Create app.py

app.py

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+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()