app.py

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

# ---------------------- Accessing data from Notion ---------------------- #


from notion_client import Client as client_notion
from config import landuseDatabaseId , subdomainAttributesDatabaseId 
from imports_utils import fetch_all_database_pages
from imports_utils import get_property_value
from imports_utils import notion
from config import landuseColumnName 
from config import subdomainColumnName 
from config import sqmPerEmployeeColumnName
from config import thresholdsColumnName 
from config import maxPointsColumnName
from config import domainColumnName 
from imports_utils import fetchDomainMapper
from imports_utils import fetchSubdomainMapper

from imports_utils import notionToken

if notionToken is None:
    raise Exception("Notion token not found. Please check the environment variables.")
else:
    print("Notion token found successfully!")
    landuse_attributes  = fetch_all_database_pages(notion, landuseDatabaseId)
    livability_attributes  = fetch_all_database_pages(notion, subdomainAttributesDatabaseId)
    landuseMapperDict = fetchDomainMapper (landuse_attributes)
    livabilityMapperDict = fetchSubdomainMapper (livability_attributes)




# ---------------------- Accessing data from Speckle ---------------------- #


from specklepy.api.client import SpeckleClient
from specklepy.api.credentials import get_default_account, get_local_accounts
from specklepy.transports.server import ServerTransport
from specklepy.api import operations
from specklepy.objects.geometry import Polyline, Point
from specklepy.objects import Base

#import imports_utils
import speckle_utils
import data_utils

from config import landuseDatabaseId , streamId,  dmBranchName, dmCommitId, luBranchName, luCommitId, distanceMatrixActivityNodes
from imports_utils import speckleToken
#from imports_utils import fetchDistanceMatrices
from config import useSpeckleData
from imports_utils import getDataFromSpeckle

if speckleToken is None:
    raise Exception("Speckle token not found")
else:
    print("Speckle token found successfully!")

if useSpeckleData == True:
    
    CLIENT = SpeckleClient(host="https://speckle.xyz/")
    account = get_default_account()
    CLIENT.authenticate_with_token(token=speckleToken)
    
    landuses, matrices = getDataFromSpeckle(speckleClient = CLIENT, streamID=streamId,matrixBranchName=dmBranchName, landuseBranchName=luBranchName)
         
    #streamDistanceMatrices = speckle_utils.getSpeckleStream(streamId,dmBranchName,CLIENT, dmCommitId)
    #matrices = fetchDistanceMatrices (streamDistanceMatrices)
    #streamLanduses = speckle_utils.getSpeckleStream(streamId,luBranchName,CLIENT, luCommitId)
    #streamData = streamLanduses["@Data"]["@{0}"]
    
    #df_speckle_lu = speckle_utils.get_dataframe(streamData, return_original_df=False)
    #df_lu = df_speckle_lu.copy()
    #df_lu = df_lu.astype(str)
    #df_lu =  df_lu.set_index("uuid", drop=False)  # variable, uuid as default

    df_dm = matrices[distanceMatrixActivityNodes]
    df_dm_dict = df_dm.to_dict('index')
     
    # Replace infinity with 10000 and NaN values with 0, then convert to integers
    df_dm = df_dm.replace([np.inf, -np.inf], 10000).fillna(0)
    df_dm = df_dm.apply(pd.to_numeric, errors='coerce')
    df_dm = df_dm.round(0).astype(int)
        
    mask_connected = df_dm.index.tolist()

    """
    lu_columns = []   # provided by user? or prefix
    for name in df_lu.columns:
      if name.startswith("lu+"):
        lu_columns.append(name)

    """
    df_lu_filtered = df_lu.loc[mask_connected]
    
    #df_lu_filtered.columns = [col.replace('lu+', '') for col in df_lu_filtered.columns]

    mergeAssetNonAssetLanduse = True
    if mergeAssetNonAssetLanduse:
        df_lu_filtered.columns = [col.replace('ASSETS+', '') for col in df_lu_filtered.columns]

    df_lu_filtered = df_lu_filtered.replace([np.inf, -np.inf], 10000).fillna(0)
    df_lu_filtered = df_lu_filtered.apply(pd.to_numeric, errors='coerce')
    df_lu_filtered = df_lu_filtered.astype(int)
    df_lu_filtered = df_lu_filtered.T.groupby(level=0).sum().T
    
    


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 input data from Grasshopper ------------------------- #

    from config import useGrasshopperData 

    useGrasshopperData = inputs['input']["useGrasshopper"]    # fetch grasshoper data or not
    
    if useGrasshopperData == True: # grasshopper input
        matrix = inputs['input']["matrix"]
        landuses = inputs['input']["landuse_areas"]    # fetch grasshoper data or not
    
        dfLanduses = pd.DataFrame(landuses).T
        dfLanduses = dfLanduses.apply(pd.to_numeric, errors='coerce')
        dfLanduses = dfLanduses.replace([np.inf, -np.inf], 0).fillna(0)  # cleaning function?
        dfLanduses = dfLanduses.round(0).astype(int)

        dfMatrix = pd.DataFrame(matrix).T
        dfMatrix = dfMatrix.apply(pd.to_numeric, errors='coerce')
        dfMatrix = dfMatrix.replace([np.inf, -np.inf], 10000).fillna(0)
        dfMatrix = dfMatrix.round(0).astype(int)
    else:
        dfLanduses = df_lu_filtered.copy()     # fetch speckl data or not
        dfMatrix = df_dm.copy()
        

    df_lu_filtered_dict = dfLanduses.to_dict('index')
    dm_dictionary = dfMatrix.to_dict('index')

    attributeMapperDict_gh = inputs['input']["attributeMapperDict"]
    landuseMapperDict_gh = inputs['input']["landuseMapperDict"]        # if fetch notion data or not, def


    from config import alpha as alphaDefault
    from config import threshold as thresholdDefault
    
    if not inputs['input']["alpha"]:
        alpha = alphaDefault
    else:
        alpha = inputs['input']["alpha"]
        alpha = float(alpha)
        
    if not inputs['input']["threshold"]:
        threshold = thresholdDefault
    else:
        threshold = inputs['input']["threshold"]
        threshold = float(threshold)

    """
    valid_indexes = [idx for idx in mask_connected if idx in dfLanduses.index]
    # Identify and report missing indexes
    missing_indexes = set(mask_connected) - set(valid_indexes)
    if missing_indexes:
        print(f"Error: The following indexes were not found in the DataFrame: {missing_indexes}, length: {len(missing_indexes)}")
    
    # Apply the filtered mask
    dfLanduses_filtered = dfLanduses.loc[valid_indexes]


    from imports_utils import findUniqueDomains
    from imports_utils import findUniqueSubdomains
    
    from imports_utils import landusesToSubdomains
    from imports_utils import FindWorkplacesNumber
    from imports_utils import computeAccessibility
    from imports_utils import computeAccessibility_pointOfInterest    
    from imports_utils import remap  
    from imports_utils import accessibilityToLivability

    
    domainsUnique = findUniqueDomains(livabilityMapperDict)
    subdomainsUnique = findUniqueSubdomains(landuseMapperDict)
       
    LivabilitySubdomainsWeights = landusesToSubdomains(dfMatrix,df_lu_filtered,landuseMapperDict,subdomainsUnique)
    
    WorkplacesNumber = FindWorkplacesNumber(dfMatrix,livabilityMapperDict,LivabilitySubdomainsWeights,subdomainsUnique)
    
    # prepare an input weights dataframe for the parameter LivabilitySubdomainsInputs
    LivabilitySubdomainsInputs =pd.concat([LivabilitySubdomainsWeights, WorkplacesNumber], axis=1)
   
    subdomainsAccessibility = computeAccessibility(dfMatrix,LivabilitySubdomainsInputs,alpha,threshold)   
    #artAccessibility = computeAccessibility_pointOfInterest(df_art_matrix,'ART',alpha,threshold)
    #gmtAccessibility = computeAccessibility_pointOfInterest(df_gmt_matrix,'GMT+HSR',alpha,threshold)
    
    #AccessibilityInputs = pd.concat([subdomainsAccessibility, artAccessibility,gmtAccessibility], axis=1)
        
    livability = accessibilityToLivability(dfMatrix,subdomainsAccessibility,livabilityMapperDict,domainsUnique)
    
    livability_dictionary = livability.to_dict('index')
    LivabilitySubdomainsInputs_dictionary = LivabilitySubdomainsInputs.to_dict('index')
    subdomainsAccessibility_dictionary = subdomainsAccessibility.to_dict('index')
    LivabilitySubdomainsWeights_dictionary = LivabilitySubdomainsWeights.to_dict('index')
    
    """
    # Prepare the output
    output = {
        #"subdomainsAccessibility_dictionary": subdomainsAccessibility_dictionary,
        #"livability_dictionary": livability_dictionary,
        #"subdomainsWeights_dictionary": LivabilitySubdomainsInputs_dictionary,
        #"luDomainMapper": landuseMapperDict,
        #"attributeMapper": livabilityMapperDict,
        "mask_connected": mask_connected,
        "dm_an": df_dm_dict,
        "landuses":df_lu_filtered_dict,
        "constants": [alpha, threshold]
    }


    
    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()