alessio21 commited on
Commit
6982727
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upload 21 files

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PATIENT/Elisabeth Smith/January_01_00_00_00.csv ADDED
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1
+ Age,Sex,ChestPainType,RestingBP,Cholesterol,FastingBS,RestingECG,MaxHR,ExerciseAngina,Oldpeak,ST_Slope,0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,HeartDisease
2
+ 66,F,ASY,155,0,1,Normal,90,N,0.0,Flat,-0.47899396,-1.3279821,-1.9630052,-2.5419049,-2.842115,-2.9779409,-2.9619698,-2.8396905,-2.7373024,-2.5030851,-2.0852846,-1.6531684,-1.3052834,-1.0453535,-0.91258439,-0.84291712,-0.62605726,-0.31009378,-0.14490503,-0.1191397,-0.10368371,-0.063717183,-0.0174915,-0.050771643,-0.1162558,-0.066098817,0.00370873,-0.031748181,-0.028119883,-0.0028080819,-0.041951363,0.0012201923,0.022962985,-0.038961863,-0.011636762,-0.033945745,-0.10409385,-0.037419821,0.030048995,0.030668639,0.06343631,0.044994873,-0.0069699494,0.034909372,0.03712576,0.0157684,0.065217049,0.019158697,-0.028116408,-0.0034261936,-0.047631622,-0.051083699,0.00047454776,-0.0077670981,-0.007549959,0.0070966386,-0.024228728,-0.037076338,-0.021506781,-0.036413066,-0.027021992,-0.000527918,-0.0043712614,0.016885555,0.048975588,0.075754176,0.11735432,0.14534487,0.1813353,0.24464304,0.27700387,0.2970474,0.35334582,0.39504374,0.4564452,0.54811028,0.54415532,0.52510299,0.56022153,0.54754362,0.54466337,0.60791459,0.66937919,0.69830768,0.68235612,0.65882534,0.71019414,0.74086485,0.6855465,0.70406076,0.73426365,0.75647882,0.88334604,0.87902182,0.79910352,0.86803552,0.86111971,0.78521487,0.82094978,0.85675716,0.8730353,0.92604005,0.91317205,0.87448704,0.94795769,1.0065038,0.95890793,0.92749952,0.9412297,0.94222368,0.91891534,0.92940373,0.95139173,0.89842472,0.83808416,0.82124751,0.82981156,0.89499201,0.93518597,0.90699599,0.93361395,0.98216677,0.92046581,0.85634063,0.9560982,1.0933071,1.0646474,0.90742317,0.68014564,0.29173526,-0.2500324,-0.77946144,-1.313335,-1.9428901,-2.4902432,-2.7089177,-2.3833794,-1.6402998,-0.85167232,0.052442871,1
PATIENT/Elisabeth Smith/status.json ADDED
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1
+ {}
PATIENT/Michael Mims/January_01_00_00_00.csv ADDED
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1
+ Age,Sex,ChestPainType,RestingBP,Cholesterol,FastingBS,RestingECG,MaxHR,ExerciseAngina,Oldpeak,ST_Slope,0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,HeartDisease
2
+ 36,M,ATA,120,166,0,Normal,180,N,0.0,Up,-0.44047864,-3.0589352,-4.039029,-4.4804406,-4.2757488,-3.267444,-1.922271,-1.6929038,-1.080047,-0.23897647,-0.18684226,-0.2368546,-0.3086703,-0.24249196,-0.27224171,-0.16826703,-0.23205733,-0.22135055,-0.27906549,-0.24973209,-0.27196028,-0.28419979,-0.21688771,-0.30782357,-0.358737,-0.35775032,-0.28973399,-0.48144311,-0.38783722,-0.49727892,-0.49264942,-0.59234927,-0.69894278,-0.70763628,-0.71283621,-0.79041512,-0.76395655,-0.72596072,-0.75009408,-0.6965548,-0.6211828,-0.55058558,-0.55185952,-0.39691642,-0.24575099,-0.24432603,-0.10616271,-0.051070086,-0.044290614,0.077233115,0.099876904,0.004933246,0.10753819,0.23436412,0.17034417,0.20399208,0.28797619,0.34029226,0.30731599,0.43908