app.py

import gradio as gr
import pandas as pd
import numpy as np
import json
from io import StringIO
from collections import OrderedDict

def test(input_json):
    print("Received input")
    # Parse the input JSON string
    try:
        inputs = json.loads(input_json)
    except json.JSONDecodeError:
        inputs = json.loads(input_json.replace("'", '"'))

    # Accessing the lists 
    ids_index = inputs['input']['ids_list']
    weightsNames = inputs['input']["weights_names"]
    
    # Extract the datatree part which is a list of dictionaries
    matrix = inputs['input']["matrix"]
    weights = inputs['input']["weights"]
    
    attributeMapperDict = inputs['input']["attributeMapperDict"]

    alpha = inputs['input']["alpha"]
    alpha = float(alpha)
    threshold = inputs['input']["threshold"]
    threshold = float(threshold)
    
    df_matrix = pd.DataFrame(matrix).T
    df_weights = pd.DataFrame(weights).T
    df_matrix = df_matrix.round(0).astype(int)
    df_weights = df_weights.round(0).astype(int)


    
    def computeAccessibility (DistanceMatrix,weightsNames, destinationWeights=None,alpha = 0.0038, threshold = 600):
    
        decay_factors = np.exp(-alpha * DistanceMatrix) * (DistanceMatrix <= threshold)
        subdomainsAccessibility = pd.DataFrame(index=DistanceMatrix.index, columns=weightsNames) #destinationWeights.columns)
        # for weighted accessibility (e. g. areas)
        if not destinationWeights.empty:
            for col,columnName in zip(destinationWeights.columns, weightsNames):
                subdomainsAccessibility[columnName] = (decay_factors * destinationWeights[col].values).sum(axis=1)
        # for unweighted accessibility (e. g. points of interest)
        else:
            for columnName in weightsNames:
                subdomainsAccessibility[columnName] = (decay_factors * 1).sum(axis=1)
        
        return subdomainsAccessibility
    
    subdomainsAccessibility = computeAccessibility(df_matrix,weightsNames,df_weights,alpha,threshold)

    # make a dictionary to output in grasshopper / etc
    subdomainsAccessibility_dictionary = subdomainsAccessibility.to_dict('index')


    def remap(value, B_min, B_max, C_min, C_max):
        return C_min + (((value - B_min) / (B_max - B_min))* (C_max - C_min))    


    
    def accessibilityToLivability (DistanceMatrix,subdomainsAccessibility, SubdomainAttributeDict):
    
        livability = pd.DataFrame(index=DistanceMatrix.index, columns=subdomainsAccessibility.columns)
        livability.fillna(0, inplace=True)
        
        # find a set of unique domains, to which subdomains are aggregated
        
        temp = []
    
        for key, values in SubdomainAttributeDict.items():
          domain = SubdomainAttributeDict[key]['domain']
          for item in domain:
            if ',' in item:
              domain_list = item.split(',')
              SubdomainAttributeDict[key]['domain'] = domain_list
              for domain in domain_list:
                temp.append(domain) 
            else:
              if item != 0: 
                  temp.append(item)  
        
        domainsUnique = list(set(temp))
             
        for domain in domainsUnique:
            livability[domain] = 0
    
    
        # remap accessibility to livability points
        
        for key, values in SubdomainAttributeDict.items():
            threshold = float(SubdomainAttributeDict[key]['thresholds'])
            max_livability = float(SubdomainAttributeDict[key]['max_points'])
            domain = SubdomainAttributeDict[key]['domain']
            sqm_per_employee = str(SubdomainAttributeDict[key]['sqmPerEmpl'])
    
            if key in subdomainsAccessibility.columns:
                livability_score = remap(subdomainsAccessibility[key], 0, threshold, 0, max_livability)
                livability.loc[subdomainsAccessibility[key] >= threshold, key] = max_livability
                livability.loc[subdomainsAccessibility[key] < threshold, key] = livability_score
                if any(domain):
                    for item in domain:
                        livability.loc[subdomainsAccessibility[key] >= threshold, domain] += max_livability
                        livability.loc[subdomainsAccessibility[key] < threshold, domain] += livability_score
            
        return livability
    
    
    

    livability = accessibilityToLivability(df_matrix,subdomainsAccessibility,attributeMapperDict)



    
    # Prepare the output
    output = {
        "subdomainsAccessibility_dictionary": subdomainsAccessibility_dictionary,
        "livability_dictionary": livability
    }


    
    return json.dumps(output)

    # Define the Gradio interface with a single JSON input
iface = gr.Interface(
    fn=test,
    inputs=gr.Textbox(label="Input JSON", lines=20, placeholder="Enter JSON with all parameters here..."),
    outputs=gr.JSON(label="Output JSON"),
    title="testspace"
)

iface.launch()