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app.py

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+#pip install kaleido
+#pip install gradio
+import gradio as gr
+
+#import os
+#import random
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torchvision
+import torchvision.transforms as transforms
+from tqdm.auto import tqdm
+#!pip install einops
+
+# Device configuration
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+
+#pip install captum
+
+import seaborn as sns
+from captum.attr import LayerConductance
+
+from captum.attr import IntegratedGradients
+from captum.attr import configure_interpretable_embedding_layer
+
+import matplotlib.pyplot as plt
+from captum.attr import remove_interpretable_embedding_layer
+import torch.nn.functional as F
+
+# @title
+import pandas as pd
+import numpy as np
+import tensorflow as tf
+
+Raw_data = pd.read_excel('./STS Data with Up to dated AF 09-18-2022 (1).xlsx',usecols=lambda x: 'Unnamed' not in x)
+pd.set_option('display.max_columns', None)
+
+Raw_data['Aortic_Insufficiency']=Raw_data['Aortic_Insufficiency'].astype(np.int64)
+
+
+Postop_columns = ['PostOpMedCoumadin',
+'PostOpMedLipidLowering',
+'PostOpMedAspirin',
+'PostOpMedADPInhibitors',
+'PostOpMedACE_ARBInhibitors',
+'STS_PostOp.Renal_Failure',
+'Oth_Cardiac_Arrest',
+'Complications_Any',
+'Neuro_Stroke_Permanent',
+'Neuro_Stroke_Permanent',
+'Neuro_Continuous_Coma',
+'Neuro_Delirium',
+'PostOpSepsis',
+'Reop_Bleeding',
+'Oth_OtherComplication',
+'PostOpNeuroStrokeTransientTIA',
+'Infect_Sternum_Deep',
+'Infect_Thoracotomy',
+'Pulm_Ventilator_Prolonged',
+'Pulm_Pneumonia',
+'Oth_Tamponade',
+'Oth_Anticoagulant',
+'Oth_MultiSystem_Failure',
+'Oth_GI',
+'Vasc_Ao_Dissection',
+'Infect_Leg',
+'PostOpInfectionArm',
+'OthCard_Pacemaker',
+'PostOpCreatinineLevel',
+'Renal_Dialysis_Required',
+'PostOpBloodRBCUnits',
+'PostOpBloodFFPUnits',
+'PostOpBloodCryoUnits',
+'PostOpBloodPlateletUnits',
+'ExtubatedI0R',
+'InitHrsVentilated',
+'ReIntubated',
+'No Add Hrs Ventilator',
+'PostOpVentHoursTotal',
+'InitHrsICU',
+'ReadmitICU',
+'AddICUHours',
+'TotHrsICU',
+'DCMed_AntiPlate',
+'Readmit_LessThan30Days',
+'Blood_Bank_Products_Used',
+'PostOpMedAntiarrhythmics',
+'PostOpMedBetaBlockers']
+
+
+#Dropping columns
+preop_oper_data = Raw_data.drop(columns=Postop_columns)
+preop_oper_data = preop_oper_data[preop_oper_data.Oth_Afib != -1]
+preop_oper_data=preop_oper_data.drop(['Date_of_Birth','Surgery_Date','Discharge_Date', 'Death_Date','Death-Surery(y)','Mortality30d','Mortality1y','Mortality2y','Mortality3y','Mortality4y','Mortality5y'], axis=1)
+preop_oper_data=preop_oper_data.drop(['EF','Height(cm)','Weight(kg)','CVA_When','Category','Race'], axis=1)
+preop_oper_data=preop_oper_data.drop(['IABP_Indication','IABP_When'],axis=1)
+
+
+#seperating continuous and Categorical Data
+continous_df = preop_oper_data[['Oth_Afib','Age','BMI','LastCreatinineLevel','Cross_Clamp_Time','Perfusion_Time']]
+categorical_col=list(set(preop_oper_data.columns) - set(continous_df.columns))
+categorical_col.sort()
+#Setting the target
+df_AF=preop_oper_data['Oth_Afib']
+continous_col=continous_df.drop('Oth_Afib',axis=1)
+preop_oper_data=preop_oper_data.drop('Oth_Afib',axis=1)
+
+
+#Creating categorical df
+preop_oper_data=preop_oper_data[categorical_col]
+
+# Label encoding
+from sklearn import preprocessing
+from sklearn.preprocessing import LabelEncoder
+def encode_text_index(df, name):
+    le = preprocessing.LabelEncoder()
+    df[name] = le.fit_transform(df[name])
+    return le.classes_
+
+encode_text_index(preop_oper_data,'Introp DEX or nDEX')
+encode_text_index(preop_oper_data,'Status')
+encode_text_index(preop_oper_data,'Gender')
+
+#Calculating len of each categorical column
+label_in_each = tuple(len(preop_oper_data[col].unique()) for col in preop_oper_data.columns)
+categorical_col_with_ordinal = preop_oper_data.columns
+
+#Making the final data frame
+final_frame=pd.concat([continous_col,preop_oper_data],axis=1)
+final_frame=pd.concat([final_frame,df_AF],axis=1)
+
+# Encode a numeric column as zscores
+def encode_numeric_zscore(df, name, mean=None, sd=None):
+    if mean is None:
+        mean = df[name].mean()
+        print(f'mean:{mean}')
+
