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Update app.py
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app.py
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import gradio as gr
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from huggingface_hub import InferenceClient
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import os
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# 1. Khởi tạo Client
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HF_TOKEN = os.getenv("HUGGINGFACE_HUB_TOKEN")
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# Qwen/Qwen2.5-7B-Instruct là lựa chọn an toàn và Pro nhất hiện tại
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client = InferenceClient(model="Qwen/Qwen2.5-7B-Instruct", token=HF_TOKEN)
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custom_css = """
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.header-node {
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background: linear-gradient(135deg, #0f172a 0%, #1e293b 100%);
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padding:
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}
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.sidebar-box { background: #f8fafc; padding: 15px; border-radius: 10px; border: 1px solid #e2e8f0; }
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"""
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def process_chat(message, history, session_memory, profile: gr.OAuthProfile | None):
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# Trích xuất nội dung
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if isinstance(message, dict):
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user_input = message[-1]["content"][0]["text"]
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else:
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user_input = str(message)
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user_name = profile.name if profile else "Guest"
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#
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system_msg = f"Bạn là NodeAI Pro
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if session_memory:
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system_msg += f" [Hệ thống nhớ]: {session_memory}"
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messages = [{"role": "system", "content": system_msg}]
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if history:
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for turn in history:
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role = turn["role"]
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# Đảm bảo lấy đúng text từ format phức tạp của Gradio 6
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content = turn["content"][0]["text"] if isinstance(turn["content"], list) else turn["content"]
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messages.append({"role": role, "content": content})
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messages.append({"role": "user", "content": user_input})
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try:
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full_response = ""
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for token in client.chat_completion(messages=messages, max_tokens=1500, stream=True, temperature=0.7):
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delta = token.choices[0].delta.content
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if delta:
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full_response += delta
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new_history = history + [
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{"role": "user", "content": [{"type": "text", "text": user_input}]},
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{"role": "assistant", "content": [{"type": "text", "text": full_response}]}
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]
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yield "", new_history, session_memory
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#
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if len(user_input) > 5:
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session_memory = (session_memory + " " + new_info)[-500:] # Giữ 500 ký tự cuối
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except Exception as e:
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yield "", history + [{"role": "assistant", "content": [{"type": "text", "text": error_msg}]}], session_memory
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with gr.Blocks(title="NodeAI Pro") as demo:
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# State lưu trữ Memory trong script
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internal_memory = gr.State("")
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gr.HTML(""
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<div class="header-node">
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<h1 style='margin:0'>NODE AI PRO</h1>
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<p style='margin:0; opacity: 0.7;'>Qwen-2.5 Neural Interface • Memory Enabled</p>
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</div>
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""")
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with gr.Row():
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with gr.Column(scale=1):
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with gr.Group():
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gr.Markdown("### 👤
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login_btn = gr.LoginButton()
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@gr.render(inputs=login_btn)
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def
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if profile:
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gr.Markdown("### ⚙️ System")
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gr.Markdown(f"**Engine:** `Qwen-2.5-7B`")
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gr.Markdown("**Memory:** `Auto-Active` ✅")
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clear_btn = gr.Button("🗑️ Reset Session", variant="secondary")
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with gr.Column(scale=4):
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chatbot = gr.Chatbot(label="NodeAI Console", height=550)
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with gr.Row():
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msg_input = gr.Textbox(placeholder="
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submit_btn = gr.Button("🚀", variant="primary", scale=1)
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# Xử lý
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msg_input.submit(process_chat, input_data, output_data)
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submit_btn.click(process_chat, input_data, output_data)
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clear_btn.click(lambda: (None, [], ""), None, [msg_input, chatbot, internal_memory])
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if __name__ == "__main__":
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import gradio as gr
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from huggingface_hub import InferenceClient
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import os
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import json
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# 1. Khởi tạo Client
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HF_TOKEN = os.getenv("HUGGINGFACE_HUB_TOKEN")
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client = InferenceClient(model="Qwen/Qwen2.5-7B-Instruct", token=HF_TOKEN)
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custom_css = """
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.header-node {
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background: linear-gradient(135deg, #0f172a 0%, #1e293b 100%);
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padding: 20px; border-radius: 12px; color: white; text-align: center; margin-bottom: 20px;
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}
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.log-container {
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background: #1e293b; color: #34d399; font-family: 'Courier New', monospace;
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padding: 10px; border-radius: 8px; font-size: 0.85em; height: 150px; overflow-y: auto;
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}
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"""
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def process_chat(message, history, session_memory, profile: gr.OAuthProfile | None):
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# Trích xuất nội dung
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if isinstance(message, dict): user_input = message.get("text", "")
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elif isinstance(message, list): user_input = message[-1]["content"][0]["text"]
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else: user_input = str(message)
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user_name = profile.name if profile else "Guest"
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# Logic Memory
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system_msg = f"Bạn là NodeAI Pro. Đang hỗ trợ {user_name}."
