import gradio as gr
from huggingface_hub import InferenceClient
import os
import requests

CHAT_URL = os.getenv("CHAT_URL")
PROJECT_ID = os.getenv("PROJECT_ID")

def respond(
    message,
    history: list[dict[str, str]],
    system_message,
    token,
    # max_tokens,
    # temperature,
    # top_p,
    # hf_token: gr.OAuthToken,
):
    """
    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(token=hf_token.token, model="openai/gpt-oss-20b")

    # messages = [{"role": "system", "content": system_message}]

    # messages.extend(history)

    # 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,
    # ):
    #     choices = message.choices
    #     token = ""
    #     if len(choices) and choices[0].delta.content:
    #         token = choices[0].delta.content

    #     response += token
    #     yield response

    req = requests.post(
        CHAT_URL,
        json={
            "project_id": PROJECT_ID,
            "session_id":system_message,
            "user_input":message,
            "update_variables": {
                "alfamidi":"",
                "alfagift":"",
                "karir":"",
                "layanan":"",
                "program":"",
                "voucher":"",
                "results":"",
                "identitas":"",
                "kerjasama":""
            },
            "output_variables": ["results"]
        },
        headers={"Authorization" : f"Bearer {token}"}
    )

    out = req.json()["data"]["results"]

    print("[OUT]",out)

    return out.encode('utf-8').decode('unicode_escape')


"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""
chatbot = gr.ChatInterface(
    respond,
    type="messages",
    additional_inputs=[
        gr.Textbox(value="<TEST_123>", label="session_id"),
        gr.Textbox(value="[TOKEN]", label="token"),
        # 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)",
        # ),
    ],
)

with gr.Blocks() as demo:
    chatbot.render()
    title = gr.HTML("<h3>Use #ai to ask the ai</h3>")


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