app.py

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

# Configuração do modelo
model_id = "lambdaindie/lambda-1v-1B"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32
)
device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)
model.eval()

# CSS visual
css = """
@import url('https://fonts.googleapis.com/css2?family=JetBrains+Mono&display=swap');
* {
    font-family: 'JetBrains Mono', monospace !important;
}
html, body, .gradio-container {
    background-color: #111 !important;
    color: #e0e0e0 !important;
}
textarea, input, button, select {
    background-color: transparent !important;
    color: #e0e0e0 !important;
    border: 1px solid #444 !important;
}
"""

# Controle global de parada
stop_signal = False

def stop_stream():
    global stop_signal
    stop_signal = True

# Geração com streaming
def generate_response(message, max_tokens, temperature, top_p):
    global stop_signal
    stop_signal = False

    prompt = f"Question: {message}\nThinking: \nAnswer:"
    inputs = tokenizer(prompt, return_tensors="pt").to(device)

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

    generation_kwargs = dict(
        input_ids=inputs["input_ids"],
        attention_mask=inputs["attention_mask"],
        streamer=streamer,
        max_new_tokens=max_tokens,
        temperature=temperature,
        top_p=top_p,
        do_sample=True,
        eos_token_id=tokenizer.eos_token_id
    )

    thread = threading.Thread(target=model.generate, kwargs=generation_kwargs)
    thread.start()

    full_text = ""
    for token in streamer:
        if stop_signal:
            break
        full_text += token
        yield full_text.strip()

    if stop_signal:
        return

# Interface Gradio
with gr.Blocks(css=css) as app:
    chatbot = gr.Chatbot(label="λ", elem_id="chatbot")
    msg = gr.Textbox(label="Mensagem", placeholder="Digite aqui...", lines=2)
    send_btn = gr.Button("Enviar")
    stop_btn = gr.Button("Parar")

    max_tokens = gr.Slider(64, 512, value=128, step=1, label="Max Tokens")
    temperature = gr.Slider(0.1, 1.5, value=0.7, step=0.1, label="Temperature")
    top_p = gr.Slider(0.1, 1.0, value=0.95, step=0.05, label="Top-p")

    state = gr.State([])  # histórico apenas visual

    def update_chat(message, chat_history):
        chat_history = chat_history + [(message, None)]  # adiciona só a pergunta
        return "", chat_history

    def generate_full(chat_history, max_tokens, temperature, top_p):
        message = chat_history[-1][0]  # última mensagem enviada
        visual_history = chat_history[:-1]  # remove temporariamente a entrada pendente

        full_response = ""
        for chunk in generate_response(message, max_tokens, temperature, top_p):
            full_response = chunk
            yield visual_history + [(message, full_response)], visual_history + [(message, full_response)]

    send_btn.click(update_chat, inputs=[msg, state], outputs=[msg, state]) \
        .then(generate_full, inputs=[state, max_tokens, temperature, top_p], outputs=[chatbot, state])

    stop_btn.click(stop_stream, inputs=[], outputs=[])

app.launch(share=True)