed Obstacle-Passing Vehicle System

**Hackathon Submission - Track 3: Agentic Demo Showcase**

## Project Description

An AI agent that iteratively designs robots or drones using an LLM and PyBullet simulation to meet user-defined functional criteria. The system demonstrates autonomous design iteration, real-time physics simulation, and intelligent performance optimization.

## 🎯 Key Innovation

- **LLM-Driven Design**: AI agent autonomously proposes and refines vehicle designs
- **Physics-Based Validation**: Real-time PyBullet simulation for accurate performance testing
- **Criteria-Driven Optimization**: User-defined success criteria guide the design process
- **Iterative Intelligence**: Agent learns from simulation feedback to improve designs

## 🚀 How to Run

### Prerequisites
- Python 3.10+
- Required packages: `pip install -r requirements.txt`

### Usage
```bash
python main_orchestrator.py
```

Open your browser to the provided URL (typically http://localhost:7860)

## 🛠️ Key Technologies Used

- **Python**: Core implementation language
- **Gradio**: Interactive web interface
- **PyBullet**: Physics simulation engine
- **Transformers/LLM**: AI agent for design generation
- **PIL/imageio**: Visualization and GIF generation

## 🎬 Demo Video

[Link to Video Overview/Demo] - *To be added*

## 🏆 Hackathon Features Demonstrated

### Technical Implementation
- Robust PyBullet physics simulation
- LLM integration with fallback mechanisms
- Real-time iterative design optimization
- Comprehensive error handling

### Usability
- Intuitive Gradio interface
- Real-time process visualization
- Downloadable design specifications
- Clear success/failure feedback

### Innovation
- AI agent designing physical entities
- Dynamic success criteria interpretation
- Physics-simulation feedback loop
- Best design tracking and analysis

### Impact
- Educational tool for understanding AI-driven design
- Framework for autonomous vehicle optimization
- Demonstration of LLM practical applications

## 📊 Current Session Results

**Vehicle Type**: {self.vehicle_type.capitalize()}
**Task**: {self.user_task_description}
**Iterations Completed**: {len(self.all_attempts)}
**Overall Success**: {'✅ Yes' if self.overall_success else '❌ No'}

## 🤝 MCP Integration Potential

This system can be extended to function as an MCP Tool/Server (Track 1) by exposing:
- Vehicle design tools
- Simulation execution tools  
- Performance evaluation tools
- Iterative optimization tools

## 📄 License

MIT License - Open source for educational and research purposes.

---
*Generated automatically by LLM-Agent-Designed Vehicle System*
*Timestamp: {datetime.now().isoformat()}*
"""
        return readme_content

# Enhanced LLM Interface Functions (add to llm_interface_enhanced.py)
def generate_initial_robot_design_prompt_with_criteria(task_description, success_criteria):
    """Generate initial robot design prompt with user-defined criteria"""
    criteria_text = "\n".join([f"- {criterion}" for criterion in success_criteria])
    
    prompt = f"""You are an expert robot design AI. Your task is to design a robot based on the following user requirements:

USER TASK: {task_description}

USER SUCCESS CRITERIA (as interpreted by the system):
{criteria_text}

ENVIRONMENT:
Obstacle: Rectangular block (5cm high, 50cm wide, 10cm deep) at x=0.75m
Robot starts at x=0m and must traverse forward

AVAILABLE ROBOT PARAMETERS (provide in JSON format within 'robot_specs'):
- "wheel_type": ["small_high_grip", "large_smooth", "tracked_base"]
- "body_clearance_cm": integer 1-10 (ground clearance in cm)
- "approach_sensor_enabled": true/false
- "main_material": ["light_plastic", "sturdy_metal_alloy"]

REQUIRED OUTPUT FORMAT:
{{
  "robot_design_iteration": 1,
  "design_reasoning": "Your detailed explanation of design choices",
  "llm_interpreted_success_conditions": ["condition 1", "condition 2", ...],
  "robot_specs": {{
    "wheel_type": "your_choice",
    "body_clearance_cm": your_number,
    "approach_sensor_enabled": your_boolean,
    "main_material": "your_choice"
  }}
}}