857,0.34954518,0.36471156,0.48396851,0.44841235,0.34015008,0.41001166,0.54918802,0.44958008,0.631039,0.56461096,0.61143985,0.60025219,0.63456657,0.74635387,0.69969196,0.60807721,0.55582234,0.48925323,0.57846001,0.50692013,0.54484525,0.51760989,0.58343134,0.40878533,0.42063216,0.44934996,0.39651202,0.40353978,0.43234808,0.46350872,0.3277348,0.39232109,0.34909481,0.32889164,0.39781027,0.35788992,0.41997218,0.29741641,0.35399178,0.5873969,0.93750726,1.1005727,1.2533827,1.3786157,1.6001485,1.7781262,1.8241755,1.8544894,1.6768503,1.4536564,1.1896788,1.0527381,0.84435875,0.38359575,-0.020145753,-0.32979729,-0.36803341,-0.35705583,-0.26818449,-0.35303211,-0.51413104,-0.57796122,-0.45875297,-0.37445816,-0.34959315,-0.43369298,-0.39538414,-0.43129934,-0.33728142,-0.1684471,0.24930914,1.2063743,1.4008974,1.480892,1.4844851,0.81622184,0.24852789,-0.47621866,0.40754928,-0.64573618,0
PATIENT/Michael Mims/status.json ADDED
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1
+ {}
current_ecg/January_01_00_00_00.csv ADDED
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1
+ Age,Sex,ChestPainType,RestingBP,Cholesterol,FastingBS,RestingECG,MaxHR,ExerciseAngina,Oldpeak,ST_Slope,0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,HeartDisease
2
+ 36,M,ATA,120,166,0,Normal,180,N,0.0,Up,-0.44047864,-3.0589352,-4.039029,-4.4804406,-4.2757488,-3.267444,-1.922271,-1.6929038,-1.080047,-0.23897647,-0.18684226,-0.2368546,-0.3086703,-0.24249196,-0.27224171,-0.16826703,-0.23205733,-0.22135055,-0.27906549,-0.24973209,-0.27196028,-0.28419979,-0.21688771,-0.30782357,-0.358737,-0.35775032,-0.28973399,-0.48144311,-0.38783722,-0.49727892,-0.49264942,-0.59234927,-0.69894278,-0.70763628,-0.71283621,-0.79041512,-0.76395655,-0.72596072,-0.75009408,-0.6965548,-0.6211828,-0.55058558,-0.55185952,-0.39691642,-0.24575099,-0.24432603,-0.10616271,-0.051070086,-0.044290614,0.077233115,0.099876904,0.004933246,0.10753819,0.23436412,0.17034417,0.20399208,0.28797619,0.34029226,0.30731599,0.43908857,0.34954518,0.36471156,0.48396851,0.44841235,0.34015008,0.41001166,0.54918802,0.44958008,0.631039,0.56461096,0.61143985,0.60025219,0.63456657,0.74635387,0.69969196,0.60807721,0.55582234,0.48925323,0.57846001,0.50692013,0.54484525,0.51760989,0.58343134,0.40878533,0.42063216,0.44934996,0.39651202,0.40353978,0.43234808,0.46350872,0.3277348,0.39232109,0.34909481,0.32889164,0.39781027,0.35788992,0.41997218,0.29741641,0.35399178,0.5873969,0.93750726,1.1005727,1.2533827,1.3786157,1.6001485,1.7781262,1.8241755,1.8544894,1.6768503,1.4536564,1.1896788,1.0527381,0.84435875,0.38359575,-0.020145753,-0.32979729,-0.36803341,-0.35705583,-0.26818449,-0.35303211,-0.51413104,-0.57796122,-0.45875297,-0.37445816,-0.34959315,-0.43369298,-0.39538414,-0.43129934,-0.33728142,-0.1684471,0.24930914,1.2063743,1.4008974,1.480892,1.4844851,0.81622184,0.24852789,-0.47621866,0.40754928,-0.64573618,0
demo.py ADDED
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1
+ import matplotlib.pyplot as plt
2
+ import numpy as np
3
+ import pandas as pd
4
+ import os
5
+ import tensorflow as tf
6
+ from tensorflow import keras
7
+ import seaborn as sns
8
+
9
+ from sklearn.metrics import accuracy_score, precision_score, recall_score, roc_auc_score
