app.py

import os
import gradio as gr
from huggingface_hub import InferenceClient

client = InferenceClient("lambdaindie/lambdai", token=os.environ["HF_TOKEN"])

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})

    messages.append({"role": "user", "content": message})
    response = ""

    for chunk in client.text_generation(
        messages,
        max_new_tokens=max_tokens,
        stream=True,
        temperature=temperature,
        top_p=top_p,
    ):
        token = chunk.choices[0].delta.content
        response += token
        yield response

with gr.Blocks() as demo:
    gr.Markdown("# 🧠 lambdai — Chat Demo")
    
    chatbot = gr.Chatbot()
    with gr.Row():
        system_msg = gr.Textbox(label="System message", placeholder="e.g. You are a helpful assistant.")
    with gr.Row():
        max_tokens = gr.Slider(1, 2048, value=512, step=1, label="Max tokens")
        temperature = gr.Slider(0.1, 4.0, value=0.7, step=0.1, label="Temperature")
        top_p = gr.Slider(0.1, 1.0, value=0.95, step=0.05, label="Top-p")
    msg = gr.Textbox(placeholder="Ask something...", label="Your message")

    state = gr.State([])

    def user_submit(user_message, history):
        return "", history + [[user_message, None]]

    def generate_response(message, history, sys_msg, max_tokens, temperature, top_p):
        gen = respond(message, history, sys_msg, max_tokens, temperature, top_p)
        return gen, history

    msg.submit(user_submit, [msg, state], [msg, state], queue=False).then(
        generate_response,
        [msg, state, system_msg, max_tokens, temperature, top_p],
        [chatbot, state]
    )

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