import gradio as gr
from huggingface_hub import InferenceClient
import requests

"""
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
"""

TRAY_API_URL = "https://1591a0e5-d083-483b-a8b8-21fc282cdb21-api.trayapp.io/getResponse"



def respond_chatgpt(message, history, system_message, max_tokens, temperature, top_p):
    messages = [{"role": "system", "content": system_message}]

    # Prepare the conversation history for the model
    for user, assistant in history:
        if user:
            messages.append({"role": "user", "content": user})
        if assistant:
            messages.append({"role": "assistant", "content": assistant})

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

    # Call the Hugging Face model
    response = ""

    # Tray.io API call
    try:
        tray_response = requests.get(TRAY_API_URL, params={"query": message})

        # Process Tray.io API response
        if tray_response.status_code == 200:
            tray_data = tray_response.json()
            tray_message = tray_data.get("message", "The agent did not return a response.")
            response += f"\n\nTAnswer: {tray_message}"
        else:
            response += "\n\nError: Failed to retrieve response from Tray.io."

    except requests.RequestException as e:
        response += f"\n\nError calling Tray.io API: {e}"

    yield response


"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""
demo = gr.ChatInterface(
    respond_chatgpt,
    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)",
        ),
    ],
)


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