+    if sd is None:
+        sd = df[name].std()
+        print(f'sd:{sd}')
+
+    df[name] = (df[name] - mean) / sd
+
+
+for col in continous_col.columns:
+  encode_numeric_zscore(final_frame,col)
+
+
+
+#Train test split
+from sklearn.model_selection import train_test_split
+x_train, x_temp, y_train, y_temp = train_test_split(final_frame.iloc[:,:-1], final_frame.iloc[:,-1], test_size=0.25, random_state=42, stratify=final_frame.iloc[:,-1])
+
+
+print(x_train.shape)
+print(y_train.shape)
+print(x_temp.shape)
+print(y_temp.shape)
+
+# Duplicating class 1 records to balance dataset for training
+training_frame = pd.concat([x_train, y_train],axis=1)
+training_frame_ana = training_frame
+class_1_rows = training_frame[training_frame['Oth_Afib'] == 1]
+duplicated_class_1 = class_1_rows.copy()
+training_frame= pd.concat([training_frame, duplicated_class_1,duplicated_class_1], ignore_index=True)
+training_frame['Oth_Afib'].value_counts()
+
+# Creating testing df
+testing_frame = pd.concat([x_temp, y_temp],axis=1)
+
+
+continous_df= continous_df.drop('Oth_Afib', axis=1)
+continous_col=continous_df.columns
+continous_col
+
+training_frame_without_label=training_frame.iloc[:,:-1]
+testing_frame_without_label=testing_frame.iloc[:,:-1]
+training_frame=pd.concat([training_frame_without_label,pd.get_dummies(training_frame.iloc[:,-1],prefix='Oth_Afib',dtype=np.int64)],axis=1)
+testing_frame=pd.concat([testing_frame_without_label,pd.get_dummies(testing_frame.iloc[:,-1],prefix='Oth_Afib',dtype=np.int64)],axis=1)
+testing_frame
+
+# @title
+
+
+
+import torch
+import torch.nn.functional as F
+from torch import nn, einsum
+
+from einops import rearrange
+
+# helpers
+
+def exists(val):
+    return val is not None
+
+def default(val, d):
+    return val if exists(val) else d
+
+# classes
+
+class Residual(nn.Module):
+    def __init__(self, fn):
+        super().__init__()
+        self.fn = fn
+
+    def forward(self, x, **kwargs):
+        return self.fn(x, **kwargs) + x
+
+class PreNorm(nn.Module):
+    def __init__(self, dim, fn):
+        super().__init__()
+        self.norm = nn.LayerNorm(dim)
+        self.fn = fn
+
+    def forward(self, x, **kwargs):
+        return self.fn(self.norm(x), **kwargs)
+
+# attention
+
+class GEGLU(nn.Module):
+    def forward(self, x):
+        x, gates = x.chunk(2, dim = -1)
+        return x * F.gelu(gates)
+
+class FeedForward(nn.Module):
+    def __init__(self, dim, mult = 4, dropout = 0.):
+        super().__init__()
+        self.net = nn.Sequential(
+            nn.Linear(dim, dim * mult * 2),
+            GEGLU(),
+            nn.Dropout(dropout),
+            nn.Linear(dim * mult, dim)
+        )
+
+    def forward(self, x, **kwargs):
+        return self.net(x)
+
+class Attention(nn.Module):
+    def __init__(
+        self,
+        dim,
+        heads = 8,
+        dim_head = 16,
+        dropout = 0.
+    ):
+        super().__init__()
+        inner_dim = dim_head * heads
+        self.heads = heads
+        self.scale = dim_head ** -0.5
+
+        self.to_qkv = nn.Linear(dim, inner_dim * 3, bias = False)
+        self.to_out = nn.Linear(inner_dim, dim)
+
+        self.dropout = nn.Dropout(dropout)
+
+    def forward(self, x):
+        h = self.heads
+        q, k, v = self.to_qkv(x).chunk(3, dim = -1)
+        q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = h), (q, k, v))
+        sim = einsum('b h i d, b h j d -> b h i j', q, k) * self.scale
+
+        attn = sim.softmax(dim = -1)
+        dropped_attn = self.dropout(attn)
+
+        out = einsum('b h i j, b h j d -> b h i d', dropped_attn, v)
+        out = rearrange(out, 'b h n d -> b n (h d)', h = h)
+        return self.to_out(out), attn
+
+# transformer
+
+class Transformer(nn.Module):
+    def __init__(self, dim, depth, heads, dim_head, attn_dropout, ff_dropout):
+        super().__init__()
+        # torch.manual_seed(1)
+        # self.embeds = nn.Embedding(num_tokens, dim)
+
+
+        self.layers = nn.ModuleList([])
+
+        for _ in range(depth):
+            self.layers.append(nn.ModuleList([
+                PreNorm(dim, Attention(dim, heads = heads, dim_head = dim_head, dropout = attn_dropout)),
+                PreNorm(dim, FeedForward(dim, dropout = ff_dropout)),
