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import gradio as gr
import torch
import time
import threading
from transformers import AutoTokenizer, AutoModelForCausalLM, TextIteratorStreamer
# === Carregar modelo local (CPU) ===
model_name = "lambdaindie/lambda-1v-1B" # troque pelo teu
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name).to("cpu") # <- CPU aqui
# === Streamer global para interrupção ===
stop_signal = {"stop": False}
def generate_stream(prompt, max_tokens=512, temperature=0.7, top_p=0.95):
stop_signal["stop"] = False
inputs = tokenizer(prompt, return_tensors="pt").to("cpu")
streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
generation_thread = threading.Thread(
target=model.generate,
kwargs=dict(
input_ids=inputs["input_ids"],
attention_mask=inputs["attention_mask"],
streamer=streamer,
max_new_tokens=max_tokens,
do_sample=True,
temperature=temperature,
top_p=top_p,
pad_token_id=tokenizer.eos_token_id,
)
)
generation_thread.start()
output = ""
for token in streamer:
if stop_signal["stop"]:
break
output += token
yield output.strip()
def stop_stream():
stop_signal["stop"] = True
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[-3:]: # Limita a 3 interações passadas
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 before answering."}]
thinking_text = "\n".join([f"{m['role']}: {m['content']}" for m in thinking_prompt])
reasoning = ""
yield '<div class="markdown-think">Thinking...</div>'
start = time.time()
for token in generate_stream(thinking_text, max_tokens, temperature, top_p):
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>
"""
final_prompt = thinking_text + f"\n\nuser: {message}\nassistant: {reasoning.strip()}\nuser: Now answer based on your reasoning above.\nassistant:"
final_answer = ""
for token in generate_stream(final_prompt, max_tokens, temperature, top_p):
final_answer = token
yield final_answer.strip()
# === Interface ===
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",
input_background_fill="#222",
input_border_color="#444",
button_primary_background_fill="#333",
button_primary_text_color="#e0e0e0",
)
chatbot = gr.ChatInterface(
fn=respond,
title="λambdAI",
css=css,
theme=theme,
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")
]
)
stop_btn = gr.Button("Parar Geração")
stop_btn.click(fn=stop_stream, inputs=[], outputs=[])
app = gr.Blocks()
with app:
chatbot.render()
stop_btn.render()
if __name__ == "__main__":
app.launch(share=True) |