10
+ from sklearn.metrics import f1_score, confusion_matrix, precision_recall_curve, roc_curve
11
+ from sklearn.metrics import ConfusionMatrixDisplay
12
+
13
+ from sklearn.model_selection import train_test_split
14
+ from tensorflow.keras import layers, losses
15
+ from tensorflow.keras.datasets import fashion_mnist
16
+ from tensorflow.keras.models import Model
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+
18
+ from plotly.subplots import make_subplots
19
+ import plotly.graph_objects as go
20
+
21
+ from sklearn.decomposition import PCA
22
+
23
+ import plotly.express as px
24
+ from scipy.interpolate import griddata
25
+ import sklearn
26
+ from sklearn.tree import DecisionTreeClassifier
27
+ from sklearn.metrics import confusion_matrix, precision_score, roc_auc_score, precision_recall_curve
28
+ from sklearn.model_selection import train_test_split, cross_val_score, GridSearchCV, cross_val_predict, StratifiedKFold
29
+ from sentence_transformers import SentenceTransformer
30
+
31
+ from sklearn import tree
32
+
33
+
34
+ import gradio as gr
35
+ import os
36
+ import json
37
+ from datetime import datetime, timedelta
38
+ import shutil
39
+ import random
40
+ import plotly.io as pio
41
+
42
+ import joblib
43
+
44
+
45
+
46
+ #load models
47
+ autoencoder = keras.models.load_model('models/autoencoder')
48
+ classifier = keras.models.load_model('models/classifier')
49
+ decision_tree = joblib.load("models/decision_tree_model.pkl")
50
+ llm_model = SentenceTransformer(r"sentence-transformers/paraphrase-MiniLM-L6-v2")
51
+
52
+ pca_2d_llm_clusters = joblib.load('models/pca_llm_model.pkl')
53
+
54
+ print("models loaded")
55
+
56
+
57
+
58
+ #compute training dataset constant (min and max) for data normalization
59
+
60
+ dataframe = pd.read_csv('ecg.csv', header=None)
61
+ dataframe[140] = dataframe[140].apply(lambda x: 1 if x==0 else 0)
62
+
63
+ df_ecg = dataframe[[i for i in range(140)]]
64
+ ecg_raw_data = df_ecg.values
65
+ labels = dataframe.values[:, -1]
66
+ ecg_data = ecg_raw_data[:, :]
67
+ train_data, test_data, train_labels, test_labels = train_test_split(
68
+ ecg_data, labels, test_size=0.2, random_state=21)
69
+
70
+ min_val = tf.reduce_min(train_data)
71
+ max_val = tf.reduce_max(train_data)
72
+
73
+ print("constant computing: OK")
74
+
75
+
76
+ #compute PCA for latent space representation
77
+
78
+ ecg_data = (ecg_data - min_val) / (max_val - min_val)
79
+
80
+ ecg_data = tf.cast(ecg_data, tf.float32)
81
+
82
+ print(ecg_data.shape)
83
+ X = autoencoder.encoder(ecg_data).numpy()
84
+
85
+ n_components=2
86
+ pca = PCA(n_components=n_components)
87
+ X_compressed = pca.fit_transform(X)
88
+
89
+
90
+ column_names = [f"Feature{i + 1}" for i in range(n_components)]
91
+ categories = ["normal","heart disease"]
92
+ target_categorical = pd.Categorical.from_codes(labels.astype(int), categories=categories)
93
+ df_compressed = pd.DataFrame(X_compressed, columns=column_names)
94
+ df_compressed["target"] = target_categorical
95
+
96
+ print("PCA: done")
97
+
98
+
99
+ #load dataset for decision tree map plot
100
+ df_plot = pd.read_csv("df_mappa.csv", sep=",", header=0)
101
+ print("df map for decision tree loaded.")