Please provide your robot design now:"""
    
    return prompt

def generate_initial_drone_design_prompt_with_criteria(task_description, success_criteria):
    """Generate initial drone design prompt with user-defined criteria"""
    criteria_text = "\n".join([f"- {criterion}" for criterion in success_criteria])
    
    prompt = f"""You are an expert drone design AI. Your task is to design a drone based on the following user requirements:

USER TASK: {task_description}

USER SUCCESS CRITERIA (as interpreted by the system):
{criteria_text}

ENVIRONMENT:
Obstacle: Rectangular block (5cm high, 50cm wide, 10cm deep) at x=0.75m
Drone starts at x=0m and must fly over/around the obstacle

AVAILABLE DRONE PARAMETERS (provide in JSON format within 'robot_specs'):
- "propeller_size": ["small_agile", "medium", "large_stable"]
- "flight_height_cm": integer 10-50 (target flight altitude)
- "stability_mode": ["auto_hover", "manual_control"]
- "main_material": ["light_carbon_fiber", "sturdy_aluminum"]

REQUIRED OUTPUT FORMAT:
{{
  "robot_design_iteration": 1,
  "design_reasoning": "Your detailed explanation of design choices",
  "llm_interpreted_success_conditions": ["condition 1", "condition 2", ...],
  "robot_specs": {{
    "propeller_size": "your_choice",
    "flight_height_cm": your_number,
    "stability_mode": "your_choice",
    "main_material": "your_choice"
  }}
}}

Please provide your drone design now:"""
    
    return prompt

# Initialize global designer instance
designer = HackathonVehicleDesigner()

def design_vehicle_task(vehicle_type, task_description, progress=gr.Progress()):
    """Main function for Gradio interface - enhanced for hackathon"""
    global designer
    
    # Reset designer for new task
    designer.reset_design_session()
    designer.vehicle_type = vehicle_type
    designer.user_task_description = task_description
    
    # Parse user criteria
    designer.log_process_step("🎯 Analyzing user task and success criteria...")
    criteria = designer.parse_user_task_for_criteria(task_description)
    
    designer.log_process_step(f"📋 Interpreted success criteria:")
    for criterion in criteria:
        designer.log_process_step(f"  • {criterion}")
    
    # Start design process
    designer.log_process_step(f"🚀 Starting {vehicle_type} design process...")
    designer.log_process_step(f"🎯 Target: {task_description}")
    
    # Run iterations
    for iteration in range(1, MAX_ITERATIONS + 1):
        if progress:
            progress((iteration - 1) / MAX_ITERATIONS, f"Running iteration {iteration}/{MAX_ITERATIONS}")
        
        success = designer.run_single_iteration(iteration)
        
        # Yield current progress
        current_log = "\n".join(designer.process_log)
        yield (
            current_log,  # process_log
            None,         # overall_status (placeholder)
            None,         # best_design_specs (placeholder)
            None,         # simulation_gif (placeholder)
            None,         # performance_summary (placeholder)
            None,         # llm_rationale (placeholder)
            None,         # download_specs (placeholder)
            None          # readme_content (placeholder)
        )
        
        if success:
            break
    
    # Generate final results
    designer.log_process_step("📊 Generating final results and visualizations...")
    
    # Create overall status
    if designer.overall_success:
        overall_status = "## 🎉 SUCCESS!\n\nThe LLM agent successfully designed a vehicle that meets all criteria!"
    else:
        overall_status = "## ❌ PROCESS COMPLETED\n\nThe agent completed all iterations but did not achieve full success. Best attempt is shown below."
    
