""" ZeroGPU Deployment Guide and Utilities for Felix Framework. This module provides comprehensive guidance, utilities, and debugging tools for deploying Felix Framework on HuggingFace Spaces with ZeroGPU acceleration. Key Features: - Deployment validation and diagnostics - Performance benchmarking and optimization recommendations - Configuration validation for HF Spaces deployment - Troubleshooting guides and automated fixes - Resource usage estimation and planning """ import os import json import logging import time import asyncio from typing import Dict, Any, List, Optional, Tuple from dataclasses import dataclass, field from enum import Enum from pathlib import Path logger = logging.getLogger(__name__) class DeploymentStatus(Enum): """Deployment validation status levels.""" READY = "ready" WARNING = "warning" ERROR = "error" CRITICAL = "critical" @dataclass class DeploymentCheck: """Individual deployment validation check.""" name: str status: DeploymentStatus message: str suggestion: Optional[str] = None technical_details: Optional[str] = None auto_fix_available: bool = False @dataclass class DeploymentReport: """Comprehensive deployment validation report.""" overall_status: DeploymentStatus checks: List[DeploymentCheck] = field(default_factory=list) performance_estimates: Dict[str, Any] = field(default_factory=dict) resource_requirements: Dict[str, Any] = field(default_factory=dict) recommendations: List[str] = field(default_factory=list) timestamp: float = field(default_factory=time.time) class ZeroGPUDeploymentValidator: """ Comprehensive deployment validator for Felix Framework on ZeroGPU. Validates configuration, dependencies, resource requirements, and provides optimization recommendations for successful HuggingFace Spaces deployment. """ def __init__(self, project_root: Optional[Path] = None): """ Initialize deployment validator. Args: project_root: Root directory of the Felix project """ self.project_root = project_root or Path.cwd() self.checks = [] self.performance_data = {} def validate_deployment(self) -> DeploymentReport: """ Perform comprehensive deployment validation. Returns: Detailed deployment report with status and recommendations """ logger.info("Starting ZeroGPU deployment validation") self.checks = [] # Core validation checks self._check_dependencies() self._check_huggingface_configuration() self._check_zerogpu_requirements() self._check_felix_configuration() self._check_gradio_integration() self._check_resource_limits() self._check_security_requirements() self._check_performance_configuration() # Determine overall status overall_status = self._determine_overall_status() # Generate recommendations recommendations = self._generate_recommendations() # Estimate performance and resources performance_estimates = self._estimate_performance() resource_requirements = self._estimate_resources() report = DeploymentReport( overall_status=overall_status, checks=self.checks.copy(), performance_estimates=performance_estimates, resource_requirements=resource_requirements, recommendations=recommendations ) logger.info(f"Deployment validation completed - Status: {overall_status.value}") return report def _check_dependencies(self): """Check required dependencies for ZeroGPU deployment.""" check_name = "Dependencies Check" try: # Check Python version import sys python_version = sys.version_info if python_version < (3, 8): self.checks.append(DeploymentCheck( name=check_name, status=DeploymentStatus.ERROR, message="Python version too old", suggestion="Upgrade to Python 3.8 or newer", technical_details=f"Current: {python_version.major}.{python_version.minor}" )) return # Check critical packages required_packages = [ ("torch", "PyTorch for GPU acceleration"), ("transformers", "HuggingFace Transformers"), ("spaces", "HuggingFace Spaces integration"), ("gradio", "Web interface framework"), ("huggingface_hub", "HuggingFace Hub API") ] missing_packages = [] version_info = {} for package, description in required_packages: try: module = __import__(package) version = getattr(module, '__version__', 'unknown') version_info[package] = version except ImportError: missing_packages.append((package, description)) if missing_packages: missing_list = ", ".join([f"{pkg} ({desc})" for pkg, desc in missing_packages]) self.checks.append(DeploymentCheck( name=check_name, status=DeploymentStatus.ERROR, message=f"Missing required packages: {missing_list}", suggestion="Install missing packages using: pip install torch transformers spaces gradio huggingface_hub", auto_fix_available=True )) else: self.checks.append(DeploymentCheck( name=check_name, status=DeploymentStatus.READY, message="All required dependencies available", technical_details=json.dumps(version_info, indent=2) )) except Exception as e: self.checks.append(DeploymentCheck( name=check_name, status=DeploymentStatus.ERROR, message=f"Dependency check failed: {e}", technical_details=str(e) )) def _check_huggingface_configuration(self): """Check HuggingFace token and account configuration.""" check_name = "HuggingFace Configuration" try: # Check for HF token hf_token = os.getenv('HF_TOKEN') or os.getenv('HUGGINGFACE_HUB_TOKEN') if not hf_token: self.checks.append(DeploymentCheck( name=check_name, status=DeploymentStatus.ERROR, message="HuggingFace token not found", suggestion="Set HF_TOKEN environment variable with your HuggingFace token", technical_details="Token required for model access and Pro account features" )) return # Test token validity try: from huggingface_hub import HfApi api = HfApi(token=hf_token) user_info = api.whoami() account_type = "Pro" if user_info.get('isPro', False) else "Free" orgs = user_info.get('orgs', []) self.checks.append(DeploymentCheck( name=check_name, status=DeploymentStatus.READY, message=f"HuggingFace token valid - {account_type} account", technical_details=f"User: {user_info.get('name', 'unknown')}, Organizations: {len(orgs)}" )) except Exception as e: self.checks.append(DeploymentCheck( name=check_name, status=DeploymentStatus.WARNING, message="Could not validate HuggingFace token", suggestion="Check token permissions and network connectivity", technical_details=str(e) )) except Exception as e: self.checks.append(DeploymentCheck( name=check_name, status=DeploymentStatus.ERROR, message=f"HuggingFace configuration check failed: {e}", technical_details=str(e) )) def _check_zerogpu_requirements(self): """Check ZeroGPU-specific requirements and configuration.""" check_name = "ZeroGPU Requirements" try: issues = [] recommendations = [] # Check for spaces decorator availability try: import spaces if hasattr(spaces, 'GPU'): recommendations.append("ZeroGPU decorator available") else: issues.append("spaces.GPU decorator not found") except ImportError: issues.append("HuggingFace Spaces package not available") # Check PyTorch GPU support try: import torch if torch.cuda.is_available(): gpu_count = torch.cuda.device_count() recommendations.append(f"CUDA available with {gpu_count} GPU(s)") # Check GPU memory for i in range(gpu_count): props = torch.cuda.get_device_properties(i) memory_gb = props.total_memory / 1024**3 recommendations.append(f"GPU {i}: {props.name}, {memory_gb:.1f}GB") if memory_gb < 8: issues.append(f"GPU {i} has insufficient memory ({memory_gb:.1f}GB < 8GB minimum)") else: issues.append("CUDA not available - ZeroGPU features will be disabled") except ImportError: issues.append("PyTorch not available") # Check model configurations try: from llm.huggingface_client import create_felix_hf_client, estimate_gpu_requirements from llm.huggingface_client import get_pro_account_models # Test with Pro account models pro_models = get_pro_account_models() requirements = estimate_gpu_requirements(pro_models) max_memory = requirements.get("max_single_model_memory", 0) if max_memory > 40: # 40GB limit for most ZeroGPU instances issues.append(f"Model memory requirement ({max_memory:.1f}GB) exceeds ZeroGPU limits") recommendations.append(f"Max model memory: {max_memory:.1f}GB") except Exception as model_e: issues.append(f"Could not validate model requirements: {model_e}") if issues: status = DeploymentStatus.WARNING if not any("not available" in issue for issue in issues) else DeploymentStatus.ERROR self.checks.append(DeploymentCheck( name=check_name, status=status, message=f"ZeroGPU issues found: {'; '.join(issues)}", suggestion="Install required packages and check GPU availability", technical_details="\n".join(recommendations) )) else: self.checks.append(DeploymentCheck( name=check_name, status=DeploymentStatus.READY, message="ZeroGPU requirements satisfied", technical_details="\n".join(recommendations) )) except Exception as e: self.checks.append(DeploymentCheck( name=check_name, status=DeploymentStatus.ERROR, message=f"ZeroGPU requirements check failed: {e}", technical_details=str(e) )) def _check_felix_configuration(self): """Check Felix Framework configuration and structure.""" check_name = "Felix Framework Configuration" try: issues = [] validations = [] # Check core modules core_modules = [ "src.core.helix_geometry", "src.agents.specialized_agents", "src.communication.central_post", "src.communication.spoke", "src.llm.huggingface_client", "src.deployment.zerogpu_monitor", "src.deployment.zerogpu_error_handler", "src.deployment.batch_optimizer" ] for module_name in core_modules: try: __import__(module_name) validations.append(f"✓ {module_name}") except ImportError as e: issues.append(f"✗ {module_name}: {e}") # Check Gradio interface try: from gradio_interface.felix_gradio_adapter import FelixGradioAdapter validations.append("✓ Gradio adapter available") except ImportError: issues.append("✗ Gradio adapter not available") # Check configuration files config_files = [ "src/llm/huggingface_client.py", "src/deployment/zerogpu_monitor.py" ] for config_file in config_files: full_path = self.project_root / config_file if full_path.exists(): validations.append(f"✓ {config_file}") else: issues.append(f"✗ Missing {config_file}") if issues: self.checks.append(DeploymentCheck( name=check_name, status=DeploymentStatus.ERROR, message=f"Felix configuration issues: {len(issues)} problems found", suggestion="Ensure all Felix modules are properly installed and configured", technical_details="\n".join(issues + validations) )) else: self.checks.append(DeploymentCheck( name=check_name, status=DeploymentStatus.READY, message="Felix Framework properly configured", technical_details="\n".join(validations) )) except Exception as e: self.checks.append(DeploymentCheck( name=check_name, status=DeploymentStatus.ERROR, message=f"Felix configuration check failed: {e}", technical_details=str(e) )) def _check_gradio_integration(self): """Check Gradio interface configuration for HF Spaces.""" check_name = "Gradio Integration" try: import gradio as gr version = gr.__version__ # Check minimum Gradio version min_version = "3.50.0" if version < min_version: self.checks.append(DeploymentCheck( name=check_name, status=DeploymentStatus.WARNING, message=f"Gradio version {version} may be outdated", suggestion=f"Consider upgrading to Gradio {min_version} or newer", technical_details=f"Current: {version}, Recommended: {min_version}+" )) else: self.checks.append(DeploymentCheck( name=check_name, status=DeploymentStatus.READY, message=f"Gradio {version} ready for HF Spaces", technical_details=f"Version: {version}" )) except ImportError: self.checks.append(DeploymentCheck( name=check_name, status=DeploymentStatus.ERROR, message="Gradio not available", suggestion="Install Gradio: pip install gradio", auto_fix_available=True )) except Exception as e: self.checks.append(DeploymentCheck( name=check_name, status=DeploymentStatus.ERROR, message=f"Gradio check failed: {e}", technical_details=str(e) )) def _check_resource_limits(self): """Check resource usage against HF Spaces limits.""" check_name = "Resource Limits" try: # HF Spaces typical limits hf_limits = { "gpu_memory_gb": 16, # T4 GPU typical "cpu_memory_gb": 16, "disk_space_gb": 50, "request_timeout_seconds": 60, "concurrent_users": 20 } warnings = [] estimates = {} # Estimate Felix resource usage try: from llm.huggingface_client import estimate_gpu_requirements, create_felix_hf_client # Get default model configuration client = create_felix_hf_client(enable_zerogpu=True) requirements = estimate_gpu_requirements(client.model_configs) max_model_memory = requirements.get("max_single_model_memory", 8.0) estimates["estimated_gpu_memory_gb"] = max_model_memory if max_model_memory > hf_limits["gpu_memory_gb"] * 0.8: # 80% threshold warnings.append(f"High GPU memory usage: {max_model_memory:.1f}GB (limit: {hf_limits['gpu_memory_gb']}GB)") # Estimate concurrent user capacity memory_per_user = max_model_memory / 4 # Rough estimate max_users = int(hf_limits["gpu_memory_gb"] / memory_per_user) estimates["estimated_max_concurrent_users"] = max_users if max_users < 3: warnings.append(f"Low concurrent user capacity: {max_users} users") except Exception as e: warnings.append(f"Could not estimate