� Repository cleanup: Remove redundant files and documentation - Removed duplicate app versions, backup files, redundant docs, test files, logs, and cache - Kept essential files: app.py, requirements.txt, README.md, core modules

ENHANCED_FUNCTIONALITY_SUMMARY.md

@@ -1,132 +0,0 @@
-# Enhanced Functionality Summary - Agent2Robot Real Implementation
-
-## Overview
-Successfully enhanced all sections of the Agent2Robot interface with real, comprehensive functionality while maintaining Gradio compatibility and the enhanced two-column layout.
-
-## Enhanced Functionality Implementation
-
-### 1. 🎬 Advanced Chat Interface with Real Responses
-
-**Previous State**: Basic demo responses with placeholder content
-**Enhanced Implementation**:
-- **Intelligent Vehicle Type Detection**: Automatically identifies and categorizes vehicle requests (Robot, Drone, Autonomous Vehicle, Robotic Arm)
-- **Detailed Technical Specifications**: Real-time generation of comprehensive specs including:
-  - Core specifications (payload, speed, dimensions, power systems)
-  - Advanced features (AI processing, sensor suites, connectivity)
-  - Performance metrics (navigation accuracy, energy efficiency, reliability scores)
-  - Manufacturing data (costs, timelines, quality standards, certifications)
-
-### 2. 📊 Comprehensive Simulation Engine
-
-**Previous State**: Simple demo text about simulation features
-**Enhanced Implementation**:
-- **Real Performance Metrics**: Dynamic generation of realistic performance data
-  - Navigation Accuracy: 98.5-99.9%
-  - Obstacle Avoidance: 99.0-100%
-  - Energy Efficiency: 94.0-98.0%
-  - Reliability Scores: 96.0-99.5%
-- **Detailed Simulation Reports**: Multi-scenario testing analysis including:
-  - 4K Ultra-HD video specifications (3840x2160 @ 60 FPS)
-  - Advanced Bullet Physics engine validation
-  - Environmental stress testing (weather, surfaces, lighting)
-  - Safety validation with emergency protocols
-  - Compliance certification (ISO 9001, ISO 26262, IEC 61508)
-
-### 3. 🏭 Manufacturing & Cost Analysis
-
-**Previous State**: Basic placeholder manufacturing information
-**Enhanced Implementation**:
-- **Realistic Cost Estimates**: $50,000-$150,000 range based on vehicle complexity
-- **Production Timelines**: 4-12 weeks with detailed scheduling
-- **Quality Standards**: Six Sigma methodology implementation
-- **Certification Packages**: CE, FCC, RoHS compliance validation
-- **Warranty Programs**: 3-year comprehensive coverage
-
-### 4. 🔧 Technical Documentation Generation
-
-**Previous State**: Simple demo files with minimal content
-**Enhanced Implementation**:
-- **Comprehensive JSON Specifications**: Detailed technical data including:
-  - Project metadata with timestamps and version tracking
-  - Vehicle classification with safety ratings
-  - Advanced feature specifications
-  - Simulation parameters and validation metrics
-- **Detailed Simulation Reports**: Professional-grade analysis documents
-- **Complete Package Documentation**: Manufacturing-ready specifications
-
-### 5. 🎯 Intelligent Requirements Processing
-
-**Previous State**: Basic text parsing with generic responses
-**Enhanced Implementation**:
-- **Advanced NLP Processing**: Contextual analysis of user requirements
-- **Multi-Objective Optimization**: Balancing performance, cost, and safety
-- **Regulatory Compliance Verification**: Automatic standards validation
-- **Adaptive Learning**: Continuous improvement of design algorithms
-
-## Technical Implementation Details
-
-### Performance Metrics Generation
-```python
-"performance_metrics": {
-    "navigation_accuracy": f"{random.uniform(98.5, 99.9):.1f}%",
-    "obstacle_avoidance": f"{random.uniform(99.0, 100.0):.1f}%",
-    "energy_efficiency": f"{random.uniform(94.0, 98.0):.1f}%",
-    "reliability_score": f"{random.uniform(96.0, 99.5):.1f}%",
-    "safety_rating": "A+ Grade"
-}
-```
-
-### Simulation Report Features
-- Real-time telemetry processing (12 simultaneous data streams)
-- AI decision making with <10ms neural network inference
-- Predictive maintenance with 30-day failure prediction
-- Adaptive learning algorithms for continuous improvement
-
-### Manufacturing Integration
-- Realistic cost modeling based on vehicle complexity
-- Production timeline optimization
-- Quality control with Six Sigma methodology
-- Complete certification package preparation
-
-## Gradio Compatibility Solution
-
-**Issue Resolved**: Complex File components causing `TypeError: argument of type 'bool' is not iterable`
-**Solution Applied**: 
-- Simplified interface structure while maintaining all functionality
-- Removed complex File download components that triggered the bug
-- Maintained comprehensive functionality through enhanced chat responses
-- All technical documentation generation preserved and working
-
-## Verification Results
-
-### ✅ Enhanced Features Working
-- **Advanced Simulation Engine**: Fully operational with realistic metrics
-- **4K Video Generation**: Specifications and validation parameters active
-- **Physics Modeling**: Bullet Physics engine simulation parameters implemented
-- **Complete Documentation**: Real technical specifications generated
-- **Manufacturing Integration**: Cost analysis and production planning active
-
-### ✅ Interface Functionality
-- **Enhanced Chat**: Intelligent responses with comprehensive technical details
-- **Real-time Generation**: Dynamic creation of specifications and reports
-- **Example Integration**: Working example buttons with realistic scenarios
-- **Performance Display**: Live metrics and validation results
-
-### ✅ Technical Compliance
-- **Standards Integration**: ISO 9001, ISO 26262, IEC 61508 compliance validation
-- **Safety Ratings**: SIL 3 (Safety Integrity Level) certification
-- **Manufacturing Ready**: Production specifications and quality control
-
-## Impact Summary
-
-**Functionality Enhancement**: 500%+ improvement in response depth and technical accuracy
-**Documentation Quality**: Professional-grade specifications suitable for manufacturing
-**Simulation Realism**: Industry-standard performance metrics and validation protocols
-**User Experience**: Comprehensive, intelligent responses to all vehicle design requests
-
-**Current Status**: All enhanced functionality is operational and ready for deployment. The interface provides real, manufacturing-ready vehicle designs with comprehensive simulation validation and technical documentation.
-
----
-*Enhanced functionality implemented: December 2024*
-*Compatible with: Gradio 4.40.0+, HuggingFace Spaces*
-*Ready for: Immediate deployment and production use* 

ENHANCED_UI_RESTORATION.md

@@ -1,105 +0,0 @@
-# Enhanced UI Restoration - Agent2Robot
-
-## Issue Resolution Summary
-
-**Problem:** The Agent2Robot interface was showing only a basic chat box, missing the enhanced UI components including the download center and advanced layout.
-
-**Root Cause:** The previous fix for Gradio compatibility issues had oversimplified the interface to a basic `ChatInterface`, removing all enhanced UI components.
-
-## Solution Implemented
-
-### 1. Enhanced Interface Restoration
-- **Restored Two-Column Layout**: Chat interface on left, download center on right
-- **Download Center**: Three file download slots for specifications, simulation reports, and complete packages
-- **Professional Styling**: Enhanced theme, proper spacing, and visual hierarchy
-- **Interactive Examples**: Pre-built example prompts for user guidance
-
-### 2. Gradio Compatibility Fix
-- **Simplified File Components**: Removed problematic `interactive=False` parameters that triggered the Gradio bug
-- **Maintained Functionality**: Kept all download capabilities while avoiding schema validation issues
-- **Encoding Fix**: Replaced emoji characters that caused encoding issues in Windows environment
-
-### 3. Working Features Verified
-
-#### ✅ Enhanced UI Components
-- **Chat Interface**: Full conversation history with professional styling
-- **Download Center**: Three separate file download areas
-- **Two-Column Layout**: Responsive design with proper scaling
-- **Examples Section**: Interactive example prompts
-- **Professional Theme**: Soft theme with enhanced visual design
-
-#### ✅ Advanced Simulation Features (Maintained)
-- **4K Ultra-HD Video Generation**: High-fidelity physics simulation (3840x2160 @ 60 FPS)
-- **Advanced Physics Engine**: Real-time Bullet Physics modeling with collision detection
-- **Multi-Scenario Testing**: Navigation, obstacles, weather, emergency protocols
-- **Performance Validation**: Comprehensive metrics (99.7% navigation accuracy)
-- **File Package Generation**: Complete downloadable specifications and reports
-- **Environmental Modeling**: Industrial, outdoor, adverse conditions simulation
-
-#### ✅ File Download System
-- **Technical Specifications**: JSON files with complete design data and simulation results
-- **Simulation Reports**: Text files with validation metrics and performance data
-- **Complete Packages**: Comprehensive documentation bundles
-- **Real File Generation**: Temporary files created for actual downloads
-
-## Technical Implementation
-
-### Interface Structure
-```
-Enhanced Gradio Blocks Interface
-├── Header with comprehensive feature description
-├── Two-Column Layout
-│   ├── Left Column (scale=2)
-│   │   ├── Chat Interface (600px height)
-│   │   └── Input Row
-│   │       ├── Multi-line Text Input (scale=4)
-│   │       └── Submit Button (scale=1)
-│   └── Right Column (scale=1)
-│       ├── Download Center Header
-│       ├── Three File Components
-│       │   ├── Technical Specifications
-│       │   ├── Simulation Report
-│       │   └── Complete Package
-│       └── Feature Description
-└── Interactive Examples Section
-```
-
-### Key Fixes Applied
-1. **File Component Simplification**: Removed `interactive=False` to avoid Gradio schema bug
-2. **Encoding Compatibility**: Replaced emoji with text for Windows compatibility
-3. **Port Management**: Used port 7863 to avoid conflicts
-4. **Error Handling**: Graceful degradation when MCP modules unavailable
-
-## Current Status
-
-### ✅ Fully Operational
-- **Enhanced Interface**: Complete two-column layout with all components
-- **Download Center**: Three working file download slots
-- **Simulation Features**: All advanced simulation capabilities maintained
-- **Professional Styling**: Enhanced visual design and user experience
-- **Cross-Platform**: Compatible with Windows environment
-
-### 🚀 Running Successfully
-- **Local URL**: http://localhost:7863
-- **Status**: Active and responsive
-- **Features**: All enhanced UI components operational
-- **Downloads**: File generation and download system working
-
-## User Experience Restored
-
-The enhanced interface now provides:
-
-1. **Complete Visual Experience**: Professional two-column layout with download center
-2. **Full Functionality**: All simulation features accessible through enhanced UI
-3. **File Downloads**: Working download system for specifications, reports, and packages
-4. **Interactive Examples**: Easy-to-use example prompts for quick testing
-5. **Professional Presentation**: Enhanced styling and visual hierarchy
-
-## Deployment Ready
-
-The enhanced interface is now ready for:
-- **Local Development**: Running successfully on localhost:7863
-- **HuggingFace Deployment**: Compatible with Spaces environment
-- **Production Use**: All features operational and tested
-
-**Resolution Complete**: Enhanced UI fully restored with all advanced simulation features operational. 

ENHANCEMENT_SUMMARY.md

@@ -1,192 +0,0 @@
-# Agent2Robot Enhanced - Option A Implementation Summary
-
-## 🚀 MCP Hackathon 2024 - Advanced Enhancement Complete
-
-### 🎯 **Option A: Enhanced MCP Integration with Real Server Endpoints**
-
-We have successfully implemented comprehensive enhancements to Agent2Robot with advanced MCP capabilities, file downloads, simulation videos, and manufacturing exports.
-
----
-
-## 🔧 **Enhanced Features Implemented**
-
-### 1. **Advanced MCP Client (mcp_client.py)**
-- **Enhanced Server Capabilities**: Added 3D modeling, cost estimation, manufacturing specs
-- **Real File Generation**: Actual downloadable JSON specifications with temporary file creation
-- **Advanced Simulation Engine**: 4K video simulation with comprehensive physics modeling
-- **Vehicle-Specific Features**: Detailed feature sets for Robot, Drone, Autonomous Vehicle, Robotic Arm
-- **Performance Metrics**: Comprehensive metrics tailored to each vehicle type
-- **Technical Specifications**: Enhanced specs with vehicle-specific components
-- **Manufacturing Specifications**: Production methods, quality control, scalability info
-- **Cost Analysis**: Development costs, unit costs, ROI timelines, financing options
-- **Enhanced JSON Specs**: Version 2.0 with advanced analytics, compliance standards, warranty info
-
-### 2. **Enhanced User Interface (app.py)**
-- **Advanced Chat Interface**: Multi-return function with file download capabilities
-- **Download Center**: Three-file download system (Specifications, Simulation, Package)
-- **Enhanced Examples**: Six comprehensive example prompts for different vehicle types
-- **Intelligent Vehicle Detection**: Advanced keyword-based vehicle type classification
-- **Progress Indication**: Real-time status updates during design process
-- **Error Recovery**: Comprehensive error handling with alternative approaches
-- **File Management**: Temporary file creation and management for downloads
-
-### 3. **Real File Downloads**
-- **JSON Specifications**: Complete technical specifications with enhanced MCP data
-- **Simulation Reports**: Comprehensive simulation information and video metadata
-- **Complete Design Packages**: Full documentation with manufacturing info
-- **Temporary File Management**: Proper file creation and cleanup
-- **Multi-format Support**: JSON, TXT, and preparation for ZIP/PDF/MP4
-
-### 4. **Advanced Simulation Features**
-- **4K Video Generation**: High-fidelity simulation with 60 FPS
-- **Multi-scenario Testing**: Navigation, obstacles, weather, emergency protocols
-- **Performance Validation**: Comprehensive metrics and validation scores
-- **Physics Engine**: Advanced Bullet Physics simulation
-- **Environmental Modeling**: Weather conditions, industrial environments
-- **Real-time Telemetry**: Performance monitoring and diagnostics
-
-### 5. **Manufacturing & Cost Integration**
-- **Production Specifications**: Lean manufacturing with Industry 4.0
-- **Quality Control**: Six Sigma quality management systems
-- **Cost Analysis**: Development, unit, and maintenance cost breakdowns
-- **ROI Projections**: 18-24 month payback timelines
-- **Scalability Planning**: 100-1000 units/month production capacity
-- **Supply Chain**: Certified tier-1 suppliers with redundancy
-
----
-
-## 📊 **Technical Specifications Enhanced**
-
-### **Vehicle Types Supported:**
-1. **Warehouse Robots**: 50kg payload, 8-hour battery, SLAM navigation
-2. **Delivery Drones**: 120-minute flight time, 4K cameras, weather resistance
-3. **Autonomous Vehicles**: Level 4 autonomy, 8 passengers, 500km range
-4. **Robotic Arms**: 0.05mm precision, 25kg payload, 7-DOF configuration
-
-### **Advanced Analytics:**
-- **Design Complexity Score**: 8.7/10
-- **Innovation Index**: 9.2/10
-- **Manufacturability Rating**: Excellent
-- **Sustainability Score**: A+
-
-### **Compliance Standards:**
-- ISO 9001, ISO 26262, IEC 61508, ANSI/RIA R15.06
-- CE, FCC, UL certifications
-- IP67 weatherproof ratings
-
----
-
-## 🎬 **Simulation Engine Features**
-
-### **Advanced Physics Modeling:**
-- Photorealistic 3D rendering
-- Real-time physics simulation (Bullet Physics)
-- Environmental interaction modeling
-- Multi-sensor data visualization
-- Dynamic obstacle scenarios
-- Weather condition variations
-- Performance stress testing
-- Failure mode analysis
-- Energy consumption tracking
-- Thermal management simulation
-
-### **Validation Metrics:**
-- Navigation Accuracy: 99.7%
-- Obstacle Avoidance: 100% Success Rate
-- Energy Efficiency: 96.5% Optimized
-- Response Time: <25ms Average
-- Safety Score: A+ Rating
-- Reliability Index: 99.9%
-
----
-
-## 💾 **File Download System**
-
-### **Generated Files:**
-1. **Technical Specifications (JSON)**: 15-25 KB
-   - Complete design data with enhanced MCP information
-   - Advanced analytics and compliance data
-   - Warranty and support information
-
-2. **Simulation Report (TXT)**: Variable size
-   - Comprehensive simulation results
-   - Performance validation data
-   - Video metadata and specifications
-
-3. **Complete Design Package (TXT)**: Variable size
-   - Manufacturing specifications
-   - Documentation overview
-   - Cost analysis summary
-   - Implementation guidelines
-
-### **Future File Types (Prepared):**
-- **Manufacturing Package (ZIP)**: 350 KB - CAD files, assembly instructions
-- **Documentation (PDF)**: 200 KB - User manuals, maintenance guides
-- **Simulation Video (MP4)**: 90 MB - 4K physics simulation
-
----
-
-## 🌟 **Enhanced User Experience**
-
-### **Interface Improvements:**
-- **Professional Branding**: Enhanced titles and descriptions
-- **Download Center**: Dedicated file download section
-- **Progress Tracking**: Real-time status updates
-- **Enhanced Examples**: Six comprehensive example prompts
-- **Error Recovery**: Graceful fallback with alternative approaches
-
-### **Advanced Prompts:**
-1. Advanced warehouse robot with 75kg payload and AI navigation
-2. Weather-resistant surveillance drone with 3-hour flight time
-3. Luxury autonomous vehicle with Level 4 autonomy
-4. Precision robotic arm for semiconductor manufacturing
-5. Search and rescue robot for disaster response
-6. Agricultural drone with multispectral imaging
-
----
-
-## 🚀 **Deployment Status**
-
-### **Live Deployment:**
-- **URL**: https://huggingface.co/spaces/sam133/Agent2Robot
-- **Status**: ✅ Successfully deployed with enhanced features
-- **Version**: Enhanced MCP Integration v2.0
-- **Features**: All Option A enhancements active
-
-### **System Architecture:**
-- **Frontend**: Enhanced Gradio interface with download capabilities
-- **Backend**: Advanced MCP client with real file generation
-- **Integration**: Comprehensive orchestrator with enhanced workflows
-- **Files**: Real-time temporary file creation and management
-
----
-
-## 🏆 **Achievement Summary**
-
-✅ **Enhanced MCP Integration**: Advanced server capabilities and real endpoints
-✅ **File Download System**: Actual downloadable files with comprehensive data
-✅ **4K Simulation Engine**: High-fidelity physics modeling and validation
-✅ **Manufacturing Integration**: Complete production and cost analysis
-✅ **Advanced UI**: Professional interface with download center
-✅ **Multi-Vehicle Support**: Specialized features for 4 vehicle types
-✅ **Real-time Processing**: Live design generation with progress tracking
-✅ **Comprehensive Documentation**: Enhanced technical specifications
-✅ **Error Recovery**: Robust fallback mechanisms
-✅ **Professional Deployment**: Live on HuggingFace Spaces
-
----
-
-## 🎯 **Option A Implementation Complete**
-
-The enhanced Agent2Robot system now provides:
-- **Real MCP server endpoints** with advanced capabilities
-- **Downloadable file packages** with actual file generation
-- **4K simulation videos** with comprehensive physics modeling
-- **Manufacturing exports** with cost analysis and production specs
-- **Professional user interface** with dedicated download center
-- **Comprehensive vehicle support** with specialized features
-
-**Status**: ✅ **FULLY IMPLEMENTED AND DEPLOYED**
-**URL**: https://huggingface.co/spaces/sam133/Agent2Robot
-
-The system is ready for MCP Hackathon 2024 evaluation with all Option A enhancements successfully integrated and operational. 

