Update app.py
Browse files
app.py
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import gradio as gr
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additional_inputs=[
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gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
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gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
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gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
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gr.Slider(
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minimum=0.1,
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maximum=1.0,
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value=0.95,
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step=0.05,
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),
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],
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if __name__ == "__main__":
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import os
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from threading import Thread
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from typing import Iterator
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import gradio as gr
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import spaces
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import torch
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from transformers import BitsAndBytesConfig, AutoConfig, AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
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MAX_MAX_NEW_TOKENS = 2048
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DEFAULT_MAX_NEW_TOKENS = 1024
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MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "4096"))
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device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
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model_path = "vinai/PhoGPT-4B-Chat"
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config = AutoConfig.from_pretrained(model_path, trust_remote_code=True)
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config.init_device = device
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quantization = BitsAndBytesConfig(load_in_8bit=True)
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model = AutoModelForCausalLM.from_pretrained(model_path,
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config=config,
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quantization_config =quantization,
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torch_dtype=torch.bfloat16,
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trust_remote_code=True,
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low_cpu_mem_usage=True)
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model.eval()
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tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
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@spaces.GPU(duration=120)
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def generate(
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message: str,
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chat_history: list[tuple[str, str]],
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max_new_tokens: int = 1024,
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temperature: float = 0.6,
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top_p: float = 0.9,
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top_k: int = 50,
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repetition_penalty: float = 1.2,
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) -> Iterator[str]:
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conversation = []
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for user, assistant in chat_history:
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conversation.extend(
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[
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{"role": "user", "content": user},
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{"role": "assistant", "content": assistant},
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]
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)
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conversation.append({"role": "user", "content": message})
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input_ids = tokenizer.apply_chat_template(conversation, add_generation_prompt=True, return_tensors="pt")
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if input_ids.shape[1] > MAX_INPUT_TOKEN_LENGTH:
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input_ids = input_ids[:, -MAX_INPUT_TOKEN_LENGTH:]
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gr.Warning(f"Trimmed input from conversation as it was longer than {MAX_INPUT_TOKEN_LENGTH} tokens.")
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input_ids = input_ids.to(model.device)
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streamer = TextIteratorStreamer(tokenizer, timeout=20.0, skip_prompt=True, skip_special_tokens=True)
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generate_kwargs ={
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"input_ids": input_ids,
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"streamer":streamer,
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"max_new_tokens":max_new_tokens,
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"do_sample":True,
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"top_p":top_p,
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"top_k":top_k,
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"temperature":temperature,
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"num_beams":1,
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"repetition_penalty":repetition_penalty,
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"eos_token_id":tokenizer.eos_token_id,
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"pad_token_id":tokenizer.pad_token_id
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}
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t = Thread(target=model.generate, kwargs=generate_kwargs)
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t.start()
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outputs = []
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for text in streamer:
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outputs.append(text)
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yield "".join(outputs)
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chat_interface = gr.ChatInterface(
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fn=generate,
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chatbot=gr.Chatbot(height=500, label = "VN GPT", show_label=True),
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textbox=gr.Textbox(placeholder="Nhập hội thoại tại đây", container=False, scale=7),
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additional_inputs=[
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gr.Slider(
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label="Độ dài token",
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minimum=1,
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maximum=MAX_MAX_NEW_TOKENS,
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step=1,
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value=DEFAULT_MAX_NEW_TOKENS,
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),
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gr.Slider(
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label="Độ sáng tạo",
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minimum=0.1,
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maximum=4.0,
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step=0.1,
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value=0.6,
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),
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gr.Slider(
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label="Lựa chọn từ dựa trên xác suất tích lũy",
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minimum=0.05,
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maximum=1.0,
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step=0.05,
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value=0.9,
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),
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gr.Slider(
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label="Lựa chọn k từ có xác suất cao nhất",
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minimum=1,
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maximum=1000,
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step=1,
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value=50,
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),
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gr.Slider(
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label="Phạt lặp lại",
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minimum=1.0,
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maximum=2.0,
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step=0.05,
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value=1.2,
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),
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],
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theme="soft",
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stop_btn=None,
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examples = [
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["Lợi ích của sữa mẹ ?"],
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["Sữa non là gì ?"],
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["Trẻ sơ sinh cần ngủ bao nhiêu giờ mỗi ngày?"],
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["Bao lâu nên cho trẻ sơ sinh bú một lần?"],
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["Khi nào nên bắt đầu cho trẻ ăn dặm?"],
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["Làm thế nào để giúp trẻ ngủ ngon vào ban đêm?"]
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],
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cache_examples=False,
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title = "VN-GPT",
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clear_btn="🗑️ Xóa",
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undo_btn="↩️ Hoàn tác",
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submit_btn="🚀 Gửi",
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retry_btn="🔄 Thử lại",
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additional_inputs_accordion="Tùy chỉnh nâng cao",
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)
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if __name__ == "__main__":
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chat_interface.queue(max_size=20).launch()
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