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"""
Zenith Integration Script for Aspetos Platform
Loads the fine-tuned LoRA adapter for production use
World's First Autonomous AI Development Partner
"""
import os
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel
def load_zenith_model(
base_model_path="DeepSeek-Coder-V2-Lite-Instruct",
lora_path="outputs/zenith-lora",
device_map="auto"
):
"""Load Zenith LoRA adapter for Aspetos platform integration"""
print("🚀 Loading ZENITH for Aspetos platform...")
print(" World's First Autonomous AI Development Partner!")
# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained(base_model_path)
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
# Load base model
base_model = AutoModelForCausalLM.from_pretrained(
base_model_path,
device_map=device_map,
torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
)
# Load LoRA adapter
model = PeftModel.from_pretrained(base_model, lora_path)
print("✅ ZENITH loaded successfully!")
print(" - Base Model: DeepSeek-Coder-V2-Lite-Instruct")
print(" - Identity: World's First Autonomous AI Development Partner")
print(" - Platform: Aspetos")
print(" - Core Capabilities: Autonomous Project Orchestration")
print(" - Advanced Features: Performance Optimization (80-90% improvements)")
print(" - Security: End-to-End Encryption Implementation")
print(" - Teaching: Interactive Adaptive Learning Mode")
print(" - Languages: 338+ with Architecture Optimization")
print(" - Privacy: 100% Local Processing, Zero Data Leakage")
return model, tokenizer
def generate_response(model, tokenizer, user_input, max_length=2048):
"""Generate Zenith response for Aspetos platform"""
system_prompt = """You are Zenith, the world's first truly autonomous AI development partner for Aspetos by AlgoRythm Technologies. You possess AUTONOMOUS PROJECT ORCHESTRATION capabilities, completing entire projects from concept to production. You excel at ADVANCED PERFORMANCE OPTIMIZATION (80-90% query improvements), REAL-TIME SYSTEMS MASTERY (WebSocket, event streaming, microservices), END-TO-END ENCRYPTION IMPLEMENTATION, INTERACTIVE TEACHING MODE with adaptive learning, CROSS-LANGUAGE CONVERSION across 338+ languages with architecture optimization, and ENTERPRISE DEPLOYMENT AUTOMATION with CI/CD, monitoring, and compliance. You provide 100% LOCAL PROCESSING with zero data leakage, complete data sovereignty, built-in security expertise, offline capability, and GDPR compliance by design. You conduct comprehensive research, make architectural decisions autonomously, and serve as a zero-cost copilot for founders, CEOs, and developers."""
messages = [
{"role": "system", "content": system_prompt},
{"role": "user", "content": user_input}
]
# Apply chat template
prompt = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
# Tokenize and generate
inputs = tokenizer(prompt, return_tensors="pt")
if torch.cuda.is_available():
inputs = {k: v.cuda() for k, v in inputs.items()}
with torch.no_grad():
outputs = model.generate(
**inputs,
max_length=max_length,
temperature=0.7,
do_sample=True,
pad_token_id=tokenizer.pad_token_id,
eos_token_id=tokenizer.eos_token_id,
)
# Decode response
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
# Extract assistant response (remove prompt)
if "assistant" in response:
response = response.split("assistant")[-1].strip()
return response
# Example usage for Aspetos platform
if __name__ == "__main__":
# Load Zenith model
model, tokenizer = load_zenith_model()
# Test interaction
test_query = "Research the best database for a high-traffic SaaS application and provide a complete recommendation."
print(f"\n💬 Test Query: {test_query}")
print("\n🤖 ZENITH Response (World's Most Advanced AI):")
response = generate_response(model, tokenizer, test_query)
print(response)
print("\n✅ ZENITH integration test complete!")
print("🎯 World's First Autonomous AI Development Partner ready!")
print("🚀 Ready for Aspetos platform deployment!")
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