app.py

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

model_id = "lambdaindie/lambdai"
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)

css = """
@import url('https://fonts.googleapis.com/css2?family=JetBrains+Mono&display=swap');
* { font-family: 'JetBrains Mono', monospace !important; }
body { background-color: #111; color: #e0e0e0; }
.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; }
}
"""

def respond(message, history, system_message, max_tokens, temperature, top_p):
    messages = [{"role": "system", "content": system_message}] if system_message else []

    for user, assistant in history:
        if user:
            messages.append({"role": "user", "content": user})
        if assistant:
            messages.append({"role": "assistant", "content": assistant})

    thinking_prompt = messages + [{"role": "user", "content": f"{message}\n\nThink step-by-step."}]
    prompt = tokenizer.apply_chat_template(thinking_prompt, tokenize=False, add_generation_prompt=True)

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

    reasoning = ""
    yield '<div class="markdown-think">Thinking...</div>'
    start = time.time()

    thread = threading.Thread(target=model.generate, kwargs={
        "inputs": inputs["input_ids"],
        "max_new_tokens": max_tokens,
        "temperature": temperature,
        "top_p": top_p,
        "streamer": streamer,
    })
    thread.start()

    for token in streamer:
        reasoning += token
        yield f'<div class="markdown-think">{reasoning.strip()}</div>'

    elapsed = time.time() - start
    yield f"""<div style="margin-top:12px;padding:8px 12px;background-color:#222;border-left:4px solid #888;
              font-family:'JetBrains Mono', monospace;color:#ccc;font-size:14px;">
              Pensou por {elapsed:.1f} segundos</div>"""

    # Segunda etapa: resposta final
    final_prompt = messages + [
        {"role": "user", "content": message},
        {"role": "assistant", "content": reasoning.strip()},
        {"role": "user", "content": "Agora responda baseado nisso."}
    ]
    prompt2 = tokenizer.apply_chat_template(final_prompt, tokenize=False, add_generation_prompt=True)
    inputs2 = tokenizer(prompt2, return_tensors="pt").to(device)
    streamer2 = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)

    thread2 = threading.Thread(target=model.generate, kwargs={
        "inputs": inputs2["input_ids"],
        "max_new_tokens": max_tokens,
        "temperature": temperature,
        "top_p": top_p,
        "streamer": streamer2,
    })
    thread2.start()

    final_answer = ""
    for token in streamer2:
        final_answer += token
        yield final_answer.strip()

demo = gr.ChatInterface(
    fn=respond,
    title="λambdAI",
    theme=gr.themes.Base(),
    css=css,
    additional_inputs=[
        gr.Textbox(value="", label="System Message"),
        gr.Slider(64, 2048, value=512, step=1, label="Max Tokens"),
        gr.Slider(0.1, 2.0, value=0.7, step=0.1, label="Temperature"),
        gr.Slider(0.1, 1.0, value=0.95, step=0.05, label="Top-p")
    ]
)

if __name__ == "__main__":
    demo.launch()