fix demos code (#76)

demos/employee_details_copilot/api_server/app/utils.py

@@ -0,0 +1,157 @@
+import pandas as pd
+import random
+import datetime
+import sqlite3
+
+def load_sql():
+    # Example Usage
+    conn = sqlite3.connect(":memory:")
+
+    # create and load the employees table
+    generate_employee_data(conn)
+
+    # create and load the projects table
+    generate_project_data(conn)
+
+    # create and load the salary_history table
+    generate_salary_history(conn)
+
+    # create and load the certifications table
+    generate_certifications(conn)
+
+    return conn
+
+# Function to generate random employee data with `eid` as the primary key
+def generate_employee_data(conn):
+    # List of possible names, positions, departments, and locations
+    names = [
+        "Alice",
+        "Bob",
+        "Charlie",
+        "David",
+        "Eve",
+        "Frank",
+        "Grace",
+        "Hank",
+        "Ivy",
+        "Jack",
+    ]
+    positions = [
+        "Manager",
+        "Engineer",
+        "Salesperson",
+        "HR Specialist",
+        "Marketing Analyst",
+    ]
+    # List of possible names, positions, departments, locations, and certifications
+    names = ["Alice", "Bob", "Charlie", "David", "Eve", "Frank", "Grace", "Hank", "Ivy", "Jack"]
+    positions = ["Manager", "Engineer", "Salesperson", "HR Specialist", "Marketing Analyst"]
+    departments = ["Engineering", "Marketing", "HR", "Sales", "Finance"]
+    locations = ["New York", "San Francisco", "Austin", "Boston", "Chicago"]
+    certifications = ["AWS Certified", "Google Cloud Certified", "PMP", "Scrum Master", "Cisco Certified"]
+
+    # Generate random hire dates
+    def random_hire_date():
+        start_date = datetime.date(2000, 1, 1)
+        end_date = datetime.date(2023, 12, 31)
+        time_between_dates = end_date - start_date
+        days_between_dates = time_between_dates.days
+        random_number_of_days = random.randrange(days_between_dates)
+        return start_date + datetime.timedelta(days=random_number_of_days)
+
+    # Generate random employee records with an employee ID (eid)
+    employees = []
+    for eid in range(1, 101):  # 100 employees with `eid` starting from 1
+        name = random.choice(names)
+        position = random.choice(positions)
+        salary = round(random.uniform(50000, 150000), 2)  # Salary between 50,000 and 150,000
+        department = random.choice(departments)
+        location = random.choice(locations)
+        hire_date = random_hire_date()
+        performance_score = round(random.uniform(1, 5), 2)  # Performance score between 1.0 and 5.0
+        years_of_experience = random.randint(1, 30)  # Years of experience between 1 and 30
+
+        employee = {
+            "eid": eid,  # Employee ID
+            "name": name,
+            "position": position,
+            "salary": salary,
+            "department": department,
+            "location": location,
+            "hire_date": hire_date,
+            "performance_score": performance_score,
+            "years_of_experience": years_of_experience
+        }
+
+        employees.append(employee)
+
+    # Convert the list of dictionaries to a DataFrame and save to DB
+    df_employees = pd.DataFrame(employees)
+    df_employees.to_sql('employees', conn, index=False, if_exists='replace')
+
+# Function to generate random project data with `eid`
+def generate_project_data(conn):
+    employees = pd.read_sql_query("SELECT eid FROM employees", conn)
+    projects = []
+
+    for _ in range(500):  # 500 projects
+        eid = random.choice(employees['eid'])
+        project_name = f"Project_{random.randint(1, 100)}"
+        start_date = datetime.date(2020, 1, 1) + datetime.timedelta(days=random.randint(0, 365 * 3))  # Within the last 3 years
+        performance_score = round(random.uniform(1, 5), 2)  # Performance score for the project between 1.0 and 5.0
+
+        project = {
+            "eid": eid,  # Foreign key from employees table
+            "project_name": project_name,
+            "start_date": start_date,
+            "performance_score": performance_score
+        }
+
+        projects.append(project)
+
+    # Convert the list of dictionaries to a DataFrame and save to DB
+    df_projects = pd.DataFrame(projects)
+    df_projects.to_sql('projects', conn, index=False, if_exists='replace')
+
+# Function to generate random salary history data with `eid`
+def generate_salary_history(conn):
+    employees = pd.read_sql_query("SELECT eid FROM employees", conn)
+    salary_history = []
+
+    for _ in range(300):  # 300 salary records
+        eid = random.choice(employees['eid'])
+        salary_increase_percentage = round(random.uniform(5, 30), 2)  # Salary increase between 5% and 30%
+        promotion_date = datetime.date(2018, 1, 1) + datetime.timedelta(days=random.randint(0, 365 * 5))  # Promotions in the last 5 years
+
+        salary_record = {
+            "eid": eid,  # Foreign key from employees table
+            "salary_increase_percentage": salary_increase_percentage,
+            "promotion_date": promotion_date
+        }
+
+        salary_history.append(salary_record)
+
+    # Convert the list of dictionaries to a DataFrame and save to DB
+    df_salary_history = pd.DataFrame(salary_history)
+    df_salary_history.to_sql('salary_history', conn, index=False, if_exists='replace')
+
+# Function to generate random certifications data with `eid`
+def generate_certifications(conn):
+    employees = pd.read_sql_query("SELECT eid FROM employees", conn)
+    certifications_list = ["AWS Certified", "Google Cloud Certified", "PMP", "Scrum Master", "Cisco Certified"]
+    employee_certifications = []
+
+    for _ in range(300):  # 300 certification records
+        eid = random.choice(employees['eid'])
+        certification = random.choice(certifications_list)
+
+        cert_record = {
+            "eid": eid,  # Foreign key from employees table
+            "certification_name": certification
+        }
+
+        employee_certifications.append(cert_record)
+
+    # Convert the list of dictionaries to a DataFrame and save to DB
+    df_certifications = pd.DataFrame(employee_certifications)
+    df_certifications.to_sql('certifications', conn, index=False, if_exists='replace')