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
):
    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=1024,
        stream=True,
        temperature=0.5,
        top_p=0.1,
    ):
        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
"""
demo = gr.ChatInterface(
    respond,
    examples=[['найди то, что еще не изучено']],
    additional_inputs=[
        gr.Textbox(value="if the prompt is 'найди то, что ещё не изучено', ask 'в какой области лингвистики или литературоведения?' after getting the answer, find the research gap using the given information and using both Google Scholar and Academia.edu and then formalize it as a bulleted list with 2-8 points", label="System message"),
    ],
    title="Research Gap Bot",
    css="""
body {
  margin: 0;
  font-family: Arial, sans-serif;
  display: flex;
  justify-content: center;
  align-items: center;
  height: 100vh;
  background: linear-gradient(135deg, #f8a8a8, #fdd9a8);
  overflow: hidden;
}
"""
)


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