# 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,
#     max_tokens,
#     temperature,
#     top_p,
# ):
#     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
# """
# demo = gr.ChatInterface(
#     respond,
#     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()

from PIL import Image
import base64
import io
import gradio as gr
from huggingface_hub import InferenceClient
import requests

# Function to convert image to base64
def image_to_base64(image: Image):
    buffered = io.BytesIO()
    image.save(buffered, format="PNG")
    return base64.b64encode(buffered.getvalue()).decode("utf-8")

# Function to respond to input
def respond(message: str, image: Image):
    # Convert image to base64
    image_base64 = image_to_base64(image)

    # Initialize the Hugging Face client
    client = InferenceClient("your_huggingface_model")

    try:
        # Call the text-to-image method
        response_data = client.text_to_image(images=image_base64, prompt=message)

        # Convert the response data (image) into a PIL Image
        image_response = Image.open(io.BytesIO(response_data))

        # Format the response in the required 'messages' format
        response_message = {
            'role': 'assistant',  # Assuming the response is from the assistant
            'content': image_response
        }
        
        return response_message
    except Exception as e:
        return {"role": "assistant", "content": str(e)}

# Define the Gradio interface
def create_interface():
    with gr.Blocks() as demo:
        chatbot = gr.Chatbot(type='messages')  # 'messages' format for chatbot
        message_input = gr.Textbox()
        image_input = gr.Image(type='pil')  # Image input as PIL image

        # Define the interaction
        message_input.submit(respond, inputs=[message_input, image_input], outputs=[chatbot])

    return demo

# Launch the interface
if __name__ == "__main__":
    create_interface().launch(share=True)  # Set share=True if you want to share the link publicly