Accessing the values of all variable in a multiscenario model for sensitivity analysis

I am trying to run sensitivity analysis on a model named scnd1 by increasing the budget by 10% in each scenario (Total 10 such scenarios are there). I want to access the value of all the variables of my model in each scenario along with the objective function value and then I want to write it to an excel file. But, cells remain blank by all the writing operations in the code sensitivity1.py. Am I doing anything wrong? Also, I am writing the .sol file using model.write() for each scenario. Each of these files contains the same values for all variables and objective function.

# -*- coding: utf-8 -*-
"""
Created on Tue May 24 14:43:16 2022

@author: hp
"""

# -*- coding: utf-8 -*-
"""
Created on Fri May 20 18:48:45 2022

@author: hp
"""

import sys
import gurobipy as gp
from gurobipy import GRB
from datetime import date
# from basemodelcode import*
from readData import*
maxScenarios = 10
import pandas as pd
# if len(sys.argv) < 2:
#     print('Usage: sensitivity.py filename')
#     sys.exit(0)
# Read model
filename=f'D:\ppr 3\\lpFiles\LP_scnd{date.today()}.lp'
# model = gp.read(sys.argv[1])
model = gp.read(filename)

if model.IsMIP == 0:
    print('Model is not a MIP')

# Solve model
model.optimize()
for v in model.getVars():
    if v.x>0.01:
        print(v.varName, v.x)
print('Objective function value:',model.objVal)
if model.Status != GRB.OPTIMAL:
    print(f'Optimization ended with status {model.Status}')

# Store the optimal solution
origObjVal = model.ObjVal
for v in model.getVars():
    v._origX = v.X

increased_budget1=[(i*0.1)*budget1 for i in range(11,21,1)]    
scenarios = 0
for i in range(len(increased_budget1)):
    scenarios+=1
    if scenarios >= maxScenarios:
        break
model.NumScenarios = scenarios

# Count number of unfixed, binary variables in model. For each we create a
# scenario.
# for v in model.getVars():
#     if (v.VType in (GRB.BINARY)):
#         scenarios += 1
#         if scenarios >= maxScenarios:
#             print(f'created scenarios={scenarios} increase the max number of scenarios->out of loop')
#             break
scenarios = 0
for p in increased_budget1:
    if scenarios<maxScenarios:
        model.Params.ScenarioNumber = scenarios
        # increased_budget1=budget1*0.1*p
        constraint = model.getConstrByName('Strategic_budget')
        # model.setAttr("RHS", constraint, p)
        constraint.ScenNRhs=p
        # budget1_list.append(increased_budget1)
        # change_budget('Strategic_budget',increased_budget1)
        scenarios+= 1
    # else:
    #     v.Start = v._origX

    


# Set the number of scenarios in the model
# model.NumScenarios = scenarios
print(f'The no. of scenarios in the model is {scenarios}')


print('############ construct multi-scenario model with {scenarios} scenarios #################')

# Create a (single) scenario model by iterating through unfixed binary
# variables in the model and create for each of these variables a scenario
# by fixing the variable to 1-X, where X is its value in the computed
# optimal solution
# scenarios = 0
# for v in model.getVars():
#     if (v.VType in (GRB.BINARY) and scenarios < maxScenarios):

#         # Set ScenarioNumber parameter to select the corresponding scenario
#         # for adjustments
#         model.Params.ScenarioNumber = scenarios

#         # Set variable to 1-X, where X is its value in the optimal solution
#         if v._origX < 0.5:
#             v.ScenNLB = 1.0
#         else:
#             v.ScenNUB = 0.0

#         scenarios += 1

#     else:
#         # Add MIP start for all other variables using the optimal solution of the base model
#         v.Start = v._origX

model.update()
# Solve multi-scenario model
model.optimize()




# In case we solved the scenario model to optimality capture the sensitivity information
if model.Status == GRB.OPTIMAL:

    modelSense = model.ModelSense
    scenarios = 0
    # Capture sensitivity information from each scenario
    # for s in range(model.NumScenarios):
    for t in model.getVars():
    # if (v.VType in (GRB.BINARY)):

        # Set scenario parameter to collect the objective value of the corresponding scenario
        model.Params.ScenarioNumber = scenarios
        # Collect objective value and bound for the scenario
        scenarioObjVal = model.ScenNObjVal
        scenarioObjBound = model.ScenNObjBound

        # Check if we found a feasible solution for this scenario
        if scenarioObjVal >= modelSense * GRB.INFINITY:
            # Check if the scenario is infeasible
            if scenarioObjBound >= modelSense * GRB.INFINITY:
                print(f'Objective sensitivity for variable {v.VarName} is infeasible')
            else:
                print(f'Objective sensitivity for variable {v.VarName}  is unknown (no solution available)')
        else:
            matrix1 = [[q_cap[f,b].X for f in food] for b in all_fb if v.VarName=='q_cap_fb']
            df1 = pd.DataFrame(matrix1, columns=food, index=all_fb)
            matrix2=[[q[f,b].ScenNX for f in food] for b in all_fb if v.VarName=='q_fb']
            df2 = pd.DataFrame(matrix2, columns=food, index=all_fb)
            
        
            with pd.ExcelWriter(f'EXCEL_SOL{date.today()}_{scenarios}.xlsx') as writer:
                df1.to_excel(writer,sheet_name='q_cap_fb')
                df2.to_excel(writer,sheet_name='q_fb')
                
        
                writer.save()
                print('Objective function value:',scenarioObjVal)
        # Scenario is feasible and a solution is available
            print(f'Objective sensitivity for budget with initial value={origObjVal}\
              and scenario obj val {scenarioObjVal}is {modelSense * (scenarioObjVal - origObjVal)}')

        model.write(f'D:\ppr 3\LPscenarios\{scenarios}.lp')
        model.write(f'D:\ppr 3\\SOLscenarios\{scenarios}.sol')
        scenarios += 1
        if scenarios >= maxScenarios:
            print(f'created scenarios={scenarios} increase the max number of scenarios->out of capture sensitivity loop')
            break