import gradio as gr
from huggingface_hub import InferenceClient

"""
For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
"""
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")


def respond(
    message,
    history: list[tuple[str, str]],
    system_message="You are a friendly Chatbot.",
    max_tokens=1024,
    temperature=0.7,
    top_p=0.95,
):
    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 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

"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""
chat_balangay = gr.ChatInterface(
    respond,
    title = "Philippine OmniCorpus: A Knowledge Hub for Legal, Media, Cultural, and Historical Insights",
    examples = ["What are the key provisions of the Philippine Data Privacy Act?", "Please provide a summary of the coverage on the Taal Volcano eruption.","Tell me about the traditional festivals celebrated in the Philippines."],
)


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