104 lines
4.3 KiB
Python
104 lines
4.3 KiB
Python
import gradio as gr
|
||
import pandas as pd
|
||
import tempfile
|
||
import csv
|
||
from http import HTTPStatus
|
||
import dashscope
|
||
from dashscope import Generation
|
||
import os
|
||
from testAny import check_df_english, check_df_tags
|
||
dashscope.api_key = os.getenv("DASHSCOPE_API_KEY") # Vincent's API key
|
||
|
||
|
||
# todo: delete instruction part or make it optional
|
||
def response(prompt, instruction=None):
|
||
messages = [{'role': 'user', 'content': prompt}]
|
||
if instruction is not None: # 如果提供了指令，则添加到messages中
|
||
messages.insert(0, {'role': 'system', 'content': instruction})
|
||
|
||
response = Generation.call(model='qwen-plus',
|
||
messages=messages,
|
||
seed=1234,
|
||
result_format='message',
|
||
stream=False,
|
||
incremental_output=False,
|
||
temperature=1.8,
|
||
top_p=0.9,
|
||
top_k=999
|
||
)
|
||
if response.status_code == HTTPStatus.OK:
|
||
message = response.output.choices[0]['message']['content']
|
||
return message
|
||
else:
|
||
print('Request id: %s, Status code: %s, error code: %s, error message: %s' % (
|
||
response.request_id, response.status_code,
|
||
response.code, response.message
|
||
))
|
||
return f"Error: Could not generate response with Status code: {response.status_code}, error code: {response.code}"
|
||
|
||
|
||
def format_full_prompt(df, introduction):
|
||
# 为每个 row 创建 context
|
||
df['context'] = df.apply(lambda row: f"{row['RAG1']}-{row['RAG2']}", axis=1)
|
||
|
||
# 准备用于 format 的字典
|
||
format_dict = df[['business_use_mark', 'context', 'question']].apply(lambda x: dict(zip(x.index, x)), axis=1)
|
||
if len(introduction) >= 100:
|
||
df['full_prompt'] = introduction
|
||
# 使用 apply() 和 lambda 函数格式化 full_prompt 列
|
||
df['full_prompt'] = df.apply(lambda row: row['full_prompt'].format(**format_dict[row.name]), axis=1)
|
||
|
||
# 可选：删除临时创建的 context 列
|
||
df.drop(columns=['context'], inplace=True)
|
||
return df
|
||
|
||
|
||
def process_xlsx(xlsx_file, instruction=None): # 这里也使instruction参数变成可选
|
||
# 读取xlsx文件到pandas DataFrame
|
||
df = pd.read_excel(xlsx_file)
|
||
# 格式化prompts
|
||
formatted_df = format_full_prompt(df, instruction)
|
||
# 假设我们要处理的提示是DataFrame的'full_prompt'列
|
||
# 调用response时，根据instruction是否为None自动处理
|
||
formatted_df['Response'] = formatted_df['full_prompt'].apply(lambda prompt: response(prompt, instruction))
|
||
|
||
# 使用tempfile创建一个临时文件路径保存处理后的xlsx
|
||
tmp_path = tempfile.NamedTemporaryFile(delete=True, suffix='.xlsx').name
|
||
formatted_df.to_excel(tmp_path, index=False, engine='openpyxl')
|
||
|
||
return formatted_df, tmp_path
|
||
|
||
|
||
def main():
|
||
with gr.Blocks() as demo:
|
||
gr.Markdown("### 大模型xlsx处理工具")
|
||
with gr.Accordion("输入说明"):
|
||
gr.Markdown("请上传一个xlsx文件，文件应包含prompts。")
|
||
system_instruction = gr.Textbox(label="System Instruction", lines=2,
|
||
value=" ")
|
||
|
||
file_input = gr.File(label="上传xlsx文件")
|
||
submit_button = gr.Button("处理xlsx")
|
||
|
||
output_table = gr.Dataframe(label="处理后的数据")
|
||
output_file = gr.File(label="下载处理后的文件")
|
||
clear_data = gr.ClearButton(components=[output_table, output_file], value="Clear processed data")
|
||
clear_all = gr.ClearButton(components=[file_input, output_table, output_file], value="Clear console")
|
||
check_tags_button = gr.Button("检查xlsx文件-tags")
|
||
check_english_button = gr.Button("检查xlsx文件-英文")
|
||
def update_output(xlsx_file, instruction):
|
||
if xlsx_file is not None:
|
||
formatted_df, tmp_path = process_xlsx(xlsx_file, instruction)
|
||
return formatted_df, tmp_path # 返回DataFrame和文件路径
|
||
|
||
submit_button.click(fn=update_output, inputs=[file_input, system_instruction],
|
||
outputs=[output_table, output_file])
|
||
check_tags_button.click(fn=check_df_tags, inputs=[output_table], outputs=[output_table])
|
||
|
||
demo.launch()
|
||
|
||
|
||
if __name__ == "__main__":
|
||
main()
|
||
|