app.py

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

# Initialize the Hugging Face client with your model
client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1")

# Define the translation functions
def translate_to_arabic(text):
    return GoogleTranslator(source='auto', target='ar').translate(text)

def translate_to_english(text):
    return GoogleTranslator(source='auto', target='en').translate(text)

# Format the prompt for the model
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

# Generate a response from the model
def generate(prompt, history=[], temperature=0.1, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0):
    # Translate the Arabic prompt to English before sending to the model
    prompt_in_english = translate_to_english(prompt)
    formatted_prompt = format_prompt(prompt_in_english, history)

    generate_kwargs = {
        "temperature": temperature,
        "max_new_tokens": max_new_tokens,
        "top_p": top_p,
        "repetition_penalty": repetition_penalty,
        "do_sample": True,
        "seed": 42,  # Seed for reproducibility, remove or change if randomness is preferred
    }

    # Generate the response
    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"]

    # Translate the English response back to Arabic
    response_in_arabic = translate_to_arabic(output)
    return response_in_arabic

# Define additional inputs for Gradio interface
additional_inputs = [
    gr.Slider(label="Temperature", value=0.9, minimum=0.0, maximum=1.0, step=0.05),
    gr.Slider(label="Max new tokens", value=256, minimum=0, maximum=1048, step=64),
    gr.Slider(label="Top-p (nucleus sampling)", value=0.90, minimum=0.0, maximum=1.0, step=0.05),
    gr.Slider(label="Repetition penalty", value=1.2, minimum=1.0, maximum=2.0, step=0.05)
]

# Set up the Gradio interface
iface = gr.Interface(
    fn=generate,
    inputs=[
        gr.Textbox(lines=5, placeholder='Type your Arabic query here...', label='Arabic Query'),
        *additional_inputs
    ],
    outputs='text',
    title="DorjGPT Arabic-English Translation Chatbot",
)

# Launch the Gradio interface
iface.launch()