app.py

import gradio as gr
from huggingface_hub import InferenceClient

# تحميل نموذج LLaMA من Hugging Face
client = InferenceClient("meta-llama/Llama-2-7b-chat-hf")

# قائمة السيناريوهات المتاحة
scenarios = {
    "restaurant": "You are in a restaurant. Help the user order food in English.",
    "airport": "You are at an airport. Help the user check in and find their gate.",
    "hotel": "You are in a hotel. Help the user book a room.",
    "shopping": "You are in a store. Help the user ask for prices and sizes.",
}

# دالة لاختيار السيناريو المناسب
def scenario_prompt(choice):
    return scenarios.get(choice, "You are a language tutor AI. Help users practice real-life conversations.")

# دالة للمحادثة مع الذكاء الاصطناعي
def respond(
    message,
    system_message="You are a language tutor AI. Help users practice real-life conversations.",
    max_tokens=512,
    temperature=0.7,
):
    messages = [{"role": "system", "content": system_message}]
    messages.append({"role": "user", "content": message})

    response = ""

    for message in client.chat_completion(
        messages,
        max_tokens=max_tokens,
        stream=True,
        temperature=temperature,
    ):
        token = message.choices[0].delta.content
        response += token
        yield response


# واجهة `Gradio` للتفاعل مع المستخدم
demo = gr.ChatInterface(
    respond,
    chatbot=gr.Chatbot(type="messages"),  # إصلاح التحذير باستخدام `type="messages"`
    additional_inputs=[
        gr.Dropdown(choices=list(scenarios.keys()), label="Choose a scenario", value="restaurant"),
        gr.Textbox(value=scenario_prompt("restaurant"), label="System message"),
        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
        gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)"),
    ],
)

if __name__ == "__main__":
    demo.launch()