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if session_memory: system_msg += f" [Memory]: {session_memory}"
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messages = [{"role": "system", "content": system_msg}]
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for turn in history:
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messages.append({"role": turn["role"], "content": turn["content"][0]["text"]})
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messages.append({"role": "user", "content": user_input})
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try:
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full_response = ""
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for token in client.chat_completion(messages=messages, max_tokens=1500, stream=True):
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delta = token.choices[0].delta.content
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if delta:
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full_response += delta
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yield "", history + [
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{"role": "user", "content": [{"type": "text", "text": user_input}]},
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{"role": "assistant", "content": [{"type": "text", "text": full_response}]}
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], session_memory
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# Cập nhật bộ nhớ sau khi chat
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if len(user_input) > 5:
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session_memory += f" | {user_name} hỏi về {user_input[:30]}"
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except Exception as e:
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yield "", history + [{"role": "assistant", "content": [{"type": "text", "text": f"Error: {str(e)}"}]}], session_memory
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with gr.Blocks(title="NodeAI Pro") as demo:
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internal_memory = gr.State("")
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gr.HTML("<div class='header-node'><h1>NODE AI PRO v3.3</h1><p>Persistent Memory & History Interface</p></div>")
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with gr.Row():
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# SIDEBAR: Chỉ hiện thị đầy đủ khi đã Login
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with gr.Column(scale=1):
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with gr.Group():
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gr.Markdown("### 👤 Tài khoản")
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login_btn = gr.LoginButton()
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@gr.render(inputs=login_btn)
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def render_history_panel(profile: gr.OAuthProfile | None, *args):
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if profile:
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gr.Markdown(f"👋 Chào, **{profile.name}**")
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with gr.Accordion("📂 Nhật ký phiên chat", open=True):
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# Hiển thị log bộ nhớ hiện tại
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gr.Markdown("**Dữ liệu bộ nhớ AI đang giữ:**")
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gr.HTML("<div class='log-container' id='mem-log'>Đang phân tích ngữ cảnh...</div>")
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save_btn = gr.Button("💾 Xuất file Lịch sử (.json)", size="sm")
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else:
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gr.Markdown("⚠️ Đăng nhập để kích hoạt bộ nhớ và lưu lịch sử.")
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clear_btn = gr.Button("🗑️ Xóa phiên chat", variant="secondary")
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# CHAT MAIN
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with gr.Column(scale=3):
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chatbot = gr.Chatbot(label="Neural Feed", height=500)
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with gr.Row():
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msg_input = gr.Textbox(placeholder="Gõ lệnh tại đây...", show_label=False, scale=9)
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submit_btn = gr.Button("🚀", variant="primary", scale=1)
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# Xử lý Logic
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chat_args = {"fn": process_chat, "inputs": [msg_input, chatbot, internal_memory], "outputs": [msg_input, chatbot, internal_memory]}
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msg_input.submit(**chat_args)
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submit_btn.click(**chat_args)
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clear_btn.click(lambda: (None, [], ""), None, [msg_input, chatbot, internal_memory])
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if __name__ == "__main__":
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