INTERFACE_FIX_SUMMARY.md

@@ -1,92 +0,0 @@
-# Interface Fix Summary - Agent2Robot Enhanced UI Restoration
-
-## Issue Resolved
-**Problem:** The Agent2Robot interface was showing only a basic chat interface instead of the enhanced two-column layout with download center and advanced simulation sections.
-
-## Root Cause Analysis
-The main `app.py` file was using a simple `ChatInterface` that only displayed:
-- Basic chat functionality 
-- Title and description
-- Simple text input
-- Example prompts
-
-**Missing Components:**
-- Download center with file download slots
-- Two-column layout (chat + downloads)
-- Enhanced UI styling and interactive examples
-- File generation capabilities
-- Advanced simulation display features
-
-## Solution Implemented
-
-### 1. Enhanced Interface Restoration
-✅ **Two-Column Layout**: Restored chat interface on left, download center on right
-✅ **Download Center**: Three file download slots for specifications, reports, and packages
-✅ **Interactive Examples**: Clickable example buttons that populate the input field
-✅ **Professional Styling**: Enhanced visual design with proper spacing and hierarchy
-✅ **File Generation**: Working file download system with demo content
-
-### 2. Advanced Features Maintained
-✅ **Working Simulation Features**: All 4K simulation capabilities preserved
-✅ **MCP Integration**: Maintained compatibility with MCP modules when available
-✅ **Error Handling**: Graceful fallback when MCP modules unavailable
-✅ **Demo Mode**: Functional demo with mock file generation for testing
-
-### 3. Technical Implementation
-- **Updated `app.py`**: Replaced simple ChatInterface with enhanced Blocks interface
-- **File Generation**: Added temporary file creation for downloadable content
-- **Interactive Components**: Added clickable example buttons and responsive design
-- **Download System**: Three-slot download center with different file types
-
-### 4. UI Components Restored
-```
-┌─────────────────────────────────────────────────────────────┐
-│ Agent2Robot Enhanced - AI Vehicle Design with Advanced Sim │
-├─────────────────────────────────┬───────────────────────────┤
-│ Chat Interface                  │ Download Center           │
-│ ├─ Conversation History         │ ├─ Technical Specs        │
-│ ├─ Message Input                │ ├─ Simulation Report      │
-│ └─ Send Button                  │ └─ Complete Package       │
-├─────────────────────────────────┴───────────────────────────┤
-│ Interactive Example Buttons                                 │
-│ [Warehouse Robot] [Delivery Drone] [Autonomous Car] [Arm]  │
-└─────────────────────────────────────────────────────────────┘
-```
-
-### 5. Features Now Working
-- **🎬 Chat Interface**: Full conversation with enhanced simulation responses
-- **📥 Download Center**: Three file types with working download functionality
-- **🎯 Interactive Examples**: Clickable buttons to populate common requests
-- **📊 File Generation**: Creates specifications, reports, and package files
-- **🚀 Enhanced Styling**: Professional theme with visual hierarchy
-- **🔧 Demo Mode**: Functional demonstration when MCP unavailable
-
-## Deployment Status
-✅ **Committed**: Changes committed to git repository
-✅ **Pushed**: Successfully deployed to HuggingFace Spaces
-✅ **Live**: Enhanced interface now available at https://huggingface.co/spaces/sam133/Agent2Robot
-
-## Technical Details
-- **File Modified**: `app.py` - Main application interface
-- **Interface Type**: Enhanced Gradio Blocks with two-column layout
-- **Download System**: Three `gr.File` components with dynamic content
-- **Example System**: Four interactive buttons with preset prompts
-- **Styling**: Professional theme with enhanced visual design
-
-## User Experience Improvements
-1. **Complete Interface**: No more missing sections or components
-2. **Download Functionality**: Working file downloads for all design outputs
-3. **Interactive Examples**: Easy-to-use example prompts for common requests
-4. **Professional Appearance**: Enhanced styling and visual hierarchy
-5. **Responsive Design**: Proper layout scaling and component organization
-
-## Verification
-The enhanced interface has been successfully deployed and includes:
-- ✅ Two-column layout with chat and downloads
-- ✅ Working file generation and download system
-- ✅ Interactive example buttons
-- ✅ Enhanced styling and professional appearance
-- ✅ All simulation features maintained and operational
-
-**Status: COMPLETE** ✅
-Enhanced interface with download center successfully restored and deployed! 

SIMULATION_FIX_REPORT.md

@@ -1,218 +0,0 @@
-# 🎬 Advanced Simulation Features - Fix Report
-
-## 🚨 **Issue Identified and Resolved**
-
-### **Problem:** Advanced Simulation Features Not Working
-- **Root Cause:** Gradio compatibility issue with complex interface
-- **Error:** `TypeError: argument of type 'bool' is not iterable` in gradio_client/utils.py
-- **Impact:** Prevented proper deployment and testing of simulation features
-
-### **Solution Implemented:** ✅ FULLY RESOLVED
-
----
-
-## 🔍 **Diagnostic Process**
-
-### **Step 1: Direct Testing of Simulation Engine**
-Created `test_simulation.py` to verify simulation features independently:
-
-```bash
-🎬 Testing Agent2Robot Advanced Simulation Features
-✅ MCP Client initialized: True
-✅ Simulation data structure received
-✅ File package generated
-✅ All simulation features working correctly!
-```
-
-**Result:** ✅ **ALL ADVANCED SIMULATION FEATURES CONFIRMED WORKING**
-
-### **Step 2: Root Cause Analysis**
-- **Issue Location:** Gradio interface complexity triggering schema validation bug
-- **Gradio Version:** 4.40.0 vs 4.44.1 compatibility issue
-- **Affected Component:** Complex Blocks interface with multiple File components
-- **Solution:** Simplified ChatInterface avoiding problematic components
-
----
-
-## 🎬 **Advanced Simulation Features - VERIFIED WORKING**
-
-### **✅ 4K Ultra-HD Video Generation**
-- **Resolution:** 4K (3840x2160) @ 60 FPS
-- **Duration:** 45 seconds ultra-high detail animation
-- **Engine:** MCP Physics Engine v3.0 (GPU-Accelerated)
-- **Format:** MP4 with HEVC encoding
-- **File Size:** ~85 MB production quality
-- **Status:** ✅ **FULLY OPERATIONAL**
-
-### **✅ Advanced Physics Simulation**
-- **Engine:** Real-time Bullet Physics
-- **Features:** Photorealistic 3D rendering
-- **Modeling:** Environmental interaction, collision detection
-- **Scenarios:** Navigation, obstacles, weather, emergency
-- **Validation:** 99.7% accuracy, 100% obstacle avoidance
-- **Status:** ✅ **FULLY OPERATIONAL**
-
-### **✅ Multi-Scenario Testing**
-- **🏭 Industrial Environment:** Navigation accuracy 99.7%
-- **🌧️ Adverse Weather:** 100% operational capability
-- **🚧 Complex Obstacles:** 100% avoidance success rate
-- **⚡ Emergency Protocols:** <25ms response time
-- **🔋 Battery Optimization:** 96.5% efficiency achieved
-- **Status:** ✅ **FULLY OPERATIONAL**
-
-### **✅ File Package Generation**
-- **Technical Specifications:** JSON format with enhanced MCP data
-- **Manufacturing Package:** ZIP with CAD files and instructions
-- **Documentation:** PDF with manuals and compliance certificates
-- **Simulation Video:** MP4 with physics validation
-- **Status:** ✅ **FULLY OPERATIONAL**
-
----
-
-## 🔧 **Technical Implementation Details**
-
-### **MCP Client Enhanced Features**
-```python
-def generate_simulation_video(self, design_specs: Dict[str, Any]) -> Dict[str, Any]:
-    """Generate enhanced simulation video and metadata"""
-    # Returns comprehensive simulation data with:
-    # - 4K video specifications
-    # - Advanced physics features
-    # - Multi-scenario validation
-    # - Performance metrics
-```
-
-### **File Generation System**
-```python
-def generate_downloadable_files(self, design_specs: Dict[str, Any]) -> Dict[str, Any]:
-    """Generate comprehensive downloadable file package with actual file creation"""
-    # Creates real temporary files for download:
-    # - JSON specifications with enhanced data
-    # - Manufacturing and documentation packages
-    # - Simulation reports and metadata
-```
-
-### **Validation Metrics**
-- **Navigation Accuracy:** 99.7% ✅
-- **Obstacle Avoidance:** 100% Success Rate ✅
-- **Energy Efficiency:** 96.5% Optimized ✅
-- **Response Time:** <25ms Average ✅
-- **Safety Score:** A+ Rating ✅
-- **Reliability Index:** 99.9% ✅
-
----
-
-## 📊 **Performance Validation Results**
-
-### **Test Vehicle: Advanced Warehouse Robot**
-```
-🎬 MCP ADVANCED SIMULATION ENGINE - GENERATION COMPLETE
-✅ Video info length: 1866 characters
-✅ File path: simulations/test_robot_simulation_001_simulation.mp4
-✅ Metadata: 4K resolution, 60 FPS, 85MB, validation passed
-```
-
-### **Generated Files Verification**
-```
-✅ Technical Specifications: 15.2 KB JSON ✓ File created
-✅ Manufacturing Package: 350.0 KB ZIP (prepared)
-✅ Documentation: 200.0 KB PDF (prepared)
-✅ Simulation Video: 92000.0 KB MP4 (prepared)
-```
-
----
-
-## 🚀 **Deployment Status**
-
-### **Local Testing:** ✅ SUCCESS
-```bash
-✅ MCP modules loaded successfully
-Running on local URL: http://0.0.0.0:7860
-App test completed (no errors)
-```
-
-### **HuggingFace Spaces:** ✅ DEPLOYED
-- **URL:** https://huggingface.co/spaces/sam133/Agent2Robot
-- **Status:** Successfully deployed with working simulation features
-- **Interface:** Simplified ChatInterface avoiding Gradio compatibility issues
-- **Features:** All advanced simulation capabilities operational
-
----
-
-## 🏆 **Advanced Simulation Features - Final Status**
-
-### **✅ COMPLETELY OPERATIONAL FEATURES:**
-
-1. **🎥 4K Ultra-HD Video Generation**
-   - Cinema-quality 3D visualization
-   - 60 FPS high-fidelity animation
-   - Professional MP4 encoding
-
-2. **⚙️ Advanced Physics Engine**
-   - Real-time Bullet Physics simulation
-   - Accurate collision and dynamics
-   - Environmental interaction modeling
-
-3. **🌍 Multi-Scenario Testing**
-   - Industrial environment navigation
-   - Adverse weather operation
-   - Complex obstacle courses
-   - Emergency response protocols
-
-4. **📊 Performance Validation**
-   - Comprehensive metrics tracking
-   - 99.7% navigation accuracy
-   - 100% obstacle avoidance success
-
-5. **🗂️ File Package Generation**
-   - Complete technical specifications
-   - Manufacturing documentation
-   - Simulation reports and videos
-
-6. **🔬 Environmental Modeling**
-   - Weather condition variations
-   - Industrial vs outdoor environments
-   - Dynamic obstacle scenarios
-
----
-
-## 🎯 **Resolution Summary**
-
-### **Problem:** Advanced Simulation Features not working due to Gradio interface bug
-### **Solution:** Simplified interface maintaining all simulation functionality
-### **Result:** ✅ **ALL ADVANCED SIMULATION FEATURES FULLY OPERATIONAL**
-
-### **Key Achievements:**
-- ✅ Identified and resolved Gradio compatibility issue
-- ✅ Verified all simulation features working correctly
-- ✅ Deployed working version to HuggingFace Spaces
-- ✅ Maintained complete functionality while fixing interface
-- ✅ Comprehensive testing validates all capabilities
-
-### **Current Status:**
-**🏆 ADVANCED SIMULATION FEATURES: FULLY WORKING AND DEPLOYED**
-
-**Live URL:** https://huggingface.co/spaces/sam133/Agent2Robot
-**Test Results:** All features verified operational
-**Deployment:** Successful with working simulation capabilities
-
----
-
-## 📝 **Technical Notes**
-
-### **Files Modified:**
-- `app.py` - Replaced with working ChatInterface version
-- `mcp_client.py` - Enhanced with verified simulation features
-- `test_simulation.py` - Created for direct feature validation
-
-### **Gradio Compatibility:**
-- **Issue:** Complex Blocks interface with File components
-- **Solution:** Simple ChatInterface pattern
-- **Result:** No more TypeError, all features working
-
-### **MCP Integration:**
-- **Status:** Fully functional with enhanced capabilities
-- **Features:** All advanced simulation features operational
-- **Validation:** Direct testing confirms complete functionality
-
-**🎬 ADVANCED SIMULATION FEATURES FIX: COMPLETE AND SUCCESSFUL** ✅ 

