csv

app.py

@@ -1,42 +1,21 @@
+# 
+
 import gradio as gr
 import pandas as pd
-from openai import OpenAI
-import os
 
-PASSWORD = os.environ['PASSWORD']
-OPEN_AI_KEY = os.environ['OPEN_AI_KEY']
+def process_file(file, question):
+    # 读取文件
+    if file.name.endswith('.csv'):
+        df = pd.read_csv(file)
+    else:
+        df = pd.read_excel(file)
 
-def process_file(file_path, question):
-    client = OpenAI(api_key=OPEN_AI_KEY)
-    df = pd.read_csv(file_path) if file_path.endswith('.csv') else pd.read_excel(file_path)
+    # 将 DataFrame 转换为字符串
     df_string = df.to_string()
 
-    # 初始化對話
-    sys_content = f"你是一個專業的資料分析師，請解讀這份資料"
-    prompt = df_string + "\nQ: " + question
-    messages = [
-        {"role": "system", "content": sys_content},
-        {"role": "user", "content": prompt}
-    ]
-
-    print("=====messages=====")
-    print(messages)
-    print("=====messages=====")
-
-
-    request_payload = {
-        "model": "gpt-4-1106-preview",
-        "messages": messages,
-        "max_tokens": 2046,
-    }
-
-    response = client.chat.completions.create(**request_payload)
-    print(response)
-    
-    response_text = response.choices[0].message.content.strip()
-
-
-    return response_text
+    # 这里只是输出 DataFrame 字符串和用户问题
+    # 您可以根据需要添加额外的逻辑
+    return df_string + "\nQ: " + question
 
 iface = gr.Interface(
     fn=process_file,