Spaces:
Running on Zero
Running on Zero
| """ | |
| Utility functions for Nova AI - ChatGPT Astra Edition. | |
| Hardware inspection, Astra prompt enhancement, latency profiling, and chat exporters (MD, JSON, HTML). | |
| """ | |
| import os | |
| import sys | |
| import time | |
| import json | |
| import html | |
| import psutil | |
| from pathlib import Path | |
| from typing import List, Dict, Any, Tuple | |
| # Ensure UTF-8 output on Windows consoles | |
| if sys.platform == "win32": | |
| try: | |
| if sys.stdout and hasattr(sys.stdout, "reconfigure"): | |
| sys.stdout.reconfigure(encoding="utf-8") | |
| if sys.stderr and hasattr(sys.stderr, "reconfigure"): | |
| sys.stderr.reconfigure(encoding="utf-8") | |
| except Exception: | |
| pass | |
| from rich.console import Console | |
| console = Console(legacy_windows=False) | |
| def get_system_hardware_info() -> Dict[str, Any]: | |
| """Inspect and return current CPU, RAM, NVIDIA GPU, and Hugging Face ZeroGPU capabilities.""" | |
| info: Dict[str, Any] = { | |
| "cpu_count_physical": psutil.cpu_count(logical=False) or 1, | |
| "cpu_count_logical": psutil.cpu_count(logical=True) or 1, | |
| "total_ram_gb": round(psutil.virtual_memory().total / (1024 ** 3), 2), | |
| "available_ram_gb": round(psutil.virtual_memory().available / (1024 ** 3), 2), | |
| "has_cuda": False, | |
| "is_zero_gpu": False, | |
| "device_name": "CPU (Intel / AMD Multi-Core)", | |
| "recommended_device": "cpu", | |
| "cuda_device_count": 0, | |
| "cuda_vram_gb": 0.0, | |
| "cuda_version": None, | |
| "compute_capability": None, | |
| "hardware_badge": "⚡ Multi-Core CPU Accelerated" | |
| } | |
| # Check for Hugging Face Spaces ZeroGPU environment | |
| if os.getenv("SPACES_ZERO_GPU") == "true" or "spaces" in sys.modules: | |
| info["is_zero_gpu"] = True | |
| info["hardware_badge"] = "🟢 NVIDIA A100 ZeroGPU Dynamic Allocation Active" | |
| try: | |
| import torch | |
| if torch.cuda.is_available(): | |
| info["has_cuda"] = True | |
| info["cuda_device_count"] = torch.cuda.device_count() | |
| dev_name = torch.cuda.get_device_name(0) | |
| info["device_name"] = dev_name | |
| info["recommended_device"] = "cuda" | |
| vram = round(torch.cuda.get_device_properties(0).total_memory / (1024 ** 3), 2) | |
| info["cuda_vram_gb"] = vram | |
| info["cuda_version"] = torch.version.cuda | |
| cap = torch.cuda.get_device_capability(0) | |
| info["compute_capability"] = f"{cap[0]}.{cap[1]}" | |
| if info["is_zero_gpu"]: | |
| info["hardware_badge"] = f"🟢 NVIDIA ZeroGPU ({dev_name}) Active" | |
| else: | |
| info["hardware_badge"] = f"🟢 NVIDIA CUDA ({dev_name} • {vram}GB VRAM • CC {cap[0]}.{cap[1]})" | |
| elif hasattr(torch.backends, "mps") and torch.backends.mps.is_available(): | |
| info["device_name"] = "Apple Silicon MPS Metal" | |
| info["recommended_device"] = "mps" | |
| info["hardware_badge"] = "🍎 Apple Silicon Metal MPS Accelerated" | |
| except ImportError: | |
| pass | |
| return info | |
| def enhance_prompt_with_astra(prompt: str) -> str: | |
| """ | |
| Astra Spark Prompt Enhancer: | |
| Transforms a concise prompt into a structured, high-yield ChatGPT Astra prompt. | |
| """ | |
| cleaned = prompt.strip() | |
| if not cleaned: | |
| return prompt | |
| # If it's already a very detailed prompt, don't over-wrap it | |
