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import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
import os
# Nonaktifkan cache Hugging Face untuk hemat penyimpanan
os.environ["HF_HUB_DISABLE_CACHE"] = "1"
# Muat model dan tokenizer
model_name = "Qwen/Qwen2-0.5B-Instruct"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.bfloat16, # bfloat16 untuk efisiensi CPU
device_map="cpu", # Paksa ke CPU untuk Space gratis
trust_remote_code=True,
low_cpu_mem_usage=True # Optimasi memori
)
# Fungsi untuk menghasilkan respons
def generate_response(user_input, chat_history):
if not user_input.strip():
return [{"role": "assistant", "content": "Masukkan teks tidak boleh kosong!"}], chat_history
if not chat_history:
chat_history = []
# Format riwayat percakapan (batasi 5 interaksi terakhir untuk efisiensi)
messages = []
for user_msg, bot_msg in chat_history[-5:]:
messages.append({"role": "user", "content": user_msg})
messages.append({"role": "assistant", "content": bot_msg})
# Tambahkan input pengguna saat ini
messages.append({"role": "user", "content": user_input})
# Buat prompt menggunakan format chat Qwen
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
# Tokenisasi input
inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to("cpu")
# Generate respons
outputs = model.generate(
**inputs,
max_new_tokens=200, # Batasi token untuk kecepatan
do_sample=True,
temperature=0.75,
top_p=0.85,
eos_token_id=tokenizer.eos_token_id,
use_cache=True # Cache untuk inferensi lebih cepat
)
# Decode respons
bot_response = tokenizer.decode(outputs[0][inputs.input_ids.shape[-1]:], skip_special_tokens=True)
# Perbarui riwayat percakapan
chat_history.append((user_input, bot_response))
# Format output untuk Gradio Chatbot (format messages)
return [
{"role": "user" if i % 2 == 0 else "assistant", "content": msg}
for i, (user_msg, bot_msg) in enumerate(chat_history)
for msg in [user_msg, bot_msg]
], chat_history
# Fungsi untuk menghapus riwayat
def clear_history():
return [], []
# Antarmuka Gradio
with gr.Blocks(
theme=gr.themes.Monochrome(), # Tema modern dan bersih
css="""
#chatbot {border-radius: 10px; border: 1px solid #e0e0e0; padding: 10px;}
.gradio-container {max-width: 800px; margin: auto;}
#input-box {border-radius: 8px;}
#submit-btn, #clear-btn {border-radius: 8px; background: #007bff; color: white;}
#submit-btn:hover, #clear-btn:hover {background: #0056b3;}
"""
) as demo:
gr.Markdown(
"""
# 💬 Chatbot Qwen (Alibaba)
Ajukan pertanyaan dan dapatkan respons cerdas dari model Qwen2-0.5B-Instruct!
"""
)
# Komponen UI
chatbot = gr.Chatbot(
type="messages", # Gunakan format messages untuk kompatibilitas
height=450,
show_label=False,
elem_id="chatbot"
)
with gr.Row():
user_input = gr.Textbox(
placeholder="Ketik pertanyaanmu di sini...",
show_label=False,
elem_id="input-box",
scale=4
)
submit_button = gr.Button("Kirim", elem_id="submit-btn", scale=1)
clear_button = gr.Button("Hapus Riwayat", elem_id="clear-btn")
# State untuk menyimpan riwayat percakapan
chat_history = gr.State([])
# Aksi tombol
submit_button.click(
fn=generate_response,
inputs=[user_input, chat_history],
outputs=[chatbot, chat_history]
)
clear_button.click(
fn=clear_history,
inputs=None,
outputs=[chatbot, chat_history]
)
# Luncurkan aplikasi
demo.launch()
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