import gradio as gr
import os
import uuid
from chat_3 import Chat

# Function to initialize a new session and create chatbot instance for that session
def initialize_session():
    session_id = str(uuid.uuid4())[:8]  # Generate unique session ID
    chatbot = Chat()  # Create a new Chat instance for this session
    # chatbot = Chat("gemini-2.0-flash")
    history = []  # Initialize history for this session
    return "", session_id, chatbot, history  # "" for clearing input

# Function to handle user input and chatbot response
def chat_function(prompt, history, session_id, chatbot):
    if chatbot is None:
        return history, "", session_id, chatbot  # Skip if chatbot not ready

    # Append the user's input to the message history
    history.append({"role": "user", "content": prompt})

    # Get the response from the chatbot
    response = chatbot.chat(prompt)

    # Append the assistant's response to the message history
    history.append({"role": "assistant", "content": response})

    return history, "", session_id, chatbot  # Clear input

# Function to save feedback with chat history
def send_feedback(feedback, history, session_id, chatbot):
    os.makedirs("app/feedback", exist_ok=True)  # Create folder if not exists
    filename = f"app/feedback/feedback_{session_id}.txt"
    with open(filename, "a", encoding="utf-8") as f:
        f.write("=== Feedback Received ===\n")
        f.write(f"Session ID: {session_id}\n")
        f.write(f"Feedback: {feedback}\n")
        f.write("Chat History:\n")
        for msg in history:
            f.write(f"{msg['role']}: {msg['content']}\n")
        f.write("\n--------------------------\n\n")
    return ""  # Clear feedback input

# Create the Gradio interface
with gr.Blocks(theme=gr.themes.Soft(primary_hue="pink")) as demo:
    gr.Markdown("# Hey Beauty Chatbot 🧖🏻‍♀️✨🌿")
    gr.Markdown("สวัสดีค่ะ Hey Beauty ยินดีให้บริการค่ะ")

    # Initialize State
    session_state = gr.State()
    chatbot_instance = gr.State()
    chatbot_history = gr.State([])

    # Chat UI
    chatbot_interface = gr.Chatbot(type="messages", label="Chat History")
    user_input = gr.Textbox(placeholder="Type your message here...", elem_id="user_input", lines=1)

    submit_button = gr.Button("Send")
    clear_button = gr.Button("Delete Chat History")

    # Submit actions
    submit_button.click(
        fn=chat_function,
        inputs=[user_input, chatbot_history, session_state, chatbot_instance],
        outputs=[chatbot_interface, user_input, session_state, chatbot_instance]
    )

    user_input.submit(
        fn=chat_function,
        inputs=[user_input, chatbot_history, session_state, chatbot_instance],
        outputs=[chatbot_interface, user_input, session_state, chatbot_instance]
    )

    # # Clear history
    # clear_button.click(lambda: [], outputs=chatbot_interface)
    clear_button.click(
        fn=initialize_session,
        inputs=[],
        outputs=[user_input, session_state, chatbot_instance, chatbot_history]
    ).then(
        fn=lambda: gr.update(value=[]),
        inputs=[],
        outputs=chatbot_interface
    )


    # Feedback section
    with gr.Row():
        feedback_input = gr.Textbox(placeholder="Send us feedback...", label="💬 Feedback")
        send_feedback_button = gr.Button("Send Feedback")

    send_feedback_button.click(
        fn=send_feedback,
        inputs=[feedback_input, chatbot_history, session_state, chatbot_instance],
        outputs=[feedback_input]
    )

    # Initialize session on load
    demo.load(
        fn=initialize_session,
        inputs=[],
        outputs=[user_input, session_state, chatbot_instance, chatbot_history]
    )

if __name__ == "__main__":
    # Launch
    demo.launch(share=True)
    # demo.launch()


# 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()