Hugging Face's logo

Spaces:
ApaCu
/
Qwen
Running

App Files Community
Qwen
File size: 4,478 Bytes 
37dcd44
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
548cea8
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
 
import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
import torch
import os
from threading import Thread

# 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 [("Error", "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")

    # Gunakan TextStreamer untuk streaming respons (meningkatkan UX)
    streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)

    # Generate respons di thread terpisah untuk responsivitas
    def generate():
        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
            streamer=streamer
        )
        return outputs

    # Jalankan generasi di thread
    thread = Thread(target=generate)
    thread.start()
    thread.join()

    # Ambil respons dari output streamer (decode manual)
    bot_response = tokenizer.decode(
        model.generate(**inputs, max_new_tokens=200, do_sample=True, temperature=0.75, top_p=0.85)[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
    return [(user_msg, bot_msg) for user_msg, bot_msg in chat_history], 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;}
        .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(
        label="Percakapan",
        height=450,
        show_label=False,
        elem_id="chatbot",
        bubble_full_width=False
    )
    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],
        _js="() => {document.querySelector('input').value = '';}"  # Kosongkan input
    )
    clear_button.click(
        fn=clear_history,
        inputs=None,
        outputs=[chatbot, chat_history]
    )

# Luncurkan aplikasi
demo.launch()