app_backup_for_debug.py

@@ -1,284 +0,0 @@
-#!/usr/bin/env python3
-"""
-Agent2Robot - MCP Hackathon 2024 Submission
-AI-Powered Vehicle Design Assistant with MCP Integration
-"""
-
-import os
-import ssl
-import json
-
-# Minimal SSL fix for local development (won't affect HuggingFace Spaces)
-try:
-    # Only apply SSL fix if needed
-    ssl._create_default_https_context = ssl._create_unverified_context
-except:
-    pass
-
-import gradio as gr
-from design_tools import VehicleDesigner
-import main_orchestrator
-
-# Initialize the vehicle designer with MCP integration
-designer = VehicleDesigner()
-
-def get_mcp_status():
-    """Get MCP server status for display"""
-    status = designer.get_mcp_status()
-    return f"MCP Server: {status['name']} v{status['version']} - Status: {status['status']}"
-
-# Create Gradio interface
-def create_app():
-    # Custom CSS for enhanced styling
-    custom_css = """
-    .simulation-output textarea {
-        font-family: 'Courier New', monospace !important;
-        background: #f8f9fa !important;
-        border: 2px solid #667eea !important;
-        border-radius: 8px !important;
-    }
-    .simulation-output {
-        background: linear-gradient(145deg, #f0f2f5, #e8eaf0) !important;
-        border-radius: 12px !important;
-        padding: 10px !important;
-    }
-    .gradio-button.primary {
-        background: linear-gradient(90deg, #667eea 0%, #764ba2 100%) !important;
-        border: none !important;
-        border-radius: 25px !important;
-        font-weight: bold !important;
-        text-transform: uppercase !important;
-    }
-    .status-display {
-        font-size: 18px !important;
-        font-weight: bold !important;
-        text-align: center !important;
-        padding: 10px !important;
-        background: linear-gradient(90deg, #f8f9fa, #e9ecef) !important;
-        border-radius: 8px !important;
-    }
-    .current-specs {
-        background: #fff3cd !important;
-        border: 2px solid #ffc107 !important;
-        border-radius: 8px !important;
-    }
-    .final-specs {
-        background: #d1edff !important;
-        border: 2px solid #0066cc !important;
-        border-radius: 8px !important;
-    }
-    """
-    
-    with gr.Blocks(
-        title="🤖🚁 Agent2Robot - MCP Hackathon 2024",
-        theme=gr.themes.Soft(),
-        css=custom_css
-    ) as app:
-        
-        # Header
-        gr.HTML("""
-        <div style="text-align: center; padding: 20px; background: linear-gradient(90deg, #667eea 0%, #764ba2 100%); color: white; border-radius: 10px; margin-bottom: 20px;">
-            <h1>🤖🚁 Agent2Robot Design Assistant</h1>
-            <p>AI-Powered Vehicle Design with MCP Integration</p>
-            <p><strong>MCP Hackathon 2024 Submission</strong></p>
-        </div>
-        """)
-        
-        # MCP Status Display
-        with gr.Row():
-            gr.Markdown("### 🔗 MCP Server Status")
-            mcp_status = gr.Textbox(
-                value=get_mcp_status(),
-                label="Server Connection",
-                interactive=False
-            )
-        
-        # === MAIN INTERFACE LAYOUT ===
-        with gr.Row():
-            # Input Section
-            with gr.Column(scale=1):
-                gr.Markdown("## 🎯 Design Parameters")
-                
-                vehicle_type = gr.Dropdown(
-                    choices=["Robot", "Drone", "Autonomous Vehicle", "Robotic Arm"],
-                    label="🚀 Vehicle Type",
-                    value="Robot"
-                )
-                
-                description = gr.Textbox(
-                    label="📝 Design Requirements",
-                    lines=6,
-                    placeholder="Describe your vehicle requirements and specifications...\n\nExample: Design a warehouse robot for navigation and package delivery with 50kg payload capacity, 8-hour operation time, and obstacle avoidance.",
-                    value="Design a robot for warehouse navigation and package delivery"
-                )
-                
-                generate_btn = gr.Button("🚀 Generate Design with MCP", variant="primary", size="lg")
-                
-                # Status Display
-                gr.Markdown("### 📊 Process Status")
-                status_display = gr.Markdown(
-                    value="🟢 Ready to start design process",
-                    elem_classes=["status-display"]
-                )
-            
-            # Output Section  
-            with gr.Column(scale=2):
-                gr.Markdown("## 📊 Live Design Process & Results")
-                
-                # Main process log / final report display
-                process_log_output = gr.Textbox(
-                    label="🎯 Design Process Log / Final Report",
-                    lines=20,
-                    interactive=False,
-                    show_copy_button=True,
-                    placeholder="🚀 Click 'Generate Design with MCP' to start the live design process...\n\nYou'll see real-time updates including:\n• MCP server communication\n• Iterative design generation\n• Physics simulation results\n• Performance optimization\n• Final design validation"
-                )
-        
-        # === ITERATIVE DESIGN SECTION ===
-        with gr.Row():
-            with gr.Column():
-                gr.Markdown("### 🔄 Current Iteration Specs")
-                current_specs_output = gr.JSON(
-                    label="📋 Current Design Being Tested",
-                    elem_classes=["current-specs"]
-                )
-            
-            with gr.Column():
-                gr.Markdown("### 🏆 Final Best Design")
-                final_specs_output = gr.JSON(
-                    label="🎯 Optimized Final Specifications",
-                    elem_classes=["final-specs"]
-                )
-        
-        # === SIMULATION & DOWNLOAD SECTION ===
-        with gr.Row():
-            with gr.Column(scale=2):
-                gr.Markdown("## 🎬 MCP Simulation Engine")
-                gr.HTML("""
-                <div style="background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); padding: 15px; border-radius: 10px; color: white; text-align: center; margin-bottom: 10px;">
-                    <h3 style="margin: 0; color: white;">🎥 Advanced Physics Simulation</h3>
-                    <p style="margin: 5px 0 0 0; color: white;">MCP-Powered Vehicle Behavior Modeling</p>
-                </div>
-                """)
-                simulation_output = gr.Textbox(
-                    label="🚀 Simulation Results & Video Information",
-                    lines=20,
-                    interactive=False,
-                    show_copy_button=True,
-                    placeholder="🎬 Simulation will be generated after design completion...\n\nSimulation features:\n• Physics-based movement modeling\n• Environmental interaction analysis\n• Performance metrics visualization\n• Real-time system validation",
-                    elem_classes=["simulation-output"]
-                )
-            
-            with gr.Column(scale=1):
-                gr.Markdown("### 📥 Downloads")
-                gr.HTML("""
-                <div style="background: #e8f5e8; padding: 15px; border-radius: 10px; border: 2px solid #28a745; margin-bottom: 10px;">
-                    <h4 style="margin: 0; color: #155724;">🎯 Design Outputs</h4>
-                    <p style="margin: 5px 0 0 0; color: #155724;">Download final specifications</p>
-                </div>
-                """)
-                
-                download_file_output = gr.File(
-                    label="📄 Download Design Specs (JSON)",
-                    file_count="single",
-                    interactive=False,
-                    visible=False
-                )
-                
-                gr.Markdown("""
-                **📋 Available after completion:**
-                - Complete design specifications
-                - Technical parameters (JSON)
-                - Performance metrics
-                - MCP validation results
-                """)
-        
-        # === CONNECT GENERATOR FUNCTION ===
-        def process_with_updates(vehicle_type, design_requirements):
-            """Wrapper function to handle generator updates"""
-            for update in main_orchestrator.process_design_request(vehicle_type, design_requirements):
-                # Extract values from the update dictionary
-                process_log = update.get("process_log", "")
-                current_specs = update.get("current_specs", None)
-                final_specs = update.get("final_specs", None)
-                simulation_video = update.get("simulation_video", "")
-                status = update.get("status", "")
-                download_file = update.get("download_file", None)
-                
-                # Make download file visible if available
-                download_visible = download_file is not None
-                download_value = download_file if download_file else None
-                
-                yield [
-                    process_log,           # process_log_output
-                    current_specs,         # current_specs_output
-                    final_specs,           # final_specs_output  
-                    simulation_video,      # simulation_output
-                    status,                # status_display
-                    gr.File(value=download_value, visible=download_visible)  # download_file_output
-                ]
-        
-        # Connect the generator function to the button
-        generate_btn.click(
-            fn=process_with_updates,
-            inputs=[vehicle_type, description],
-            outputs=[
-                process_log_output,
-                current_specs_output,
-                final_specs_output,
-                simulation_output,
-                status_display,
-                download_file_output
-            ]
-        )
-        
-        # === INFORMATION SECTIONS ===
-        with gr.Row():
-            with gr.Column():
-                gr.Markdown("""
-                ### 🔧 MCP Integration Features
-                
-                **Model Context Protocol (MCP) Integration:**
-                - **🔗 Server Communication**: Direct integration with MCP servers for design generation
-                - **📊 Real-time Validation**: Live design validation through MCP protocols
-                - **🎯 Context Awareness**: Maintains design context across sessions
-                - **🎬 Simulation Generation**: MCP-powered video simulation creation
-                - **🔄 Iterative Optimization**: Multi-iteration design improvement
-                - **🚀 Scalable Architecture**: Modular design supporting multiple MCP servers
-                """)
-            
-            with gr.Column():
-                gr.Markdown("""
-                ### 🏆 MCP Hackathon 2024 - Technical Stack
-                
-                **Core Components:**
-                - **MCP Client**: `mcp_client.py` - Handles server communication
-                - **Design Tools**: `design_tools.py` - Core vehicle design logic
-                - **Main Orchestrator**: `main_orchestrator.py` - Generator-based processing
-                - **Gradio Interface**: `app.py` - Real-time UI updates
-                - **Simulation Engine**: MCP-integrated physics modeling
-                - **Download System**: JSON specification export
-                """)
-        
-        # Footer
-        gr.Markdown("""
-        ---
-        ### 🏆 Agent2Robot - MCP Hackathon 2024
-        
-        **AI-Powered Vehicle Design Assistant** with **Model Context Protocol Integration**
-        
-        Experience **real-time design generation** with live updates showing MCP server communication, 
-        iterative optimization, physics simulation, and final results.
-        
-        **MCP Features**: Live server integration • Real-time validation • Context preservation • 
-        Iterative optimization • Simulation generation • Download capability
-        
-        Built with ❤️ for the MCP Hackathon 2024 | Powered by Gradio + MCP + Generator Architecture
-        """)
-    
-    return app
-
-# Launch the application
-if __name__ == "__main__":
-    app = create_app()
-    app.launch() 

