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import gradio as gr
from huggingface_hub import InferenceClient
import time
client = InferenceClient("lambdaindie/lambdai")
css = """
@import url('https://fonts.googleapis.com/css2?family=JetBrains+Mono&display=swap');
body {
background-color: #111;
color: #e0e0e0;
font-family: 'JetBrains Mono', monospace;
margin: 0;
padding: 0;
width: 100vw;
overflow-x: hidden;
}
.gradio-container, .gr-block.gr-box {
width: 100vw !important;
max-width: 100vw !important;
box-sizing: border-box;
}
.gr-button {
background: linear-gradient(to right, #2a2a2a, #1f1f1f);
color: white;
border-radius: 10px;
padding: 8px 16px;
font-weight: bold;
font-family: 'JetBrains Mono', monospace;
}
.gr-button:hover {
background: #333;
}
.gr-textbox textarea {
background-color: #181818 !important;
color: #fff !important;
font-family: 'JetBrains Mono', monospace !important;
border-radius: 8px;
}
.gr-chat-message, .gr-chatbot {
font-family: 'JetBrains Mono', monospace !important;
width: 100% !important;
max-width: 100% !important;
}
.markdown-think {
background-color: #1e1e1e;
border-left: 4px solid #555;
padding: 10px;
margin-bottom: 8px;
font-style: italic;
white-space: pre-wrap;
font-family: 'JetBrains Mono', monospace;
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 before answering."
}
]
reasoning = ""
yield '<div class="markdown-think">Thinking...</div>'
for chunk in client.chat_completion(
thinking_prompt,
max_tokens=max_tokens,
stream=True,
temperature=temperature,
top_p=top_p,
):
token = chunk.choices[0].delta.content or ""
reasoning += token
styled_thought = f'<div class="markdown-think">{reasoning.strip()}</div>'
yield styled_thought
time.sleep(0.5)
final_prompt = messages + [
{"role": "user", "content": message},
{"role": "assistant", "content": reasoning.strip()},
{"role": "user", "content": "Now answer based on your reasoning above."}
]
final_answer = ""
for chunk in client.chat_completion(
final_prompt,
max_tokens=max_tokens,
stream=True,
temperature=temperature,
top_p=top_p,
):
token = chunk.choices[0].delta.content or ""
final_answer += token
yield final_answer.strip()
demo = gr.ChatInterface(
fn=respond,
title="λmabdAI", # Título visível agora
theme=gr.themes.Base(primary_hue="gray"),
css=css,
additional_inputs=[
gr.Textbox(
value="You are a concise, logical AI that explains its reasoning clearly before answering.",
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() |