from huggingface_hub import InferenceClient
import gradio as gr

# Initialize the Inference Client
chat_client = InferenceClient("mistralai/Mistral-7B-Instruct-v0.3")
image_client = InferenceClient("UnfilteredAI/NSFW-gen-v2.1")

# 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_response(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)
    
    if "generate an image of" in prompt.lower():
        image_prompt = prompt.lower().split("generate an image of")[1].strip()
        image = image_client.text_to_image(image_prompt).images[0]
        return None, image

    stream = chat_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, None

with gr.Blocks(theme="Nymbo/Alyx_Theme") as demo:
    gr.Markdown("# Chatbot with Image Generation")
    
    with gr.Row():
        with gr.Column(scale=3):
            chat_history = gr.Chatbot()
            chat_input = gr.Textbox(label="User Input", placeholder="Type your message here...")
            chat_output = gr.Textbox(label="Chatbot Response")
            image_output = gr.Image(label="Generated Image", visible=False)
        with gr.Column(scale=1):
            temperature = gr.Slider(label="Temperature", minimum=0.1, maximum=1.0, value=0.7, step=0.1)
            max_tokens = gr.Slider(label="Max Tokens", minimum=10, maximum=512, value=100, step=10)
            top_p = gr.Slider(label="Top-p", minimum=0.1, maximum=1.0, value=0.9, step=0.1)
            repetition_penalty = gr.Slider(label="Repetition Penalty", minimum=1.0, maximum=2.0, value=1.2, step=0.1)
            chat_button = gr.Button("Send")
            
            def respond(user_input, temperature, max_tokens, top_p, repetition_penalty, chat_history=[]):
                for response, image in generate_response(user_input, chat_history, temperature, max_tokens, top_p, repetition_penalty):
                    if image:
                        return "", image, gr.update(visible=True)
                    return response, None, gr.update(visible=False)
            
            chat_button.click(respond, inputs=[chat_input, temperature, max_tokens, top_p, repetition_penalty], outputs=[chat_output, image_output, image_output])

demo.launch()