Update app.py

app.py

@@ -14,21 +14,20 @@ import psycopg2
 import time
 import gradio as gr
 import sqlparse
-
-# from google.colab import drive
+import os
 
 #EA_key
-openai.api_key = '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'
-
+openai.api_key = os.getenv("api_key")
 
 pd.set_option('display.max_columns', None)
 pd.set_option('display.max_rows', None)
 
-db_name = 'express'
-user_db = "amardeep"
-pwd_db = 'Welcome!23'
-host_db = "ea-non-prod.cxw4zfxatj9b.us-west-1.redshift.amazonaws.com"
-port_db = "5439"
+#database credential
+db_name = os.getenv("db_name")
+user_db = os.getenv("user_db")
+pwd_db = os.getenv("pwd_db")
+host_db = os.getenv("host_db")
+port_db = os.getenv("port_db")
 
 conn = psycopg2.connect(database=db_name, user = user_db, password = pwd_db, host = host_db, port = port_db)
 
@@ -54,9 +53,6 @@ df_calendar = pd.read_sql_query(sql9, con=conn)
 
 conn.close()
 df_customer.head(2)
-# df_customer.head(2)
-# df_product.head(2)
-# df_sales.head(2)
 
 customer_col=['customer_id','customer_type', 'first_name', 'middle_name', 'household_name', 'last_name', 'personal_email', 'city', 'state', 'zip_code', 'address1', 'country', 'gender', 'phone_number', 'reward_number']
 product_col=['product_id', 'product_name', 'product_price', 'department', 'class',  'discount', 'category', 'department_desc', 'department_type', 'product_type', 'manufacturer', 'color']
@@ -170,51 +166,19 @@ def generate_sql_query(text):
     sql_query = request['choices'][0]['message']['content']
     return sql_query
 
-# text="the customer who made a total transaction with more than 50 dollars ?"
-# text="give me the list of male customer from california ?"
-
 text = "for female customer who did a transaction of more than 100 dollars in year 2020 please write sql query ?"
 
-
 schema_name = 'lpdatamart'
 prompt = """Given an input text, and You will generate the corresponding SQL query. The schema name is {}. The first table name is {} and the following data:\n {}. The second table name is {} and the following data for second table:\n {}. The third table name is {} and the following data for third table:\n {}. The fourth table name is {} and the following data for fourth table:\n {}. The fifth table name is {} and the following data for fifth table:\n {}. The sixth table name is {} and the following data for sixth table:\n {}. The seventh table name is {} and the following data for seventh table:\n {} \n""".format(schema_name,table_name1,df_customer.loc[:5], table_name2, df_product.loc[:5], table_name3, df_sales.loc[:5], table_name5, df_store.loc[:5],  table_name6, df_channel.loc[:5],table_name7, df_lineaction.loc[:5], table_name8, df_calendar.loc[:5])
 messages = [{"role": "system", "content": prompt}]
 
 sql_query=generate_sql_query(text)
 print("Generated SQL query: ",sql_query)
-# if sql_query:
-#     result=execute_sql_query(sql_query)
-#     print("ChatGPT Response=>",result)
-
-# Close database connection
-# cursor1.close()
-# conn1.close()
-
-# cursor2.close()
-# conn2.close()
-
-# cursor3.close()
-# conn3.close()
-
 
 # prompt = """Given an input text, and You will generate the corresponding SQL query. The first table name is {} and the following data:\n {}. The second table name is {} and the following data for second table:\n {}. The third table name is {} and the following data for third table:\n {}.\n""".format(table_name1,df2.loc[:5], table_name2, df3.loc[:5], table_name3, df4.loc[:5])
 prompt = """Given an input text, and You will generate the corresponding SQL query. The schema name is {}. The first table name is {} and the following data:\n {}. The second table name is {} and the following data for second table:\n {}. The third table name is {} and the following data for third table:\n {}. The fourth table name is {} and the following data for fourth table:\n {}. The fifth table name is {} and the following data for fifth table:\n {}. The sixth table name is {} and the following data for sixth table:\n {}. The seventh table name is {} and the following data for seventh table:\n {} \n""".format(schema_name,table_name1,df_customer.loc[:5], table_name2, df_product.loc[:5], table_name3, df_sales.loc[:5], table_name5, df_store.loc[:5],  table_name6, df_channel.loc[:5],table_name7, df_lineaction.loc[:5], table_name8, df_calendar.loc[:5])
 messages = [{"role": "system", "content": prompt}]
 
-# def CustomChatGPT(Question):
-#     messages.append({"role": "user", "content": Question})
-#     response = openai.ChatCompletion.create(
-#         model = "gpt-4",
-#         messages = messages
-#     )
-#     ChatGPT_reply = response["choices"][0]["message"]["content"]
-#     messages.append({"role": "assistant", "content": ChatGPT_reply})
-#     return ChatGPT_reply
-
-# demo = gradio.Interface(fn=CustomChatGPT, inputs = "text", outputs = "text", title = "Query Helper")
-
-# demo.launch(share=True)
-
 import time
 import gradio as gr
 def CustomChatGPT(user_inp):
@@ -240,12 +204,6 @@ def test_Sql(sql):
   sql = str(sql)
   sql = sqlparse.format(sql, reindent=True, keyword_case='upper')
 
-  db_name = 'express'
-  user_db = "amardeep"
-  pwd_db = 'Welcome!23'
-  host_db = "ea-non-prod.cxw4zfxatj9b.us-west-1.redshift.amazonaws.com"
-  port_db = "5439"
-
   conn = psycopg2.connect(database=db_name, user = user_db, password = pwd_db, host = host_db, port = port_db)
   df = pd.read_sql_query(sql, con=conn)
   conn.close()
@@ -267,5 +225,4 @@ with gr.Blocks() as demo:
         clear = gr.ClearButton([text_input, text_output])
         text_button.click(test_Sql, inputs=text_input, outputs=text_output)
 
-demo.launch(share=True)
-# inf.launch(share=True)
+demo.launch(share=True)