import gradio as gr
from huggingface_hub import InferenceClient
import time

client = InferenceClient("lambdaindie/lambdai")

def respond(message, history, system_message, max_tokens, temperature, top_p):
    # Build base message history
    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})

    # Phase 1 — Thinking aloud (reasoning step)
    thinking_prompt = messages + [
        {
            "role": "user",
            "content": f"{message}\n\nThink step-by-step before answering."
        }
    ]

    reasoning = ""
    yield "**Thinking...**\n```markdown\n```"  # Trigger gray markdown block

    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
        yield f"**Thinking...**\n```markdown\n{reasoning.strip()}```"

    time.sleep(0.5)  # Optional dramatic pause

    # Phase 2 — Final answer
    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(
    respond,
    title="LENIRΛ",
    theme=gr.themes.Base(primary_hue="gray", font=["JetBrains Mono", "monospace"]),
    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()