from huggingface_hub import InferenceClient
from resume import data
import markdowm as md
import gradio as gr

client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")

# Chatbot response function with integrated system message
def respond(
        message,
        history: list[tuple[str, str]],
        max_tokens=1024,
        temperature=0.5,
        top_p=0.95,
):
    # System message defining assistant behavior
    system_message = {
        "role": "SARATH",
        "content": f"Act and chat as SARATH to thw user question. SARATH is a professional fresher seeking a job pursuing Btech-4-1 and your name is SARATH."
                   f"Here is about SARATH:```{data}```. You should answer questions based on this information only."
                   f"If the question is irreverent to the contex=[about sarath], polity reply the user"
    }

    messages = [system_message]

    # Adding conversation history
    for val in history:
        if val[0]:
            messages.append({"role": "user", "content": val[0]})
        if val[1]:
            messages.append({"role": "assistant", "content": val[1]})

    # Adding the current user input
    messages.append({"role": "user", "content": message})

    response = ''

    # Streaming the response from the API
    for message in client.chat_completion(
            messages,
            max_tokens=max_tokens,
            stream=True,
            temperature=temperature,
            top_p=top_p,
    ):
        token = message.choices[0].delta.content
        response += token
        yield response


# Gradio interface with additional sliders for control
with gr.Blocks(theme=gr.themes.Ocean(font=[gr.themes.GoogleFont("Roboto Mono")])) as main:
    gr.Markdown(md.title)
    with gr.Tabs():
        with gr.TabItem("Intro"):
            gr.Markdown(md.description)
            gr.Markdown("# Resume:")
            gr.Markdown(data)

        with gr.TabItem("My2.0"):
            gr.ChatInterface(respond)

if __name__ == "__main__":
    main.launch(share=True)