105 lines
3.9 KiB
Python
105 lines
3.9 KiB
Python
import gradio as gr
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import pandas as pd
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import tempfile
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import csv
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from http import HTTPStatus
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import dashscope
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from dashscope import Generation
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def pross_instruction(system, rag_dict):
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"""
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使用format_map()替换字符串中的变量。
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"""
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return system.format_map(rag_dict)
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def response(prompt, instruction):
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messages = [{'role': 'system', 'content': instruction},
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{'role': 'user', 'content': prompt}]
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response = Generation.call(model='qwen-plus',
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messages=messages,
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seed=1234,
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result_format='message',
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stream=False,
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incremental_output=False,
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temperature=1.8,
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top_p=0.9,
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top_k=999
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)
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if response.status_code == HTTPStatus.OK:
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message = response.output.choices[0]['message']['content']
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return message
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else:
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print('Request id: %s, Status code: %s, error code: %s, error message: %s' % (
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response.request_id, response.status_code,
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response.code, response.message
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))
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return f"Error: Could not generate response with Status code: {response.status_code}, error code: {response.code}"
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def format_full_prompt(df):
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# 为每个 row 创建 context
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df['context'] = df.apply(lambda row: f"{row['RAG1']}-{row['RAG2']}", axis=1)
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# 准备用于 format 的字典
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format_dict = df[['business_use_mark', 'context', 'question']].apply(lambda x: dict(zip(x.index, x)), axis=1)
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# 使用 apply() 和 lambda 函数格式化 full_prompt 列
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df['full_prompt'] = df.apply(lambda row: row['full_prompt'].format(**format_dict[row.name]), axis=1)
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# 可选：删除临时创建的 context 列
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df.drop(columns=['context'], inplace=True)
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return df
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def process_xlsx(xlsx_file, instruction):
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# 读取xlsx文件到pandas DataFrame
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df = pd.read_excel(xlsx_file)
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# 格式化prompts
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formatted_df = format_full_prompt(df)
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# 假设我们要处理的提示是DataFrame的'full_prompt'列
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formatted_df['Response'] = formatted_df['full_prompt'].apply(lambda prompt: response(prompt, instruction))
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# 使用tempfile创建一个临时文件路径保存处理后的xlsx
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tmp_path = tempfile.NamedTemporaryFile(delete=True, suffix='.xlsx').name
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formatted_df.to_excel(tmp_path, index=False, engine='openpyxl')
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return formatted_df, tmp_path
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def main():
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with gr.Blocks() as demo:
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gr.Markdown("### 大模型xlsx处理工具")
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with gr.Accordion("输入说明"):
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gr.Markdown("请上传一个xlsx文件，文件应包含prompts。")
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system_instruction = gr.Textbox(label="System Instruction", lines=2,
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value="A conversation between a user and an LLM-based AI assistant. The assistant gives helpful and honest answers.")
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file_input = gr.File(label="上传xlsx文件")
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submit_button = gr.Button("处理xlsx")
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output_table = gr.Dataframe(label="处理后的数据")
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output_file = gr.File(label="下载处理后的文件")
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clear_data = gr.ClearButton(components=[output_table, output_file], value="Clear processed data")
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clear_all = gr.ClearButton(components=[file_input, output_table, output_file], value="Clear console")
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def update_output(xlsx_file, instruction):
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if xlsx_file is not None:
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formatted_df, tmp_path = process_xlsx(xlsx_file, instruction)
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return formatted_df, tmp_path # 返回DataFrame和文件路径
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submit_button.click(fn=update_output, inputs=[file_input, system_instruction],
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outputs=[output_table, output_file])
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demo.launch()
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if __name__ == "__main__":
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main()
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