add new py file for file process

gradio_multiInputs.py

@@ -7,6 +7,13 @@ from dashscope import Generation
 dashscope.api_key = 'sk-73e9b0452a7e40048495d8ac8ab1afe4'  # Vincent's API key
 
 
+def pross_instruction(system, rag_dict):
+    """
+        使用format_map()替换字符串中的变量。
+    """
+    return system.format_map(rag_dict)
+
+
 def response(prompt, instruction):
     messages = [{'role': 'system', 'content': instruction},
                 {'role': 'user', 'content': prompt}]

testGrDownload.py

@@ -0,0 +1,104 @@
+import gradio as gr
+import pandas as pd
+import tempfile
+import csv
+from http import HTTPStatus
+import dashscope
+from dashscope import Generation
+
+
+
+
+def pross_instruction(system, rag_dict):
+    """
+        使用format_map()替换字符串中的变量。
+    """
+    return system.format_map(rag_dict)
+
+
+def response(prompt, instruction):
+    messages = [{'role': 'system', 'content': instruction},
+                {'role': 'user', 'content': prompt}]
+
+    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):
+    # 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)
+
+    # 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):
+    # 读取xlsx文件到pandas DataFrame
+    df = pd.read_excel(xlsx_file)
+
+    # 格式化prompts
+    formatted_df = format_full_prompt(df)
+
+    # 假设我们要处理的提示是DataFrame的'full_prompt'列
+    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="A conversation between a user and an LLM-based AI assistant. The assistant gives helpful and honest answers.")
+
+        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):
+            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])
+
+    demo.launch()
+
+
+if __name__ == "__main__":
+    main()
+