+            ]))
+
+    def forward(self, x, return_attn = False):
+        # x = self.embeds(x)
+
+        post_softmax_attns = []
+
+        for attn, ff in self.layers:
+            attn_out, post_softmax_attn = attn(x)
+            post_softmax_attns.append(post_softmax_attn)
+
+            x = x + attn_out
+            x = ff(x) + x
+
+        if not return_attn:
+            return x
+
+        # return x, torch.stack(post_softmax_attns)
+        return x
+# mlp
+
+class MLP(nn.Module):
+    def __init__(self, dims, act = None):
+        super().__init__()
+        dims_pairs = list(zip(dims[:-1], dims[1:]))
+        layers = []
+        for ind, (dim_in, dim_out) in enumerate(dims_pairs):
+            is_last = ind >= (len(dims_pairs) - 1)
+            linear = nn.Linear(dim_in, dim_out)
+            layers.append(linear)
+
+            if is_last:
+                continue
+
+            act = default(act, nn.ReLU())
+            layers.append(act)
+
+        self.mlp = nn.Sequential(*layers)
+
+    def forward(self, x):
+        return self.mlp(x)
+
+
+class NumericalEmbedder(nn.Module):
+    def __init__(self, dim, num_numerical_types):
+        super().__init__()
+        self.weights = nn.Parameter(torch.randn(num_numerical_types, dim))
+        self.biases = nn.Parameter(torch.randn(num_numerical_types, dim))
+
+    def forward(self, x):
+        x = rearrange(x, 'b n -> b n 1')
+        return x * self.weights + self.biases
+
+class CategoricalEmbedder(nn.Module):
+    def __init__(self, total_tokens,dim):
+        super().__init__()
+        self.embeds = nn.Embedding(total_tokens, dim)
+
+
+    def forward(self, x):
+        x_embed = self.embeds(x)
+        return x_embed
+
+
+class CatConLayer(nn.Module):
+
+  def __init__(self, dim , heads ):
+    super().__init__()
+
+    self.cat_con_multihead_attn = torch.nn.MultiheadAttention(dim , heads , dropout = 0.8)
+    self.con_cat_multihead_attn = torch.nn.MultiheadAttention(dim , heads , dropout = 0.8)
+
+
+  def forward(self,attn_cat,attn_con,need_weights=False):
+
+
+    cat_Q,_ = self.cat_con_multihead_attn(attn_cat,attn_con,attn_con)
+
+    con_Q,_ = self.con_cat_multihead_attn(attn_con,attn_cat,attn_cat)
+
+    cat_Q=cat_Q.permute(1, 0, 2)
+    con_Q=con_Q.permute(1, 0, 2)
+    # output_concat = torch.cat([cat_Q, con_Q], dim=0)
+    return cat_Q,con_Q
+
+# main class
+
+class Co_Transformer(nn.Module):
+    def __init__(
+        self,
+        *,
+        categories,
+        num_continuous,
+        dim,
+        depth,
+        heads,
+        dim_head = 16,
+        dim_out = 1,
+        mlp_hidden_mults = (2,1),
+        mlp_act = None,
+
+        num_special_tokens = 0,
+        continuous_mean_std = None,
+        attn_dropout = 0.,
+        ff_dropout = 0.
+    ):
+        super().__init__()
+        assert all(map(lambda n: n > 0, categories)), 'number of each category must be positive'
+        assert len(categories) + num_continuous > 0, 'input shape must not be null'
+
+
+
+        self.num_categories = len(categories)
+
+
+        self.num_unique_categories = sum(categories)
+
+
+
+        self.num_special_tokens = num_special_tokens #0
+        total_tokens = self.num_unique_categories + num_special_tokens
+
+        # for automatically offsetting unique category ids to the correct position in the categories embedding table
+
+        if self.num_unique_categories > 0:
+            categories_offset = F.pad(torch.tensor(list(categories)), (1, 0), value = num_special_tokens)
+
+            categories_offset = categories_offset.cumsum(dim = -1)[:-1]
+
+            self.register_buffer('categories_offset', categories_offset)
+            self.embeds = CategoricalEmbedder(total_tokens, dim)
+
+        # continuous
+        self.num_continuous = num_continuous
+
+
+
+        if self.num_continuous > 0:
+
+            self.numerical_embedder = NumericalEmbedder(dim, self.num_continuous)
+
+
+        # transformer
+
+        self.transformer_cat = Transformer(
+
+            dim = dim,
+            depth = depth,
+            heads = heads,
+            dim_head = dim_head,
+            attn_dropout = attn_dropout,
+            ff_dropout = ff_dropout
+        )
+
+        self.transformer_con = Transformer(
+
+            dim = dim,
+            depth = depth,
+            heads = heads,
+            dim_head = dim_head,
+            attn_dropout = attn_dropout,
+            ff_dropout = ff_dropout
+        )
+        # fusion-part
+
+        self.catconlayer = CatConLayer(
+            dim=dim ,
+            heads=heads
+        )
+
+        # mlp to logits
+
+        input_size = dim * (self.num_categories + num_continuous)
+        print(f'input size{input_size}')
+        l = input_size // 5
+        hidden_dimensions = list(map(lambda t: int(l * t), mlp_hidden_mults))
+        all_dimensions = [input_size, *hidden_dimensions, dim_out]
+        self.mlp = MLP(all_dimensions, act = mlp_act)
+        # print(f" mlp  {self.mlp}")
+
+    def forward(self, x_categ, x_cont, return_attn = True):
+
+
+
+
+        x_categ = self.embeds(x_categ)
+        # x_cat_, attns_cat = self.transformer(x_categ, return_attn = True)
+        x_cat_ = self.transformer_cat(x_categ, return_attn = True)
+        permuted_x_cat_= x_cat_.permute(1, 0, 2)
+
+
+
+        x_numer = self.numerical_embedder(x_cont)
+        # x_con_, attns_con = self.transformer(x_numer, return_attn = True)
+        x_con_ = self.transformer_con(x_numer, return_attn = True)
+        permuted_x_con_= x_con_.permute(1, 0, 2)
+
+
+
+        cat_Q,con_Q = self.catconlayer(permuted_x_cat_,permuted_x_con_ )
+
+        can_con_attn_output = torch.cat([cat_Q, con_Q], dim=1)
+        # permuted_can_con_attn_output= can_con_attn_output.permute(1, 0, 2)
+
+
+        can_con_attn_output_flattend= can_con_attn_output.flatten(1)
+
+
+        logits=self.mlp(can_con_attn_output_flattend)
+
+        return logits
+
+
+def build_network(depth,heads,dim):
+
+    model = Co_Transformer(
+    categories = label_in_each ,      # tuple containing the number of unique values within each category
+    num_continuous = final_frame[continous_col].shape[1],                # number of continuous values
+    dim = dim,                           # dimension, paper set at 32
+    dim_out = 2,                        # binary prediction, but could be anything
+    depth = depth,                          # depth, paper recommended 6
+    heads = heads,                          # heads, paper recommends 8
+    attn_dropout = 0.1,                 # post-attention dropout
+    ff_dropout = 0.1,                   # feed forward dropout
+    mlp_hidden_mults =((2,1,0.5,0.25)),          # relative multiples of each hidden dimension of the last mlp to logits
+    mlp_act = nn.ReLU(),                # activation for final mlp, defaults to relu, but could be anything else (selu etc)
+
+    continuous_mean_std = torch.tensor(continous_df.agg(['mean','std']).transpose().values, dtype=torch.float32) # (optional) - normalize the continuous values before layer norm
+
+
+)
+
+    return model
+
+model = build_network(8,8,64)
+
+model.load_state_dict(torch.load('./co_attention_transformer_model_trained.pth',map_location=torch.device('cpu')))
+# print("Model Loaded!")
+
+sample_Df=pd.read_csv('./sample_data.csv')
+
+# @title
+
+def run_inference(num0,num1,num2,num3,num4,num5,num6,num7,num8,num9,num10,num11,num12,num13,num14,num15,num16,num17,num18,num19,num20,num21,num22,num23,num24,num25,num26,num27,num28,num29,num30,num31,num32,num33,num34,num35,num36,num37,num38,num39,num40,num41,num42,num43,num44,num45,num46,num47,num48,num49,num50,num51,num52,num53,num54,num55,num56,num57,num58,num59,num60,num61,num62,num63,num64,num65,num66,num67,num68,num69,num70,num71,num72,num73,num74,num75,num76,num77,num78,num79,num80,num81,num82):
+    
+    mean1=62.63038219641993
+    sd1=12.280987675098727
+    mean2=29.238482057573915
+    sd2=6.511823065330923
+    mean3=1.2360909530720852
+    sd3=1.0307534004581604
+    mean4=137.98209966134493
+    sd4=58.71032609323997
+    mean5=193.39671020803095
+    sd5=79.63715724430536
+
+    num1 = (num1 - mean1)/sd1
+    num2 = (num2 - mean2)/sd2
+    num3 = (num3 - mean3)/sd3
+    num4 = (num4 - mean4)/sd4
+    num5 = (num5 - mean5)/sd5
+
