app.py

import os
import base64
import requests
from typing import Dict
import gradio as gr
from huggingface_hub import InferenceClient

# -----------------------------
# GitHub Prompt Configuration
# -----------------------------
GITHUB_OWNER = "Tony14100"
GITHUB_REPO = "Prompt-Tonic"
GITHUB_REF = "main"
GITHUB_PATH = "OzonConsultant"
GITHUB_API_URL = f"https://api.github.com/repos/{GITHUB_OWNER}/{GITHUB_REPO}/contents/{GITHUB_PATH}?ref={GITHUB_REF}"
LOCAL_PROMPT_FILE = "prompt_cache.txt"
LOCAL_SHA_FILE = "prompt_cache_sha.txt"

# -----------------------------
# Load system prompt from GitHub
# -----------------------------
def load_system_prompt():
    github_token = os.getenv("GITHUB_TOKEN")
    if not github_token:
        return "Вы — полезный консультант по Ozon. Отвечайте четко и профессионально."
    try:
        headers = {'Authorization': f'token {github_token}', 'User-Agent': 'Gradio-App'}
        response = requests.get(GITHUB_API_URL, headers=headers, timeout=10)
        response.raise_for_status()
        data = response.json()
        current_sha = data['sha']
        cached_sha = None
        if os.path.exists(LOCAL_SHA_FILE):
            with open(LOCAL_SHA_FILE, 'r') as f:
                cached_sha = f.read().strip()
        if cached_sha == current_sha and os.path.exists(LOCAL_PROMPT_FILE):
            with open(LOCAL_PROMPT_FILE, 'r', encoding='utf-8') as f:
                return f.read().strip()
        content_b64 = data['content']
        prompt = base64.b64decode(content_b64).decode('utf-8').strip()
        with open(LOCAL_PROMPT_FILE, 'w', encoding='utf-8') as f:
            f.write(prompt)
        with open(LOCAL_SHA_FILE, 'w') as f:
            f.write(current_sha)
        return prompt
    except:
        return "Вы — полезный консультант по Ozon. Отвечайте четко и профессионально."

SYSTEM_PROMPT = load_system_prompt()

# -----------------------------
# HF Client
# -----------------------------
MODEL_ID = "zai-org/GLM-4.5"
MAX_TOKENS = 1024

def get_client() -> InferenceClient:
    token = os.getenv("HF_TOKEN") or os.getenv("HF_API_KEY")
    if not token:
        raise RuntimeError("HF_TOKEN не найден!")
    return InferenceClient(token=token)

# -----------------------------
# State management
# -----------------------------
def reset_state():
    return {"messages": [], "system_used": False}

def mock_predict(user_message: str):
    return f"[Mock reply] Ты написал: {user_message}"

def on_user_message(user_message: str, state: Dict):
    if not user_message.strip():
        return [], state, gr.update(value="")
    messages = state["messages"]
    if not state["system_used"]:
        messages.insert(0, {"role": "system", "content": SYSTEM_PROMPT})
        state["system_used"] = True
    messages.append({"role": "user", "content": user_message})
    try:
        client = get_client()
        response = client.chat_completion(
            model=MODEL_ID,
            messages=messages,
            max_tokens=MAX_TOKENS,
            temperature=0.5,
            stream=False,
        )
        assistant_reply = response.choices[0].message["content"].strip()
    except:
        assistant_reply = mock_predict(user_message)
    messages.append({"role": "assistant", "content": assistant_reply})
    state["messages"] = messages
    chat_history = []
    for msg in messages:
        if msg["role"] != "system" and msg["content"].strip():
            html = (
                f'<div style="margin:5px;">'
                f'<span style="color:#00aaff; font-family:Arial, sans-serif; font-size:14px;">'
                f'{msg["content"]}'
                f'</span></div>'
            )
            chat_history.append({"role": msg["role"], "content": html})
    return chat_history, state, gr.update(value="")

# -----------------------------
# Build UI
# -----------------------------
def build_ui():
    css = """
    body {background-color:#000000; color:#00aaff;}
    .chat-container {display:flex; flex-direction:row; gap:20px; height:600px;}
    .chat-box { flex:2; overflow-y:auto; padding:10px; border:1px solid #00aaff; border-radius:5px; background:#000; width:100%; }
    .ads-box {flex:1; display:flex; flex-direction:column; gap:10px; height:600px;}
    .ad { background-color:#111; color:#fff; flex:1; display:flex; justify-content:center; align-items:center; font-weight:bold; border:1px solid #00aaff; border-radius:5px; }
    /* СТИЛЬ ОКНА ВВОДА — КАК ОКНО ЧАТА */
    .input-styled {
        width:100% !important;
        max-width: none !important;
    }
    .input-styled .wrap {
        width:100% !important;
    }
    .input-styled textarea {
        width:100% !important;
        background:#000000 !important;
        color:#00aaff !important;
        border:1px solid #00aaff !important;
        border-radius:5px !important;
        padding:10px !important;
        font-size:14px !important;
        font-family:Arial, sans-serif !important;
        resize: vertical;
        min-height: 40px;
    }
    .input-styled label {
        color: #00aaff !important;
        font-family: Arial, sans-serif;
        font-size: 14px;
    }
    """
    autoscroll_js = """
    <script>
    const chatObserver = new MutationObserver((mutations) => {
        const chatboxes = document.querySelectorAll('.chat-box');
        chatboxes.forEach(chat => chat.scrollTop = chat.scrollHeight);
    });
    document.querySelectorAll('.chat-box').forEach(el => chatObserver.observe(el, {childList:true, subtree:true}));
    </script>
    """
    initial_bot_message = (
        "Привет! Я консультант Ozon. Могу помочь с товарами, заказами и продажами. "
        "С чем хотите помочь первым?"
    )
    chat_history_initial = [
        {
            "role": "assistant",
            "content": (
                f'<div style="margin:5px;">'
                f'<span style="color:#00aaff; font-family:Arial; font-size:14px;">'
                f'{initial_bot_message}</span></div>'
            ),
        }
    ]
    with gr.Blocks() as app:
        gr.HTML(f"<style>{css}</style>")
        gr.HTML(autoscroll_js)
        with gr.Row(elem_classes="chat-container"):
            with gr.Column(scale=3):
                chat = gr.Chatbot(value=chat_history_initial, elem_classes="chat-box")
                input_box = gr.Textbox(
                    placeholder="Введите сообщение…",
                    label="Сообщение",
                    elem_classes="input-styled",
                    lines=1
                )
                state = gr.State(reset_state())
                input_box.submit(
                    on_user_message,
                    inputs=[input_box, state],
                    outputs=[chat, state, input_box]
                )
                gr.Button("Очистить чат").click(
                    lambda: (chat_history_initial, reset_state(), gr.update(value="")),
                    None,
                    [chat, state, input_box]
                )
            with gr.Column(scale=1):
                with gr.Row(elem_classes="ads-box"):
                    gr.HTML('<div class="ad">Здесь может быть ваша реклама</div>')
                    gr.HTML('<div class="ad">Здесь может быть ваша реклама</div>')
    return app

app = build_ui()
if __name__ == "__main__":
    app.queue(max_size=5)
    app.launch(server_name="0.0.0.0", server_port=7860, debug=True, share=False)