102
+
103
+ #load dataset form llm pca
104
+ df_pca_llm = pd.read_csv("df_PCA_llm.csv",sep=",",header=0)
105
+
106
+
107
+
108
+
109
+
110
+
111
+ #useful functions
112
+
113
+ def df_encoding(df):
114
+ df.ExerciseAngina.replace(
115
+ {
116
+ 'N' : 'No',
117
+ 'Y' : 'exercise-induced angina'
118
+ },
119
+ inplace = True
120
+ )
121
+ df.FastingBS.replace(
122
+ {
123
+ 0 : 'Not Diabetic',
124
+ 1 : 'High fasting blood sugar'
125
+ },
126
+ inplace = True
127
+ )
128
+ df.Sex.replace(
129
+ {
130
+ 'M' : 'Man',
131
+ 'F' : 'Female'
132
+ },
133
+ inplace = True
134
+ )
135
+ df.ChestPainType.replace(
136
+ {
137
+ 'ATA' : 'Atypical',
138
+ 'NAP' : 'Non-Anginal Pain',
139
+ 'ASY' : 'Asymptomatic',
140
+ 'TA' : 'Typical Angina'
141
+ },
142
+ inplace = True
143
+ )
144
+ df.RestingECG.replace(
145
+ {
146
+ 'Normal' : 'Normal',
147
+ 'ST' : 'ST-T wave abnormality',
148
+ 'LVH' : 'Probable left ventricular hypertrophy'
149
+ },
150
+ inplace = True
151
+ )
152
+ df.ST_Slope.replace(
153
+ {
154
+ 'Up' : 'Up',
155
+ 'Flat' : 'Flat',
156
+ 'Down' : 'Downsloping'
157
+ },
158
+ inplace = True
159
+ )
160
+
161
+ return df
162
+
163
+
164
+
165
+ def compile_text_no_target(x):
166
+
167
+
168
+ text = f"""Age: {x['Age']},
169
+ Sex: {x['Sex']},
170
+ Chest Pain Type: {x['ChestPainType']},
171
+ RestingBP: {x['RestingBP']},
172
+ Cholesterol: {x['Cholesterol']},
173
+ FastingBS: {x['FastingBS']},
174
+ RestingECG: {x['RestingECG']},
175
+ MaxHR: {x['MaxHR']}
176
+ Exercise Angina: {x['ExerciseAngina']},
177
+ Old peak: {x['Oldpeak']},
178
+ ST_Slope: {x['ST_Slope']}
179
+ """
180
+
181
+ return text
182
+
183
+ def LLM_transform(df , model = llm_model):
184
+ sentences = df.apply(lambda x: compile_text_no_target(x), axis=1).tolist()
185
+
186
+
187
+
188
+ #model = SentenceTransformer(r"sentence-transformers/paraphrase-MiniLM-L6-v2")
189
+
190
+ output = model.encode(sentences=sentences, show_progress_bar= True, normalize_embeddings = True)
191
+
192
+ df_embedding = pd.DataFrame(output)
193
+
194
+ return df_embedding
195
+
196
+
197
+
198
+
199
+
200
+
201
+
202
+
203
+
204
+ def upload_ecg(file):
205
+
206
+
207
+
208
+ if len(os.listdir("current_ecg"))>0: # se ci sono file nella cartella, eliminali
209
+
210
+ try:
211
+ for filename in os.listdir("current_ecg"):
212
+ file_path = os.path.join("current_ecg", filename)
213
+ if os.path.isfile(file_path):
214
+ os.remove(file_path)
215
+ print(f"I file nella cartella 'current_ecg' sono stati eliminati.")
216
+
217
+ except Exception as e:
218
+ print(f"Errore nell'eliminazione dei file: {str(e)}")
219
+
220
+
221
+
222
+ df = pd.read_csv(file.name,header=None) #file.name è il path temporaneo del file caricato
223
+
224
+
225
+ source_directory = os.path.dirname(file.name) # Replace with the source directory path
226
+ destination_directory = 'current_ecg' # Replace with the destination directory path
227
+
228
+
229
+ # Specify the filename (including the extension) of the CSV file you want to copy
230
+ file_to_copy = os.path.basename(file.name) # Replace with the actual filename
231
+
232
+
233
+ # Construct the full source and destination file paths
234
+ source_file_path = f"{source_directory}/{file_to_copy}"
235
+ destination_file_path = f"{destination_directory}/{file_to_copy}"
236
+
237
+ # Copy the file from the source directory to the destination directory
238
+ shutil.copy(source_file_path, destination_file_path)
239
+
240
+
241
+ return "Your ECG is ready, you can analyze it!"
