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Update app.py
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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
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@@ -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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# Global variables
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config = {"db": "localhost", "user": "admin", "password": "admin"}
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connection = None
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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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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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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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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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if __name__ == "__main__":
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main()
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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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# 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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# 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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# 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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# 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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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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["""
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import pandas as pd
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import re
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import ast
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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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# Stream text - stream tokens with InferenceClient from TGI
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