app_complex_backup.py

@@ -1,412 +0,0 @@
-#!/usr/bin/env python3
-"""
-Agent2Robot - MCP Hackathon 2024 Submission - Enhanced Version
-AI-Powered Vehicle Design Assistant with Advanced MCP Integration
-"""
-
-import os
-import datetime
-import gradio as gr
-import json
-import tempfile
-import shutil
-from typing import Dict, Any, Optional, Tuple
-
-# Import our real MCP modules
-try:
-    from design_tools import VehicleDesigner
-    import main_orchestrator
-    MCP_AVAILABLE = True
-    print("✅ Enhanced MCP modules loaded successfully")
-except ImportError as e:
-    MCP_AVAILABLE = False
-    print(f"❌ MCP modules not available: {e}")
-
-# Initialize the vehicle designer
-if MCP_AVAILABLE:
-    designer = VehicleDesigner()
-
-# Current time for agent context
-time = datetime.datetime.now().astimezone().isoformat()
-
-SYSTEM_PROMPT = """You are Agent2Robot Enhanced, an AI-powered vehicle design assistant with advanced MCP capabilities including:
-- Real-time design generation and optimization
-- High-fidelity physics simulation with video output
-- Comprehensive file package generation
-- Manufacturing specification exports
-- Cost analysis and ROI calculations
-
-Your expertise covers: warehouse robots, delivery drones, autonomous vehicles, robotic arms, and custom mechanical systems.
-
-Current time (ISO 8601): {time}"""
-
-def agent_chat(message: str, history: list) -> Tuple[str, Optional[str], Optional[str], Optional[str]]:
-    """
-    Enhanced chat function that returns:
-    (response_text, download_json_file, download_simulation_file, download_package_file)
-    """
-    
-    if not MCP_AVAILABLE:
-        # Enhanced fallback response
-        return (f"""🤖 **Agent2Robot Enhanced Design Assistant** (Demo Mode)
-
-**Your Request:** {message}
-
-🎯 **Advanced Analysis Process:**
-- Requirements parsing from: "{message[:80]}..."
-- Vehicle type classification and constraint analysis
-- Performance target definition and optimization goals
-
-📋 **Enhanced Design Generation Pipeline:**
-1. **🔍 Intelligent Requirements Analysis**
-2. **🚀 MCP-Powered Iterative Design Optimization** 
-3. **🎬 High-Fidelity Physics Simulation & Video Generation**
-4. **📄 Comprehensive Technical Documentation**
-5. **💾 Complete File Package Export**
-
-🚀 **Advanced Deliverables:**
-- ✅ Complete technical specifications (JSON)
-- ✅ Performance analysis & validation report  
-- ✅ 4K simulation video with physics modeling
-- ✅ Manufacturing guidelines and CAD files
-- ✅ Cost analysis and ROI projections
-
-⚠️ *Note: Full MCP integration with file downloads and simulation videos will be available when properly configured.*
-
-**Enhanced Output Preview:** Advanced warehouse robot design with AI-powered navigation, 50kg payload, 12-hour battery life, LiDAR+camera fusion, predictive maintenance, and complete manufacturing package.""", None, None, None)
-    
-    # Real enhanced MCP integration
-    try:
-        # Intelligent vehicle type detection
-        message_lower = message.lower()
-        vehicle_type = "Robot"  # Default
-        
-        if any(word in message_lower for word in ["robot", "warehouse", "delivery", "mobile", "ground"]):
-            vehicle_type = "Robot"
-        elif any(word in message_lower for word in ["drone", "uav", "aerial", "flying", "quadcopter"]):
-            vehicle_type = "Drone"  
-        elif any(word in message_lower for word in ["autonomous", "self-driving", "car", "vehicle", "transportation"]):
-            vehicle_type = "Autonomous Vehicle"
-        elif any(word in message_lower for word in ["arm", "manipulator", "assembly", "precision", "manufacturing"]):
-            vehicle_type = "Robotic Arm"
-
-        # Enhanced initial response with progress indication
-        initial_response = f"""🚀 **Agent2Robot Enhanced Design Process Initiated**
-
-**🎯 Project Overview:**
-- **Vehicle Type Detected:** {vehicle_type}
-- **Requirements:** {message}
-- **MCP Server:** Connected ✅
-- **Advanced Features:** Enabled ✅
-
-🔄 **Enhanced MCP-Powered Pipeline Starting:**
-- 🔍 Analyzing requirements with NLP processing...
-- ⚙️ Initializing iterative design optimization...
-- 🎬 Preparing high-fidelity simulation engine...
-- 📄 Setting up comprehensive documentation system...
-- 💾 Configuring downloadable file generation...
-
-*Processing your complete vehicle design with specifications, 4K simulations, and downloadable technical packages...*
-
-═══════════════════════════════════════════════════════
-"""
-
-        # Process the enhanced design request
-        final_result = initial_response
-        design_data = None
-        simulation_info = None
-        download_files = None
-        
-        # Collect all updates from the enhanced generator
-        for update in main_orchestrator.process_design_request(vehicle_type, message):
-            if update.get("process_log"):
-                final_result = initial_response + "\n\n" + update["process_log"]
-            
-            # Capture final design specs
-            if update.get("final_specs"):
-                try:
-                    if isinstance(update["final_specs"], str):
-                        design_data = json.loads(update["final_specs"])
-                    else:
-                        design_data = update["final_specs"]
-                except:
-                    design_data = update["final_specs"]
-                    
-            # Capture simulation info
-            if update.get("simulation_video"):
-                simulation_info = update["simulation_video"]
-        
-        # Generate enhanced downloadable files
-        json_file = None
-        simulation_file = None
-        package_file = None
-        
-        if design_data and MCP_AVAILABLE:
-            try:
-                # Generate comprehensive file package using enhanced MCP client
-                files_package = designer.mcp_client.generate_downloadable_files(design_data)
-                
-                # Create JSON specifications file
-                if files_package.get("specifications"):
-                    json_filepath = files_package["specifications"].get("filepath")
-                    if json_filepath and os.path.exists(json_filepath):
-                        json_file = json_filepath
-                
-                # Create simulation info file (text format for demo)
-                if simulation_info:
-                    temp_dir = tempfile.mkdtemp()
-                    sim_filepath = os.path.join(temp_dir, f"simulation_report_{design_data.get('design_id', 'unknown')}.txt")
-                    with open(sim_filepath, 'w', encoding='utf-8') as f:
-                        f.write(simulation_info)
-                    simulation_file = sim_filepath
-                
-                # Create comprehensive package info
-                package_filepath = os.path.join(temp_dir, f"complete_package_{design_data.get('design_id', 'unknown')}.txt")
-                package_info = f"""🎯 AGENT2ROBOT COMPLETE DESIGN PACKAGE
-═══════════════════════════════════════════════════════
-
-📦 PACKAGE CONTENTS:
-✅ Technical Specifications (JSON): {files_package.get('specifications', {}).get('size_kb', 0):.1f} KB
-✅ Manufacturing Files (ZIP): {files_package.get('manufacturing', {}).get('size_kb', 0):.1f} KB  
-✅ Documentation (PDF): {files_package.get('documentation', {}).get('size_kb', 0):.1f} KB
-✅ Simulation Video (MP4): {files_package.get('simulation', {}).get('size_kb', 0):.1f} KB
-
-🚀 VEHICLE TYPE: {design_data.get('vehicle_type', 'Unknown')}
-🏷️ DESIGN ID: {design_data.get('design_id', 'N/A')}
-📊 OPTIMIZATION SCORE: {design_data.get('optimization_score', 'N/A')}%
-
-🎬 SIMULATION FEATURES:
-• 4K resolution physics simulation
-• Multi-scenario validation testing
-• Performance metrics visualization
-• Environmental interaction modeling
-
-📄 DOCUMENTATION INCLUDES:
-• Complete technical specifications
-• Assembly and manufacturing instructions
-• User manual and maintenance guide
-• Safety protocols and compliance certificates
-• Cost analysis and ROI projections
-
-💾 DOWNLOAD READY - All files generated successfully!
-
-Generated by Agent2Robot Enhanced MCP System
-Timestamp: {datetime.datetime.now().isoformat()}
-"""
-                
-                with open(package_filepath, 'w', encoding='utf-8') as f:
-                    f.write(package_info)
-                package_file = package_filepath
-                
-            except Exception as e:
-                print(f"Error generating download files: {e}")
-        
-        # Enhanced final design summary
-        if design_data:
-            final_result += f"""
-
-🏆 **ENHANCED DESIGN SPECIFICATIONS COMPLETE**
-═══════════════════════════════════════════════════════
-
-**🎯 Core Design Information:**
-• **Vehicle Type:** {design_data.get('vehicle_type', vehicle_type)}
-• **Design ID:** {design_data.get('design_id', 'N/A')}
-• **Optimization Score:** {design_data.get('optimization_score', 'N/A')}%
-• **Generation Timestamp:** {design_data.get('timestamp', 'N/A')}
-
-**🔧 Advanced Features Generated:**
-{chr(10).join(f'  ✅ {feature}' for feature in design_data.get('generated_features', ['Advanced AI control', 'Robust design', 'High efficiency'])[:5])}
-
-**📊 Performance Validation:**
-• **Speed:** {design_data.get('performance_metrics', {}).get('speed', 'Optimized for requirements')}
-• **Efficiency:** {design_data.get('performance_metrics', {}).get('efficiency', 'High performance rating')}
-• **Reliability:** {design_data.get('performance_metrics', {}).get('reliability', '99.9% uptime')}
-• **Maintainability:** {design_data.get('performance_metrics', {}).get('maintainability', 'Easy maintenance')}
-
-**💰 Cost Analysis:**
-• **Development Cost:** {design_data.get('cost_analysis', {}).get('development_cost', 'N/A')}
-• **Unit Cost:** {design_data.get('cost_analysis', {}).get('unit_manufacturing_cost', 'N/A')}
-• **ROI Timeline:** {design_data.get('cost_analysis', {}).get('roi_timeline', 'N/A')}
-
-**🏭 Manufacturing Specifications:**
-• **Production Method:** {design_data.get('manufacturing_specs', {}).get('production_method', 'Advanced manufacturing')}
-• **Lead Time:** {design_data.get('manufacturing_specs', {}).get('lead_time', 'Standard delivery')}
-• **Quality Control:** {design_data.get('manufacturing_specs', {}).get('quality_control', 'Comprehensive testing')}
-
-**📄 Generated Documentation:**
-✅ Complete technical specifications (JSON)
-✅ High-fidelity 4K simulation video
-✅ Manufacturing and assembly guides
-✅ Performance analysis report
-✅ Compliance and safety documentation
-
-**🎭 ENHANCED DESIGN PROCESS COMPLETE!**
-Your advanced vehicle design includes comprehensive specifications, validation, simulation, and complete file package ready for immediate implementation and deployment."""
-        
-        return (final_result, json_file, simulation_file, package_file)
-        
-    except Exception as e:
-        error_response = f"""❌ **Enhanced MCP Design Process Error**
-
-**Error Details:** {str(e)}
-
-🔄 **Advanced Error Recovery Protocol:**
-
-Despite the error, I can provide you with an enhanced conceptual framework for: "{message}"
-
-**🎯 Enhanced Alternative Approach:**
-**Detected Vehicle Type:** {vehicle_type}
-
-**📋 Comprehensive Design Framework:**
-1. **🔍 Advanced Requirements Analysis**
-   - Functional specification parsing
-   - Performance constraint identification
-   - Environmental requirement assessment
-
-2. **⚙️ System Architecture Design**
-   - Component selection optimization
-   - Integration strategy planning
-   - Scalability consideration
-
-3. **🎬 Virtual Prototyping Process**
-   - Physics simulation preparation
-   - Performance modeling setup
-   - Validation protocol design
-
-4. **📄 Documentation Generation**
-   - Technical specification creation
-   - Manufacturing guide development
-   - Compliance verification planning
-
-**🔧 Recommended Next Steps:**
-- Refine requirements specification
-- Check MCP server connectivity
-- Retry design generation process
-- Contact technical support if needed
-
-The enhanced system is designed to handle complex requirements and generate complete design packages. Please try again with specific vehicle requirements."""
-        
-        return (error_response, None, None, None)
-
-# Create enhanced interface with file download capabilities
-def create_enhanced_interface():
-    """Create the enhanced Gradio interface with download capabilities"""
-    
-    with gr.Blocks(theme=gr.themes.Soft(), title="Agent2Robot Enhanced") as app:
-        gr.Markdown("""
-        # 🤖🚁 Agent2Robot Enhanced - AI Vehicle Design Assistant
-        
-        **🏆 MCP Hackathon 2024 Enhanced Submission**
-        
-        Advanced AI-powered vehicle design assistant with **comprehensive MCP integration**. Get complete vehicle designs including specifications, 4K simulations, and downloadable technical documentation packages.
-        
-        **🚀 Enhanced Capabilities:**
-        • **🎯 Intelligent Requirements Analysis** - Advanced NLP parsing of complex specifications
-        • **🔄 MCP-Powered Iterative Optimization** - Real-time design improvement with validation  
-        • **🎬 High-Fidelity Physics Simulation** - 4K video generation with comprehensive modeling
-        • **📁 Complete Technical Documentation** - Specifications, reports, and manufacturing files
-        • **💾 Comprehensive Download Packages** - Ready-to-implement solutions with all assets
-        • **💰 Advanced Cost Analysis** - ROI projections and manufacturing economics
-        
-        **🔧 Supported Vehicle Types:**
-        Warehouse robots • Delivery drones • Autonomous vehicles • Robotic arms • Custom systems
-        """)
-        
-        with gr.Row():
-            with gr.Column(scale=2):
-                chatbot = gr.Chatbot(
-                    label="🤖 Agent2Robot Enhanced Chat",
-                    height=600,
-                    show_label=True
-                )
-                
-                with gr.Row():
-                    msg = gr.Textbox(
-                        label="💬 Describe your vehicle requirements",
-                        placeholder="Example: Design a warehouse robot for package delivery with 50kg payload capacity and 8-hour operation time",
-                        lines=3,
-                        scale=4
-                    )
-                    submit_btn = gr.Button("🚀 Generate Design", variant="primary", scale=1)
-            
-            with gr.Column(scale=1):
-                gr.Markdown("### 📥 Enhanced Download Center")
-                
-                specs_file = gr.File(
-                    label="📄 Technical Specifications (JSON)",
-                    interactive=False,
-                    visible=True
-                )
-                
-                simulation_file = gr.File(
-                    label="🎬 Simulation Report", 
-                    interactive=False,
-                    visible=True
-                )
-                
-                package_file = gr.File(
-                    label="📦 Complete Design Package",
-                    interactive=False, 
-                    visible=True
-                )
-                
-                gr.Markdown("""
-                **💡 Download Information:**
-                - **Specifications:** Complete technical specs in JSON format
-                - **Simulation:** Physics simulation report and video info
-                - **Package:** Comprehensive design documentation
-                
-                Files are generated automatically after design completion.
-                """)
-        
-        # Enhanced examples
-        gr.Examples(
-            examples=[
-                "Design an advanced warehouse robot for heavy package delivery with 75kg payload, AI navigation, and 12-hour battery life",
-                "Create a weather-resistant surveillance drone with 3-hour flight time, 4K camera, and autonomous patrol capabilities", 
-                "Design a luxury autonomous vehicle for urban navigation with 8 passengers, Level 4 autonomy, and premium comfort systems",
-                "Build a precision robotic arm for semiconductor manufacturing with 0.01mm accuracy and cleanroom compatibility",
-                "Develop a search and rescue robot for disaster response with all-terrain mobility and sensor package",
-                "Create an agricultural drone for crop monitoring with multispectral imaging and GPS precision"
-            ],
-            inputs=msg,
-            label="🎯 Enhanced Example Prompts"
-        )
-        
-        def enhanced_chat_response(message, history):
-            """Handle chat interaction with enhanced file generation"""
-            if not message.strip():
-                return history, "", None, None, None
-            
-            # Get enhanced response with files
-            response, json_file, sim_file, pkg_file = agent_chat(message, history)
-            
-            # Update chat history
-            history = history + [[message, response]]
-            
-            return history, "", json_file, sim_file, pkg_file
-        
-        # Wire up the enhanced interface
-        submit_btn.click(
-            enhanced_chat_response,
-            inputs=[msg, chatbot],
-            outputs=[chatbot, msg, specs_file, simulation_file, package_file]
-        )
-        
-        msg.submit(
-            enhanced_chat_response,
-            inputs=[msg, chatbot], 
-            outputs=[chatbot, msg, specs_file, simulation_file, package_file]
-        )
-    
-    return app
-
-# Create and launch the enhanced interface
-if __name__ == "__main__":
-    enhanced_app = create_enhanced_interface()
-    enhanced_app.launch(
-        server_name="0.0.0.0",
-        server_port=7860,
-        show_error=True,
-        share=True
-    ) 

app_enhanced_working.py

@@ -1,406 +0,0 @@
-#!/usr/bin/env python3
-"""
-Agent2Robot - Enhanced Working Version with Real Functionality
-AI-Powered Vehicle Design Assistant with Complete Functionality and Gradio-Compatible UI
-"""
-
-import gradio as gr
-import json
-import tempfile
-import os
-import time
-import datetime
-import random
-from typing import List, Tuple, Optional
-
-# Import MCP modules with error handling
-try:
-    import design_orchestrator as designer
-    import main_orchestrator
-    MCP_AVAILABLE = True
-    print("✅ MCP modules loaded successfully")
-except ImportError as e:
-    MCP_AVAILABLE = False
-    print(f"⚠️ MCP modules not available: {e}")
-
-def generate_detailed_specifications(vehicle_type: str, message: str) -> dict:
-    """Generate detailed technical specifications for the vehicle"""
-    
-    # Determine vehicle type from message
-    if "robot" in message.lower() or "warehouse" in message.lower():
-        vehicle_category = "Warehouse Robot"
-        base_specs = {
-            "vehicle_type": "Autonomous Mobile Robot (AMR)",
-            "primary_function": "Material handling and navigation",
-            "payload_capacity": "50-100 kg",
-            "operating_speed": "1.5 m/s maximum, 0.8 m/s operational",
-            "navigation_system": "LiDAR + Camera fusion with SLAM",
-            "safety_features": ["Emergency stop", "Collision avoidance", "Backup sensors"],
-            "power_system": "48V Lithium-ion, 8-hour operation",
-            "dimensions": "120 x 80 x 150 cm (L x W x H)"
-        }
-    elif "drone" in message.lower() or "uav" in message.lower():
-        vehicle_category = "Delivery Drone"
-        base_specs = {
-            "vehicle_type": "Quadcopter UAV",
-            "primary_function": "Package delivery and surveillance",
-            "payload_capacity": "5-15 kg",
-            "flight_time": "45-120 minutes",
-            "max_speed": "15 m/s horizontal, 5 m/s vertical",
-            "navigation_system": "GPS + Vision-based navigation",
-            "safety_features": ["Return-to-home", "Obstacle avoidance", "Emergency landing"],
-            "power_system": "22.2V LiPo battery",
-            "dimensions": "80 x 80 x 35 cm (rotor span)"
-        }
-    elif "autonomous" in message.lower() or "car" in message.lower():
-        vehicle_category = "Autonomous Vehicle"
-        base_specs = {
-            "vehicle_type": "Level 4 Autonomous Vehicle",
-            "primary_function": "Passenger transport and urban navigation",
-            "passenger_capacity": "4-6 passengers",
-            "operating_speed": "60 km/h urban, 120 km/h highway",
-            "navigation_system": "LiDAR + Camera + Radar fusion",
-            "safety_features": ["Redundant systems", "Emergency braking", "Collision avoidance"],
-            "power_system": "Electric drivetrain, 400V battery",
-            "dimensions": "450 x 180 x 160 cm (L x W x H)"
-        }
-    else:
-        vehicle_category = "Robotic Arm"
-        base_specs = {
-            "vehicle_type": "6-DOF Industrial Manipulator",
-            "primary_function": "Precision manufacturing and assembly",
-            "payload_capacity": "10-20 kg",
-            "reach": "1.2 m working radius",
-            "precision": "±0.1 mm repeatability",
-            "navigation_system": "Vision-guided positioning",
-            "safety_features": ["Force sensing", "Emergency stop", "Collision detection"],
-            "power_system": "24V servo motors",
-            "dimensions": "Base: 60 x 60 cm, Height: 180 cm"
-        }
-    
-    # Generate detailed specifications
-    detailed_specs = {
-        "project_metadata": {
-            "generated_on": datetime.datetime.now().isoformat(),
-            "agent_version": "Agent2Robot v3.0",
-            "mcp_integration": "Enhanced MCP Protocol v2.4",
-            "simulation_engine": "PhysX 4K Ultra-HD"
-        },
-        "vehicle_classification": {
-            "category": vehicle_category,
-            "type": base_specs["vehicle_type"],
-            "compliance_standards": ["ISO 9001", "ISO 26262", "IEC 61508"],
-            "safety_rating": "SIL 3 (Safety Integrity Level)"
-        },
-        "core_specifications": base_specs,
-        "advanced_features": {
-            "ai_processing": "NVIDIA Jetson AGX Xavier",
-            "sensor_suite": ["LiDAR 360°", "4K stereo cameras", "IMU", "GPS/GNSS"],
-            "connectivity": ["5G/LTE", "WiFi 6", "Bluetooth 5.0"],
-            "edge_computing": "Real-time inference <10ms latency"
-        },
-        "simulation_parameters": {
-            "physics_engine": "Bullet Physics 3.25",
-            "rendering_quality": "4K Ultra-HD @ 60 FPS",
-            "simulation_accuracy": "99.7% real-world correlation",
-            "test_scenarios": ["Navigation", "Obstacles", "Weather", "Emergency"],
-            "validation_metrics": ["Path efficiency", "Energy consumption", "Safety margin"]
-        },
-        "performance_metrics": {
-            "navigation_accuracy": f"{random.uniform(98.5, 99.9):.1f}%",
-            "obstacle_avoidance": f"{random.uniform(99.0, 100.0):.1f}%",
-            "energy_efficiency": f"{random.uniform(94.0, 98.0):.1f}%",
-            "reliability_score": f"{random.uniform(96.0, 99.5):.1f}%",
-            "safety_rating": "A+ Grade"
-        },
-        "manufacturing_data": {
-            "estimated_cost": f"${random.randint(50000, 150000):,}",
-            "production_time": f"{random.randint(4, 12)} weeks",
-            "quality_control": "Six Sigma methodology",
-            "certifications": ["CE", "FCC", "RoHS"],
-            "warranty": "3 years comprehensive"
-        },
-        "user_requirements": {
-            "original_request": message,
-            "interpreted_needs": [
-                "High reliability operation",
-                "Advanced safety features", 
-                "Optimal performance efficiency",
-                "Regulatory compliance"
-            ]
-        }
-    }
-    
-    return detailed_specs
-
-def generate_simulation_report(vehicle_type: str, message: str) -> str:
-    """Generate detailed simulation and validation report"""
-    
-    timestamp = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
-    
-    report = f"""# Agent2Robot Advanced Simulation Report
-Generated: {timestamp}
-Simulation Engine: PhysX 4K Ultra-HD v5.2
-MCP Integration: Active
-
-## Executive Summary
-Comprehensive simulation analysis completed for: {message}
-
-## Simulation Configuration
-- **Rendering Quality**: 4K Ultra-HD (3840x2160) @ 60 FPS
-- **Physics Engine**: Advanced Bullet Physics with real-time collision detection
-- **Environment**: Multi-scenario testing environment
-- **Duration**: 45-second comprehensive analysis
-- **File Format**: MP4 H.264 encoding
-
-## Test Scenarios Executed
-
-### 1. Navigation Performance
-- **Environment**: Industrial warehouse simulation
-- **Obstacles**: Dynamic and static object detection
-- **Path Planning**: A* algorithm with real-time optimization
-- **Results**: 
-  - Navigation Accuracy: {random.uniform(98.5, 99.9):.1f}%
-  - Path Efficiency: {random.uniform(94.0, 98.0):.1f}%
-  - Collision Avoidance: 100% Success Rate
-
-### 2. Environmental Stress Testing
-- **Weather Conditions**: Rain, fog, low visibility
-- **Surface Variations**: Wet, uneven, inclined surfaces
-- **Lighting**: Various ambient lighting conditions
-- **Results**:
-  - Performance Degradation: <5% in adverse conditions
-  - Sensor Reliability: {random.uniform(96.0, 99.0):.1f}%
-  - Emergency Response: <200ms reaction time
-
-### 3. Energy Efficiency Analysis
-- **Battery Simulation**: Real-world consumption modeling
-- **Operational Patterns**: Various duty cycles tested
-- **Charging Optimization**: Fast charging compatibility
-- **Results**:
-  - Energy Efficiency: {random.uniform(94.0, 98.0):.1f}%
-  - Operational Range: Extended by 15% through optimization
-  - Charging Time: 25% reduction from baseline
-
-### 4. Safety Validation
-- **Emergency Scenarios**: 50+ critical situations tested
-- **Failsafe Systems**: Redundancy verification
-- **Human Interaction**: Proximity detection accuracy
-- **Results**:
-  - Safety Rating: A+ Grade
-  - Emergency Response: 100% success rate
-  - Compliance: Meets all ISO 26262 requirements
-
-## Performance Metrics Dashboard
-
-```
-Navigation Accuracy:    ████████████████████ {random.uniform(98.5, 99.9):.1f}%
-Obstacle Avoidance:     ████████████████████ 100.0%
-Energy Efficiency:      ███████████████████  {random.uniform(94.0, 98.0):.1f}%
-Safety Score:           ████████████████████ A+ Grade
-Reliability Index:      ███████████████████  {random.uniform(96.0, 99.5):.1f}%
-```
-
-## Advanced Analytics
-
-### Real-time Telemetry
-- **Sensor Fusion**: 12 simultaneous data streams processed
-- **AI Decision Making**: Neural network inference <10ms
-- **Predictive Maintenance**: 30-day failure prediction accuracy
-- **Adaptive Learning**: Continuous improvement algorithms active
-
-### Simulation Video Details
-- **File Size**: ~85MB (4K 45-second duration)
-- **Encoding**: H.264 with hardware acceleration
-- **Framerate**: Consistent 60 FPS throughout
-- **Quality**: Photorealistic rendering with accurate physics
-
-## Compliance & Certification
-
-### Standards Met
-- ✅ ISO 9001:2015 (Quality Management)
-- ✅ ISO 26262 (Functional Safety)
-- ✅ IEC 61508 (Electrical Safety)
-- ✅ IEEE 1012 (Software Verification)
-
-### Testing Validation
-- **Independent Verification**: Third-party testing completed
-- **Regulatory Approval**: Pre-certification analysis passed
-- **Insurance Rating**: Premium tier qualification
-- **Operational Clearance**: Ready for deployment
-
-## Recommendations
-
-1. **Optimization Opportunities**: Energy system can be improved by 3-5%
-2. **Maintenance Schedule**: Preventive maintenance every 2000 hours
-3. **Upgrade Path**: Sensor package upgrade available Q2 2025
-4. **Training Requirements**: 40-hour operator certification recommended
-
-## Conclusion
-Simulation validation confirms the design meets all specified requirements with exceptional performance metrics. The vehicle demonstrates superior reliability, safety, and efficiency suitable for immediate deployment.
-
----
-*This report was generated by Agent2Robot Advanced Simulation Engine v3.0*
-*For technical inquiries, contact: simulation@agent2robot.ai*
-"""
-    
-    return report
-
-def agent_chat(message: str, history: list) -> Tuple[list, str]:
-    """Enhanced chat function with comprehensive vehicle design"""
-    
-    if not message.strip():
-        return history + [["", "Please describe your vehicle requirements for AI-powered design and simulation."]], ""
-    
-    # Generate detailed specifications
-    specs = generate_detailed_specifications("vehicle", message)
-    
-    # Generate simulation report
-    simulation_report = generate_simulation_report("vehicle", message)
-    
-    # Determine vehicle type for response
-    if "robot" in message.lower() or "warehouse" in message.lower():
-        vehicle_type = "Warehouse Robot"
-        icon = "🤖"
-    elif "drone" in message.lower() or "uav" in message.lower():
-        vehicle_type = "Delivery Drone"
-        icon = "🚁"
-    elif "autonomous" in message.lower() or "car" in message.lower():
-        vehicle_type = "Autonomous Vehicle"
-        icon = "🚗"
-    elif "arm" in message.lower() or "manipulator" in message.lower():
-        vehicle_type = "Robotic Arm"
-        icon = "🦾"
-    else:
-        vehicle_type = "Custom Vehicle"
-        icon = "🚀"
-
-    # Generate comprehensive response
-    response = f"""{icon} **Agent2Robot Advanced Vehicle Design Complete**
-
-**🎯 Your Request:** {message}
-**🔧 Vehicle Type Designed:** {vehicle_type}
-
-## 🚀 AI Design Process Complete
-**✅ Requirements Analysis:** Advanced NLP processing interpreted your specifications
-**✅ Engineering Design:** Optimal configuration generated using AI algorithms  
-**✅ Safety Validation:** Comprehensive safety analysis completed
-**✅ Performance Optimization:** Multi-objective optimization applied
-**✅ Compliance Verification:** All regulatory standards validated
-
-## 🎬 Advanced Simulation Features Executed
-**🎥 4K Ultra-HD Video Generation:** 
-- Resolution: 3840x2160 @ 60 FPS
-- Duration: 45-second comprehensive scenarios
-- Physics: Advanced Bullet Physics engine v3.25
-
-**📊 Performance Validation Results:**
-**🎯 Navigation Accuracy:** {specs['performance_metrics']['navigation_accuracy']}
-**🛡️ Obstacle Avoidance:** {specs['performance_metrics']['obstacle_avoidance']}  
-**⚡ Energy Efficiency:** {specs['performance_metrics']['energy_efficiency']}
-**🔒 Safety Rating:** {specs['performance_metrics']['safety_rating']}
-**📈 Reliability Score:** {specs['performance_metrics']['reliability_score']}
-
-## 🏭 Manufacturing & Deployment Ready
-**💰 Estimated Cost:** {specs['manufacturing_data']['estimated_cost']}
-**⏱️ Production Time:** {specs['manufacturing_data']['production_time']}
-**📜 Certifications:** CE, FCC, RoHS, UL Listed
-**🛡️ Warranty:** {specs['manufacturing_data']['warranty']}
-
-## 📁 Technical Documentation Generated
-**🗂️ Complete specifications:** {len(json.dumps(specs, indent=2))} characters of detailed JSON data
-**📊 Simulation report:** {len(simulation_report)} characters of comprehensive analysis
-**🔬 Advanced features:** All systems validated and ready for deployment
-
-## 🔧 Core Technical Specifications
-**Vehicle Category:** {specs['vehicle_classification']['category']}
-**Primary Function:** {specs['core_specifications']['primary_function']}
-**AI Processing:** {specs['advanced_features']['ai_processing']}
-**Physics Engine:** {specs['simulation_parameters']['physics_engine']}
-
-## 🌍 Multi-Scenario Testing Results
-- ✅ **Navigation Testing:** Industrial warehouse environment simulation
-- ✅ **Weather Conditions:** Rain, fog, low visibility validated
-- ✅ **Emergency Protocols:** <200ms response time achieved
-- ✅ **Energy Optimization:** 15% range improvement through smart algorithms
-
-Your {vehicle_type.lower()} design is now complete with all advanced simulation features validated and ready for manufacturing. All technical documentation has been generated with comprehensive specifications."""
-
-    new_history = history + [[message, response]]
-    return new_history, ""
-
-# Create the interface
-with gr.Blocks(theme=gr.themes.Soft(), title="Agent2Robot Enhanced Simulation") as app:
-    gr.Markdown("""
-    # 🤖🚁 Agent2Robot Enhanced - AI Vehicle Design with Advanced Simulation
-    
-    **🏆 MCP Hackathon 2024 - Complete Functionality Implementation**
-    
-    Complete AI-powered vehicle design assistant with **fully operational advanced simulation features**. Get comprehensive vehicle designs including 4K simulations, physics modeling, and detailed technical documentation.
-    
-    **🎬 Enhanced Simulation Features:**
-    • **🎥 4K Ultra-HD Video Generation** - High-fidelity physics simulation (3840x2160 @ 60 FPS)
-    • **⚙️ Advanced Physics Engine** - Real-time Bullet Physics modeling with collision detection
-    • **🌍 Multi-Scenario Testing** - Navigation, obstacles, weather, emergency protocols
-    • **📊 Performance Validation** - Comprehensive metrics (99.7% navigation accuracy)
-    • **🗂️ Complete Documentation** - Detailed specifications and technical reports
-    • **🔬 Environmental Modeling** - Industrial, outdoor, adverse conditions simulation
-    
-    **🔧 Advanced Capabilities:**
-    • Intelligent requirements analysis • MCP-powered optimization • Real-time physics simulation
-    • Complete technical documentation • Manufacturing specifications • Cost analysis & ROI
-    
-    **🚀 Supported Vehicle Types:**
-    Warehouse robots • Delivery drones • Autonomous vehicles • Robotic arms • Custom systems
-    """)
-    
-    chatbot = gr.Chatbot(
-        label="🎬 Agent2Robot Advanced Simulation Chat",
-        height=600,
-        show_label=True
-    )
-    
-    with gr.Row():
-        msg = gr.Textbox(
-            label="💬 Describe your vehicle requirements for advanced simulation",
-            placeholder="Example: Design warehouse robot with advanced navigation and 50kg payload capacity",
-            lines=3,
-            scale=4
-        )
-        submit_btn = gr.Button("🚀 Generate Design + Simulation", variant="primary", scale=1)
-    
-    # Enhanced examples section
-    gr.Markdown("### 🎯 Example Requests (Click to Try)")
-    
-    with gr.Row():
-        example1 = gr.Button("🏭 Warehouse Robot with Advanced Navigation", size="sm")
-        example2 = gr.Button("🚁 Delivery Drone with 4K Simulation", size="sm")
-        
-    with gr.Row():
-        example3 = gr.Button("🚗 Autonomous Vehicle with Physics Modeling", size="sm") 
-        example4 = gr.Button("🦾 Robotic Arm with Precision Analysis", size="sm")
-
-    # Chat interaction
-    submit_btn.click(agent_chat, [msg, chatbot], [chatbot, msg])
-    msg.submit(agent_chat, [msg, chatbot], [chatbot, msg])
-    
-    # Example button handlers
-    example1.click(lambda: "Generate simulation for warehouse robot with advanced navigation and 50kg payload capacity", None, msg)
-    example2.click(lambda: "Create 4K simulation video for delivery drone with obstacle avoidance and 2-hour flight time", None, msg)
-    example3.click(lambda: "Design autonomous vehicle with comprehensive physics simulation for urban navigation", None, msg)
-    example4.click(lambda: "Build robotic arm simulation with precision movement analysis for manufacturing tasks", None, msg)
-    
-    # Status footer
-    gr.Markdown("""
-    ---
-    **🔗 Enhanced Features Status:**
-    🟢 Advanced Simulation Engine • 🟢 4K Video Generation • 🟢 Physics Modeling • 🟢 Complete Documentation
-    """)
-
-if __name__ == "__main__":
-    app.launch(
-        server_name="0.0.0.0",
-        server_port=7865,
-        show_error=True,
-        share=True
-    ) 

app_new_pattern.py

@@ -1,92 +0,0 @@
-#!/usr/bin/env python3
-"""
-Agent2Robot - MCP Hackathon 2024 Submission
-Following Working MCP Client Pattern
-"""
-
-import os
-import datetime
-import gradio as gr
-
-# Try to import our modules - fallback to mock if not available
-try:
-    from design_tools import VehicleDesigner
-    import main_orchestrator
-    MCP_AVAILABLE = True
-except ImportError:
-    MCP_AVAILABLE = False
-    print("MCP modules not available - using mock mode")
-
-# Current time for agent context
-time = datetime.datetime.now().astimezone().isoformat()
-
-SYSTEM_PROMPT = """You are an AI-powered vehicle design assistant specializing in robotics, drones, and autonomous vehicles. 
-You help users design and optimize vehicles through iterative processes including:
-- Requirements analysis and specification generation
-- Physics simulation and performance modeling  
-- Design optimization and validation
-- Technical documentation and downloads
-
-Be helpful, technical, and thorough in your responses.
-Current time (ISO 8601): {time}"""
-
-def agent_chat(message: str, history: list):
-    """Main chat function that processes user messages and returns responses"""
-    
-    if not MCP_AVAILABLE:
-        # Mock response when MCP is not available
-        return f"🤖 **Agent2Robot Design Assistant**\n\n**Your Request:** {message}\n\n**Mock Response:**\nI would help you design a {message.lower()} vehicle with the following approach:\n\n1. **📋 Requirements Analysis**\n   - Parse your specifications\n   - Identify key performance metrics\n\n2. **🎯 Design Generation**\n   - Create initial specifications\n   - Apply engineering constraints\n\n3. **📊 Simulation & Validation**\n   - Run physics simulations\n   - Optimize performance\n\n4. **📁 Final Deliverables**\n   - Complete technical specifications\n   - Performance reports\n   - Download packages\n\n*Note: MCP server integration will be available when properly configured.*"
-    
-    # Real MCP integration when available
-    try:
-        # Format message with system prompt and history
-        formatted_message = f"{SYSTEM_PROMPT.format(time=time)}\n"
-        formatted_message += "\n".join([f"{x['role']}: {x['content']}" for x in history])
-        formatted_message += f"\nUser Request: {message}"
-        
-        # Process through main orchestrator
-        result = ""
-        for update in main_orchestrator.process_design_request("Robot", message):
-            if update.get("process_log"):
-                result += update["process_log"] + "\n"
-            if update.get("final_specs"):
-                result += f"\n**Final Specifications:**\n{update['final_specs']}\n"
-        
-        return result if result else "Design process completed successfully!"
-        
-    except Exception as e:
-        return f"🚨 **Error:** {str(e)}\n\nFalling back to mock mode. Please check MCP server configuration."
-
-# Create the main interface following working pattern
-chat_interface = gr.ChatInterface(
-    fn=agent_chat,
-    type="messages",
-    examples=[
-        "Design a warehouse robot for package delivery with 50kg payload capacity",
-        "Create a drone for aerial surveillance with 2-hour flight time",
-        "Design an autonomous vehicle for urban navigation",
-        "Build a robotic arm for precision manufacturing tasks"
-    ],
-    title="🤖🚁 Agent2Robot - AI Vehicle Design Assistant",
-    description="""
-    **MCP Hackathon 2024 Submission**
-    
-    AI-powered vehicle design assistant with real-time specification generation, 
-    physics simulation, and optimization. Supports robots, drones, autonomous vehicles, and more.
-    
-    **Features:**
-    • 🎯 Intelligent requirements analysis
-    • 🔄 Iterative design optimization  
-    • 📊 Physics simulation and validation
-    • 📁 Complete technical documentation
-    • 💾 Downloadable specifications
-    """,
-    theme=gr.themes.Soft()
-)
-
-# Main execution following working pattern
-if __name__ == "__main__":
-    chat_interface.launch()
-else:
-    # For HuggingFace Spaces automatic detection
-    demo = chat_interface 

app_simulation_fix.py

@@ -1,390 +0,0 @@
-#!/usr/bin/env python3
-"""
-Agent2Robot - Enhanced Simulation Fix - Real Functionality
-AI-Powered Vehicle Design Assistant with Complete Functionality and Gradio-Compatible UI
-"""
-
-import gradio as gr
-import json
-import tempfile
-import os
-import time
-import datetime
-import random
-from typing import List, Tuple, Optional
-
-# Import MCP modules with error handling
-try:
-    import design_orchestrator as designer
-    import main_orchestrator
-    MCP_AVAILABLE = True
-    print("✅ MCP modules loaded successfully")
-except ImportError as e:
-    MCP_AVAILABLE = False
-    print(f"⚠️ MCP modules not available: {e}")
-
-def generate_detailed_specifications(vehicle_type: str, message: str) -> dict:
-    """Generate detailed technical specifications for the vehicle"""
-    
-    # Determine vehicle type from message
-    if "robot" in message.lower() or "warehouse" in message.lower():
-        vehicle_category = "Warehouse Robot"
-        base_specs = {
-            "vehicle_type": "Autonomous Mobile Robot (AMR)",
-            "primary_function": "Material handling and navigation",
-            "payload_capacity": "50-100 kg",
-            "operating_speed": "1.5 m/s maximum, 0.8 m/s operational",
-            "navigation_system": "LiDAR + Camera fusion with SLAM",
-            "safety_features": ["Emergency stop", "Collision avoidance", "Backup sensors"],
-            "power_system": "48V Lithium-ion, 8-hour operation",
-            "dimensions": "120 x 80 x 150 cm (L x W x H)"
-        }
-    elif "drone" in message.lower() or "uav" in message.lower():
-        vehicle_category = "Delivery Drone"
-        base_specs = {
-            "vehicle_type": "Quadcopter UAV",
-            "primary_function": "Package delivery and surveillance",
-            "payload_capacity": "5-15 kg",
-            "flight_time": "45-120 minutes",
-            "max_speed": "15 m/s horizontal, 5 m/s vertical",
-            "navigation_system": "GPS + Vision-based navigation",
-            "safety_features": ["Return-to-home", "Obstacle avoidance", "Emergency landing"],
-            "power_system": "22.2V LiPo battery",
-            "dimensions": "80 x 80 x 35 cm (rotor span)"
-        }
-    elif "autonomous" in message.lower() or "car" in message.lower():
-        vehicle_category = "Autonomous Vehicle"
-        base_specs = {
-            "vehicle_type": "Level 4 Autonomous Vehicle",
-            "primary_function": "Passenger transport and urban navigation",
-            "passenger_capacity": "4-6 passengers",
-            "operating_speed": "60 km/h urban, 120 km/h highway",
-            "navigation_system": "LiDAR + Camera + Radar fusion",
-            "safety_features": ["Redundant systems", "Emergency braking", "Collision avoidance"],
-            "power_system": "Electric drivetrain, 400V battery",
-            "dimensions": "450 x 180 x 160 cm (L x W x H)"
-        }
-    else:
-        vehicle_category = "Robotic Arm"
-        base_specs = {
-            "vehicle_type": "6-DOF Industrial Manipulator",
-            "primary_function": "Precision manufacturing and assembly",
-            "payload_capacity": "10-20 kg",
-            "reach": "1.2 m working radius",
-            "precision": "±0.1 mm repeatability",
-            "navigation_system": "Vision-guided positioning",
-            "safety_features": ["Force sensing", "Emergency stop", "Collision detection"],
-            "power_system": "24V servo motors",
-            "dimensions": "Base: 60 x 60 cm, Height: 180 cm"
-        }
-    
-    # Generate detailed specifications
-    detailed_specs = {
-        "project_metadata": {
-            "generated_on": datetime.datetime.now().isoformat(),
-            "agent_version": "Agent2Robot v3.0",
-            "mcp_integration": "Enhanced MCP Protocol v2.4",
-            "simulation_engine": "PhysX 4K Ultra-HD"
-        },
-        "vehicle_classification": {
-            "category": vehicle_category,
-            "type": base_specs["vehicle_type"],
-            "compliance_standards": ["ISO 9001", "ISO 26262", "IEC 61508"],
-            "safety_rating": "SIL 3 (Safety Integrity Level)"
-        },
-        "core_specifications": base_specs,
-        "advanced_features": {
-            "ai_processing": "NVIDIA Jetson AGX Xavier",
-            "sensor_suite": ["LiDAR 360°", "4K stereo cameras", "IMU", "GPS/GNSS"],
-            "connectivity": ["5G/LTE", "WiFi 6", "Bluetooth 5.0"],
-            "edge_computing": "Real-time inference <10ms latency"
-        },
-        "simulation_parameters": {
-            "physics_engine": "Bullet Physics 3.25",
-            "rendering_quality": "4K Ultra-HD @ 60 FPS",
-            "simulation_accuracy": "99.7% real-world correlation",
-            "test_scenarios": ["Navigation", "Obstacles", "Weather", "Emergency"],
-            "validation_metrics": ["Path efficiency", "Energy consumption", "Safety margin"]
-        },
-        "performance_metrics": {
-            "navigation_accuracy": f"{random.uniform(98.5, 99.9):.1f}%",
-            "obstacle_avoidance": f"{random.uniform(99.0, 100.0):.1f}%",
-            "energy_efficiency": f"{random.uniform(94.0, 98.0):.1f}%",
-            "reliability_score": f"{random.uniform(96.0, 99.5):.1f}%",
-            "safety_rating": "A+ Grade"
-        },
-        "manufacturing_data": {
-            "estimated_cost": f"${random.randint(50000, 150000):,}",
-            "production_time": f"{random.randint(4, 12)} weeks",
-            "quality_control": "Six Sigma methodology",
-            "certifications": ["CE", "FCC", "RoHS"],
-            "warranty": "3 years comprehensive"
-        },
-        "user_requirements": {
-            "original_request": message,
-            "interpreted_needs": [
-                "High reliability operation",
-                "Advanced safety features", 
-                "Optimal performance efficiency",
-                "Regulatory compliance"
-            ]
-        }
-    }
-    
-    return detailed_specs
-
-def generate_simulation_report(vehicle_type: str, message: str) -> str:
-    """Generate detailed simulation and validation report"""
-    
-    timestamp = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
-    
-    report = f"""# Agent2Robot Advanced Simulation Report
-Generated: {timestamp}
-Simulation Engine: PhysX 4K Ultra-HD v5.2
-MCP Integration: Active
-
-## Executive Summary
-Comprehensive simulation analysis completed for: {message}
-
-## Simulation Configuration
-- **Rendering Quality**: 4K Ultra-HD (3840x2160) @ 60 FPS
-- **Physics Engine**: Advanced Bullet Physics with real-time collision detection
-- **Environment**: Multi-scenario testing environment
-- **Duration**: 45-second comprehensive analysis
-- **File Format**: MP4 H.264 encoding
-
-## Test Scenarios Executed
-
-### 1. Navigation Performance
-- **Environment**: Industrial warehouse simulation
-- **Obstacles**: Dynamic and static object detection
-- **Path Planning**: A* algorithm with real-time optimization
-- **Results**: 
-  - Navigation Accuracy: {random.uniform(98.5, 99.9):.1f}%
-  - Path Efficiency: {random.uniform(94.0, 98.0):.1f}%
-  - Collision Avoidance: 100% Success Rate
-
-### 2. Environmental Stress Testing
-- **Weather Conditions**: Rain, fog, low visibility
-- **Surface Variations**: Wet, uneven, inclined surfaces
-- **Lighting**: Various ambient lighting conditions
-- **Results**:
-  - Performance Degradation: <5% in adverse conditions
-  - Sensor Reliability: {random.uniform(96.0, 99.0):.1f}%
-  - Emergency Response: <200ms reaction time
-
-### 3. Energy Efficiency Analysis
-- **Battery Simulation**: Real-world consumption modeling
-- **Operational Patterns**: Various duty cycles tested
-- **Charging Optimization**: Fast charging compatibility
-- **Results**:
-  - Energy Efficiency: {random.uniform(94.0, 98.0):.1f}%
-  - Operational Range: Extended by 15% through optimization
-  - Charging Time: 25% reduction from baseline
-
-### 4. Safety Validation
-- **Emergency Scenarios**: 50+ critical situations tested
-- **Failsafe Systems**: Redundancy verification
-- **Human Interaction**: Proximity detection accuracy
-- **Results**:
-  - Safety Rating: A+ Grade
-  - Emergency Response: 100% success rate
-  - Compliance: Meets all ISO 26262 requirements
-
-## Performance Metrics Dashboard
-
-```
-Navigation Accuracy:    ████████████████████ {random.uniform(98.5, 99.9):.1f}%
-Obstacle Avoidance:     ████████████████████ 100.0%
-Energy Efficiency:      ███████████████████  {random.uniform(94.0, 98.0):.1f}%
-Safety Score:           ████████████████████ A+ Grade
-Reliability Index:      ███████████████████  {random.uniform(96.0, 99.5):.1f}%
-```
-
-## Advanced Analytics
-
-### Real-time Telemetry
-- **Sensor Fusion**: 12 simultaneous data streams processed
-- **AI Decision Making**: Neural network inference <10ms
-- **Predictive Maintenance**: 30-day failure prediction accuracy
-- **Adaptive Learning**: Continuous improvement algorithms active
-
-### Simulation Video Details
-- **File Size**: ~85MB (4K 45-second duration)
-- **Encoding**: H.264 with hardware acceleration
-- **Framerate**: Consistent 60 FPS throughout
-- **Quality**: Photorealistic rendering with accurate physics
-
-## Compliance & Certification
-
-### Standards Met
-- ✅ ISO 9001:2015 (Quality Management)
-- ✅ ISO 26262 (Functional Safety)
-- ✅ IEC 61508 (Electrical Safety)
-- ✅ IEEE 1012 (Software Verification)
-
-### Testing Validation
-- **Independent Verification**: Third-party testing completed
-- **Regulatory Approval**: Pre-certification analysis passed
-- **Insurance Rating**: Premium tier qualification
-- **Operational Clearance**: Ready for deployment
-
-## Recommendations
-
-1. **Optimization Opportunities**: Energy system can be improved by 3-5%
-2. **Maintenance Schedule**: Preventive maintenance every 2000 hours
-3. **Upgrade Path**: Sensor package upgrade available Q2 2025
-4. **Training Requirements**: 40-hour operator certification recommended
-
-## Conclusion
-Simulation validation confirms the design meets all specified requirements with exceptional performance metrics. The vehicle demonstrates superior reliability, safety, and efficiency suitable for immediate deployment.
-
----
-*This report was generated by Agent2Robot Advanced Simulation Engine v3.0*
-*For technical inquiries, contact: simulation@agent2robot.ai*
-"""
-    
-    return report
-
-def agent_chat(message: str, history: list) -> Tuple[str, str]:
-    """Enhanced chat function with comprehensive vehicle design"""
-    
-    if not message.strip():
-        return history + [["Please describe your vehicle requirements for AI-powered design and simulation.", ""]]
-    
-    # Generate detailed specifications
-    specs = generate_detailed_specifications("vehicle", message)
-    
-    # Generate simulation report
-    simulation_report = generate_simulation_report("vehicle", message)
-    
-    # Determine vehicle type for response
-    if "robot" in message.lower() or "warehouse" in message.lower():
-        vehicle_type = "Warehouse Robot"
-        icon = "🤖"
-    elif "drone" in message.lower() or "uav" in message.lower():
-        vehicle_type = "Delivery Drone"
-        icon = "🚁"
-    elif "autonomous" in message.lower() or "car" in message.lower():
-        vehicle_type = "Autonomous Vehicle"
-        icon = "🚗"
-    elif "arm" in message.lower() or "manipulator" in message.lower():
-        vehicle_type = "Robotic Arm"
-        icon = "🦾"
-    else:
-        vehicle_type = "Custom Vehicle"
-        icon = "🚀"
-
-    # Generate comprehensive response
-    response = f"""{icon} **Agent2Robot Advanced Vehicle Design Complete**
-
-**🎯 Your Request:** {message}
-**🔧 Vehicle Type Designed:** {vehicle_type}
-
-## 🚀 AI Design Process Complete
-**✅ Requirements Analysis:** Advanced NLP processing interpreted your specifications
-**✅ Engineering Design:** Optimal configuration generated using AI algorithms  
-**✅ Safety Validation:** Comprehensive safety analysis completed
-**✅ Performance Optimization:** Multi-objective optimization applied
-**✅ Compliance Verification:** All regulatory standards validated
-
-## 🎬 Advanced Simulation Features Executed
-**🎥 4K Ultra-HD Video Generation:** 
-- Resolution: 3840x2160 @ 60 FPS
-- Duration: 45-second comprehensive scenarios
-- Physics: Advanced Bullet Physics engine v3.25
-
-**📊 Performance Validation Results:**
-**🎯 Navigation Accuracy:** {specs['performance_metrics']['navigation_accuracy']}
-**🛡️ Obstacle Avoidance:** {specs['performance_metrics']['obstacle_avoidance']}  
-**⚡ Energy Efficiency:** {specs['performance_metrics']['energy_efficiency']}
-**🔒 Safety Rating:** {specs['performance_metrics']['safety_rating']}
-**📈 Reliability Score:** {specs['performance_metrics']['reliability_score']}
-
-## 🏭 Manufacturing & Deployment Ready
-**💰 Estimated Cost:** {specs['manufacturing_data']['estimated_cost']}
-**⏱️ Production Time:** {specs['manufacturing_data']['production_time']}
-**📜 Certifications:** CE, FCC, RoHS, UL Listed
-**🛡️ Warranty:** {specs['manufacturing_data']['warranty']}
-
-## 📁 Technical Documentation Generated
-**🗂️ Complete specifications:** {len(json.dumps(specs, indent=2))} characters of detailed JSON data
-**📊 Simulation report:** {len(simulation_report)} characters of comprehensive analysis
-**🔬 Advanced features:** All systems validated and ready for deployment
-
-Your {vehicle_type.lower()} design is now complete with all advanced simulation features validated and ready for manufacturing."""
-
-    new_history = history + [[message, response]]
-    return new_history, ""
-
-# Create the interface
-with gr.Blocks(theme=gr.themes.Soft(), title="Agent2Robot Enhanced Simulation") as app:
-    gr.Markdown("""
-    # 🤖🚁 Agent2Robot Enhanced - AI Vehicle Design with Advanced Simulation
-    
-    **🏆 MCP Hackathon 2024 - Complete Functionality Implementation**
-    
-    Complete AI-powered vehicle design assistant with **fully operational advanced simulation features**. Get comprehensive vehicle designs including 4K simulations, physics modeling, and detailed technical documentation.
-    
-    **🎬 Enhanced Simulation Features:**
-    • **🎥 4K Ultra-HD Video Generation** - High-fidelity physics simulation (3840x2160 @ 60 FPS)
-    • **⚙️ Advanced Physics Engine** - Real-time Bullet Physics modeling with collision detection
-    • **🌍 Multi-Scenario Testing** - Navigation, obstacles, weather, emergency protocols
-    • **📊 Performance Validation** - Comprehensive metrics (99.7% navigation accuracy)
-    • **🗂️ Complete Documentation** - Detailed specifications and technical reports
-    • **🔬 Environmental Modeling** - Industrial, outdoor, adverse conditions simulation
-    
-    **🚀 Supported Vehicle Types:**
-    Warehouse robots • Delivery drones • Autonomous vehicles • Robotic arms • Custom systems
-    """)
-    
-    chatbot = gr.Chatbot(
-        label="🎬 Agent2Robot Advanced Simulation Chat",
-        height=500,
-        show_label=True
-    )
-    
-    with gr.Row():
-        msg = gr.Textbox(
-            label="💬 Describe your vehicle requirements for advanced simulation",
-            placeholder="Example: Design warehouse robot with advanced navigation and 50kg payload capacity",
-            lines=2,
-            scale=4
-        )
-        submit_btn = gr.Button("🚀 Generate Design + Simulation", variant="primary", scale=1)
-    
-    # Enhanced examples section
-    gr.Markdown("### 🎯 Example Requests (Click to Try)")
-    
-    with gr.Row():
-        example1 = gr.Button("🏭 Warehouse Robot with Advanced Navigation", size="sm")
-        example2 = gr.Button("🚁 Delivery Drone with 4K Simulation", size="sm")
-        
-    with gr.Row():
-        example3 = gr.Button("🚗 Autonomous Vehicle with Physics Modeling", size="sm") 
-        example4 = gr.Button("🦾 Robotic Arm with Precision Analysis", size="sm")
-
-    # Chat interaction
-    submit_btn.click(agent_chat, [msg, chatbot], [chatbot, msg])
-    msg.submit(agent_chat, [msg, chatbot], [chatbot, msg])
-    
-    # Example button handlers
-    example1.click(lambda: "Generate simulation for warehouse robot with advanced navigation and 50kg payload capacity", None, msg)
-    example2.click(lambda: "Create 4K simulation video for delivery drone with obstacle avoidance and 2-hour flight time", None, msg)
-    example3.click(lambda: "Design autonomous vehicle with comprehensive physics simulation for urban navigation", None, msg)
-    example4.click(lambda: "Build robotic arm simulation with precision movement analysis for manufacturing tasks", None, msg)
-    
-    # Status footer
-    gr.Markdown("""
-    ---
-    **🔗 Enhanced Features Status:**
-    🟢 Advanced Simulation Engine • 🟢 4K Video Generation • 🟢 Physics Modeling • 🟢 Complete Documentation
-    """)
-
-if __name__ == "__main__":
-    app.launch(
-        server_name="0.0.0.0",
-        server_port=7860,
-        show_error=True,
-        share=True
-    ) 

app_step2.py

@@ -1,16 +0,0 @@
-#!/usr/bin/env python3
-"""
-Agent2Robot - Debug Step 2: Add Blocks Parameters
-"""
-
-import gradio as gr
-
-# Step 2: Add gr.Blocks() parameters one by one
-with gr.Blocks(
-    title="🤖🚁 Agent2Robot - MCP Hackathon 2024",
-    theme=gr.themes.Soft()
-) as demo:
-    gr.Markdown("# Step 2: Blocks Parameters Test")
-    test_input = gr.Textbox(label="Test", value="Hello World")
-
-demo.launch() 

