app.py

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

# Carregar modelo local
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
)
model.to("cuda" if torch.cuda.is_available() else "cpu")
model.eval()

# Estilo
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;
}
.markdown-think {
    background-color: #1e1e1e;
    border-left: 4px solid #555;
    padding: 10px;
    margin-bottom: 8px;
    font-style: italic;
    white-space: pre-wrap;
    animation: pulse 1.5s infinite ease-in-out;
}
@keyframes pulse {
    0% { opacity: 0.6; }
    50% { opacity: 1.0; }
    100% { opacity: 0.6; }
}
"""

theme = gr.themes.Base(
    primary_hue="gray",
    font=[gr.themes.GoogleFont("JetBrains Mono"), "monospace"]
).set(
    body_background_fill="#111",
    body_text_color="#e0e0e0",
    button_primary_background_fill="#333",
    button_primary_text_color="#e0e0e0",
    input_background_fill="#222",
    input_border_color="#444",
    block_title_text_color="#fff"
)

# Flag de parada
stop_signal = False

def stop_stream():
    global stop_signal
    stop_signal = True

def respond(history, system_message, max_tokens, temperature, top_p):
    global stop_signal
    stop_signal = False

    # Construir prompt
    prompt = ""
    if system_message:
        prompt += system_message + "\n\n"

    for msg in history:
        role = msg["role"]
        content = msg["content"]
        if role == "user":
            prompt += f"User: {content}\n"
        elif role == "assistant":
            prompt += f"Assistant: {content}\n"

    prompt += "Assistant:"

    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)

    generation_kwargs = dict(
        **inputs,
        streamer=streamer,
        max_new_tokens=max_tokens,
        temperature=temperature,
        top_p=top_p,
        do_sample=True,
    )

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

    output = ""
    start = time.time()

    for token in streamer:
        if stop_signal:
            break
        output += token
        yield history + [{"role": "assistant", "content": output}]

    end = time.time()
    yield history + [
        {"role": "assistant", "content": output},
        {"role": "system", "content": f"Pensou por {end - start:.1f} segundos"}
    ]

# Interface
with gr.Blocks(css=css, theme=theme) as app:
    chatbot = gr.Chatbot(label="λ", type="messages")
    state = gr.State([])

    with gr.Row():
        msg = gr.Textbox(label="Mensagem")
        send_btn = gr.Button("Enviar")
        stop_btn = gr.Button("Parar")

    with gr.Accordion("Configurações Avançadas", open=False):
        system_message = gr.Textbox(label="System Message", value="")
        max_tokens = gr.Slider(64, 2048, value=256, step=1, label="Max Tokens")
        temperature = gr.Slider(0.1, 2.0, 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")

    def handle_user_msg(user_msg, chat_history):
        if user_msg:
            chat_history = chat_history + [{"role": "user", "content": user_msg}]
        return "", chat_history

    send_btn.click(fn=handle_user_msg, inputs=[msg, state], outputs=[msg, state])\
        .then(fn=respond, inputs=[state, system_message, max_tokens, temperature, top_p], outputs=[chatbot, state])

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

app.launch(share=True)