def generate_df_summarise(df_string):

app.py

@@ -20,18 +20,40 @@ def process_file(file):
 
     # 根据上传的文件内容生成问题
     questions = generate_questions(df_string)
+    df_summarise = generate_df_summarise(df_string)
 
     # 返回按钮文本和 DataFrame 字符串
     return questions[0] if len(questions) > 0 else "", \
            questions[1] if len(questions) > 1 else "", \
            questions[2] if len(questions) > 2 else "", \
+           df_summarise, \
            df_string
 
-def update_buttons(df_string):
-    # 根据上传的文件内容生成问题
-    questions = generate_questions(df_string)
-    return questions[:3]  # 确保返回三个问题
+def generate_df_summarise(df_string):
+    # 使用 OpenAI 生成基于上传数据的问题
+    sys_content = "你是一個資料分析師，服務對象為老師，請精讀資料，使用 zh-TW"
+    user_content = f"請根據 {df_string}，大概描述這張表的欄位敘述，以及內容的資料樣態與解析"
+    messages = [
+        {"role": "system", "content": sys_content},
+        {"role": "user", "content": user_content}
+    ]
+    print("=====messages=====")
+    print(messages)
+    print("=====messages=====")
+
+    request_payload = {
+        "model": "gpt-4-1106-preview",
+        "messages": messages,
+        "max_tokens": 2000,
+    }
+
+    response = client.chat.completions.create(**request_payload)
+    df_summarise = response.choices[0].message.content.strip()
+    print("=====df_summarise=====")
+    print(df_summarise)
+    print("=====df_summarise=====")
 
+    return df_summarise
 
 def generate_questions(df_string):
     # 使用 OpenAI 生成基于上传数据的问题
@@ -106,11 +128,6 @@ def respond(user_message, df_string_output, chat_history):
     # 返回聊天历史和空字符串清空输入框
     return "", chat_history
 
-
-def on_button_click(button, df_string_output, chat_history):
-    # 当按钮被点击时发送对应的问题
-    return respond(button, df_string_output, chat_history)
-
 with gr.Blocks() as demo:
     with gr.Row():
         with gr.Column():
@@ -120,8 +137,12 @@ with gr.Blocks() as demo:
             send_button = gr.Button("Send")
 
         with gr.Column():
-            df_string_output = gr.Textbox(label="raw data")
-
+            with gr.Group():
+                df_string_output = gr.Textbox(label="raw data")
+            with gr.Group():
+                gr.Markdown("## 這是一張什麼表？")
+                df_summarise = gr.Textbox()
+               
             with gr.Group():
                 gr.Markdown("## 常用問題")
                 btn_1 = gr.Button()
@@ -134,16 +155,17 @@ with gr.Blocks() as demo:
         inputs=[msg, df_string_output, chatbot], 
         outputs=[msg, chatbot]
     )
+    # 连接按钮点击事件
+    btn_1.click(respond, inputs=[btn_1, df_string_output, chatbot], outputs=[msg, chatbot])
+    btn_2.click(respond, inputs=[btn_2, df_string_output, chatbot], outputs=[msg, chatbot])
+    btn_3.click(respond, inputs=[btn_3, df_string_output, chatbot], outputs=[msg, chatbot])
 
-    # file_upload.change(process_file, inputs=file_upload, outputs=df_string_output)
-    file_upload.change(process_file, inputs=file_upload, outputs=[btn_1, btn_2, btn_3, df_string_output])
 
+    # file_upload.change(process_file, inputs=file_upload, outputs=df_string_output)
+    file_upload.change(process_file, inputs=file_upload, outputs=[btn_1, btn_2, btn_3, df_summarise, df_string_output])
 
-    # 连接按钮点击事件
-    btn_1.click(on_button_click, inputs=[btn_1, df_string_output, chatbot], outputs=[msg, chatbot])
-    btn_2.click(on_button_click, inputs=[btn_2, df_string_output, chatbot], outputs=[msg, chatbot])
-    btn_3.click(on_button_click, inputs=[btn_3, df_string_output, chatbot], outputs=[msg, chatbot])
 
+    
 
 
 demo.launch()