| import gradio as gr |
| import torch |
| from transformers import AutoTokenizer, AutoModelForCausalLM |
| from peft import PeftModel |
|
|
| BASE_MODEL = "TinyLlama/TinyLlama-1.1B-Chat-v1.0" |
| LORA_MODEL = "Logi6023/LogiAI-tiny" |
|
|
| print("🤖 LogiAI betöltése...") |
| tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL) |
| model = AutoModelForCausalLM.from_pretrained( |
| BASE_MODEL, |
| torch_dtype=torch.float32, |
| device_map="cpu", |
| ) |
| model = PeftModel.from_pretrained(model, LORA_MODEL) |
| model.eval() |
| print("✅ LogiAI kész!") |
|
|
| TEMPLATE = "### Instruction:\n{}\n\n### Input:\n\n\n### Response:\n" |
|
|
| def logiai_chat(message, history): |
| prompt = TEMPLATE.format(message) |
| inputs = tokenizer([prompt], return_tensors="pt") |
| with torch.no_grad(): |
| outputs = model.generate( |
| **inputs, |
| max_new_tokens=256, |
| temperature=0.7, |
| do_sample=True, |
| pad_token_id=tokenizer.eos_token_id, |
| ) |
| result = tokenizer.decode(outputs[0], skip_special_tokens=True) |
| return result.split("### Response:")[-1].strip() |
|
|
| demo = gr.ChatInterface( |
| fn=logiai_chat, |
| title="🤖 LogiAI", |
| description="Saját AI asszisztens — kód, fájlok, CMD parancsok", |
| examples=["Ki vagy te?", "Írj Python hello world-öt!", "Mit tudsz csinálni?"], |
| ) |
|
|
| demo.launch() |
