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| import os | |
| import torch | |
| import gradio gr | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| # 1. Load Model dan Tokenizer (Qwen3-0.6B) | |
| MODEL_NAME = "Qwen/Qwen3-0.6B" | |
| print("Loading tokenizer...") | |
| tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME) | |
| print("Loading model...") | |
| model = AutoModelForCausalLM.from_pretrained( | |
| MODEL_NAME, | |
| torch_dtype="auto", | |
| device_map="auto" | |
| ) | |
| def respond(message, history, system_message, max_tokens, temperature, top_p): | |
| # Susun percakapan sesuai format yang disarankan Qwen3 | |
| # Catatan: Best practice Qwen3 menyarankan riwayat chat tidak mengikutsertakan proses berpikir sebelumnya | |
| messages = [{"role": "system", "content": system_message}] | |
| # Pada Gradio 6, riwayat dikirim dalam format objek khusus atau dictionary. | |
| # Kode ini mengekstrak konten user dan assistant secara aman. | |
| for turn in history: | |
| user_content = turn.get("user") if isinstance(turn, dict) else getattr(turn, "user", None) | |
| assistant_content = turn.get("assistant") if isinstance(turn, dict) else getattr(turn, "assistant", None) | |
| if user_content: | |
| messages.append({"role": "user", "content": user_content}) | |
| if assistant_content: | |
| messages.append({"role": "assistant", "content": assistant_content}) | |
| messages.append({"role": "user", "content": message}) | |
| # Terapkan template chat bawaan Qwen3 (default: enable_thinking=True) | |
| text = tokenizer.apply_chat_template( | |
| messages, | |
| tokenize=False, | |
| add_generation_prompt=True, | |
| enable_thinking=True | |
| ) | |
| model_inputs = tokenizer([text], return_tensors="pt").to(model.device) | |
| # Generate respons | |
| generated_ids = model.generate( | |
| **model_inputs, | |
| max_new_tokens=max_tokens, | |
| temperature=temperature, | |
| top_p=top_p, | |
| top_k=20, # Sesuai rekomendasi dokumentasi Qwen3 | |
| do_sample=True # Wajib True untuk thinking mode agar tidak berulang | |
| ) | |
| # Ambil token baru saja | |
| output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() | |
| # Parsing konten berfikir berdasarkan token khusus </think> (ID: 151668) | |
| try: | |
| index = len(output_ids) - output_ids[::-1].index(151668) | |
| except ValueError: | |
| index = 0 | |
| thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n") | |
| final_content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n") | |
| # Format output agar cantik di UI Gradio | |
| full_response = "" | |
| if thinking_content: | |
| full_response += f"💭 *Thinking Process:*\n```text\n{thinking_content}\n```\n\n" | |
| full_response += final_content | |
| return full_response | |
| # 2. Desain Antarmuka Gradio (Zela 3.0 UI untuk Gradio 6) | |
| with gr.Blocks() as demo: | |
| gr.ChatInterface( | |
| respond, | |
| title="Zela 3.0 🤖✨", | |
| description="Asisten pintar bertenaga Qwen3-0.6B dengan fitur *Thinking Mode* bawaan.", | |
| examples=[ | |
| ["Berapa jumlah huruf 'r' pada kata 'strawberry'?"], | |
| ["Berikan penjelasan singkat apa itu Black Hole."], | |
| ["Buat skrip python sederhana untuk menghitung faktorial."] | |
| ], | |
| additional_inputs=[ | |
| gr.Textbox(value="Kamu adalah Zela 3.0, asisten AI yang ramah, jenius, dan membantu.", label="System Message"), | |
| gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max New Tokens"), | |
| # Nilai default disesuaikan dengan rekomendasi Qwen3 (Thinking Mode = 0.6) | |
| gr.Slider(minimum=0.1, maximum=1.0, value=0.6, step=0.05, label="Temperature"), | |
| gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p"), | |
| ] | |
| ) | |
| if __name__ == "__main__": | |
| # Sesuai standar Gradio 6, tema dipasangkan di fungsi launch() | |
| demo.launch(theme="soft") |