resource usage: {e}") # Check disk space for model cache try: import shutil free_space = shutil.disk_usage(self.project_root).free / 1024**3 estimates["available_disk_space_gb"] = free_space if free_space < 10: # 10GB minimum warnings.append(f"Low disk space: {free_space:.1f}GB available") except Exception as e: warnings.append(f"Could not check disk space: {e}") if warnings: self.checks.append(DeploymentCheck( name=check_name, status=DeploymentStatus.WARNING, message=f"Resource limit concerns: {'; '.join(warnings)}", suggestion="Consider optimizing model selection or implementing more aggressive memory management", technical_details=json.dumps(estimates, indent=2) )) else: self.checks.append(DeploymentCheck( name=check_name, status=DeploymentStatus.READY, message="Resource usage within HF Spaces limits", technical_details=json.dumps(estimates, indent=2) )) except Exception as e: self.checks.append(DeploymentCheck( name=check_name, status=DeploymentStatus.ERROR, message=f"Resource limits check failed: {e}", technical_details=str(e) )) def _check_security_requirements(self): """Check security requirements for HF Spaces deployment.""" check_name = "Security Requirements" try: issues = [] validations = [] # Check for sensitive information in environment sensitive_vars = ["API_KEY", "SECRET", "PASSWORD", "TOKEN"] env_vars = list(os.environ.keys()) for var in env_vars: if any(sensitive in var.upper() for sensitive in sensitive_vars): if var not in ["HF_TOKEN", "HUGGINGFACE_HUB_TOKEN"]: # These are expected issues.append(f"Potential sensitive variable in environment: {var}") # Check for hardcoded secrets in common files code_files = list(self.project_root.glob("**/*.py"))[:20] # Sample check for file_path in code_files: try: content = file_path.read_text(encoding='utf-8') if any(pattern in content.lower() for pattern in ["api_key =", "secret =", "password ="]): issues.append(f"Potential hardcoded secret in {file_path.name}") except Exception: continue # Skip files that can't be read # Validate token handling try: from llm.huggingface_client import HuggingFaceClient # Check that client properly handles token from environment validations.append("✓ HuggingFace client uses environment token") except Exception: issues.append("Could not validate token handling") if issues: self.checks.append(DeploymentCheck( name=check_name, status=DeploymentStatus.WARNING, message=f"Security concerns found: {len(issues)} issues", suggestion="Review and remove any hardcoded secrets, use HF Spaces secrets for sensitive data", technical_details="\n".join(issues[:5]) # Limit output )) else: self.checks.append(DeploymentCheck( name=check_name, status=DeploymentStatus.READY, message="Security requirements satisfied", technical_details="\n".join(validations) )) except Exception as e: self.checks.append(DeploymentCheck( name=check_name, status=DeploymentStatus.ERROR, message=f"Security check failed: {e}", technical_details=str(e) )) def _check_performance_configuration(self): """Check performance optimization configuration.""" check_name = "Performance Configuration" try: recommendations = [] optimizations = [] # Check ZeroGPU optimizations try: from deployment.zerogpu_monitor import ZeroGPUMonitor from deployment.batch_optimizer import ZeroGPUBatchOptimizer optimizations.append("✓ ZeroGPU monitoring available") optimizations.append("✓ Batch processing optimization available") except ImportError: recommendations.append("Enable ZeroGPU monitoring and batch optimization") # Check token budget configuration try: from llm.token_budget import TokenBudgetManager manager = TokenBudgetManager(strict_mode=True) optimizations.append("✓ Token budget management configured") except Exception: recommendations.append("Configure token budget management") # Check caching configuration try: from gradio_interface.felix_gradio_adapter import FelixGradioAdapter adapter = FelixGradioAdapter(enable_cache=True) optimizations.append("✓ Response caching enabled") except Exception: recommendations.append("Enable response caching in Gradio adapter") # Performance recommendations perf_recommendations = [ "Use batch processing for multiple agent requests", "Enable model caching to reduce loading time", "Implement progressive complexity levels", "Monitor GPU memory usage and implement cleanup", "Use token budgets to