app.py

import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer

# 加载本地模型和tokenizer
model_name = "ganchengguang/OIELLM-8B-Instruction"  # 替换为你的模型名称
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# 定义语言和选项的映射
options = {
    'English': {'NER': '/NER/', 'Sentimentrw': '/Sentiment related word/', 'Sentimentadjn': '/Sentiment Adj and N/', 'Sentimentadj': '/Sentiment Adj/', 'Sentimentn': '/Sentiment N/', 'Relation': '/relation extraction/', 'Event': '/event extraction/'},
    '中文': {'NER': '/实体命名识别/', 'Sentimentrw': '/感情分析关联单词/', 'Sentimentadjn': '/感情分析形容词名词/', 'Sentimentadj': '/感情分析形容词/', 'Sentimentn': '/感情分析名词/', 'Relation': '/关系抽取/', 'Event': '/事件抽取/'},
    '日本語': {'NER': '/固有表現抽出/', 'Sentimentrw': '/感情分析関連単語/', 'Sentimentadjn': '/感情分析形容詞名詞/', 'Sentimentadj': '/感情分析形容詞/', 'Sentimentn': '/感情分析名詞/', 'Relation': '/関係抽出/', 'Event': '/事件抽出/'}
}

# 定义聊天函数
def respond(message, language, task, system_message, max_tokens, temperature, top_p):
    # 初始化对话历史
    messages = [{"role": "system", "content": system_message}]
    messages.append({"role": "user", "content": message + " " + options[language][task]})

    # 编码输入
    inputs = tokenizer(messages, return_tensors="pt", padding=True, truncation=True)
    # 生成回复
    outputs = model.generate(
        inputs["input_ids"],
        max_length=max_tokens,
        temperature=temperature,
        top_p=top_p,
        do_sample=True
    )
    # 解码回复
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    yield response

# 更新任务选项的函数
def update_tasks(language):
    return gr.update(choices=list(options[language].keys()))

# 创建Gradio接口
demo = gr.ChatInterface(
    respond,
    inputs=[
        gr.Textbox(label="Input Text"),
        gr.Dropdown(label="Language", choices=list(options.keys()), value="English"),
        gr.Dropdown(label="Task", choices=list(options['English'].keys())),
        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
        gr.Slider(minimum=0.1, maximum4.0, value=0.7, step=0.1, label="Temperature"),
        gr.Slider(
            minimum=0.1,
            maximum=1.0,
            value=0.95,
            step=0.05,
            label="Top-p (nucleus sampling)"
        ),
    ],
    live=True
)

# 设置语言选择框的动态更新
demo.components[1].change(update_tasks, inputs=demo.components[1], outputs=demo.components[2])

if __name__ == "__main__":
    demo.launch()