import pandas as pd
import similarity as sm
import json

def tenant_visualization(similarity_matrix, requested_tenants):
    #TODO VIEW COMPATIBILITY BETWEEN REQUESTED TENANTS
    print(f"The options for visualizatio are:\n"+
          "1.Compatibility for each tenants with requested ones\n"+
          "2.Mean compatibility for each tenant\n"+
          "3.Most compatible tenant for the requested ones\n"+
          "4.Obtain registers from most compatible tenants\n")
    option = int(input("What option are u choosing: "))
    tenant_lines = similarity_matrix[requested_tenants].mean()
    match(option):

        case 1:
            tenant_lines.head(5)
            print(f"Compatibility for each tenants with requested ones \n{tenant_lines}")

        case 2:
            mean_compatibility = tenant_lines.mean(axis = 0)
            print(f"Mean compatibility for each tenant:\n {mean_compatibility} ")

        case 3:
            most_compatible = tenant_lines.sort_values(ascending = False)
            print(f"Max compatibility for each tenant:\n {most_compatible} ")
        
        case 4:
            most_compatible = tenant_lines.sort_values(ascending = False)
        
            print(f"Most compatible tenants registers\n {most_compatible}")



def tenant_inference(similarity_matrix, requested_tenants,dataframe):
    print("Tenant inference check")
    #TODO THIS FUNCTION IS THE ONE USED DURING INFERENCE TIME THE MODEL WILL CALCULATE THE 4 TENANTS WITH THE HIGHER COMPATIBILITY
    try:
        
        similarity_matrix = similarity_matrix.astype(int)
        similarity_tenant = similarity_matrix[requested_tenants].sort_values(ascending = False).head(4)
        final_tenants = similarity_tenant.index
        tenant_features = dataframe.loc[final_tenants, ["names", "age", "smoking", "email"]]
        print(tenant_features)
        
        tenants_dict = {
            key : list(tenant_features[key]) for key in tenant_features.columns
        }
        
        tenants_dict["Similarity"] = list(similarity_tenant)
        return tenants_dict
    
    except Exception as error:
        print(f"Error:  {error}")

def algo_start(id):

    dataframe, original_dataframe = sm.data_preparing()
    print("Received data preparing")
    #sm.data_checking(dataframe)
    similarity_matrix = sm.encoder_matrix(dataframe, min_range = 0, max_range=100)
    print("Received similarity")
    tenant_list = tenant_inference(similarity_matrix, id,original_dataframe)
    print("Fin algo start check")
    return tenant_list
    #json_convert(tenant_list)
    #tenant_visualization(similarity_matrix, [20,40,50,18,15])

def json_convert(tenant_list):
    print("Entering json_convert")
    #THE ALGO START FUNCTION COULD BE DOING THIS DIRECTLY, IS A POSSIBLE IMPROVEMENT IF THE WEB ISN'T WORKING FAST ENOUGH
    
    tenants_dict = {
        str(i) : { "Names" : " ", "Age": " ", "Smoking": " ", "Email" : " ", "Compatibility" : " "} for i in range(4)
    }

    tenant_counter = 0
    for tenant in tenant_list:
        tenant_features = tenant[0]
        tenant_compatibility = tenant[1]
    
        for feature in tenant_features.index:
            tenants_dict[str(tenant_counter)][feature] = str(tenant_features[feature])

        tenants_dict[str(tenant_counter)]["Compatibility"] = str(tenant_compatibility)    
        tenant_counter += 1
    
    tenants_json = json.dumps(tenants_dict, indent = 4)
    return tenants_json