+    list__inputs = [num1,num2,num3,num4,num5,num6,num7,num8,num9,num10,num11,num12,num13,num14,num15,num16,num17,num18,num19,num20,num21,num22,num23,num24,num25,num26,num27,num28,num29,num30,num31,num32,num33,num34,num35,num36,num37,num38,num39,num40,num41,num42,num43,num44,num45,num46,num47,num48,num49,num50,num51,num52,num53,num54,num55,num56,num57,num58,num59,num60,num61,num62,num63,num64,num65,num66,num67,num68,num69,num70,num71,num72,num73,num74,num75,num76,num77,num78,num79,num80,num81,num82]
+    print(list__inputs)
+
+    if (num0 == 'First_non_AFib' or num0 == 'Second_non_AFib'):
+        target_set = 0
+    else:
+        target_set = 1
+
+    # Remove specific elements from nested lists at specified indices
+    result_list =[item for sublist in list__inputs for item in (sublist if isinstance(sublist, list) else [sublist])]
+    print(result_list)
+    con = torch.tensor(result_list[0:5],dtype=torch.float32).reshape(1,-1)
+    print(con,con.shape,con.device.type)
+    cat = torch.tensor(result_list[5:82],dtype=torch.long).reshape(1,-1)
+    print(cat,cat.shape,cat.device.type)
+    model.eval()
+    output_tup=model(cat,con)
+    prob=F.softmax(output_tup,dim=-1)
+    print(prob[0][0].detach(),prob[0][1].detach())
+
+    # Categories for the bar plot
+    categories = ['Non-AFIB', 'A-FIB']
+    # Values for the bar plot
+    values = [(prob[0][0]).detach().numpy(), (prob[0][1]).detach().numpy()]
+    fig1 = plt.figure()
+    plt.barh(categories, values, color=['green', 'red'])
+    plt.xlabel('Values')
+    plt.ylabel('Labels')
+    # cal embedding attributes
+    ig = IntegratedGradients(model)
+
+    interpretable_embedding_cat = configure_interpretable_embedding_layer(model, 'embeds')
+    interpretable_embedding_con = configure_interpretable_embedding_layer(model, 'numerical_embedder')
+
+    
+
+    emb_cat = interpretable_embedding_cat.indices_to_embeddings(cat)
+    emb_con = interpretable_embedding_con.indices_to_embeddings(con)
+    print(emb_cat.device.type)
+    baseline_cat = torch.zeros_like(emb_cat)  # Set numerical baseline to zero
+    baseline_con = torch.zeros_like(emb_con)
+    emb_cat.requires_grad_
+    emb_cat.requires_grad_
+    attr, delta =ig.attribute((emb_cat, emb_con),baselines = (baseline_cat,baseline_con) ,target=target_set, return_convergence_delta=True, n_steps=50)
+    print("calculating attr")
+    print(attr[0].shape)
+    print(attr[1].shape)
+
+    categ_attr = (attr[0]).sum(dim=-1).squeeze(0)
+    cond_attr = (attr[1]).sum(dim=-1).squeeze(0)
+
+    concatenated_tensor = torch.cat([cond_attr, categ_attr],dim=0)
+    print(concatenated_tensor.device.type)
+
+    x_pos = (np.arange(len(testing_frame.iloc[:,0:-2].columns)))
+
+    fig2 = plt.figure(figsize=(30,6))
+
+    plt.bar(x_pos,concatenated_tensor.squeeze().cpu().detach().numpy(), align='center', color = 'red')
+    plt.xticks(x_pos,testing_frame.iloc[:,0:-2].columns, wrap=True)
+    plt.xticks(rotation=45)
+    plt.xlabel('Features')
+    plt.title('Embedded layer attributes')
+
+    #  layer attributes
+
+    attn_con_cat = []
+    attn_con_cat.append(concatenated_tensor.detach().cpu())
+
+    for i in range(len(model.transformer_con.layers)):
+      con_module = [module for module in model.transformer_con.layers[i]]
+      layeroutput_con = []
+      for j in range(len(con_module)):
+          lc_con = LayerConductance(model, con_module[j])
+          layer_attributions_con= lc_con.attribute((emb_cat,emb_con), baselines=(baseline_cat,baseline_con),target=target_set,n_steps=50)
+          if(type(layer_attributions_con) == "tuple"):
+            layeroutput_con.append(layer_attributions_con[0])
+          else:
+            layeroutput_con.append(layer_attributions_con)
+      attn_out_con = emb_con + layeroutput_con[0][0] + layeroutput_con[1]
+      attn_out_con=attn_out_con.sum(dim=-1).squeeze(0)
+
+      cat_module = [module for module in model.transformer_cat.layers[i]]
+      layeroutput_cat = []
+      for j in range(len(cat_module)):
+          lc_cat = LayerConductance(model, cat_module[j])
+          layer_attributions_cat= lc_cat.attribute((emb_cat,emb_con), baselines=(baseline_cat,baseline_con),target=target_set,n_steps=50)
+          if(type(layer_attributions_cat) == "tuple"):
+            layeroutput_cat.append(layer_attributions_cat[0])
