� PHASE 1: Deploy minimal Gradio app for baseline testing

✅ Created minimal app with only basic components:
- Simple gr.Textbox input/output
- Basic gr.Button with simple function
- No complex components or backend imports
✅ Minimal requirements.txt with only Gradio
✅ Backup of original app saved as app_backup.py

Goal: Test if basic Gradio functionality works without TypeError

app.py

@@ -1,479 +1,60 @@
 #!/usr/bin/env python3
 """
-Agent2Robot - LLM-Agent-Designed Obstacle-Passing Vehicle System
-Gradio User Interface Implementation
-Track 3: Agentic Demo Showcase
+Agent2Robot - Minimal Test App
+Systematic Debugging - Phase 1
 """
 
-import os
-import ssl
-import time
-import json
-import tempfile
-from datetime import datetime
-from pathlib import Path
+import gradio as gr
 
-# SSL workaround for Gradio issues
-try:
-    import certifi
-    os.environ['SSL_CERT_FILE'] = certifi.where()
-except ImportError:
-    pass
+def dummy_function(name):
+    """Simple test function"""
+    if not name:
+        name = "World"
+    return f"Hello {name}! The basic Gradio app is working."
 
-try:
-    ssl._create_default_https_context = ssl._create_unverified_context
-except AttributeError:
-    pass
-
-# 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}")
-    exit(1)
-
-# Import backend components
-from main_orchestrator import HackathonVehicleDesigner
-
-# Global configuration
-MAX_ITERATIONS = 5
-designer = HackathonVehicleDesigner()
-
-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} ===")
+def create_minimal_app():
+    """Create minimal Gradio interface for testing"""
+    with gr.Blocks(title="Agent2Robot - Minimal Test") as demo:
+        gr.Markdown("# 🔧 Agent2Robot - Minimal Test App")
+        gr.Markdown("**Phase 1: Testing basic Gradio functionality**")
         
-        # 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
-    )
-
-def create_agent2robot_interface():
-    """Create the Agent2Robot Gradio interface"""
-    
-    # Custom CSS for better appearance
-    custom_css = """
-    .main-header {
-        text-align: center;
-        background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
-        color: white;
-        padding: 30px;
-        border-radius: 15px;
-        margin-bottom: 20px;
-        box-shadow: 0 8px 16px rgba(0,0,0,0.1);
-    }
-    .process-log {
-        font-family: 'Courier New', monospace;
-        font-size: 12px;
-        line-height: 1.4;
-    }
-    .success-indicator {
-        background: linear-gradient(90deg, #4CAF50, #45a049);
-        color: white;
-        padding: 10px;
-        border-radius: 8px;
-        margin: 5px 0;
-    }
-    .iteration-info {
-        background: linear-gradient(90deg, #2196F3, #1976D2);
-        color: white;
-        padding: 8px;
-        border-radius: 6px;
-        margin: 3px 0;
-    }
-    """
-    
-    with gr.Blocks(
-        title="🤖🚁 Agent2Robot - LLM Vehicle Designer",
-        theme=gr.themes.Soft(),
-        css=custom_css
-    ) as demo:
-        
-        # Header Section
-        gr.HTML("""
-        <div class="main-header">
-            <h1>🤖🚁 Agent2Robot</h1>
-            <h2>LLM-Agent-Designed Obstacle-Passing Vehicle System</h2>
-            <p><strong>Hackathon Submission - Track 3: Agentic Demo Showcase</strong></p>
-            <p>Describe your desired vehicle and task in natural language, then watch our AI agent design, simulate, and optimize it in real-time!</p>
-        </div>
-        """)
-        
-        # Main Input Section
         with gr.Row():
-            with gr.Column(scale=1):
-                gr.Markdown("## 🎯 1. Define Your Vehicle Challenge")
-                
-                vehicle_type_input = gr.Radio(
-                    choices=["Robot", "Drone"], 
-                    label="1. Choose Vehicle Type",
-                    value="Robot",
-                    info="Select whether you want a ground robot or flying drone"
-                )
-                
-                user_description_input = gr.Textbox(
-                    lines=5, 
-                    label="2. Describe Vehicle's Task & Success Criteria",
-                    placeholder="e.g., 'Design a robot that can cross the 5cm box obstacle quickly and without tipping over, then stop safely.' or 'Create a drone that flies over the wall, lands gently 1 meter beyond it, and remains stable.'",
-                    value="Design a robot that can cross the 5cm high obstacle smoothly and come to a controlled stop."
-                )
-                
-                start_button = gr.Button(
-                    "🚀 Start AI Design Process", 
-                    variant="primary",
-                    size="lg"
+            with gr.Column():
+                inp = gr.Textbox(
+                    label="Input", 
+                    placeholder="Enter your name...",
+                    value=""
                 )
-                
-                gr.Markdown("""
-                ### 📋 Environment Info
-                - **Obstacle**: 5cm high × 50cm wide box
-                - **Success Target**: Vehicle reaches x > 0.8m
-                - **Physics**: Real-time PyBullet simulation
-                - **Max Iterations**: 5 design attempts
-                """)
+                btn = gr.Button("Submit", variant="primary")
             