    # Get best design specs
    best_specs = designer.best_attempt['vehicle_specs'] if designer.best_attempt else {}
    
    # Create visualization
    simulation_gif = designer.create_final_visualization()
    
    # Format performance summary
    if designer.best_attempt:
        eval_results = designer.best_attempt['evaluation_results']
        performance_summary = f"""## 📊 Performance Summary of Best Design

**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 ACHIEVED'}

**Target Distance**: 0.8m
**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%'}
"""
    else:
        performance_summary = "## ❌ No successful attempts recorded"
    
    # Get LLM rationale
    llm_rationale = designer.best_attempt['design_reasoning'] if designer.best_attempt else "No design reasoning available"
    
    # Create downloadable specs
    download_specs = designer.save_design_specs_json()
    
    # Generate README content
    readme_content = designer.generate_readme_content()
    
    # Final log
    final_log = "\n".join(designer.process_log)
    final_log += f"\n\n🏁 DESIGN PROCESS COMPLETED"
    final_log += f"\n📊 Total iterations: {len(designer.all_attempts)}"
    final_log += f"\n🏆 Best iteration: {designer.best_iteration}"
    final_log += f"\n✅ Overall success: {designer.overall_success}"
    
    return (
        final_log,           # process_log
        overall_status,      # overall_status
        best_specs,          # best_design_specs
        simulation_gif,      # simulation_gif
        performance_summary, # performance_summary
        llm_rationale,       # llm_rationale
        download_specs,      # download_specs
        readme_content       # readme_content
    )

def create_hackathon_gradio_interface():
    """Create enhanced Gradio interface for hackathon submission"""
    
    # Custom CSS for better appearance
    custom_css = """
    .main-header {
        text-align: center;
        background: linear-gradient(90deg, #667eea 0%, #764ba2 100%);
        color: white;
        padding: 20px;
        border-radius: 10px;
        margin-bottom: 20px;
    }
    .success-box {
        background-color: #d4edda;
        border: 1px solid #c3e6cb;
        color: #155724;
        padding: 15px;
        border-radius: 5px;
        margin: 10px 0;
    }
    .failure-box {
        background-color: #f8d7da;
        border: 1px solid #f5c6cb;
        color: #721c24;
        padding: 15px;
        border-radius: 5px;
        margin: 10px 0;
    }
    """
    
    with gr.Blocks(
        title="🤖🚁 LLM Vehicle Designer - Hackathon Demo",
        theme=gr.themes.Soft(),
        css=custom_css
    ) as iface:
        
        # Header
        gr.HTML("""
        <div class="main-header">
            <h1>🤖🚁 LLM-Agent-Designed Obstacle-Passing Vehicle System</h1>
            <h3>Hackathon Submission - Track 3: Agentic Demo Showcase</h3>
            <p>An intelligent system where an LLM agent iteratively designs robots and drones to meet your custom criteria!</p>
        </div>
        """)
        
        # User Input Section
        with gr.Row():
            with gr.Column(scale=2):
                gr.Markdown("## 🎯 Define Your Challenge")
                
                vehicle_type = gr.Dropdown(
                    label="Select Vehicle Type",
                    choices=["robot", "drone"],
                    value="robot",
                    info="Choose between ground robot or flying drone"
                )
                
                task_description = gr.Textbox(
                    label="Describe the Vehicle's Task & Success Criteria",
                    placeholder="e.g., 'Robot to cross a 5cm high box quickly and without falling over, then stop.' or 'Drone to fly over a 10cm wall, land 1m beyond it, and stay stable.'",
                    lines=3,
                    value="Design a robot that can cross the 5cm high obstacle smoothly and come to a controlled stop."
                )
                
                submit_btn = gr.Button(
                    "🚀 Start LLM Agent Design Process",
                    variant="primary",
                    size="lg"
                )
                
            with gr.Column(scale=1):
                gr.Markdown("## 📋 Process Info")
                gr.Markdown("""
                **Environment Setup:**
                - 📦 Obstacle: 5cm high × 50cm wide × 10cm deep
                - 📍 Position: x = 0.75m
                - 🎯 Success: Vehicle must reach x > 0.8m
                
                **Agent Capabilities:**
                - 🤖 **Robot**: Wheel types, clearance, materials
                - 🚁 **Drone**: Propellers, flight height, stability
                - 🔄 **Max Iterations**: 5
                - 🧠 **LLM-Driven**: AI interprets your criteria
                """)
        
        gr.Markdown("---")
        
