import gradio as gr
from huggingface_hub import InferenceClient

# Initialize the client with your desired model
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")

# Define the system prompt as an AI Dermatologist
def format_prompt(message, history):
    prompt = "<s>"
    # Start the conversation with a system message
    prompt += "[INST] You are an AI Dermatologist chatbot designed to assist users with skin by only providing text and if user information is not provided related to skin then ask what they want to know related to skin.[/INST]"
    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

# Function to generate responses with the AI Dermatologist context
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
    return output

# Customizable input controls for the chatbot interface
Settings = [
    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",
    )
]
# Define the chatbot interface with the starting system message as AI Dermatologist
gr.ChatInterface(
    fn=generate,
    chatbot=gr.Chatbot(show_label=False, show_share_button=False, show_copy_button=True, layout="panel"),
    additional_inputs = Settings,
    title="Skin Bot"
).launch(show_api=False)

# Load your model after launching the interface
gr.load("models/Bhaskar2611/Capstone").launch()