from huggingface_hub import InferenceClient
import gradio as gr
from deep_translator import GoogleTranslator

# Initialize the InferenceClient and the translators
client = InferenceClient("mistralai/Mistral-7B-Instruct-v0.3")
translator_to_en = GoogleTranslator(source='hindi', target='english')
translator_to_ar = GoogleTranslator(source='english', target='hindi')

def format_prompt(message, history):
    prompt = "<s>"
    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=0.1, 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,
    )

    # Translate the Arabic prompt to English
    translated_prompt = translator_to_en.translate(prompt)
    formatted_prompt = format_prompt(translated_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 translator_to_ar.translate(output)  # Translate the response back to Arabic
    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",
    )
]

examples = [
    ["How many languages can you accept input"],
    ["Tell me about sarcopenia and how to avoid it"],
    ["Give me a low carb food menu for a day"],
    ["Give me an exercise plan for a week that include resistance training and cardio"],
]


# Custom title component with an additional line and color change
title = """
<div style='text-align: center;'>
    <div style='font-weight: bold; color: red; font-size: 24px;'>A Multilingual Chatbot accept input in any language, answers in Hindi</div>
    <div style='font-size: 18px;'>Large language model mistralai/Mistral-7B-Instruct-v0.3</div>
</div>
"""

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,
    #gr.Markdown("**Hindi_Mistral8-7b**"),  # Title in bold
    title=title,
    examples=examples
).launch(show_api=True)