        # Real-time Process Section
        with gr.Row():
            with gr.Column(scale=3):
                gr.Markdown("## 🔄 Live Agent Process")
                process_log = gr.Textbox(
                    label="Full Process Log - Real-time Agent Activity",
                    lines=25,
                    max_lines=40,
                    show_copy_button=True,
                    interactive=False,
                    placeholder="Agent process log will appear here in real-time..."
                )
                
            with gr.Column(scale=2):
                gr.Markdown("## 🎬 Current Simulation")
                current_iteration_info = gr.Markdown("Ready to start...")
                
                simulation_gif = gr.Image(
                    label="Simulation Recording of Best Design's Trial",
                    type="filepath",
                    interactive=False
                )
        
        gr.Markdown("---")
        
        # Results Section
        gr.Markdown("## 🏆 Final Results & Analysis")
        
        overall_status = gr.Markdown(
            label="Overall Run Status",
            value="Waiting for process to complete..."
        )
        
        gr.Markdown("### --- Best Design Found ---")
        
        with gr.Row():
            with gr.Column(scale=2):
                best_design_specs = gr.JSON(
                    label="Best Vehicle Design Specifications (JSON)",
                    show_label=True
                )
                
                performance_summary = gr.Markdown(
                    label="Performance Summary of Best Design"
                )
                
            with gr.Column(scale=1):
                download_specs = gr.File(
                    label="📄 Download Design Specs (JSON)",
                    file_count="single",
                    type="filepath",
                    interactive=False
                )
                
                llm_rationale = gr.Textbox(
                    label="🧠 LLM's Rationale for Best Design",
                    lines=8,
                    interactive=False
                )
        
        gr.Markdown("---")
        
        # Hackathon Submission Section
        gr.Markdown("## 📝 Hackathon Submission Materials")
        
        readme_content = gr.Textbox(
            label="📋 Generated README.md Content",
            lines=15,
            show_copy_button=True,
            interactive=False,
            placeholder="README content will be generated after process completion..."
        )
        
        # Set up interface interaction
        submit_btn.click(
            fn=design_vehicle_task,
            inputs=[vehicle_type, task_description],
            outputs=[
                process_log,
                overall_status,
                best_design_specs,
                simulation_gif,
                performance_summary,
                llm_rationale,
                download_specs,
                readme_content
            ],
            show_progress=True
        )
        
        gr.Markdown("---")
        
        # Footer Information
        gr.Markdown("""
        ## 🎯 How the LLM Agent Works

        1. **🎯 Criteria Interpretation**: Agent analyzes your task description and defines success conditions
        2. **🔧 Initial Design**: LLM proposes vehicle specifications based on requirements  
        3. **⚗️ Physics Simulation**: Design tested in PyBullet with real physics
        4. **📊 Performance Analysis**: Results evaluated against interpreted criteria
        5. **🔄 Iterative Refinement**: Agent uses feedback to improve design
        6. **🏆 Best Design Selection**: System tracks and presents optimal solution
        
        **Key Innovation**: This demonstrates an autonomous AI agent that can design physical systems to meet user-defined functional requirements through simulation-based optimization.
        """)
    
    return iface

if __name__ == "__main__":
    print("🤖🚁 LLM-Agent-Designed Vehicle System - Hackathon Edition")
    print("=" * 70)
    
    if GRADIO_AVAILABLE:
        print("🚀 Starting enhanced Gradio interface for hackathon...")
        try:
            # Create and launch enhanced interface
            interface = create_hackathon_gradio_interface()
            interface.launch(
                server_name="0.0.0.0",
                server_port=7860,
                share=True,
                show_error=True,
                inbrowser=True
            )
        except Exception as e:
            print(f"❌ Failed to start Gradio interface: {e}")
            print("Please check your installation and try again.")
    else:
        print("❌ Gradio not available. Please install requirements:")
        print("pip install -r requirements.txt")