control resource usage" ] if recommendations: self.checks.append(DeploymentCheck( name=check_name, status=DeploymentStatus.WARNING, message=f"Performance optimizations available: {len(recommendations)} improvements", suggestion="; ".join(recommendations[:3]), technical_details="\n".join(optimizations + perf_recommendations) )) else: self.checks.append(DeploymentCheck( name=check_name, status=DeploymentStatus.READY, message="Performance optimizations configured", technical_details="\n".join(optimizations + perf_recommendations) )) except Exception as e: self.checks.append(DeploymentCheck( name=check_name, status=DeploymentStatus.ERROR, message=f"Performance configuration check failed: {e}", technical_details=str(e) )) def _determine_overall_status(self) -> DeploymentStatus: """Determine overall deployment status from individual checks.""" if any(check.status == DeploymentStatus.CRITICAL for check in self.checks): return DeploymentStatus.CRITICAL elif any(check.status == DeploymentStatus.ERROR for check in self.checks): return DeploymentStatus.ERROR elif any(check.status == DeploymentStatus.WARNING for check in self.checks): return DeploymentStatus.WARNING else: return DeploymentStatus.READY def _generate_recommendations(self) -> List[str]: """Generate deployment recommendations based on checks.""" recommendations = [] # Collect recommendations from failed checks for check in self.checks: if check.status != DeploymentStatus.READY and check.suggestion: recommendations.append(f"{check.name}: {check.suggestion}") # General recommendations general_recommendations = [ "Test deployment in development environment before production", "Monitor GPU memory usage during high-load scenarios", "Implement gradual rollout for production deployment", "Set up error monitoring and alerting", "Document deployment configuration and troubleshooting steps" ] recommendations.extend(general_recommendations[:3]) # Add top 3 general recommendations return recommendations[:10] # Limit to top 10 def _estimate_performance(self) -> Dict[str, Any]: """Estimate performance characteristics for deployment.""" estimates = { "cold_start_time_seconds": 30, # Typical for model loading "warm_inference_time_seconds": 2, # Per agent request "batch_processing_efficiency": 0.7, # 70% efficiency for batching "concurrent_users_target": 5, # Conservative estimate "memory_efficiency_ratio": 0.8 # 80% GPU memory utilization } try: # Try to get actual estimates from configuration from llm.huggingface_client import estimate_gpu_requirements, create_felix_hf_client client = create_felix_hf_client() requirements = estimate_gpu_requirements(client.model_configs) # Update estimates based on model requirements max_memory = requirements.get("max_single_model_memory", 8.0) estimates["model_loading_time_seconds"] = max(5, max_memory * 2) # Rough estimate estimates["max_model_memory_gb"] = max_memory except Exception as e: logger.warning(f"Could not get detailed performance estimates: {e}") return estimates def _estimate_resources(self) -> Dict[str, Any]: """Estimate resource requirements for deployment.""" requirements = { "minimum_gpu_memory_gb": 8, "recommended_gpu_memory_gb": 16, "cpu_memory_gb": 8, "disk_space_gb": 20, "network_bandwidth_mbps": 10 } try: # Get specific requirements from model configuration from llm.huggingface_client import estimate_gpu_requirements, create_felix_hf_client client = create_felix_hf_client() model_requirements = estimate_gpu_requirements(client.model_configs) requirements.update({ "estimated_gpu_memory_gb": model_requirements.get("recommended_gpu_memory", 16), "minimum_gpu_memory_gb": model_requirements.get("minimum_gpu_memory", 8), "peak_memory_usage_gb": model_requirements.get("total_memory_if_all_loaded", 20) }) except Exception as e: logger.warning(f"Could not get detailed resource estimates: {e}") return requirements def generate_deployment_config(self, output_path: Optional[Path] = None) -> Dict[str, Any]: """Generate optimized deployment configuration for HF Spaces.""" config = { "title": "Felix Framework - Multi-Agent Research Assistant", "emoji": "🧬", "colorFrom": "blue", "colorTo": "purple", "sdk": "gradio", "sdk_version": "4.0.0", "app_file": "app.py", "pinned": False, "license": "mit", "hardware": "t4-small", # Default to T4 small "python_version": "3.10", "requirements": [ "torch", "transformers", "spaces", "gradio>=4.0.0", "huggingface_hub", "numpy", "asyncio", "aiohttp" ], "environment_variables": { "HF_TOKEN": "{{HF_TOKEN}}", # To be set in Spaces secrets "PYTORCH_CUDA_ALLOC_CONF": "max_split_size_mb:128" }, "deployment_settings": { "enable_zerogpu": True, "max_concurrent_users": 10, "request_timeout": 60, "enable_caching": True, "default_complexity": "medium", "gpu_memory_threshold": 0.85 } } if output_path: output_path.write_text(json.dumps(config, indent=2)) logger.info(f"Deployment configuration saved to {output_path}") return config def create_app_file(self, output_path: Optional[Path] = None) -> str: """Create optimized app.py file for HF Spaces deployment.""" app_content = '''""" Felix Framework - Multi-Agent Research Assistant HuggingFace Spaces deployment with ZeroGPU acceleration. """ import gradio as gr import os import logging from pathlib import Path # Configure logging logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) # Import Felix components try: from src.gradio_interface.felix_gradio_adapter import FelixGradioAdapter, ComplexityLevel from src.llm.huggingface_client import create_felix_hf_client from src.deployment.zerogpu_monitor import create_zerogpu_monitor # Initialize components logger.info("Initializing Felix Framework for HF Spaces...") # Create optimized HF client for Spaces llm_client = create_felix_hf_client( token_budget=50000, concurrent_requests=5, enable_zerogpu=True, debug_mode=False ) # Create GPU monitor gpu_monitor = create_zerogpu_monitor() # Create Gradio adapter felix_adapter = FelixGradioAdapter( llm_client=llm_client, enable_cache=True, max_sessions=50, session_timeout=3600, default_complexity=ComplexityLevel.MEDIUM ) logger.info("Felix Framework initialized successfully") except Exception as e: logger.error(f"Failed to initialize Felix Framework: {e}") # Fallback to demo mode felix_adapter = None def process_request(topic, complexity="medium"): """Process a blog writing request.""" if not felix_adapter: return "Felix Framework initialization failed. Please contact support.", {} try: # Process request with progress tracking results = [] for status, progress in felix_adapter.process_blog_request( topic=topic, complexity=complexity, use_cache=True ): results.append((status, progress)) yield status, progress, {} # Return final result final_result = results[-1] if results else ({}, 0) return final_result[0], final_result[1], final_result[2] if len(final_result) > 2 else {} except Exception as e: logger.error(f"Request processing failed: {e}") return f"Error: {e}", 0, {} def create_interface(): """Create Gradio interface.""" with gr.Blocks( title="Felix Framework - Multi-Agent Research Assistant", theme=gr.themes.Soft(), css=""" .container { max-width: 1200px; margin: auto; } .header { text-align: center; margin-bottom: 2rem; } .progress-bar { margin: 1rem 0; } """ ) as demo: gr.Markdown(""" # 🧬 Felix Framework ### Multi-Agent Research Assistant with Helix-Based Coordination Experience the power of geometric multi-agent coordination for research and analysis. """, elem_classes=["header"]) with gr.Row(): with gr.Column(scale=2): topic_input = gr.Textbox( label="Research Topic", placeholder="Enter your research topic (e.g., 'artificial intelligence ethics')", lines=2 ) complexity_input = gr.Dropdown( choices=["demo", "simple", "medium", "complex", "research"], value="medium", label="Processing Complexity", info="Higher complexity uses more agents and provides deeper analysis" ) submit_btn = gr.Button("Start Research", variant="primary", scale=1) with gr.Column(scale=3): progress_bar = gr.Progress() status_output = gr.Textbox( label="Processing Status", lines=2, interactive=False ) result_output = gr.Markdown( label="Research Results", height=400 ) with gr.Accordion("Technical Details", open=False): metrics_output = gr.JSON(label="Performance Metrics") # Event handlers submit_btn.click( fn=process_request, inputs=[topic_input, complexity_input], outputs=[status_output, progress_bar, metrics_output], show_progress=True ) # Add examples gr.Examples( examples=[ ["Renewable energy technologies", "medium"], ["Machine learning ethics", "complex"], ["Climate change mitigation", "research"], ["Quantum computing applications", "simple"] ], inputs=[topic_input, complexity_input] ) gr.Markdown(""" ### About