import gradio as gr
from huggingface_hub import InferenceClient

# Step 1: Read your background info
with open("BACKGROUND.md", "r", encoding="utf-8") as f:
    background_text = f.read()

# Step 2: Set up your InferenceClient (same as before)
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")

def respond(
    message,
    history: list[tuple[str, str]],
    system_message,
    max_tokens,
    temperature,
    top_p,
):
    messages = [{"role": "system", "content": system_message}]
    
    for val in history:
        if val[0]:
            messages.append({"role": "user", "content": val[0]})
        if val[1]:
            messages.append({"role": "assistant", "content": val[1]})

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

    response = ""

    for msg in client.chat_completion(
        messages,
        max_tokens=max_tokens,
        stream=True,
        temperature=temperature,
        top_p=top_p,
    ):
        token = msg.choices[0].delta.content
        response += token
        # 'yield' returns partial responses for streaming
        yield response

# Step 3: Build a Gradio Blocks interface with two Tabs
with gr.Blocks() as demo:
    # (A) First Tab: Chat Interface
    with gr.Tab("GPT Chat Agent"):
        gr.Markdown("## Welcome to Varun's GPT Agent")
        gr.Markdown("Feel free to ask questions about Varun’s journey, skills, and more!")
        chat = gr.ChatInterface(
            respond,
            additional_inputs=[
                gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
                gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
                gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
                gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)"),
            ],
        )

    # (B) Second Tab: Background Document
    with gr.Tab("Varun's Background"):
        gr.Markdown("# About Varun")
        gr.Markdown(background_text)

# Step 4: Launch
if __name__ == "__main__":
    demo.launch()