app.py

import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
import torch
import threading

model_name = "lambdaindie/lambda-1v-1B"

# Carrega modelo na CPU de forma mais leve
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.float16,  # ou torch.bfloat16 se suportar
    low_cpu_mem_usage=True
)
tokenizer = AutoTokenizer.from_pretrained(model_name)

stop_flag = {"stop": False}

def respond(prompt, history):
    stop_flag["stop"] = False
    history = history[-3:]  # Mantém apenas os últimos 3 pares

    full_prompt = f"\nThink a bit step-by-step before answering.\nQuestion: {prompt}\nAnswer:"
    inputs = tokenizer(full_prompt, return_tensors="pt")

    streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)

    generation_thread = threading.Thread(
        target=model.generate,
        kwargs={
            "input_ids": inputs["input_ids"],
            "attention_mask": inputs["attention_mask"],
            "max_new_tokens": 512,
            "do_sample": True,
            "temperature": 0.7,
            "top_p": 0.9,
            "pad_token_id": tokenizer.eos_token_id,
            "streamer": streamer,
        }
    )
    generation_thread.start()

    reasoning = ""
    for new_text in streamer:
        if stop_flag["stop"]:
            return "", history
        reasoning += new_text
        yield "", history + [(prompt, f"<div class='final-answer'>{reasoning}</div>")]

def stop_generation():
    stop_flag["stop"] = True

# Interface Gradio
with gr.Blocks(css="""
    #chatbot, .gr-markdown, .gr-button, .gr-textbox {  
        font-family: 'JetBrains Mono', monospace !important;  
        font-size: 11px !important;  
    }  
    .final-answer {  
        background-color: #1e1e1e;  
        color: #ffffff;  
        padding: 10px;  
        border-left: 4px solid #4caf50;  
        white-space: pre-wrap;  
        font-size: 11px !important;  
    }
""") as demo:
    gr.Markdown("## λambdAI — Reasoning Chat")

    chatbot = gr.Chatbot(elem_id="chatbot")
    with gr.Row():
        txt = gr.Textbox(placeholder="Digite sua pergunta...", show_label=False)
        send_btn = gr.Button("Enviar")
        stop_btn = gr.Button("Parar")

    send_btn.click(respond, [txt, chatbot], [txt, chatbot])
    txt.submit(respond, [txt, chatbot], [txt, chatbot])
    stop_btn.click(stop_generation, None, None)

    demo.launch(share=True)