Felix Framework The Felix Framework demonstrates helix-based cognitive architecture for multi-agent systems, where autonomous agents traverse spiral processing paths with spoke-based communication to a central coordination system. **Key Features:** - 🌀 Helix-based agent coordination - 🤖 Specialized agent types (Research, Analysis, Synthesis, Critic) - ⚡ ZeroGPU acceleration - 📊 Real-time progress tracking - 🧠 Adaptive complexity levels """) return demo # Create and launch interface if __name__ == "__main__": demo = create_interface() demo.launch( server_name="0.0.0.0", server_port=7860, share=False ) ''' if output_path: output_path.write_text(app_content) logger.info(f"App file saved to {output_path}") return app_content def export_full_report(self, output_dir: Path) -> Dict[str, Path]: """Export comprehensive deployment report and configuration files.""" output_dir.mkdir(parents=True, exist_ok=True) files_created = {} try: # Generate validation report report = self.validate_deployment() report_file = output_dir / "deployment_report.json" report_data = { "overall_status": report.overall_status.value, "timestamp": report.timestamp, "checks": [ { "name": check.name, "status": check.status.value, "message": check.message, "suggestion": check.suggestion, "technical_details": check.technical_details, "auto_fix_available": check.auto_fix_available } for check in report.checks ], "performance_estimates": report.performance_estimates, "resource_requirements": report.resource_requirements, "recommendations": report.recommendations } report_file.write_text(json.dumps(report_data, indent=2)) files_created["report"] = report_file # Generate deployment configuration config_file = output_dir / "hf_spaces_config.json" self.generate_deployment_config(config_file) files_created["config"] = config_file # Generate app.py app_file = output_dir / "app.py" self.create_app_file(app_file) files_created["app"] = app_file # Generate requirements.txt requirements_file = output_dir / "requirements.txt" requirements_content = """torch>=2.0.0 transformers>=4.30.0 spaces>=0.10.0 gradio>=4.0.0 huggingface_hub>=0.15.0 numpy>=1.24.0 aiohttp>=3.8.0 psutil>=5.9.0 """ requirements_file.write_text(requirements_content) files_created["requirements"] = requirements_file # Generate README readme_file = output_dir / "README.md" readme_content = f"""# Felix Framework - HuggingFace Spaces Deployment ## Deployment Status: {report.overall_status.value.upper()} This directory contains the complete deployment configuration for Felix Framework on HuggingFace Spaces with ZeroGPU acceleration. ## Files - `app.py` - Main Gradio application - `requirements.txt` - Python dependencies - `hf_spaces_config.json` - HF Spaces configuration - `deployment_report.json` - Validation report ## Quick Deployment 1. Create a new HuggingFace Space with ZeroGPU hardware 2. Upload all files to your Space repository 3. Set your HF_TOKEN in Space secrets 4. The Space will automatically build and deploy ## Performance Estimates {json.dumps(report.performance_estimates, indent=2)} ## Resource Requirements {json.dumps(report.resource_requirements, indent=2)} ## Recommendations {chr(10).join(f"- {rec}" for rec in report.recommendations[:5])} --- Generated on {time.strftime('%Y-%m-%d %H:%M:%S')} by Felix Framework Deployment Validator """ readme_file.write_text(readme_content) files_created["readme"] = readme_file logger.info(f"Deployment package created in {output_dir}") return files_created except Exception as e: logger.error(f"Failed to export deployment package: {e}") raise # Utility functions def validate_zerogpu_deployment(project_root: Optional[Path] = None) -> DeploymentReport: """Quick validation function for ZeroGPU deployment readiness.""" validator = ZeroGPUDeploymentValidator(project_root) return validator.validate_deployment() def create_deployment_package(output_dir: Path, project_root: Optional[Path] = None) -> Dict[str, Path]: """Create complete deployment package for HuggingFace Spaces.""" validator = ZeroGPUDeploymentValidator(project_root) return validator.export_full_report(output_dir) # Export main classes and functions __all__ = [ 'ZeroGPUDeploymentValidator', 'DeploymentReport', 'DeploymentCheck', 'DeploymentStatus', 'validate_zerogpu_deployment', 'create_deployment_package' ]