karths commited on
Commit
62e5f0b
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1 Parent(s): f660680

Update app.py

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Files changed (1) hide show
  1. app.py +111 -23
app.py CHANGED
@@ -21,7 +21,116 @@ description = """
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  Please give it 4 to 5 minutes for the model to load and Run , consider using Python code with less than 120 lines of code due to GPU constrainst
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  """
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  css = """.toast-wrap { display: none !important } """
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- examples=[["""
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  import pandas as pd
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  import re
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  import ast
@@ -66,28 +175,7 @@ def evaluate_dataframe_multiple_runs(df, runs=3):
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  df_metrics_mean = pd.concat(all_results).groupby(level=0).mean()
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  df_metrics_std = pd.concat(all_results).groupby(level=0).std()
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  return df_metrics_mean, df_metrics_std
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- """ ] ,
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- ["""
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- def analyze_sales_data(sales_records):
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- active_sales = filter(lambda record: record['status'] == 'active', sales_records)
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- sales_by_category = {}
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- for record in active_sales:
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- category = record['category']
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- total_sales = record['units_sold'] * record['price_per_unit']
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- if category not in sales_by_category:
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- sales_by_category[category] = {'total_sales': 0, 'total_units': 0}
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- sales_by_category[category]['total_sales'] += total_sales
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- sales_by_category[category]['total_units'] += record['units_sold']
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- average_sales_data = []
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- for category, data in sales_by_category.items():
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- average_sales = data['total_sales'] / data['total_units']
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- sales_by_category[category]['average_sales'] = average_sales
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- average_sales_data.append((category, average_sales))
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- average_sales_data.sort(key=lambda x: x[1], reverse=True)
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- for rank, (category, _) in enumerate(average_sales_data, start=1):
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- sales_by_category[category]['rank'] = rank
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- return sales_by_category
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- """]]
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  # Stream text - stream tokens with InferenceClient from TGI
 
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  Please give it 4 to 5 minutes for the model to load and Run , consider using Python code with less than 120 lines of code due to GPU constrainst
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  """
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  css = """.toast-wrap { display: none !important } """
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+ examples=[ ["""
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+ import sys
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+ import os
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+ import someDatabaseLib
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+
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+ # Global variables
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+ config = {"db": "localhost", "user": "admin", "password": "admin"}
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+ connection = None
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+
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+ def dbConnect():
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+ global connection
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+ try:
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+ connection = someDatabaseLib.connect(config["db"], config["user"], config["password"])
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+ except Exception as e:
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+ print(e)
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+ sys.exit(1)
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+
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+ def fetchData():
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+ global connection
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+ if connection is None:
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+ print("Not connected to DB")
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+ return None
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+ try:
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+ cursor = connection.cursor()
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+ cursor.execute("SELECT * FROM someTable WHERE someColumn='someValue'")
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+ return cursor.fetchall()
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+ except Exception as e:
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+ print("Failed to fetch data: ", e)
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+ return None
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+
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+ def processData(data):
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+ if data is None:
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+ print("No data provided")
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+ return None
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+ result = []
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+ for row in data:
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+ # Processing logic here
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+ result.append(row)
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+ return result
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+
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+ def main():
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+ dbConnect()
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+ data = fetchData()
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+ if data is None:
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+ print("No data fetched")
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+ sys.exit(1)
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+ processedData = processData(data)
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+ print("Data processed")
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+
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+ if __name__ == "__main__":
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+ main()
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+
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+ # Additional functions and logic mixed together without clear separation or modularisation
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+ def someOtherFunction():
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+ pass
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+
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+ # Hardcoded paths and configuration details
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+ path_to_files = "/path/to/some/files"
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+ for file_name in os.listdir(path_to_files):
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+ with open(os.path.join(path_to_files, file_name), 'r') as file:
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+ data = file.read()
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+ # Do something with the data
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+
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+ # Poor error handling and mixing of concerns (e.g., UI logic with business logic)
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+ def userInterfaceFunction():
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+ choice = input("Enter your choice: ")
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+ if choice == "1":
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+ print("User chose 1")
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+ # Proceed with option 1
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+ elif choice == "2":
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+ print("User chose 2")
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+ # Proceed with option 2
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+ else:
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+ print("Invalid choice")
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+
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+ # Direct database access mixed with business logic without any abstraction layer
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+ def directDBAccess():
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+ global config
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+ try:
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+ conn = someDatabaseLib.connect(config["db"], config["user"], config["password"])
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+ cursor = conn.cursor()
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+ cursor.execute("UPDATE someTable SET someColumn='newValue' WHERE anotherColumn='value'")
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+ except Exception as e:
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+ print("Database operation failed: ", e)
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+
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+ # Mixing of different levels of abstraction, lack of consistent error handling, and no use of classes or functions to encapsulate related operations
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+
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+ """] ,
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+ ["""
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+ def analyze_sales_data(sales_records):
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+ active_sales = filter(lambda record: record['status'] == 'active', sales_records)
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+ sales_by_category = {}
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+ for record in active_sales:
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+ category = record['category']
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+ total_sales = record['units_sold'] * record['price_per_unit']
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+ if category not in sales_by_category:
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+ sales_by_category[category] = {'total_sales': 0, 'total_units': 0}
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+ sales_by_category[category]['total_sales'] += total_sales
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+ sales_by_category[category]['total_units'] += record['units_sold']
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+ average_sales_data = []
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+ for category, data in sales_by_category.items():
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+ average_sales = data['total_sales'] / data['total_units']
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+ sales_by_category[category]['average_sales'] = average_sales
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+ average_sales_data.append((category, average_sales))
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+ average_sales_data.sort(key=lambda x: x[1], reverse=True)
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+ for rank, (category, _) in enumerate(average_sales_data, start=1):
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+ sales_by_category[category]['rank'] = rank
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+ return sales_by_category
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+ """] ,
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+ ["""
134
  import pandas as pd
135
  import re
136
  import ast
 
175
  df_metrics_mean = pd.concat(all_results).groupby(level=0).mean()
176
  df_metrics_std = pd.concat(all_results).groupby(level=0).std()
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  return df_metrics_mean, df_metrics_std
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+ """ ] ]
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  # Stream text - stream tokens with InferenceClient from TGI