242
+
243
+
244
+
245
+
246
+
247
+
248
+
249
+
250
+
251
+
252
+ def ecg_availability(patient_name):
253
+
254
+ folder_path = os.path.join("PATIENT",patient_name)
255
+ status_file_path = os.path.join(folder_path, "status.json")
256
+
257
+ # Check if the "status.json" file exists
258
+ if not os.path.isfile(status_file_path):
259
+ return None # If the file doesn't exist, return None
260
+
261
+ # Load the JSON data from the "status.json" file
262
+ with open(status_file_path, 'r') as status_file:
263
+ status_data = json.load(status_file)
264
+
265
+ # Extract the last datetime from the status JSON (if available)
266
+ last_datetime_str = status_data.get("last_datetime", None)
267
+
268
+ # Get the list of CSV files in the folder
269
+ csv_files = [f for f in os.listdir(folder_path) if f.endswith(".csv")]
270
+
271
+ if last_datetime_str is None:
272
+ return f"New ECG available" # If the JSON is empty, return all CSV files
273
+
274
+ last_datetime = datetime.strptime(last_datetime_str, "%B_%d_%H_%M_%S")
275
+
276
+ # Find successive CSV files
277
+ successive_csv_files = []
278
+ for csv_file in csv_files:
279
+ csv_datetime_str = csv_file.split('.')[0]
280
+ csv_datetime = datetime.strptime(csv_datetime_str, "%B_%d_%H_%M_%S")
281
+
282
+ # Check if the CSV datetime is successive to the last saved datetime
283
+ if csv_datetime > last_datetime:
284
+ successive_csv_files.append(csv_file)
285
+
286
+ if len(successive_csv_file)>0:
287
+ return f"New ECG available (last ECG: {last_datetime})"
288
+
289
+ else:
290
+ return f"No ECG available (last ECG: {last_datetime})"
291
+
292
+
293
+
294
+
295
+ def ecg_analysis():
296
+
297
+ df = pd.read_csv(os.path.join("current_ecg",os.listdir("current_ecg")[0]))
298
+
299
+
300
+ df_ecg = df[[str(i) for i in range(140)]] #ecg data columns
301
+ df_data = df_ecg.values #raw data. shape: (n_rows , 140)
302
+ df_data = (df_data - min_val) / (max_val - min_val)
303
+ df_data = tf.cast(df_data, tf.float32) #raw data. shape: (n_rows , 140)
304
+
305
+
306
+ df_tree = df[["ChestPainType","ST_Slope"]].copy() #dataset for decision tree
307
+
308
+ df_llm = df[["Age","Sex","ChestPainType","RestingBP","Cholesterol","FastingBS","RestingECG","MaxHR","ExerciseAngina","Oldpeak","ST_Slope"]].copy() # dataset for LLM
309
+
310
+ true_label = df.values[:,-1]
311
+
312
+ # ----------------ECG ANALYSIS WITH AUTOENCODER-------------------------------
313
+ heartbeat_encoder_preds = autoencoder.encoder(df_data).numpy() #encoder data representation. shape: (n_rows , 8)
314
+ heartbeat_decoder_preds = autoencoder.decoder(heartbeat_encoder_preds).numpy() #decoder data reconstruction. shape: (n_rows , 140)
315
+
316
+ classification_res = classifier.predict(df_data) #shape: (n_rows , 1)
317
+
318
+
319
+ print("shapes of: encoder preds, decoder preds, classification preds/n",heartbeat_encoder_preds.shape,heartbeat_decoder_preds.shape,classification_res.shape)
320
+
321
+ #heartbeat_indexes = [i for i, pred in enumerate(classification_res) if pred == 0]
322
+
323
+ p_encoder_preds = heartbeat_encoder_preds[0,:] #encoder representation of the chosen row
324
+ p_decoder_preds = heartbeat_decoder_preds[0,:] #decoder reconstruction of the chosen row
325
+ p_class_res = classification_res[0,:] # classification res of the chosen row
326
+ p_true = true_label[0]
327
+
328
+
329
+
330
+
331
+ #LATENT SPACE PLOT
332
+
333
+ # Create the scatter plot
334
+ fig = px.scatter(df_compressed, x='Feature1', y='Feature2', color='target', color_discrete_map={0: 'red', 1: 'blue'},
335
+ labels={'Target': 'Binary Target'},size_max=18)
336
+
337
+
338
+ # