Function re-running each time I want to sort output

Using Streamlit
1

Hiya,
I have created a function to create a permutation of models and cached it.
However, each time I change how i want to select the best model (which sits outside the function), the function re-runs even though I have cached it.

I dont know why this is happening, can someone help?
Below is my code

@st.cache(suppress_st_warning=True)
def create_model(input_df):
    combi = []
    modelNumber = 1
    output = pd.DataFrame()
    placeholder_text = st.empty()   
    for i in range(1,len(feat_importances)):
        combi = (list(combinations(feat_importances.index, i)))
        for c in combi:
            print('-------------> Model Number:', modelNumber)
            variable_string = str(option + ' ~ 1 ')
            var_iter =1
            for j in list(c):
                #print(len(list(c)))
                #print(' + ' ,j)
                variable_string  +=' + ' + str(j)
                #final_string = ""
                if var_iter == len(list(c)):
                    #print(variable_string)
                    #print(var_iterator )
                    placeholder = 'Model Number: ' + str(modelNumber) + ' Model Variables: ' + str(variable_string)
                    placeholder_text.text(placeholder)
                    try:
                        result = smf.logit(formula= variable_string, data=input_df).fit()
                        coeffs = result.params
                        coeffs = pd.DataFrame({'Variable':coeffs.index, 'Values':coeffs.values})
                        predTable = result.pred_table()
                        #result.summary()
                        prsq = result.prsquared
    
                        tp = predTable[1,1]
                        tn = predTable[0,0]
                        fp = predTable[0,1]
                        fn = predTable[1,0]
                        
                        
                        precision = tp/(tp+fp)
                        recall = tp/(tp+fn)
                        accuracy = (tp + tn)/(tp+tn+fp+fn)
    
                        #new row as dictionary
                        row1 = [{'Variable':'modelNumber', 'Values':modelNumber}
                                , {'Variable':'pRSQ', 'Values':prsq}
                                , {'Variable':'precision', 'Values':precision}
                                , {'Variable':'recall', 'Values':recall}
                                , {'Variable':'accuracy', 'Values':accuracy}
                                , {'Variable':'truepos', 'Values':tp}
                                , {'Variable':'trueneg', 'Values':tn}
                                , {'Variable':'falsepos', 'Values':fp}
                                , {'Variable':'falseneg', 'Values':fn}
                                , {'Variable':'variableString', 'Values':variable_string}
                                , {'Variable':'NumVars', 'Values':len(c)}                           
                                ]
                        coeffs = coeffs.append(row1, ignore_index=True)
                        #append row to dataframe
                        output= output.append(coeffs.set_index('Variable').T)
                        modelNumber += 1
                    except :
                       pass
                var_iter +=1
    placeholder_text.text('Finished')
    placeholder_text.text('')   
    output.name = 'ModelOutput'
    return output

output_df = create_model(uploaded_df)

best_model = output_df.sort_values(by='accuracy',ascending=False)['modelNumber'][0]

Is it happening because I am sorting output_df which is dependent on the create_model function?

Bilal