app.py

import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline

model_id = "psy191190/LimitlessAGI"  # e.g. your Llama 3.2 3B Instruct

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")

pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, max_new_tokens=1024)

def respond(history):
    messages = []
    for msg in history:
        messages.append({"role": msg["role"], "content": msg["content"]})
    
    prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
    output = pipe(prompt)[0]["generated_text"]
    response = output[len(prompt):].strip()
    
    yield response

with gr.Blocks() as demo:
    gr.ChatInterface(respond)

demo.queue().launch()