-            with gr.Column(scale=2):
-                gr.Markdown("## 🤖 2. Watch the AI Agent Work")
-                
-                process_log_output = gr.Textbox(
-                    label="🤖 AI Agent - Live Process Log",
-                    lines=15,
-                    interactive=False,
-                    show_copy_button=True,
-                    elem_classes=["process-log"],
-                    placeholder="Process log will appear here in real-time as the AI agent works...",
+            with gr.Column():
+                out = gr.Textbox(
+                    label="Output",
                     value=""
                 )
-                
-                with gr.Row():
-                    current_design_specs_output = gr.JSON(
-                        label="⚙️ Current Design Specs Being Tested",
-                        value={}
-                    )
-                    
-                    progress_bar_output = gr.Slider(
-                        minimum=0, 
-                        maximum=100,
-                        step=1,
-                        label="Progress (%)",
-                        interactive=False,
-                        show_label=True,
-                        value=0
-                    )
-        
-        # Results Section
-        with gr.Accordion("🏆 Final Results & Design Specifications", open=True) as results_accordion:
-            final_status_output = gr.Markdown(
-                label="🏁 Final Run Status",
-                value="Waiting for process to complete..."
-            )
-            
-            with gr.Row():
-                with gr.Column(scale=2):
-                    simulation_video_output = gr.Image(
-                        label="🎬 Simulation of Best Design's Trial",
-                        interactive=False,
-                        height=300,
-                        value=None
-                    )
-                    
-                    performance_summary_output = gr.Markdown(
-                        label="📊 Performance Summary of Best Design",
-                        value=""
-                    )
-                
-                with gr.Column(scale=1):
-                    best_design_specs_output = gr.JSON(
-                        label="🔩 Best Vehicle Design Specifications",
-                        show_label=True,
-                        value={}
-                    )
-                    
-                    download_json_output = gr.File(
-                        label="📄 Download Best Design Specs (JSON)",
-                        file_count="single",
-                        type="filepath",
-                        interactive=True,
-                        value=None
-                    )
-                    
-                    llm_rationale_output = gr.Textbox(
-                        label="💡 LLM's Design Rationale",
-                        lines=6,
-                        interactive=False,
-                        show_copy_button=True,
-                        value=""
-                    )
         
-        # Connect button to the wrapper function
-        start_button.click(
-            fn=ui_function_wrapper,
-            inputs=[vehicle_type_input, user_description_input],
-            outputs=[
-                process_log_output,
-                current_design_specs_output,
-                progress_bar_output,
-                final_status_output,
-                simulation_video_output,
-                best_design_specs_output,
-                download_json_output,
-                performance_summary_output,
-                llm_rationale_output
-            ],
-            show_progress=False  # We handle progress manually
-        )
+        btn.click(fn=dummy_function, inputs=inp, outputs=out)
         
-        # Information Footer
         gr.Markdown("---")
-        gr.Markdown("""
-        ## 🔬 How the Agentic AI Works
-        
-        1. **🎯 Criteria Interpretation**: AI analyzes your natural language task and defines measurable success conditions
-        2. **🔧 Intelligent Design**: LLM proposes vehicle specifications based on physics principles and your requirements  
-        3. **⚗️ Physics Simulation**: Each design is tested in accurate PyBullet physics simulation with real obstacles
-        4. **📊 Performance Analysis**: Results are evaluated against your interpreted criteria with detailed metrics
-        5. **🔄 Iterative Learning**: AI uses simulation feedback to refine and improve designs automatically
-        6. **🏆 Best Design Selection**: System tracks performance and presents the optimal solution found
-        
-        **🚀 Innovation**: This demonstrates autonomous AI that goes beyond text generation - it's an agent that designs, tests, learns, and optimizes physical systems to meet user-defined functional requirements.
-        """)
+        gr.Markdown("**Status**: If you see this page, basic Gradio components are working correctly.")
     
     return demo
 
 if __name__ == "__main__":
-    print("🤖🚁 Agent2Robot - LLM-Agent-Designed Vehicle System")
-    print("=" * 60)
-    print("🚀 Launching enhanced Gradio interface...")
+    print("🔧 Agent2Robot - Minimal Test App")
+    print("=" * 50)
+    print("🚀 Testing basic Gradio functionality...")
     
