Evandro Fonseca Junior
fb37807c31
- Updated `package.json` to include new dependencies: Emotion, MUI Joy, Lucide React, and Tailwind CSS. - Refactored `src/App.tsx` to implement a new layout with a sidebar and chat interface using MUI components. - Added Tailwind CSS import in `src/App.css` for styling. - Improved user interaction elements with icons and tooltips for a better user experience.
135 lines
4.7 KiB
Python
135 lines
4.7 KiB
Python
import os
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import yaml
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import psutil
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from abc import ABC, abstractmethod
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from concurrent.futures import ThreadPoolExecutor
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from huggingface_hub import hf_hub_download
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from llama_cpp import Llama
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# --- Camada de Abstração (Interface) ---
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class AIModelEngine(ABC):
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@abstractmethod
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def generate_stream(self, prompt: str):
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pass
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@abstractmethod
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def reset_context(self):
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pass
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# --- Gerenciador de Recursos de Engenharia ---
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class ResourceManager:
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@staticmethod
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def get_optimal_threads() -> int:
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# Prioriza núcleos físicos para evitar perdas por troca de contexto de hardware (SMT/Hyper-Threading)
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physical_cores = psutil.cpu_count(logical=False)
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return max(1, physical_cores - 1)
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@staticmethod
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def get_available_ram_mb() -> int:
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return psutil.virtual_memory().available // (1024 * 1024)
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# --- Implementação Llama-CPP ---
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class LlamaCPPEngine(AIModelEngine):
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def __init__(self, config: dict, model_path: str):
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threads = ResourceManager.get_optimal_threads()
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self.llm = Llama(
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model_path=model_path,
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n_ctx=config.get('context_window', 2048),
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n_threads=threads,
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n_gpu_layers=0, # Auto-ajuste para estabilidade inicial
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n_batch=512,
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verbose=False
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)
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self.history = []
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self.config = config
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def generate_stream(self, user_input: str):
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self.history.append({"role": "user", "content": user_input})
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# Implementação de Sliding Window rudimentar (mantém últimos 5 turnos)
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if len(self.history) > 10:
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self.history = self.history[-10:]
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try:
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stream = self.llm.create_chat_completion(
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messages=self.history,
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stream=True,
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temperature=self.config.get('temperature', 0.1),
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max_tokens=self.config.get('max_tokens', 512)
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)
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full_response = ""
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for chunk in stream:
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delta = chunk['choices'][0].get('delta', {})
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if 'content' in delta:
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token = delta['content']
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full_response += token
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yield token
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self.history.append({"role": "assistant", "content": full_response})
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except Exception as e:
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self.reset_context()
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yield f"\n[ERRO CRÍTICO]: {str(e)}. Memória de contexto limpa."
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def reset_context(self):
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self.history = []
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self.llm.reset()
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# --- Engine Factory ---
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class EngineFactory:
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@staticmethod
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def create_engine(model_key: str, config_path: str, models_dir: str) -> AIModelEngine:
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with open(config_path, 'r') as f:
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config = yaml.safe_load(f)['models'][model_key]
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model_path = hf_hub_download(
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repo_id=config['repo_id'],
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filename=config['filename'],
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local_dir=models_dir
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)
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if config['type'] == 'llama-cpp':
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return LlamaCPPEngine(config, model_path)
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raise ValueError("Tipo de modelo não suportado.")
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# --- Loop Principal com Threading ---
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def main():
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MODELS_DIR = "./src-python/app/models"
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CONFIG_FILE = "modelConfig.yaml"
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print(f"[Sistema]: Detectando Hardware... Threads Otimizadas: {ResourceManager.get_optimal_threads()}")
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print(f"[Sistema]: RAM Disponível: {ResourceManager.get_available_ram_mb()} MB")
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engine = EngineFactory.create_engine('lfm_2.5_1.2b', CONFIG_FILE, MODELS_DIR)
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# Executor para gerenciar inferência fora da Main Thread
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with ThreadPoolExecutor(max_workers=1) as executor:
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while True:
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try:
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user_in = input("\n[Você]: ").strip()
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if user_in.lower() in ['sair', 'exit']: break
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if user_in.lower() == 'clear':
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engine.reset_context()
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print("Contexto resetado.")
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continue
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if not user_in: continue
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print("[IA]: ", end="", flush=True)
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# Executa a geração em thread separada para não bloquear o poll de IO do sistema
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future = executor.submit(list, engine.generate_stream(user_in))
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# Enquanto a thread processa, podemos exibir o stream
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# Nota: Para stream real em thread, usaríamos filas (Queue),
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# aqui simplificamos o consumo do gerador.
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for token in engine.generate_stream(user_in):
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print(token, end="", flush=True)
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print()
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except KeyboardInterrupt:
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break
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if __name__ == "__main__":
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main() |