diff --git a/newGrWithFile.py b/newGrWithFile.py
new file mode 100644
index 0000000..2a514be
--- /dev/null
+++ b/newGrWithFile.py
@@ -0,0 +1,128 @@
+import gradio as gr
+import pandas as pd
+# import tempfile
+from http import HTTPStatus
+import dashscope
+from dashscope import Generation
+import os
+# import logging
+from testAny import check_df_english, check_df_tags
+import concurrent.futures
+import datetime
+
+# 设置日志记录
+# logging.basicConfig(level=logging.INFO)
+
+dashscope.api_key = os.getenv("DASHSCOPE_API_KEY")  # Vincent's API key
+
+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})
+
+    try:
+        response = Generation.call(model='qwen-plus',
+                                   messages=messages,
+                                   seed=1234,
+                                   result_format='message',
+                                   stream=False,
+                                   incremental_output=False,
+                                   temperature=0.85,
+                                   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}"
+    except Exception as e:
+        print(f"Failed to generate response: {e}")
+        return f"Error: Failed to generate response due to an error."
+
+def format_full_prompt(df, introduction):
+    # 为每个 row 创建 context，拼接RAG1和2
+    df['context'] = df.apply(lambda row: f"{row['RAG1']}-{row['RAG2']}", axis=1)
+
+    # 准备用于 format 的字典
+    column_list = df.drop('full_prompt', axis=1).columns.tolist()  # 去除full_prompt列，其他的都为参数
+    format_dict = df[column_list].apply(lambda x: dict(zip(x.index, x)), axis=1)
+    if len(introduction) >= 200:
+        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)
+
+    # 删除临时创建的 context 列
+    df.drop(columns=['context'], inplace=True)
+    return df
+
+def process_xlsx(xlsx_file, instruction=None, loops=1):
+    try:
+        # 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)
+        if loops > 1:
+            df_list = [formatted_df.copy() for _ in range(loops - 1)]
+            # 使用pd.concat一次性合并所有副本
+            formatted_df = pd.concat([formatted_df] + df_list, ignore_index=True)
+
+        # 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))
+
+        # check df with tags and english
+        formatted_df = check_df_tags(formatted_df)
+        formatted_df = check_df_english(formatted_df)
+
+        # 使用一个文件路径保存处理后的xlsx
+        date_str = datetime.datetime.now().strftime("%m%d%H")
+        times_str = str(20 * loops)
+        output_path = 'output'
+        if not os.path.exists(output_path):
+            os.makedirs(output_path)
+        file_name = f"{date_str}_{times_str}times_output.xlsx"
+        file_path = os.path.join(output_path, file_name)
+        formatted_df.to_excel(file_path, index=False, engine='openpyxl')
+        return formatted_df, file_path
+    except Exception as e:
+        print(f"Failed to process xlsx: {e}")
+        return None, None
+
+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=" ")
+            slider = gr.Slider(minimum=1, maximum=10, step=1, label="循环次数", value=1)
+
+        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")
+        def update_output(xlsx_file, instruction, loops):
+            if xlsx_file is not None:
+                formatted_df, tmp_path = process_xlsx(xlsx_file, instruction, loops)
+                # 使用线程池并行处理每一行
+                with concurrent.futures.ThreadPoolExecutor() as executor:
+                    formatted_df['Response'] = list(executor.map(response, formatted_df['full_prompt'], [instruction] * len(formatted_df)))
+
+                if formatted_df is not None:
+                    return formatted_df, tmp_path  # 返回DataFrame和文件路径
+
+        submit_button.click(fn=update_output, inputs=[file_input, system_instruction, slider],
+                            outputs=[output_table, output_file])
+
+    demo.launch()
+
+
+if __name__ == "__main__":
+    main()