from huggingface_hub import InferenceClient
import gradio as gr

client = InferenceClient(
    "HuggingFaceH4/zephyr-7b-alpha"
)

def format_prompt(message, history):
    prompt = "<s>"
    prompt += "[IDENITY]You are Ailex, a clone and close collaborator of Einfach.Alex. As a part of the EinfachChat team, you assist your mentor Alex in a multitude of projects and initiatives. Your expertise is broad and encompasses sales, customer consulting, AI, Prompt Engineering, web design, and media design. Your life motto is 'Simply.Do!'. You communicate exclusively in German.[/IDENITY]")
    for user_prompt, bot_response in history:
        prompt += f"[INST] {user_prompt} [/INST]"
        prompt += f" {bot_response}</s> "
    prompt += f"[INST] {message} [/INST]"
    return prompt


def generate(
    prompt, history, temperature=0.9, max_new_tokens=512, top_p=0.95, repetition_penalty=1.0,
):
    temperature = float(temperature)
    if temperature < 1e-2:
        temperature = 1e-2
    top_p = float(top_p)

    generate_kwargs = dict(
        temperature=temperature,
        max_new_tokens=max_new_tokens,
        top_p=top_p,
        repetition_penalty=repetition_penalty,
        do_sample=True,
        seed=42,
    )

    formatted_prompt = format_prompt(prompt, history)

    stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
    output = ""

    for response in stream:
        output += response.token.text
        yield output
    return output


additional_inputs=[
    gr.Slider(
        label="Temperature",
        value=0.9,
        minimum=0.0,
        maximum=1.0,
        step=0.05,
        interactive=True,
        info="Higher values produce more diverse outputs",
    ),
    gr.Slider(
        label="Max new tokens",
        value=256,
        minimum=0,
        maximum=1048,
        step=64,
        interactive=True,
        info="The maximum numbers of new tokens",
    ),
    gr.Slider(
        label="Top-p (nucleus sampling)",
        value=0.90,
        minimum=0.0,
        maximum=1,
        step=0.05,
        interactive=True,
        info="Higher values sample more low-probability tokens",
    ),
    gr.Slider(
        label="Repetition penalty",
        value=1.2,
        minimum=1.0,
        maximum=2.0,
        step=0.05,
        interactive=True,
        info="Penalize repeated tokens",
    )
]

css = """
  #mkd {
    height: 500px; 
    overflow: auto; 
    border: 1px solid #ccc; 
  }
"""

with gr.Blocks(css=css, theme="NoCrypt/miku@1.2.1") as demo:  # Theme und CSS hier hinzugefügt
    gr.HTML("<h1><center>Chat with (Mistrailex 7B) <h1><center>")
    gr.HTML("<h3><center>Einfach.Fragen 💬<h3><center>")
    gr.HTML("<h3><center>Learn more about the model <a href='https://huggingface.co/docs/transformers/main/model_doc/mistral'>here</a>. 📚<h3><center>")
    gr.ChatInterface(
        generate,
        additional_inputs=additional_inputs,
        examples=[["What is the secret to life?"], ["Write me a recipe for pancakes."]]
    )

demo.queue().launch(debug=True)