| if len(cleaned) > 250 and ("###" in cleaned or "Requirements:" in cleaned): | |
| return cleaned | |
| # Generate an elevated Astra prompt template | |
| enhanced = ( | |
| f"{cleaned}\n\n" | |
| f"--- Astra Guidance ---\n" | |
| f"Please provide an in-depth, structured response covering:\n" | |
| f"1. Core concept and fundamental explanation.\n" | |
| f"2. Practical real-world examples or code implementation with best practices.\n" | |
| f"3. Key trade-offs, common pitfalls, and edge cases to consider.\n" | |
| f"4. Summary checklist or actionable next steps." | |
| ) | |
| return enhanced | |
| def format_chat_as_markdown(history: List[Dict[str, str]], title: str = "Nova AI - Conversation Record") -> str: | |
| """Format conversation list into structured Markdown text.""" | |
| timestamp = time.strftime("%Y-%m-%d %H:%M:%S") | |
| md = [ | |
| f"# 🌟 {title}\n", | |
| f"*Generated by Nova AI (ChatGPT Astra Edition) on: {timestamp}*\n\n", | |
| "---\n\n" | |
| ] | |
| for msg in history: | |
| role = msg.get("role", "user").lower() | |
| content = msg.get("content", "").strip() | |
| if role == "system": | |
| md.append(f"> 🧠 **Astra System Persona:**\n> {content}\n\n") | |
| elif role == "user": | |
| md.append(f"### 👤 User:\n{content}\n\n") | |
| else: | |
| md.append(f"### 🌟 Nova Astra:\n{content}\n\n") | |
| return "".join(md) | |
| def format_chat_as_json(history: List[Dict[str, str]]) -> str: | |
| """Format conversation history into rich JSON string.""" | |
| return json.dumps({ | |
| "generator": "Nova AI - ChatGPT Astra Edition", | |
| "version": "2.5.0-Astra", | |
| "timestamp": time.time(), | |
| "date": time.strftime("%Y-%m-%d %H:%M:%S"), | |
| "total_messages": len(history), | |
| "messages": history | |
| }, indent=2, ensure_ascii=False) | |
| def format_chat_as_html(history: List[Dict[str, str]], title: str = "Nova AI Conversation") -> str: | |
| """Format conversation into an exportable standalone cosmic dark-mode HTML document.""" | |
| timestamp = time.strftime("%Y-%m-%d %H:%M:%S") | |
| messages_html = [] | |
| for msg in history: | |
| role = msg.get("role", "user").lower() | |
| content = html.escape(msg.get("content", "").strip()).replace("\n", "<br/>") | |
| if role == "system": | |
| card = f""" | |
| <div class="msg-box system"> | |
| <div class="msg-header">🧠 Astra Persona Directive</div> | |
| <div class="msg-body">{content}</div> | |
| </div> | |
| """ | |
| elif role == "user": | |
| card = f""" | |
| <div class="msg-box user"> | |
| <div class="msg-header">👤 User</div> | |
| <div class="msg-body">{content}</div> | |
| </div> | |
| """ | |
| else: | |
| card = f""" | |
| <div class="msg-box astra"> | |
| <div class="msg-header">🌟 Nova Astra</div> | |
| <div class="msg-body">{content}</div> | |
| </div> | |
| """ | |
| messages_html.append(card) | |
| html_content = f"""<!DOCTYPE html> | |
| <html lang="en"> | |
| <head> | |
| <meta charset="UTF-8"> | |
| <meta name="viewport" content="width=device-width, initial-scale=1.0"> | |
| <title>{html.escape(title)} - Nova AI</title> | |
| <style> | |
| :root {{ | |
| --bg-color: #0b0f19; | |
| --card-bg: #111827; | |
| --text-main: #f3f4f6; | |
| --text-dim: #9ca3af; | |
| --accent-purple: #8b5cf6; | |
