import os
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("meta-llama/Meta-Llama-3-8B",token=os.getenv('HF_API_TOKEN'))

# ## None type 
# def respond(
#     message: str,
#     history: list[tuple[str, str]],  # This will not be used
#     system_message: str,
#     max_tokens: int,
#     temperature: float,
#     top_p: float,
# ):
#     messages = [{"role": "system", "content": system_message}]
    
#     # Append only the latest user message
#     messages.append({"role": "user", "content": message})

#     response = ""

#     try:
#         # Generate response from the model
#         for message in client.chat_completion(
#             messages,
#             max_tokens=max_tokens,
#             stream=True,
#             temperature=temperature,
#             top_p=top_p,
#         ):
#             if message.choices[0].delta.content is not None:
#                 token = message.choices[0].delta.content
#                 response += token
#             yield response
#     except Exception as e:
#         yield f"An error occurred: {e}"

# """
# 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()


####19
def respond(
    message: str,
    history: list[tuple[str, str]],  # This will not be used
    system_message: str,
    max_tokens: int,
    temperature: float,
    top_p: float,
):
    # Combine the system message and user input into a single prompt
    prompt = f"{system_message}\n{message}"

    response = ""

    try:
        # Generate response from the model using text generation method
        for message in client.text_generation(
            prompt=prompt,
            max_new_tokens=max_tokens,
            temperature=temperature,
            top_p=top_p,
        ):
            if message.token is not None:
                response += message.token
            yield response
    except Exception as e:
        yield f"An error occurred: {e}"

# Define the Gradio interface
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()

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

# # Initialize the Hugging Face Inference Client
# client = InferenceClient(
#     "meta-llama/Meta-Llama-3.1-8B-Instruct",
#     token=  os.getenv("HF_API_TOKEN"),# Replace with your actual token
# )

# # Define a function to handle the chat input and get a response from the model
# def chat_with_model(user_input):
#     # Call the client to get the model's response
#     response = ""
#     for message in client.chat_completion(
#         messages=[{"role": "user", "content": user_input}],
#         max_tokens=500,
#         stream=True,
#     ):
#         response += message.choices[0].delta.content
#     return response

# # Create a Gradio interface with a chat component
# with gr.Blocks() as demo:
#     chatbot = gr.Chatbot()
#     with gr.Row():
#         txt = gr.Textbox(show_label=False, placeholder="Type your message here...")
#         txt.submit(chat_with_model, inputs=txt, outputs=chatbot)

# demo.launch()