app.py

import gradio as gr
from huggingface_hub import InferenceClient

# Use Arabic-optimized model
client = InferenceClient("aubmindlab/aragpt2-base")

def respond(
    message: str,
    history: list[list[str]],  # Updated format
    system_message="أنت مساعد مفيد يتحدث العربية",
    max_tokens=512,
    temperature=0.7,
    top_p=0.95,
):
    # Force Arabic responses
    prompt = f"باللغة العربية: {message}"
    
    # Format history in messages format
    messages = [{"role": "system", "content": system_message}]
    
    for user_msg, bot_msg in history:
        if user_msg:
            messages.append({"role": "user", "content": user_msg})
        if bot_msg:
            messages.append({"role": "assistant", "content": bot_msg})

    messages.append({"role": "user", "content": prompt})

    # Generate Arabic response
    response = ""
    for chunk in client.text_generation(
        prompt=prompt,
        max_new_tokens=max_tokens,
        stream=True,
        temperature=temperature,
        repetition_penalty=1.2,
    ):
        response += chunk
        yield response

# Mobile-optimized Arabic interface
demo = gr.ChatInterface(
    respond,
    additional_inputs=[
        gr.Textbox(value="أنت مساعد عربي مفيد", label="الرسالة النظام"),
        gr.Slider(minimum=1, maximum=512, value=256, label="الحد الأقصى للكلمات"),
        gr.Slider(minimum=0.1, maximum=1.0, value=0.5, label="الابتكار"),
        gr.Slider(minimum=0.1, maximum=1.0, value=0.9, label="الدقة"),
    ],
    css="""
    .gradio-container {direction: rtl;}
    textarea {font-family: 'Amiri', serif;}
    """,
    examples=[
        ["ما هو أفضل حل للزراعة في السودان؟"],  # Sudan example
        ["كيف يمكن تطوير الذكاء الاصطناعي في السعودية؟"]  # KSA example
    ]
)

if __name__ == "__main__":
    # Correct launch parameters
    demo.launch(
        share=False,  # Set to True for temporary public link
        auth=("user", "pass")  # Optional basic auth
    )