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,
):
    # Prepare the messages, starting with the system message
    messages = [{"role": "system", "content": system_message}]

    # Add the conversation history to the messages
    for user_message, assistant_response in history:
        if user_message:
            messages.append({"role": "user", "content": user_message})
        if assistant_response:
            messages.append({"role": "assistant", "content": assistant_response})

    # Add the current user message
    messages.append({"role": "user", "content": message})

    response = ""

    # Stream the response from the model
    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 tasked with labeling text data based on both emotion temperature and text type categories. The final output must be a 13-character code that consists of the following structure:

1. Emotion Temperature Code (2 characters):
   - If the emotion is purely Cold: Use CC
   - If the emotion is purely Warm: Use WW
   - If the emotion is purely Hot: Use HH
   - If the emotion is a mix, use one of the following:
     - Cold and Warm: Use CW
     - Warm and Hot: Use WH
     - Cold and Hot: Use CH

2. Text Type Codes (next 9 digits):
   Assign a digit for each of the following categories based on the presence in the text. Use 0 for categories not applicable:
   1: Toxic
   2: Appreciation
   3: Constructive Criticism
   4: Genuine Questions
   5: Advice/Suggestions
   6: Requests
   7: Spam
   8: Off-Topic
   9: Engagement Boosters

3. Special Categories (last 2 digits):
   If the text is Neutral/General: Set the 10th digit to 1; otherwise, set it to 0.
   If the text contains Hate: Set the last digit (11th) to 1; otherwise, set it to 0.

Example:
For the text "I love your videos but still something is missing":
   - Emotion: Cold and Warm (CW)
   - Types Detected: 2 (Appreciation), 3 (Constructive Criticism), 5 (Advice/Suggestions)
   - Special Categories: Neutral/General (set the 10th digit to 1), no Hate

The output would be: CW02305000010

Output Format:
Always return a 13-character code following this structure.""",
            label="each index of 13 digit have 0 to 9 , you need to extract the 13 digit number from the user input",
            lines=10,
        ),
        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()