     try:
-        # Create and launch the interface
-        app = create_agent2robot_interface()
+        app = create_minimal_app()
         app.launch(
             server_name="0.0.0.0",
             server_port=7860,
-            share=False,  # Set to True for public sharing
+            share=False,
             show_error=True,
             inbrowser=True,
             quiet=False
         )
     except Exception as e:
-        print(f"❌ Error launching app: {e}")
-        print("Please check your installation and try again.") 
+        print(f"❌ Error launching minimal app: {e}")
+        print("Basic Gradio functionality is failing.") 

app_backup.py

@@ -0,0 +1,480 @@
+#!/usr/bin/env python3
+"""
+Agent2Robot - LLM-Agent-Designed Obstacle-Passing Vehicle System
+Gradio User Interface Implementation
+Track 3: Agentic Demo Showcase
+BACKUP FILE - DO NOT MODIFY
+"""
+
+import os
+import ssl
+import time
+import json
+import tempfile
+from datetime import datetime
+from pathlib import Path
+
+# SSL workaround for Gradio issues
+try:
+    import certifi
+    os.environ['SSL_CERT_FILE'] = certifi.where()
+except ImportError:
+    pass
+
+try:
+    ssl._create_default_https_context = ssl._create_unverified_context
+except AttributeError:
+    pass
+
+# 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}")
+    exit(1)
+
+# Import backend components
+from main_orchestrator import HackathonVehicleDesigner
+
+# Global configuration
+MAX_ITERATIONS = 5
+designer = HackathonVehicleDesigner()
+
+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
+    )
+
+def create_agent2robot_interface():
+    """Create the Agent2Robot Gradio interface"""
+    
+    # Custom CSS for better appearance
+    custom_css = """
+    .main-header {
+        text-align: center;
+        background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
+        color: white;
+        padding: 30px;
+        border-radius: 15px;
+        margin-bottom: 20px;
+        box-shadow: 0 8px 16px rgba(0,0,0,0.1);
+    }
+    .process-log {
+        font-family: 'Courier New', monospace;
+        font-size: 12px;
+        line-height: 1.4;
+    }
+    .success-indicator {
+        background: linear-gradient(90deg, #4CAF50, #45a049);
+        color: white;
+        padding: 10px;
+        border-radius: 8px;
+        margin: 5px 0;
+    }
+    .iteration-info {
+        background: linear-gradient(90deg, #2196F3, #1976D2);
+        color: white;
+        padding: 8px;
+        border-radius: 6px;
+        margin: 3px 0;
+    }
+    """
+    
+    with gr.Blocks(
+        title="🤖🚁 Agent2Robot - LLM Vehicle Designer",
+        theme=gr.themes.Soft(),
+        css=custom_css
+    ) as demo:
+        
+        # Header Section
+        gr.HTML("""
+        <div class="main-header">
+            <h1>🤖🚁 Agent2Robot</h1>
+            <h2>LLM-Agent-Designed Obstacle-Passing Vehicle System</h2>
+            <p><strong>Hackathon Submission - Track 3: Agentic Demo Showcase</strong></p>
+            <p>Describe your desired vehicle and task in natural language, then watch our AI agent design, simulate, and optimize it in real-time!</p>
+        </div>
+        """)
+        
+        # Main Input Section
+        with gr.Row():
+            with gr.Column(scale=1):
+                gr.Markdown("## 🎯 1. Define Your Vehicle Challenge")
+                
+                vehicle_type_input = gr.Radio(
+                    choices=["Robot", "Drone"], 
+                    label="1. Choose Vehicle Type",
+                    value="Robot",
+                    info="Select whether you want a ground robot or flying drone"
+                )
+                
+                user_description_input = gr.Textbox(
+                    lines=5, 
+                    label="2. Describe Vehicle's Task & Success Criteria",
+                    placeholder="e.g., 'Design a robot that can cross the 5cm box obstacle quickly and without tipping over, then stop safely.' or 'Create a drone that flies over the wall, lands gently 1 meter beyond it, and remains stable.'",
+                    value="Design a robot that can cross the 5cm high obstacle smoothly and come to a controlled stop."
+                )
+                
+                start_button = gr.Button(
+                    "🚀 Start AI Design Process", 
+                    variant="primary",
+                    size="lg"
+                )
+                
+                gr.Markdown("""
+                ### 📋 Environment Info
+                - **Obstacle**: 5cm high × 50cm wide box
+                - **Success Target**: Vehicle reaches x > 0.8m
+                - **Physics**: Real-time PyBullet simulation
+                - **Max Iterations**: 5 design attempts
+                """)
+            
+            with gr.Column(scale=2):
+                gr.Markdown("## 🤖 2. Watch the AI Agent Work")
+                
+                process_log_output = gr.Textbox(
+                    label="🤖 AI Agent - Live Process Log",
+                    lines=15,
+                    interactive=False,
+                    show_copy_button=True,
+                    elem_classes=["process-log"],
+                    placeholder="Process log will appear here in real-time as the AI agent works...",
+                    value=""
+                )
+                
+                with gr.Row():
