import json
import gurobipy as gp
from gurobipy import GRB
from datetime import datetime

def load_data_from_json(jsonfile):
    with open(jsonfile, 'r') as file:
        data = json.load(file)
    return data

def optimize_course_scheduling(data):
    # Create a new Gurobi model
    model = gp.Model("course_scheduling")

    # Create decision variables
    x = {}  # Classroom assignment variables
    classroom_ids = []  # List to store ClassroomIds
    section_ids = []  # List to store SectionIds

    for classroom in data['Classrooms']:
        id = str(classroom["Id"])  # Convert Id to string
        capacity = classroom["Capacity"]
        name = classroom["Name"]

        x[id] = {}
        for section in data['Sections']:
            section_id = str(section["Id"])  # Convert Id to string
            section_ids.append(section_id)
            section_start_time = datetime.strptime(section["StartTime"], "%H:%M:%S")
            section_end_time = datetime.strptime(section["EndTime"], "%H:%M:%S")

            x[id][section_id] = model.addVar(vtype=GRB.BINARY, name=f"x_{id}_{section_id}")
            classroom_ids.append(id)

            # Constraint: Classroom capacity should not be exceeded
            model.addConstr(classroom["Capacity"] <= capacity * x[id][section_id])

            # Constraint: No time conflicts
            for other_section in data['Sections']:
                if other_section["Id"] != section_id:
                    other_start_time = datetime.strptime(other_section["StartTime"], "%H:%M:%S")
                    other_end_time = datetime.strptime(other_section["EndTime"], "%H:%M:%S")

                    # Check for time overlap
                    if section_start_time < other_end_time and other_start_time < section_end_time:
                        model.addConstr(x[id][section_id] + x[id][str(other_section["Id"])] <= 1)

    # Set objective function
    model.setObjective(gp.quicksum(x[id][section_id] for id in x for section_id in x[id]), GRB.MAXIMIZE)

    # Optimize the model
    model.optimize()

    # Print the solution
    if model.status == GRB.OPTIMAL:
        print("Optimal solution found!")
        solution = []
        assigned_classrooms = []

        for id in x:
            for section_id in x[id]:
                if x[id][section_id].x > 0.5:
                    classroom_id = classroom_ids.index(id) + 1  # Find the index of the id in classroom_ids and add 1 to get ClassroomId
                    section_id = int(section_id)  # Convert section_id back to int to get SectionId

                    # Get additional data for ClassroomId
                    classroom_data = data['Classrooms'][classroom_id - 1]  # Subtract 1 to match the index
                    classroom_name = classroom_data['Name']
                    classroom_capacity = classroom_data['Capacity']

                    # Get additional data for SectionId
                    section_data = data['Sections'][section_id - 1]  # Subtract 1 to match the index
                    section_start_time = section_data['StartTime']
                    section_end_time = section_data['EndTime']
                    section_name = section_data['CourseCode']
                    section_day = section_data['Day']

                    tempdata = {
                        'StartTime': section_start_time,
                        'EndTime': section_end_time,
                        'CourseCode': section_name,
                        'Name