| --accent-cyan: #06b6d4; | |
| --border-color: #1f2937; | |
| }} | |
| body {{ | |
| background-color: var(--bg-color); | |
| color: var(--text-main); | |
| font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, "Helvetica Neue", Arial, sans-serif; | |
| line-height: 1.6; | |
| margin: 0; | |
| padding: 30px 15px; | |
| }} | |
| .container {{ | |
| max-width: 900px; | |
| margin: 0 auto; | |
| }} | |
| .header {{ | |
| background: linear-gradient(135deg, #1e1b4b 0%, #312e81 50%, #4338ca 100%); | |
| padding: 24px 30px; | |
| border-radius: 16px; | |
| margin-bottom: 24px; | |
| box-shadow: 0 10px 25px -5px rgba(49, 46, 129, 0.4); | |
| }} | |
| .header h1 {{ | |
| margin: 0 0 6px 0; | |
| font-size: 26px; | |
| color: #fff; | |
| }} | |
| .header p {{ | |
| margin: 0; | |
| color: #c7d2fe; | |
| font-size: 14px; | |
| }} | |
| .msg-box {{ | |
| margin-bottom: 16px; | |
| padding: 16px 20px; | |
| border-radius: 14px; | |
| background: var(--card-bg); | |
| border: 1px solid var(--border-color); | |
| }} | |
| .msg-box.user {{ | |
| border-left: 4px solid var(--accent-cyan); | |
| }} | |
| .msg-box.astra {{ | |
| border-left: 4px solid var(--accent-purple); | |
| background: #141c2e; | |
| }} | |
| .msg-box.system {{ | |
| border-left: 4px solid #f59e0b; | |
| font-size: 13px; | |
| color: var(--text-dim); | |
| }} | |
| .msg-header {{ | |
| font-weight: 700; | |
| font-size: 13px; | |
| text-transform: uppercase; | |
| letter-spacing: 0.05em; | |
| margin-bottom: 8px; | |
| color: #e0e7ff; | |
| }} | |
| .msg-body {{ | |
| white-space: pre-wrap; | |
| word-break: break-word; | |
| }} | |
| .footer {{ | |
| text-align: center; | |
| color: var(--text-dim); | |
| font-size: 13px; | |
| margin-top: 30px; | |
| }} | |
| </style> | |
| </head> | |
| <body> | |
| <div class="container"> | |
| <div class="header"> | |
| <h1>🌟 Nova AI (ChatGPT Astra Edition)</h1> | |
| <p>Exported Transcript • {timestamp}</p> | |
| </div> | |
| {''.join(messages_html)} | |
| <div class="footer">Generated by Nova AI • Next-Gen Conversational Intelligence</div> | |
| </div> | |
| </body> | |
| </html>""" | |
| return html_content | |
| class LatencyTracker: | |
| """High-resolution latency, time-to-first-token, and throughput tracker.""" | |
| def __init__(self): | |
| self.start_time: float = 0.0 | |
| self.first_token_time: float = 0.0 | |
| self.end_time: float = 0.0 | |
| self.token_count: int = 0 | |
| def start(self): | |
| self.start_time = time.perf_counter() | |
| self.first_token_time = 0.0 | |
| self.end_time = 0.0 | |
| self.token_count = 0 | |
| def record_token(self, count: int = 1): | |
| self.token_count += count | |
| if self.first_token_time == 0.0: | |
| self.first_token_time = time.perf_counter() | |
| def stop(self) -> Dict[str, float]: | |
| self.end_time = time.perf_counter() | |
| total_time = max(self.end_time - self.start_time, 0.0001) | |
| ttft = max(self.first_token_time - self.start_time, 0.0) if self.first_token_time > 0 else 0.0 | |
| tps = self.token_count / total_time if self.token_count > 0 else 0.0 | |
| return { | |
| "total_latency_seconds": round(total_time, 3), | |
| "time_to_first_token_ms": round(ttft * 1000, 1), | |
| "tokens_generated": self.token_count, | |
| "tokens_per_second": round(tps, 1) | |
| } | |