app.py

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');

{
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 a bit step-by-step before answering."  
}]  

reasoning = ""  
yield '<div class="markdown-think">Thinking...</div>'  

start = time.time()  

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  

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>  
"""  

time.sleep(2)  

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="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()