app.py

import gradio as gr
from huggingface_hub import InferenceClient
from transformers import pipeline
from langdetect import detect  # 🔥 new for auto language detection

# ---------------- CONFIG ----------------
MODEL_REPO = "HuggingFaceH4/zephyr-7b-beta"
TRANSLATOR_MODEL = "facebook/m2m100_418M"  # multilingual translator

SYSTEM_PROMPT_DEFAULT = (
    "You are Zephyr, a concise and polite AI assistant. "
    "Answer briefly unless the user specifically asks for detail."
)

MAX_NEW_TOKENS_DEFAULT = 128
TEMP_DEFAULT = 0.7
TOP_P_DEFAULT = 0.95

# Clients
client = InferenceClient(MODEL_REPO)
translator = pipeline("translation", model=TRANSLATOR_MODEL)


# ---------------- HELPERS ----------------
def is_translation_request(message: str) -> bool:
    """
    Heuristics: if user explicitly asks to translate OR if message is not English.
    """
    triggers = ["translate", "traduce", "ترجم", "traduire", "übersetze"]
    if any(t in message.lower() for t in triggers):
        return True
    # naive non-English detection (if >40% chars non-ASCII)
    non_ascii_ratio = sum(1 for c in message if ord(c) > 127) / max(len(message), 1)
    return non_ascii_ratio > 0.4


def translate_text(text: str) -> str:
    """Detects language and translates to English."""
    try:
        src_lang = detect(text)  # auto-detect like "ru", "es", "fr"
        result = translator(text, src_lang=src_lang, tgt_lang="en")
        return result[0]["translation_text"]
    except Exception as e:
        return f"⚠️ Translation failed: {str(e)}"


# ---------------- CHAT FUNCTION ----------------
def stream_response(message, chat_history, system_message, max_tokens, temperature, top_p, response_style):
    # check if translation
    if is_translation_request(message):
        translated = translate_text(message)
        chat_history.append({"role": "user", "content": message})
        chat_history.append({"role": "assistant", "content": translated})
        yield "", chat_history
        return

    # Otherwise → normal Zephyr response
    if response_style == "Concise":
        system_message += " Keep answers short and direct."
    elif response_style == "Detailed":
        system_message += " Provide more explanation and context when helpful."
    elif response_style == "Essay":
        system_message += " Write long, structured, essay-style responses."

    messages = [{"role": "system", "content": system_message}] + chat_history
    messages.append({"role": "user", "content": message})

    # Append user first
    chat_history.append({"role": "user", "content": message})
    response = ""
    chat_history.append({"role": "assistant", "content": ""})  # placeholder for streaming

    for msg in client.chat_completion(
        messages,
        max_tokens=max_tokens,
        stream=True,
        temperature=temperature,
        top_p=top_p,
    ):
        token = msg.choices[0].delta.content or ""
        response += token
        chat_history[-1]["content"] = response  # update last assistant message
        yield "", chat_history

    # finally clear input box once after streaming is done
    yield "", chat_history


# ---------------- UI ----------------
with gr.Blocks(theme=gr.themes.Soft(primary_hue="violet", secondary_hue="pink")) as demo:
    gr.Markdown(
        """
        # 🤖 Zephyr-7B Chat + 🌍 Translator  
        Welcome! This app combines **Zephyr-7B** (for smart chat) with **M2M100** (for multilingual translation).  
        **How to use:**
        - 💬 Ask *anything* → Zephyr will reply.  
        - 🌐 Paste non-English text or say "translate" → auto translation into English.  
        - ⚙️ Adjust settings in the panel if you want different styles.  
        ---
        """
    )

    chatbot = gr.Chatbot(
        type="messages",
        height=500,
        show_copy_button=True,
        label="Chat Assistant"
    )

    with gr.Row():
        msg = gr.Textbox(
            label="💬 Your Message",
            placeholder="Type here and press Enter or click 🚀",
            scale=6
        )
        send_btn = gr.Button("🚀 Send", variant="primary", scale=1)
        clear_btn = gr.Button("🧹 Clear Chat", scale=1)

    with gr.Accordion("⚙️ Advanced Settings", open=False):
        system_prompt = gr.Textbox(label="System Prompt", value=SYSTEM_PROMPT_DEFAULT, lines=3)
        response_style = gr.Dropdown(
            ["Concise", "Detailed", "Essay"], value="Concise", label="Response Style"
        )
        temperature = gr.Slider(0.1, 1.5, value=TEMP_DEFAULT, step=0.1, label="Temperature")
        top_p = gr.Slider(0.1, 1.0, value=TOP_P_DEFAULT, step=0.05, label="Top-p")
        max_tokens = gr.Slider(32, 2048, value=MAX_NEW_TOKENS_DEFAULT, step=16, label="Max new tokens")

    # Events
    send_btn.click(
        stream_response,
        [msg, chatbot, system_prompt, max_tokens, temperature, top_p, response_style],
        [msg, chatbot]
    )
    msg.submit(
        stream_response,
        [msg, chatbot, system_prompt, max_tokens, temperature, top_p, response_style],
        [msg, chatbot]
    )
    clear_btn.click(lambda: [], None, chatbot, queue=False)

    gr.Markdown("---")
    gr.Markdown("🔗 Built with ❤️ using [Zephyr-7B](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) & [M2M100](https://huggingface.co/facebook/m2m100_418M).")

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