| import gradio as gr | |
| from langchain_community.llms import LlamaCpp | |
| import_llm = LlamaCpp( | |
| model_path = "./model/model.gguf", | |
| n_ctx=4096, | |
| max_tokens=4096, | |
| temperature=0.0, | |
| top_p = 0.95, | |
| top_k = 50, | |
| verbose=False, | |
| streaming=True | |
| ) | |
| PROMPT = """Anda adalah seorang asisten virtual, tugas Anda adalah menjawab pertanyaan sesuai pengetahuan umum Anda. | |
| Pertanyaan Pengguna: {} | |
| Jawaban Anda:""" | |
| PROMPT_HISTORY = """Anda adalah seorang asisten virtual, tugas Anda adalah menjawab pertanyaan sesuai pengetahuan umum Anda. | |
| Berikut adalah riwayat percakapan anda: | |
| [RIWAYAT] | |
| {} | |
| [/RIWAYAT] | |
| Pertanyaan Lanjutan: {} | |
| Jawaban Anda:""" | |
| global llm | |
| llm = None | |
| chat_template = { | |
| "user": "<start_of_turn>user\n{}<end_of_turn>\n", | |
| "assistant": "<start_of_turn>model\n{}<end_of_turn>\n", | |
| "generation_prompt": "<start_of_turn>model\n" | |
| } | |
| api_version_file = open("./versions.txt", "r") | |
| api_version = api_version_file.read().strip() | |
| model_version_file = open("./versions.txt", "r") | |
| model_version = model_version_file.read().strip() | |
| with gr.Blocks(title="Gemma2-2b-it-GGUF") as nlp: | |
| gr.HTML("<h2 style= 'font-size: 3rem'>Gemma2-2b-it-GGUF</h2> \ | |
| <div style = 'display: flex; flex-direction: row; align-items: center; gap: 0 5px;'>\ | |
| <div><span style ='font-size: 16px; background-color: gray; padding: 3px 5px; border-radius: 5px;'>API Version: {}</span></div>\ | |
| <div><span style ='font-size: 16px; background-color: gray; padding: 3px 5px; margin: 2px 0; border-radius: 5px;'>Model Version: {}</span></div>\ | |
| </div>".format(api_version, model_version)) | |
| bot = gr.Chatbot(label="Gemma2-2b-it") | |
| msg = gr.Textbox( | |
| placeholder="Tanya sesuatu! (Tekan \"enter\" untuk mengirim)", label="", elem_id="inputTextBox") | |
| clear = gr.Button('Clear', variant='stop') | |
| if llm == None: | |
| llm = import_llm | |
| def user(user_message, history): | |
| return "", history + [[user_message, None]] | |
| def predict(history): | |
| prompt = "" | |
| if len(history) == 1: | |
| chat = history[0] | |
| user_input = PROMPT.format(chat[0]) | |
| prompt += chat_template['user'].format(user_input) | |
| prompt += chat_template['generation_prompt'] | |
| else: | |
| chat_history = "" | |
| for chat in history[:-1]: | |
| if chat[0] is not None: | |
| chat_history += f"Pertanyaan: {chat[0]}\n" | |
| if chat[1] is not None: | |
| chat_history += f"Jawaban: {chat[1]}\n" | |
| user_input = PROMPT_HISTORY.format( | |
| chat_history, history[-1][0] if history[-1][0] is not None else "" | |
| ) | |
| prompt += chat_template['user'].format(user_input) | |
| prompt += chat_template['generation_prompt'] | |
| history[-1][1] = "" | |
| for token in llm.stream(prompt): | |
| history[-1][1] += token | |
| yield history | |
| msg.submit(user, [msg, bot], [msg, bot], queue=False).then( | |
| predict, bot, bot | |
| ) | |
| clear.click(lambda: None, None, bot, queue=False) | |
| nlp.launch(max_threads=1) |