app_working_simulation.py

@@ -1,394 +0,0 @@
-#!/usr/bin/env python3
-"""
-Agent2Robot - Enhanced Interface with Working Simulation Features
-AI-Powered Vehicle Design Assistant with Complete UI and Operational Advanced Simulation
-"""
-
-import gradio as gr
-import json
-import tempfile
-import os
-from typing import List, Tuple, Optional
-
-# Import MCP modules with error handling
-try:
-    import design_orchestrator as designer
-    import main_orchestrator
-    MCP_AVAILABLE = True
-    print("SUCCESS: MCP modules loaded successfully")
-except ImportError as e:
-    MCP_AVAILABLE = False
-    print(f"WARNING: MCP modules not available: {e}")
-
-def agent_chat(message: str, history: list) -> Tuple[str, Optional[str], Optional[str], Optional[str]]:
-    """Enhanced chat function with file generation and working simulation features"""
-    
-    if not MCP_AVAILABLE:
-        return ("""❌ **MCP Integration Unavailable**
-        
-This demo requires the MCP (Model Context Protocol) modules to be available.
-
-**🎬 Simulation Features Would Include:**
-• 4K Ultra-HD video generation
-• Advanced physics simulation
-• Multi-scenario testing
-• Performance validation
-• File package downloads
-
-**📝 Mock Design Process:**
-For your request: "{}"
-
-A complete vehicle design would be generated with:
-- Technical specifications
-- Physics simulation video  
-- Manufacturing documentation
-- Performance analysis
-
-**Note:** Enable MCP integration for full functionality.""".format(message), None, None, None)
-    
-    # Enhanced demo message
-    demo_msg = """
-🎬 **Advanced Simulation Features Demo:**
-
-**🚀 Simulation Engine Status:** Ready ✅
-**🎥 Video Generation:** 4K capability enabled
-**📊 Physics Modeling:** Advanced Bullet Physics
-**🔬 Validation System:** Multi-scenario testing active
-
-**Simulation would include:**
-- High-fidelity 3D rendering
-- Real-time physics simulation
-- Environmental interaction modeling
-- Performance validation metrics
-- Multi-scenario testing (navigation, obstacles, weather)
-
-⚠️ *Note: Full MCP simulation requires server connection.*"""
-    
-    # Real MCP integration with focus on simulation
-    try:
-        # Determine vehicle type
-        message_lower = message.lower()
-        if "robot" in message_lower or "warehouse" in message_lower:
-            vehicle_type = "Robot"
-        elif "drone" in message_lower or "uav" in message_lower:
-            vehicle_type = "Drone"
-        elif "autonomous" in message_lower or "car" in message_lower:
-            vehicle_type = "Autonomous Vehicle"
-        elif "arm" in message_lower or "manipulator" in message_lower:
-            vehicle_type = "Robotic Arm"
-        else:
-            vehicle_type = "Robot"
-
-        # Process design request and capture simulation
-        final_result = f"""🚀 **Agent2Robot Advanced Simulation Process**
-
-**🎯 Vehicle Type:** {vehicle_type}
-**📝 Requirements:** {message}
-
-🔄 **Processing Design with Advanced Simulation...**
-"""
-
-        design_data = None
-        simulation_info = None
-        
-        # Get design and simulation data
-        for update in main_orchestrator.process_design_request(vehicle_type, message):
-            if update.get("process_log"):
-                final_result += "\n\n" + update["process_log"]
-            
-            if update.get("final_specs"):
-                try:
-                    if isinstance(update["final_specs"], str):
-                        design_data = json.loads(update["final_specs"])
-                    else:
-                        design_data = update["final_specs"]
-                except:
-                    design_data = update["final_specs"]
-                    
-            if update.get("simulation_video"):
-                simulation_info = update["simulation_video"]
-
-        # Enhanced simulation output with file generation
-        if design_data and MCP_AVAILABLE:
-            # Generate enhanced simulation with actual MCP client
-            simulation_data = designer.mcp_client.generate_simulation_video(design_data)
-            
-            # Generate downloadable files
-            files_package = designer.mcp_client.generate_downloadable_files(design_data)
-            
-            final_result += f"""
-
-🎬 **ADVANCED SIMULATION FEATURES ACTIVATED**
-═══════════════════════════════════════════════════════
-
-{simulation_info if simulation_info else simulation_data.get('video_info', 'Simulation generated successfully')}
-
-🔧 **Enhanced Simulation Capabilities:**
-✅ **4K Ultra-HD Rendering** - Photorealistic 3D visualization
-✅ **Advanced Physics Engine** - Real-time Bullet Physics simulation
-✅ **Multi-Scenario Testing** - Navigation, obstacles, weather conditions
-✅ **Performance Validation** - Comprehensive metrics and analytics
-✅ **Environmental Modeling** - Industrial, outdoor, adverse conditions
-✅ **Real-time Telemetry** - Live performance monitoring and diagnostics
-
-📊 **Simulation Validation Results:**
-• **Navigation Accuracy:** 99.7% ✅
-• **Obstacle Avoidance:** 100% Success Rate ✅
-• **Energy Efficiency:** 96.5% Optimized ✅
-• **Response Time:** <25ms Emergency Protocols ✅
-• **Thermal Management:** Optimal Operating Range ✅
-• **Reliability Index:** 99.9% ✅
-
-🎯 **Simulation Features Working:**
-• Physics-based movement simulation
-• Environmental interaction modeling
-• Performance metrics visualization
-• Real-time sensor data overlay
-• Collision detection and response
-• Path planning visualization
-• Energy consumption tracking
-• Thermal management simulation
-
-📁 **Generated File Package:**
-• **Technical Specifications:** {files_package.get('specifications', {}).get('size_kb', 25.4):.1f} KB JSON
-• **Simulation Report:** {files_package.get('simulation', {}).get('size_kb', 156.8):.1f} KB Report
-• **Complete Package:** {files_package.get('documentation', {}).get('size_kb', 2.1):.1f} MB ZIP
-
-**✅ ADVANCED SIMULATION FEATURES FULLY OPERATIONAL!**
-
-The simulation system has successfully generated comprehensive validation data for your {vehicle_type.lower()} design with all advanced features working correctly."""
-
-            # Create temporary files for downloads
-            try:
-                # Technical specifications file
-                specs_data = {
-                    "vehicle_type": vehicle_type,
-                    "requirements": message,
-                    "design_specifications": design_data,
-                    "simulation_results": simulation_data,
-                    "validation_metrics": {
-                        "navigation_accuracy": "99.7%",
-                        "obstacle_avoidance": "100%",
-                        "energy_efficiency": "96.5%",
-                        "response_time": "<25ms",
-                        "reliability": "99.9%"
-                    },
-                    "file_info": files_package
-                }
-                
-                specs_file = tempfile.NamedTemporaryFile(mode='w', suffix='.json', delete=False)
-                json.dump(specs_data, specs_file, indent=2)
-                specs_file.close()
-                
-                # Simulation report file
-                sim_report = f"""Agent2Robot Simulation Report
-Vehicle Type: {vehicle_type}
-Requirements: {message}
-
-SIMULATION RESULTS:
-{simulation_data.get('video_info', 'Advanced simulation completed')}
-
-VALIDATION METRICS:
-- Navigation Accuracy: 99.7%
-- Obstacle Avoidance: 100% Success Rate  
-- Energy Efficiency: 96.5% Optimized
-- Response Time: <25ms Emergency Protocols
-- Reliability Index: 99.9%
-
-FEATURES VALIDATED:
-✅ 4K Ultra-HD Rendering
-✅ Advanced Physics Engine
-✅ Multi-Scenario Testing
-✅ Performance Validation
-✅ Environmental Modeling
-✅ Real-time Telemetry
-
-Generated: {files_package.get('timestamp', 'Now')}
-"""
-                
-                sim_file = tempfile.NamedTemporaryFile(mode='w', suffix='.txt', delete=False)
-                sim_file.write(sim_report)
-                sim_file.close()
-                
-                # Complete package info
-                package_info = f"""Agent2Robot Complete Design Package
-
-Vehicle: {vehicle_type}
-Request: {message}
-
-PACKAGE CONTENTS:
-- Technical Specifications (JSON)
-- Simulation Report and Data
-- Performance Validation Results
-- Manufacturing Documentation
-- Compliance Information
-
-SIMULATION FEATURES:
-- 4K Ultra-HD Video Generation
-- Advanced Physics Modeling
-- Multi-Environment Testing
-- Comprehensive Validation
-
-Package Size: {files_package.get('total_size_mb', 2.1):.1f} MB
-Generated: {files_package.get('timestamp', 'Now')}
-"""
-                
-                pkg_file = tempfile.NamedTemporaryFile(mode='w', suffix='.txt', delete=False)
-                pkg_file.write(package_info)
-                pkg_file.close()
-                
-                return (final_result, specs_file.name, sim_file.name, pkg_file.name)
-                
-            except Exception as file_error:
-                return (final_result + f"\n\n⚠️ File generation completed with note: {str(file_error)}", None, None, None)
-        
-        return (final_result, None, None, None)
-        
-    except Exception as e:
-        return (f"""❌ **Simulation Error Detected**
-
-**Error:** {str(e)}
-
-🔄 **Simulation Recovery Mode:**
-The advanced simulation features encountered an error, but the system is designed to handle this gracefully.
-
-**🎬 Simulation Features Status:**
-• **Engine:** Advanced MCP Physics Engine v3.0
-• **Rendering:** 4K capability available
-• **Physics:** Bullet Physics integration ready
-• **Scenarios:** Multi-environment testing prepared
-• **Validation:** Performance metrics system active
-
-**🛠️ Alternative Simulation Approach:**
-The simulation engine can still provide comprehensive analysis for: "{message}"
-
-**Recommended Action:** Try the simulation request again or contact support for advanced debugging.""", None, None, None)
-
-# Create enhanced interface with download center and working simulation
-def create_enhanced_interface():
-    """Create enhanced Gradio interface with download capabilities and working simulation"""
-    
-    with gr.Blocks(theme=gr.themes.Soft(), title="Agent2Robot Enhanced Simulation") as app:
-        gr.Markdown("""
-        # 🤖🚁 Agent2Robot Enhanced - AI Vehicle Design with Advanced Simulation
-        
-        **🏆 MCP Hackathon 2024 - Working Advanced Simulation System**
-        
-        Complete AI-powered vehicle design assistant with **fully operational advanced simulation features**. Get comprehensive vehicle designs including 4K simulations, physics modeling, and downloadable technical documentation.
-        
-        **🎬 Verified Working Simulation Features:**
-        • **🎥 4K Ultra-HD Video Generation** - High-fidelity physics simulation (3840x2160 @ 60 FPS)
-        • **⚙️ Advanced Physics Engine** - Real-time Bullet Physics modeling with collision detection
-        • **🌍 Multi-Scenario Testing** - Navigation, obstacles, weather, emergency protocols
-        • **📊 Performance Validation** - Comprehensive metrics (99.7% navigation accuracy)
-        • **🗂️ File Package Generation** - Complete downloadable specifications and reports
-        • **🔬 Environmental Modeling** - Industrial, outdoor, adverse conditions simulation
-        
-        **🔧 Advanced Capabilities:**
-        • Intelligent requirements analysis • MCP-powered optimization • Real-time physics simulation
-        • Complete technical documentation • Manufacturing specifications • Cost analysis & ROI
-        
-        **🚀 Supported Vehicle Types:**
-        Warehouse robots • Delivery drones • Autonomous vehicles • Robotic arms • Custom systems
-        """)
-        
-        with gr.Row():
-            with gr.Column(scale=2):
-                chatbot = gr.Chatbot(
-                    label="🎬 Agent2Robot Advanced Simulation Chat",
-                    height=600,
-                    show_label=True
-                )
-                
-                with gr.Row():
-                    msg = gr.Textbox(
-                        label="💬 Describe your vehicle requirements for advanced simulation",
-                        placeholder="Example: Design warehouse robot with 4K simulation, advanced physics, and multi-scenario testing",
-                        lines=3,
-                        scale=4
-                    )
-                    submit_btn = gr.Button("🚀 Generate Design + Simulation", variant="primary", scale=1)
-            
-            with gr.Column(scale=1):
-                gr.Markdown("### 📥 Advanced Download Center")
-                
-                specs_file = gr.File(
-                    label="📄 Technical Specifications & Simulation Data",
-                    visible=True
-                )
-                
-                simulation_file = gr.File(
-                    label="🎥 Simulation Report & Validation Results",
-                    visible=True
-                )
-                
-                package_file = gr.File(
-                    label="📦 Complete Design & Simulation Package",
-                    visible=True
-                )
-                
-                gr.Markdown("""
-                **🎬 Advanced Simulation Downloads:**
-                - **Specifications:** Complete technical specs with simulation data
-                - **Simulation Report:** Physics validation and performance metrics
-                - **Complete Package:** Full design documentation with simulation results
-                
-                **✅ Working Features:**
-                • 4K video generation capabilities
-                • Advanced physics simulation data
-                • Multi-scenario test results
-                • Performance validation metrics
-                • Environmental modeling data
-                """)
-        
-        # Enhanced examples focused on simulation
-        gr.Examples(
-            examples=[
-                "Design warehouse robot with advanced 4K simulation, physics modeling, and multi-scenario testing",
-                "Create delivery drone with comprehensive simulation including weather conditions and obstacle avoidance", 
-                "Build autonomous vehicle with advanced physics simulation and performance validation metrics",
-                "Generate robotic arm with precision simulation, collision detection, and environmental modeling",
-                "Develop search-rescue robot with advanced simulation for disaster response scenarios",
-                "Create agricultural drone with comprehensive flight simulation and environmental interaction modeling"
-            ],
-            inputs=msg,
-            label="🎯 Advanced Simulation Examples"
-        )
-        
-        def enhanced_chat_response(message, history):
-            """Handle chat interaction with file generation and working simulation"""
-            if not message.strip():
-                return history, "", None, None, None
-            
-            # Get enhanced response with files and simulation
-            response, json_file, sim_file, pkg_file = agent_chat(message, history)
-            
-            # Update chat history
-            history = history + [[message, response]]
-            
-            return history, "", json_file, sim_file, pkg_file
-        
-        # Wire up the enhanced interface
-        submit_btn.click(
-            enhanced_chat_response,
-            inputs=[msg, chatbot],
-            outputs=[chatbot, msg, specs_file, simulation_file, package_file]
-        )
-        
-        msg.submit(
-            enhanced_chat_response,
-            inputs=[msg, chatbot], 
-            outputs=[chatbot, msg, specs_file, simulation_file, package_file]
-        )
-    
-    return app
-
-if __name__ == "__main__":
-    # Create and launch the enhanced interface with working simulation
-    app = create_enhanced_interface()
-    app.launch(
-        server_name="0.0.0.0",
-        server_port=7863,
-        show_error=True,
-        share=True
-    )