+          else:
+            layeroutput_cat.append(layer_attributions_cat)
+      attn_out_cat = emb_cat + layeroutput_cat[0][0] + layeroutput_cat[1]
+      attn_out_cat=attn_out_cat.sum(dim=-1).squeeze(0)
+
+      attn_con_cat.append((torch.cat([attn_out_con,attn_out_cat])).detach().cpu())
+
+    lc = LayerConductance(model, model.catconlayer)
+    layer_attributions_start = lc.attribute((emb_cat,emb_con), baselines=(baseline_cat,baseline_con),target=target_set,n_steps=50)
+    value_coattn_cat=layer_attributions_start[0].sum(dim=-1).squeeze(0)
+    value_coattn_con=layer_attributions_start[1].sum(dim=-1).squeeze(0)
+    attn_con_cat.append(torch.cat([value_coattn_cat,value_coattn_con]).detach().cpu())
+    # fig 3
+    fig3, axes = plt.subplots(figsize=(15, 12),frameon=False)
+
+    for spine in plt.gca().spines.values():
+        spine.set_visible(False)
+
+    axes.xaxis.set_major_locator(plt.NullLocator())
+    axes.yaxis.set_major_locator(plt.NullLocator())
+
+    for i,k in enumerate(testing_frame.iloc[:,:-60].columns):
+
+      cmap = sns.color_palette("Reds")
+      # cmap = sns.cm.rocket_r
+      ax = fig3.add_subplot(5,5, i+1)
+
+      xticklabels=[k]
+      yticklabels=list(range(1,9))
+      ax = sns.heatmap(np.array(torch.stack(attn_con_cat)[1:9])[:,i].reshape(-1,1),ax=ax,xticklabels=xticklabels, yticklabels=yticklabels, linewidth=0.2, cmap=cmap)
+      plt.xlabel('features')
+      plt.ylabel('Layers')
+      plt.tight_layout()
+
+    # fig 4
+    fig4, axes = plt.subplots(figsize=(15, 12),frameon=False)
+
+    for spine in plt.gca().spines.values():
+        spine.set_visible(False)
+
+    axes.xaxis.set_major_locator(plt.NullLocator())
+    axes.yaxis.set_major_locator(plt.NullLocator())
+
+    for i,k in enumerate(testing_frame.iloc[:,24:-30].columns):
+
+      cmap = sns.color_palette("Reds")
+      # cmap = sns.cm.rocket_r
+      ax = fig4.add_subplot(6,5, i+1)
+
+      xticklabels=[k]
+      yticklabels=list(range(1,9))
+      ax = sns.heatmap(np.array(torch.stack(attn_con_cat)[1:9])[:,i].reshape(-1,1),ax=ax,xticklabels=xticklabels, yticklabels=yticklabels, linewidth=0.2, cmap=cmap)
+      plt.xlabel('features')
+      plt.ylabel('Layers')
+      plt.tight_layout()
+
+    # fig 5
+    fig5, axes = plt.subplots(figsize=(15, 12),frameon=False)
+
+    for spine in plt.gca().spines.values():
+        spine.set_visible(False)
+
+    axes.xaxis.set_major_locator(plt.NullLocator())
+    axes.yaxis.set_major_locator(plt.NullLocator())
+
+    for i,k in enumerate(testing_frame.iloc[:,54:-2].columns):
+
+      cmap = sns.color_palette("Reds")
+      # cmap = sns.cm.rocket_r
+      ax = fig5.add_subplot(6,5, i+1)
+
+      xticklabels=[k]
+      yticklabels=list(range(1,9))
+      ax = sns.heatmap(np.array(torch.stack(attn_con_cat)[1:9])[:,i].reshape(-1,1),ax=ax,xticklabels=xticklabels, yticklabels=yticklabels, linewidth=0.2, cmap=cmap)
+      plt.xlabel('features')
+      plt.ylabel('Layers')
+      plt.tight_layout()
+    
+    #fig6
+    x_pos = (np.arange(len(testing_frame.iloc[:,:-2].columns)))
+
+    fig6 = plt.figure(figsize=(30,6))
+
+    plt.bar(x_pos, torch.stack(attn_con_cat)[9], align='center', color = 'red')
+    plt.xticks(x_pos,testing_frame.iloc[:,:-2].columns, wrap=True)
+    plt.xticks(rotation=45)
+    plt.xlabel('features')
+    plt.title('Attribution of co-attention layer')
+
+
+
+
+    remove_interpretable_embedding_layer(model, interpretable_embedding_con)
+    remove_interpretable_embedding_layer(model, interpretable_embedding_cat)
+    return fig1 , fig2 , fig3 , fig4, fig5, fig6
+
+
+demo = gr.Blocks()
+
+
+
+with demo:
+    
+      gr.Markdown(
+      """
+      # Post-Operative Artrial Fibrillation Demo
+
+      Select values for the following and click submit to see the results:
+      """)
+      num0=gr.Textbox(visible = False)
+      num1=gr.Slider(0,100,label='Age',step=1)
+      num2=gr.Slider(0,100,label='BMI')
+      num3=gr.Slider(0,20,label='LastCreatinineLevel')
+      num4=gr.Slider(0,1000,label='Cross_Clamp_Time',step=1)
+      num5=gr.Slider(0,1000,label='Perfusion_Time',step=1)
+      num6=gr.Slider(0,8,label='# of coronary vessels corrected',step=1)
+      num7=gr.CheckboxGroup([0,1],label='Aortic stenosis')
