"""
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", "
")
if role == "system":
card = f"""
"""
elif role == "user":
card = f"""
"""
else:
card = f"""
"""
messages_html.append(card)
html_content = f"""
{html.escape(title)} - Nova AI
{''.join(messages_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)
}