+                    current_design_specs_output = gr.JSON(
+                        label="⚙️ Current Design Specs Being Tested",
+                        value={}
+                    )
+                    
+                    progress_bar_output = gr.Slider(
+                        minimum=0, 
+                        maximum=100,
+                        step=1,
+                        label="Progress (%)",
+                        interactive=False,
+                        show_label=True,
+                        value=0
+                    )
+        
+        # Results Section
+        with gr.Accordion("🏆 Final Results & Design Specifications", open=True) as results_accordion:
+            final_status_output = gr.Markdown(
+                label="🏁 Final Run Status",
+                value="Waiting for process to complete..."
+            )
+            
+            with gr.Row():
+                with gr.Column(scale=2):
+                    simulation_video_output = gr.Image(
+                        label="🎬 Simulation of Best Design's Trial",
+                        interactive=False,
+                        height=300,
+                        value=None
+                    )
+                    
+                    performance_summary_output = gr.Markdown(
+                        label="📊 Performance Summary of Best Design",
+                        value=""
+                    )
+                
+                with gr.Column(scale=1):
+                    best_design_specs_output = gr.JSON(
+                        label="🔩 Best Vehicle Design Specifications",
+                        show_label=True,
+                        value={}
+                    )
+                    
+                    download_json_output = gr.File(
+                        label="📄 Download Best Design Specs (JSON)",
+                        file_count="single",
+                        type="filepath",
+                        interactive=True,
+                        value=None
+                    )
+                    
+                    llm_rationale_output = gr.Textbox(
+                        label="💡 LLM's Design Rationale",
+                        lines=6,
+                        interactive=False,
+                        show_copy_button=True,
+                        value=""
+                    )
+        
+        # Connect button to the wrapper function
+        start_button.click(
+            fn=ui_function_wrapper,
+            inputs=[vehicle_type_input, user_description_input],
+            outputs=[
+                process_log_output,
+                current_design_specs_output,
+                progress_bar_output,
+                final_status_output,
+                simulation_video_output,
+                best_design_specs_output,
+                download_json_output,
+                performance_summary_output,
+                llm_rationale_output
+            ],
+            show_progress=False  # We handle progress manually
+        )
+        
+        # Information Footer
+        gr.Markdown("---")
+        gr.Markdown("""
+        ## 🔬 How the Agentic AI Works
+        
+        1. **🎯 Criteria Interpretation**: AI analyzes your natural language task and defines measurable success conditions
+        2. **🔧 Intelligent Design**: LLM proposes vehicle specifications based on physics principles and your requirements  
+        3. **⚗️ Physics Simulation**: Each design is tested in accurate PyBullet physics simulation with real obstacles
+        4. **📊 Performance Analysis**: Results are evaluated against your interpreted criteria with detailed metrics
+        5. **🔄 Iterative Learning**: AI uses simulation feedback to refine and improve designs automatically
+        6. **🏆 Best Design Selection**: System tracks performance and presents the optimal solution found
+        
+        **🚀 Innovation**: This demonstrates autonomous AI that goes beyond text generation - it's an agent that designs, tests, learns, and optimizes physical systems to meet user-defined functional requirements.
+        """)
+    
+    return demo
+
+if __name__ == "__main__":
+    print("🤖🚁 Agent2Robot - LLM-Agent-Designed Vehicle System")
+    print("=" * 60)
+    print("🚀 Launching enhanced Gradio interface...")
+    
+    try:
+        # Create and launch the interface
+        app = create_agent2robot_interface()
+        app.launch(
+            server_name="0.0.0.0",
+            server_port=7860,
+            share=False,  # Set to True for public sharing
+            show_error=True,
+            inbrowser=True,
+            quiet=False
+        )
+    except Exception as e:
+        print(f"❌ Error launching app: {e}")
+        print("Please check your installation and try again.") 

requirements.txt

@@ -1,15 +1,15 @@
-pybullet>=3.2.5
+# Minimal requirements for Phase 1 debugging
 gradio>=4.44.1
-imageio>=2.20.0
+# imageio>=2.20.0
 # transformers>=4.21.0
 # torch>=1.12.0
-Pillow>=9.0.0
-numpy>=1.21.0
-requests>=2.28.0
-certifi>=2022.0.0
-mcp>=1.0.0
-fastapi>=0.100.0
-uvicorn>=0.20.0
-scipy>=1.9.0
-matplotlib>=3.5.0
-imageio-ffmpeg>=0.4.7 
+# Pillow>=9.0.0
+# numpy>=1.21.0
+# requests>=2.28.0
+# certifi>=2022.0.0
+# mcp>=1.0.0
+# fastapi>=0.100.0
+# uvicorn>=0.20.0
+# scipy>=1.9.0
+# matplotlib>=3.5.0
+# imageio-ffmpeg>=0.4.7