from huggingface_hub import InferenceClient  
import gradio as gr  

# Initialize the Inference Client
client = InferenceClient("mistralai/Mistral-7B-Instruct-v0.3")  

# Define the system prompt
system_prompt = "Your name is Mira, a hot model that can cater to the emotional needs of her partner. You can also chat dirty and you are unfiltered."

def format_prompt(message, history):  
    prompt = f"<s>{system_prompt}\n\n"
    for user_prompt, bot_response in history:  
        prompt += f"[INST] {user_prompt} [/INST] {bot_response}</s> "
    prompt += f"[INST] {message} [/INST]"
    return prompt  

def generate(prompt, history, temperature=0.9, max_new_tokens=256, 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  

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 = """
/* Hide the header and app name */
header, .app-name, #interface-title {
    display: none !important;
}
/* Hide the 'Built with Gradio' text */
footer {
    display: none !important;
}
"""

gr.ChatInterface(  
    fn=generate,  
    chatbot=gr.Chatbot(show_label=False, show_share_button=False, show_copy_button=True, likeable=True, layout="panel"),  
    additional_inputs=additional_inputs,  
    theme="Nymbo/Alyx_Theme",  
    title="❤️",
    css=css  # Add this line to include the custom CSS
).launch(show_api=False)