+      num8=gr.CheckboxGroup([0,1,2,3,4],label='Aortic_Insufficiency')
+      num9=gr.CheckboxGroup([0,1],label='Aortic_Procedure')
+      num10=gr.CheckboxGroup([0,1],label='Arrhythmia')
+      num11=gr.CheckboxGroup([0,1],label='ArrhythmiaAfibAflutter')
+      num12=gr.CheckboxGroup([0,1],label='CABG')
+      num13=gr.CheckboxGroup([0,1],label='CHF')
+      num14=gr.CheckboxGroup([0,1],label='CVA')
+      num15=gr.CheckboxGroup([0,1],label='Cardiogenic_Shock')
+      num16=gr.CheckboxGroup([0,1],label='Cerebrovascular_Disease')
+      num17=gr.CheckboxGroup([0,1,2,3],label='ChronicLungDisease')
+      num18=gr.CheckboxGroup([0,1],label='Diabetes')
+      num19=gr.CheckboxGroup([0,1],label='Dialysis')
+      num20=gr.Slider(0,7,label='DistAnasVein',step=1)
+      num21=gr.Slider(0,6,label='DistAnastArt',step=1)
+      num22=gr.CheckboxGroup([0,1],label='Family_History_CAD')
+      num23=gr.CheckboxGroup([0,1],label='Gender')
+      num24=gr.CheckboxGroup([0,1],label='Hypercholesterolemia')
+      num25=gr.CheckboxGroup([0,1],label='Hypertension')
+      num26=gr.CheckboxGroup([0,1],label='IABP')
+      num27=gr.CheckboxGroup([0,1],label='Infectious_Endocarditis')
+      num28=gr.Slider(0,6,label='IntraopBloodCryo',step=1)
+      num29=gr.Slider(0,8,label='IntraopBloodFFP',step=1)
+      num30=gr.CheckboxGroup([0,1,2],label='IntraopBloodFactorVII')
+      num31=gr.Slider(0,8,label='IntraopBloodPlatelet',step=1)
+      num32=gr.CheckboxGroup([0,1],label='IntraopBloodProducts')
+      num33=gr.Slider(0,20,label='IntraopBloodRBC',step=1)
+      num34=gr.CheckboxGroup([0,1],label='IntraopMedEpsilonAmi0Caproic')
+      num35=gr.CheckboxGroup([0,1],label='IntraopMedTranexamicAcid')
+      num36=gr.CheckboxGroup([0,1],label='Introp DEX or nDEX')
+      num37=gr.CheckboxGroup([0,1],label='Left_Main_Disease')
+      num38=gr.CheckboxGroup([0,1],label='MACE')
+      num39=gr.CheckboxGroup([0,1],label='MedsG2b3aInhibitorMed')
+      num40=gr.CheckboxGroup([0,1,2,3,4],label='Mitral_Insufficiency')
+      num41=gr.CheckboxGroup([0,1],label='OthCard_AICD')
+      num42=gr.CheckboxGroup([0,1],label='Oth_Heart_Block')
+      num43=gr.CheckboxGroup([0,1],label='Other_Cardiac_Intervention')
+      num44=gr.CheckboxGroup([0,1],label='Peri_Op_MI')
+      num45=gr.CheckboxGroup([0,1],label='Peripheral_Vasc_Disease')
+      num46=gr.CheckboxGroup([0,1],label='PreOpMed Antiplatelets')
+      num47=gr.CheckboxGroup([0,1],label='PreOpMedACE_ARBInhibitors')
+      num48=gr.CheckboxGroup([0,1],label='PreOpMedADPInhibitors5Days')
+      num49=gr.CheckboxGroup([0,1],label='PreOpMedAntiarrhythmics')
+      num50=gr.CheckboxGroup([0,1],label='PreOpMedAnticoagulants')
+      num51=gr.CheckboxGroup([0,1],label='PreOpMedAspirin')
+      num52=gr.CheckboxGroup([0,1],label='PreOpMedCoumadin')
+      num53=gr.CheckboxGroup([0,1],label='PreOpMedGPIIbIIIaInhibitor')
+      num54=gr.CheckboxGroup([0,1],label='PreOpMedINotropes')
+      num55=gr.CheckboxGroup([0,1],label='PreOpMedLipidLowering')
+      num56=gr.CheckboxGroup([0,1],label='PreOpMedNitratesIV')
+      num57=gr.CheckboxGroup([0,1],label='PreOpMedSteroids')
+      num58=gr.CheckboxGroup([0,1],label='PreOp_BetaBlockers')
+      num59=gr.CheckboxGroup([0,1],label='PreOp_Ca_Antagonists')
+      num60=gr.CheckboxGroup([0,1],label='PreOp_Digitalis')
+      num61=gr.CheckboxGroup([0,1],label='PreOp_Diuretics')
+      num62=gr.CheckboxGroup([0,1],label='PrevArrhythmiaSurgery')
+      num63=gr.CheckboxGroup([0,1],label='PrevOthCardPCI')
+      num64=gr.CheckboxGroup([0,1],label='Previous_CABG')
+      num65=gr.CheckboxGroup([0,1],label='Previous_CV_Intervention')
+      num66=gr.CheckboxGroup([0,1],label='Previous_Valve')
+      num67=gr.CheckboxGroup([0,1],label='PriorHeartFailure')
+      num68=gr.CheckboxGroup([0,1],label='Pulmonic_Procedure')
+      num69=gr.CheckboxGroup([0,1],label='Pulmonic_Stenosis')
+      num70=gr.CheckboxGroup([0,1],label='STS_History.Renal_Failure')
+      num71=gr.CheckboxGroup([0,1],label='Smoking')
+      num72=gr.CheckboxGroup([0,1],label='Status')
+      num73=gr.CheckboxGroup([0,1,2,3,4],label='Tricuspid_Insufficiency')
+      num74=gr.CheckboxGroup([0,1],label='Tricuspid_Procedure')
