teaevo commited on
Commit
d7a34dd
1 Parent(s): 5782838

Update app.py

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Files changed (1) hide show
  1. app.py +38 -9
app.py CHANGED
@@ -7,6 +7,9 @@ import gradio as gr
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  import numpy as np
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  import time
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  import os
 
 
 
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  #import pkg_resources
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  '''
@@ -18,6 +21,38 @@ for package, version in installed_packages.items():
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  print(f"{package}=={version}")
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  '''
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  # Load the chatbot model
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  chatbot_model_name = "microsoft/DialoGPT-medium"
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  tokenizer = AutoTokenizer.from_pretrained(chatbot_model_name)
@@ -28,19 +63,13 @@ sql_model_name = "microsoft/tapex-large-finetuned-wtq"
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  sql_tokenizer = TapexTokenizer.from_pretrained(sql_model_name)
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  sql_model = BartForConditionalGeneration.from_pretrained(sql_model_name)
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- data = {
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- "year": [1896, 1900, 1904, 2004, 2008, 2012],
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- "city": ["athens", "paris", "st. louis", "athens", "beijing", "london"]
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- }
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- table = pd.DataFrame.from_dict(data)
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-
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- sql_response = None
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  def predict(input, history=[]):
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- global sql_response
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  # Check if the user input is a question
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- is_question = "?" in input
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  '''
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  if is_question:
 
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  import numpy as np
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  import time
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  import os
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+
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+ import pyodbc
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+
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  #import pkg_resources
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  '''
 
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  print(f"{package}=={version}")
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  '''
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+ '''
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+ # Replace the connection parameters with your SQL Server information
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+ server = 'your_server'
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+ database = 'your_database'
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+ username = 'your_username'
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+ password = 'your_password'
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+ driver = 'SQL Server' # This depends on the ODBC driver installed on your system
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+
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+ # Create the connection string
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+ connection_string = f'DRIVER={{{driver}}};SERVER={server};DATABASE={database};UID={username};PWD={password}'
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+
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+ # Connect to the SQL Server
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+ conn = pyodbc.connect(connection_string)
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+
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+ #============================================================================
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+ # Replace "your_query" with your SQL query to fetch data from the database
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+ query = 'SELECT * FROM your_table_name'
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+
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+ # Use pandas to read data from the SQL Server and store it in a DataFrame
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+ df = pd.read_sql_query(query, conn)
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+
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+ # Close the SQL connection
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+ conn.close()
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+ '''
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+
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+ data = {
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+ "year": [1896, 1900, 1904, 2004, 2008, 2012],
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+ "city": ["athens", "paris", "st. louis", "athens", "beijing", "london"]
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+ }
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+ table = pd.DataFrame.from_dict(data)
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+
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+
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  # Load the chatbot model
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  chatbot_model_name = "microsoft/DialoGPT-medium"
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  tokenizer = AutoTokenizer.from_pretrained(chatbot_model_name)
 
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  sql_tokenizer = TapexTokenizer.from_pretrained(sql_model_name)
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  sql_model = BartForConditionalGeneration.from_pretrained(sql_model_name)
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+ #sql_response = None
 
 
 
 
 
 
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  def predict(input, history=[]):
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+ #global sql_response
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  # Check if the user input is a question
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+ #is_question = "?" in input
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  '''
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  if is_question: