Update app.py

app.py

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