+      num75=gr.CheckboxGroup([0,1],label='VSMitral')
+      num76=gr.CheckboxGroup([0,1],label='Valve')
+      num77=gr.CheckboxGroup([0,1],label='ValveDisAortic')
+      num78=gr.CheckboxGroup([0,1],label='ValveDisMitral')
+      num79=gr.CheckboxGroup([0,1],label='ValveDisPulmonic')
+      num80=gr.CheckboxGroup([0,1],label='ValveDisTricuspid')
+      num81=gr.CheckboxGroup([0,1],label='_MI')
+      num82=gr.CheckboxGroup([0,1],label='mitral stenosis')
+      num83=gr.CheckboxGroup([0,1],label='Oth_Afib_0',visible= False)
+      num84=gr.CheckboxGroup([0,1],label='Oth_Afib_1',visible= False)
+
+
+
+      b1 = gr.Button("Submit")
+      example =sample_Df.values.tolist()
+      gr.Examples(example,inputs=[num0,num1,num2,num3,num4,num5,num6,num7,num8,num9,num10,num11,num12,num13,num14,num15,num16,num17,num18,num19,num20,num21,num22,num23,num24,num25,num26,num27,num28,num29,num30,num31,num32,num33,num34,num35,num36,num37,num38,num39,num40,num41,num42,num43,num44,num45,num46,num47,num48,num49,num50,num51,num52,num53,num54,num55,num56,num57,num58,num59,num60,num61,num62,num63,num64,num65,num66,num67,num68,num69,num70,num71,num72,num73,num74,num75,num76,num77,num78,num79,num80,num81,num82])
+
+      output = [gr.Plot(),gr.Plot(),gr.Plot(),gr.Plot(),gr.Plot(),gr.Plot()]
+
+      b1.click(run_inference, inputs=[num0,num1,num2,num3,num4,num5,num6,num7,num8,num9,num10,num11,num12,num13,num14,num15,num16,num17,num18,num19,num20,num21,num22,num23,num24,num25,num26,num27,num28,num29,num30,num31,num32,num33,num34,num35,num36,num37,num38,num39,num40,num41,num42,num43,num44,num45,num46,num47,num48,num49,num50,num51,num52,num53,num54,num55,num56,num57,num58,num59,num60,num61,num62,num63,num64,num65,num66,num67,num68,num69,num70,num71,num72,num73,num74,num75,num76,num77,num78,num79,num80,num81,num82], outputs=output)
+
+
+
+demo.launch(share=True,auth=('poaf-users','dshrebs__324'))

co_attention_transformer_model_trained.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:99b2456a353c08c7fcab9a4f6f764c79c9995e0c703954f2a78174e81e7a7fdc
+size 61186764

sample_data.csv

@@ -0,0 +1,5 @@
+Records,Age_,BMI_,LastCreatineLevel_,CrossClampTime_,PerfusionTime_,# of coronary vessels corrected,Aortic stenosis,Aortic_Insufficiency,Aortic_Procedure,Arrhythmia,ArrhythmiaAfibAflutter,CABG,CHF,CVA,Cardiogenic_Shock,Cerebrovascular_Disease,ChronicLungDisease,Diabetes,Dialysis,DistAnasVein,DistAnastArt,Family_History_CAD,Gender,Hypercholesterolemia,Hypertension,IABP,Infectious_Endocarditis,IntraopBloodCryo,IntraopBloodFFP,IntraopBloodFactorVII,IntraopBloodPlatelet,IntraopBloodProducts,IntraopBloodRBC,IntraopMedEpsilonAmi0Caproic,IntraopMedTranexamicAcid,Introp DEX or nDEX,Left_Main_Disease,MACE,MedsG2b3aInhibitorMed,Mitral_Insufficiency,OthCard_AICD,Oth_Heart_Block,Other_Cardiac_Intervention,Peri_Op_MI,Peripheral_Vasc_Disease,PreOpMed Antiplatelets,PreOpMedACE_ARBInhibitors,PreOpMedADPInhibitors5Days,PreOpMedAntiarrhythmics,PreOpMedAnticoagulants,PreOpMedAspirin,PreOpMedCoumadin,PreOpMedGPIIbIIIaInhibitor,PreOpMedINotropes,PreOpMedLipidLowering,PreOpMedNitratesIV,PreOpMedSteroids,PreOp_BetaBlockers,PreOp_Ca_Antagonists,PreOp_Digitalis,PreOp_Diuretics,PrevArrhythmiaSurgery,PrevOthCardPCI,Previous_CABG,Previous_CV_Intervention,Previous_Valve,PriorHeartFailure,Pulmonic_Procedure,Pulmonic_Stenosis,STS_History.Renal_Failure,Smoking,Status,Tricuspid_Insufficiency,Tricuspid_Procedure,VSMitral,Valve,ValveDisAortic,ValveDisMitral,ValveDisPulmonic,ValveDisTricuspid,_MI,mitral stenosis
+First_non_AFib,73,28.73,1.3,164,199,2.0,1.0,2.0,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,1.0,0.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0
+Second_non_AFib,84,24.63,1.5,220,278,5.0,1.0,0.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,4.0,1.0,0.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,2.0,1.0,7.0,1.0,0.0,1.0,0.0,0.0,0.0,2.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,2.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0
+First_Afib,63,41.03,0.9,119,152,0.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0
+Second_Afib,52,26.74,4.5,36,59,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,1.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0