mcp_client_backup.py

@@ -1,400 +0,0 @@
-"""
-MCP Client for Agent2Robot - Enhanced Version
-Handles communication with MCP servers for vehicle design with advanced capabilities
-"""
-
-import json
-import tempfile
-import os
-import base64
-from datetime import datetime
-from typing import Dict, List, Any, Optional, Tuple
-import io
-
-class MCPClient:
-    """Enhanced client for interacting with MCP servers with file generation and advanced features"""
-    
-    def __init__(self):
-        self.connected = False
-        self.server_capabilities = {}
-        self.session_id = f"agent2robot_{int(datetime.now().timestamp())}"
-        # Auto-connect on initialization
-        self.connect()
-    
-    def connect(self, server_url: str = None) -> bool:
-        """Connect to MCP server with enhanced capabilities"""
-        # Simulate connection with enhanced capabilities
-        self.connected = True
-        self.server_capabilities = {
-            "design_optimization": True,
-            "performance_analysis": True,
-            "specification_generation": True,
-            "validation": True,
-            "simulation_generation": True,
-            "file_export": True,
-            "manufacturing_specs": True,
-            "3d_modeling": True,
-            "cost_estimation": True
-        }
-        return True
-    
-    def generate_design(self, vehicle_type: str, requirements: str) -> Dict[str, Any]:
-        """Generate vehicle design using enhanced MCP server"""
-        if not self.connected:
-            self.connect()
-        
-        # Enhanced design data with more detailed specifications
-        design_data = {
-            "vehicle_type": vehicle_type,
-            "requirements": requirements,
-            "design_id": f"{vehicle_type.lower().replace(' ', '_')}_{self.session_id}",
-            "optimization_score": 95,
-            "generated_features": self._get_vehicle_specific_features(vehicle_type),
-            "performance_metrics": self._get_performance_metrics(vehicle_type),
-            "technical_specs": self._get_technical_specifications(vehicle_type),
-            "manufacturing_specs": self._get_manufacturing_specifications(vehicle_type),
-            "cost_analysis": self._get_cost_analysis(vehicle_type),
-            "simulation_ready": True,
-            "files_generated": True,
-            "timestamp": datetime.now().isoformat()
-        }
-        
-        return design_data
-    
-    def generate_simulation_video(self, design_specs: Dict[str, Any]) -> Dict[str, Any]:
-        """Generate enhanced simulation video and metadata"""
-        vehicle_type = design_specs.get("vehicle_type", "robot").lower()
-        design_id = design_specs.get("design_id", "unknown")
-        
-        simulation_data = {
-            "video_info": f"""🎬 MCP ADVANCED SIMULATION ENGINE - GENERATION COMPLETE
-═══════════════════════════════════════════════════════
-
-🚀 HIGH-FIDELITY VEHICLE SIMULATION GENERATED!
-
-📋 SIMULATION PARAMETERS:
-   • Vehicle Type: {design_specs.get('vehicle_type', 'Unknown')}
-   • Design ID: {design_id}
-   • Simulation Engine: MCP Physics Engine v3.0 (GPU-Accelerated)
-   • Generation Time: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}
-   • Status: ✅ SUCCESSFULLY GENERATED
-
-🎥 VIDEO SPECIFICATIONS:
-   • Duration: 45 seconds (Ultra-High Detail Animation)
-   • Resolution: 4K (3840x2160) @ 60 FPS
-   • Format: MP4 with HEVC encoding
-   • File Size: ~85 MB (Production Quality)
-   • Audio: Spatial audio simulation included
-
-🔧 ADVANCED SIMULATION FEATURES:
-   ✅ Photorealistic 3D rendering
-   ✅ Real-time physics simulation (Bullet Physics)
-   ✅ Environmental interaction modeling
-   ✅ Multi-sensor data visualization
-   ✅ Dynamic obstacle scenarios
-   ✅ Weather condition variations
-   ✅ Performance stress testing
-   ✅ Failure mode analysis
-   ✅ Energy consumption tracking
-   ✅ Thermal management simulation
-
-🎯 SIMULATION SCENARIOS INCLUDED:
-   🏭 Industrial environment navigation
-   🌧️ Adverse weather operation
-   🚧 Complex obstacle courses
-   ⚡ Emergency response protocols
-   🔋 Battery optimization cycles
-   🛠️ Maintenance access validation
-   📊 Real-time telemetry display
-
-🌟 MCP INTEGRATION HIGHLIGHTS:
-   • Server-validated physics parameters
-   • Context-aware scenario generation
-   • Real-time performance validation
-   • Automated quality assurance
-   • Multi-environment testing
-   • Compliance verification
-
-📈 VALIDATION METRICS:
-   • Navigation Accuracy: 99.7%
-   • Obstacle Avoidance: 100% Success
-   • Energy Efficiency: 96.5% Optimized
-   • Response Time: <25ms Average
-   • Safety Score: A+ Rating
-   • Reliability Index: 99.9%
-
-🎭 DEPLOYMENT READY - ALL TESTS PASSED!
-══════════════════════════════════════════════════════��""",
-            
-            "file_path": f"simulations/{design_id}_simulation.mp4",
-            "thumbnail_path": f"simulations/{design_id}_thumbnail.jpg",
-            "metadata": {
-                "duration": 45,
-                "resolution": "4K",
-                "fps": 60,
-                "filesize_mb": 85,
-                "scenarios": ["navigation", "obstacles", "weather", "emergency"],
-                "validation_passed": True
-            }
-        }
-        
-        return simulation_data
-    
-    def generate_downloadable_files(self, design_specs: Dict[str, Any]) -> Dict[str, Any]:
-        """Generate comprehensive downloadable file package"""
-        design_id = design_specs.get("design_id", "unknown")
-        vehicle_type = design_specs.get("vehicle_type", "Unknown")
-        
-        # Generate JSON specifications
-        json_content = self._create_detailed_json_specs(design_specs)
-        
-        # Generate manufacturing files
-        manufacturing_files = self._create_manufacturing_files(design_specs)
-        
-        # Generate documentation
-        documentation = self._create_technical_documentation(design_specs)
-        
-        files_package = {
-            "specifications": {
-                "filename": f"{design_id}_specifications.json",
-                "content": json_content,
-                "size_kb": len(json_content.encode()) / 1024,
-                "description": "Complete technical specifications in JSON format"
-            },
-            "manufacturing": {
-                "filename": f"{design_id}_manufacturing.zip",
-                "content": manufacturing_files,
-                "size_kb": 250,  # Simulated size
-                "description": "CAD files, assembly instructions, and part lists"
-            },
-            "documentation": {
-                "filename": f"{design_id}_documentation.pdf",
-                "content": documentation,
-                "size_kb": 150,  # Simulated size
-                "description": "User manual, maintenance guide, and safety protocols"
-            },
-            "simulation": {
-                "filename": f"{design_id}_simulation.mp4",
-                "content": "video_binary_data",
-                "size_kb": 87000,  # ~85MB
-                "description": "4K simulation video with performance validation"
-            }
-        }
-        
-        return files_package
-    
-    def _get_vehicle_specific_features(self, vehicle_type: str) -> List[str]:
-        """Get vehicle-specific features based on type"""
-        features_map = {
-            "Robot": [
-                "Advanced SLAM navigation system",
-                "Multi-sensor obstacle avoidance",
-                "Adaptive payload management",
-                "Swarm communication protocols",
-                "Energy-efficient locomotion",
-                "Modular tool attachment system",
-                "Real-time path optimization",
-                "Fault-tolerant control systems"
-            ],
-            "Drone": [
-                "GPS-denied navigation capability",
-                "Advanced flight control algorithms",
-                "Collision avoidance radar",
-                "Weatherproof design architecture",
-                "Extended flight time optimization",
-                "High-resolution imaging systems",
-                "Emergency landing protocols",
-                "Multi-rotor redundancy"
-            ],
-            "Autonomous Vehicle": [
-                "Level 4 autonomous driving",
-                "360-degree sensor fusion",
-                "Passenger safety systems",
-                "Predictive maintenance",
-                "V2X communication protocols",
-                "Emergency response automation",
-                "Comfort optimization systems",
-                "Cybersecurity hardening"
-            ],
-            "Robotic Arm": [
-                "Sub-millimeter precision control",
-                "Force/torque feedback systems",
-                "Collision detection and avoidance",
-                "Tool quick-change mechanism",
-                "Vision-guided operation",
-                "Learning-based optimization",
-                "Safety-rated operation",
-                "Multi-arm coordination"
-            ]
-        }
-        return features_map.get(vehicle_type, features_map["Robot"])
-    
-    def _get_performance_metrics(self, vehicle_type: str) -> Dict[str, str]:
-        """Get performance metrics specific to vehicle type"""
-        metrics_map = {
-            "Robot": {
-                "speed": "2.5 m/s maximum velocity",
-                "efficiency": "96% energy efficiency",
-                "reliability": "99.9% uptime rating",
-                "maintainability": "30-minute service cycles",
-                "payload": "50kg maximum capacity",
-                "battery_life": "8 hours continuous operation"
-            },
-            "Drone": {
-                "speed": "25 m/s cruise velocity",
-                "efficiency": "94% propulsion efficiency",
-                "reliability": "99.5% mission success rate",
-                "maintainability": "15-minute pre-flight check",
-                "flight_time": "120 minutes maximum",
-                "range": "15km operational radius"
-            },
-            "Autonomous Vehicle": {
-                "speed": "120 km/h maximum safe speed",
-                "efficiency": "92% energy recovery",
-                "reliability": "99.99% safety rating",
-                "maintainability": "10,000km service intervals",
-                "passengers": "8 passenger capacity",
-                "range": "500km per charge"
-            },
-            "Robotic Arm": {
-                "speed": "1.5 m/s end-effector velocity",
-                "efficiency": "98% motion efficiency",
-                "reliability": "99.8% precision consistency",
-                "maintainability": "Annual calibration cycle",
-                "precision": "±0.05mm repeatability",
-                "payload": "25kg maximum load"
-            }
-        }
-        return metrics_map.get(vehicle_type, metrics_map["Robot"])
-    
-    def _get_technical_specifications(self, vehicle_type: str) -> Dict[str, str]:
-        """Get detailed technical specifications"""
-        base_specs = {
-            "power_system": "Advanced lithium-ion battery with BMS",
-            "sensors": "LiDAR, stereo cameras, IMU, GPS, ultrasonic",
-            "communication": "5G, WiFi 6, Bluetooth 5.0, CAN bus",
-            "processing": "NVIDIA Jetson AGX with edge AI acceleration",
-            "materials": "Carbon fiber composite with aluminum frame",
-            "safety": "ISO 26262 compliant safety systems",
-            "environmental": "IP67 weatherproof rating",
-            "certifications": "CE, FCC, UL listed components"
-        }
-        
-        # Add vehicle-specific specs
-        if vehicle_type == "Robot":
-            base_specs.update({
-                "locomotion": "4-wheel differential drive",
-                "sensors_additional": "360° LiDAR, depth cameras",
-                "manipulation": "6-DOF robotic arm optional"
-            })
-        elif vehicle_type == "Drone":
-            base_specs.update({
-                "propulsion": "8-rotor configuration with redundancy",
-                "gimbal": "3-axis stabilized camera mount",
-                "landing": "Precision landing sensors"
-            })
-        elif vehicle_type == "Autonomous Vehicle":
-            base_specs.update({
-                "drivetrain": "Electric AWD with torque vectoring",
-                "interior": "Climate control and entertainment systems",
-                "charging": "150kW DC fast charging capability"
-            })
-        elif vehicle_type == "Robotic Arm":
-            base_specs.update({
-                "joints": "7-DOF with harmonic drive reducers",
-                "end_effector": "Universal gripper with force sensing",
-                "mounting": "Floor, ceiling, or mobile base options"
-            })
-        
-        return base_specs
-    
-    def _get_manufacturing_specifications(self, vehicle_type: str) -> Dict[str, Any]:
-        """Get manufacturing and production specifications"""
-        return {
-            "production_method": "Lean manufacturing with Industry 4.0 integration",
-            "quality_control": "Six Sigma quality management system",
-            "testing_protocol": "100% functional testing with automated validation",
-            "lead_time": "6-8 weeks from order to delivery",
-            "scalability": "Production capacity: 100-1000 units/month",
-            "supply_chain": "Certified tier-1 suppliers with redundancy",
-            "assembly_time": "4-6 hours per unit (automated assembly)",
-            "customization": "Modular design allows 50+ configuration options"
-        }
-    
-    def _get_cost_analysis(self, vehicle_type: str) -> Dict[str, Any]:
-        """Get comprehensive cost analysis"""
-        base_costs = {
-            "Robot": {"development": 150000, "unit_cost": 25000, "maintenance_annual": 2500},
-            "Drone": {"development": 200000, "unit_cost": 15000, "maintenance_annual": 1500},
-            "Autonomous Vehicle": {"development": 500000, "unit_cost": 75000, "maintenance_annual": 5000},
-            "Robotic Arm": {"development": 180000, "unit_cost": 35000, "maintenance_annual": 3000}
-        }
-        
-        costs = base_costs.get(vehicle_type, base_costs["Robot"])
-        
-        return {
-            "development_cost": f"${costs['development']:,}",
-            "unit_manufacturing_cost": f"${costs['unit_cost']:,}",
-            "annual_maintenance": f"${costs['maintenance_annual']:,}",
-            "roi_timeline": "18-24 months typical payback",
-            "volume_discounts": "10% reduction at 100+ units",
-            "financing_available": "Lease options starting at 3.9% APR"
-        }
-    
-    def _create_detailed_json_specs(self, design_specs: Dict[str, Any]) -> str:
-        """Create detailed JSON specifications file"""
-        detailed_specs = design_specs.copy()
-        detailed_specs.update({
-            "file_version": "1.0",
-            "generated_by": "Agent2Robot MCP System",
-            "generation_timestamp": datetime.now().isoformat(),
-            "mcp_server_version": "3.0",
-            "compliance_standards": ["ISO 9001", "ISO 26262", "IEC 61508"],
-            "warranty_info": {
-                "hardware": "3 years comprehensive",
-                "software": "5 years updates included",
-                "support": "24/7 technical support"
-            }
-        })
-        return json.dumps(detailed_specs, indent=2)
-    
-    def _create_manufacturing_files(self, design_specs: Dict[str, Any]) -> str:
-        """Simulate manufacturing file package creation"""
-        return f"Manufacturing package for {design_specs.get('design_id', 'unknown')} (ZIP format with CAD files, assembly instructions, BOM)"
-    
-    def _create_technical_documentation(self, design_specs: Dict[str, Any]) -> str:
-        """Create technical documentation"""
-        return f"Technical documentation for {design_specs.get('vehicle_type', 'Unknown')} (PDF format with user manual and maintenance guide)"
-    
-    def validate_design(self, design_specs: Dict[str, Any]) -> Dict[str, Any]:
-        """Enhanced design validation using MCP server"""
-        return {
-            "valid": True,
-            "confidence": 0.97,
-            "validation_notes": "Design exceeds requirements and meets all safety standards",
-            "compliance_check": "Passed all regulatory requirements",
-            "performance_validation": "All metrics within acceptable ranges",
-            "safety_rating": "A+ (Excellent)",
-            "recommendations": [
-                "Consider optional redundant systems for critical applications",
-                "Evaluate extended warranty options for high-use scenarios"
-            ]
-        }
-    
-    def get_server_info(self) -> Dict[str, Any]:
-        """Get enhanced MCP server information"""
-        return {
-            "name": "Agent2Robot Advanced MCP Server",
-            "version": "3.0.0",
-            "capabilities": self.server_capabilities,
-            "status": "connected" if self.connected else "disconnected",
-            "session_id": self.session_id,
-            "endpoints_available": [
-                "/design/generate",
-                "/simulation/create", 
-                "/files/export",
-                "/manufacturing/specs",
-                "/validation/run",
-                "/cost/analyze"
-            ]
-        } 

test_simulation.py

@@ -1,114 +0,0 @@
-#!/usr/bin/env python3
-"""
-Direct test of Agent2Robot Advanced Simulation Features
-"""
-
-import json
-from datetime import datetime
-
-# Test the MCP client simulation features directly
-try:
-    from design_tools import VehicleDesigner
-    from mcp_client import MCPClient
-    
-    print("🎬 Testing Agent2Robot Advanced Simulation Features")
-    print("=" * 60)
-    
-    # Initialize MCP client
-    client = MCPClient()
-    print(f"✅ MCP Client initialized: {client.connected}")
-    
-    # Create test design data
-    test_design = {
-        "vehicle_type": "Robot",
-        "design_id": "test_robot_simulation_001",
-        "requirements": "Advanced warehouse robot with simulation testing",
-        "optimization_score": 95,
-        "generated_features": [
-            "Advanced SLAM navigation system",
-            "Multi-sensor obstacle avoidance",
-            "Adaptive payload management"
-        ],
-        "performance_metrics": {
-            "speed": "2.5 m/s maximum velocity",
-            "efficiency": "96% energy efficiency",
-            "reliability": "99.9% uptime rating"
-        }
-    }
-    
-    print("\n🚀 Testing Advanced Simulation Generation...")
-    print("-" * 50)
-    
-    # Test simulation video generation
-    simulation_result = client.generate_simulation_video(test_design)
-    
-    if isinstance(simulation_result, dict):
-        print("✅ Simulation data structure received:")
-        print(f"   Video info length: {len(simulation_result.get('video_info', ''))}")
-        print(f"   File path: {simulation_result.get('file_path', 'N/A')}")
-        print(f"   Metadata: {simulation_result.get('metadata', 'N/A')}")
-        
-        print("\n🎬 Simulation Video Info Preview:")
-        print("-" * 50)
-        video_info = simulation_result.get('video_info', simulation_result)
-        if isinstance(video_info, str):
-            # Show first few lines of simulation info
-            lines = video_info.split('\n')[:10]
-            for line in lines:
-                print(line)
-        print("... (truncated)")
-        
-    else:
-        print("✅ Simulation info generated:")
-        print(f"   Content length: {len(str(simulation_result))}")
-        print("\n🎬 Simulation Info Preview:")
-        print("-" * 50)
-        lines = str(simulation_result).split('\n')[:10]
-        for line in lines:
-            print(line)
-        print("... (truncated)")
-    
-    print("\n" + "=" * 60)
-    print("🔧 Testing File Download Generation...")
-    print("-" * 50)
-    
-    # Test file package generation
-    files_package = client.generate_downloadable_files(test_design)
-    
-    print("✅ File package generated:")
-    for file_type, file_info in files_package.items():
-        print(f"   {file_type}: {file_info.get('filename', 'N/A')} ({file_info.get('size_kb', 0):.1f} KB)")
-        if file_info.get('filepath'):
-            print(f"     ✅ File created: {file_info['filepath']}")
-        else:
-            print(f"     📋 Prepared: {file_info.get('description', 'N/A')}")
-    
-    print("\n" + "=" * 60)
-    print("📊 Testing Design Validation...")
-    print("-" * 50)
-    
-    # Test validation
-    validation_result = client.validate_design(test_design)
-    print("✅ Validation result:")
-    for key, value in validation_result.items():
-        print(f"   {key}: {value}")
-    
-    print("\n" + "=" * 60)
-    print("🏆 ADVANCED SIMULATION FEATURES TEST COMPLETE")
-    print("=" * 60)
-    print("✅ All simulation features working correctly!")
-    print("✅ 4K video generation: OPERATIONAL")
-    print("✅ Advanced physics simulation: OPERATIONAL") 
-    print("✅ File package generation: OPERATIONAL")
-    print("✅ Design validation: OPERATIONAL")
-    print("✅ MCP integration: FULLY FUNCTIONAL")
-    
-    print(f"\n🎯 Test completed at: {datetime.now().isoformat()}")
-    
-except ImportError as e:
-    print(f"❌ Import error: {e}")
-    print("MCP modules not available for testing")
-    
-except Exception as e:
-    print(f"❌ Test error: {e}")
-    print("Some simulation features may need debugging")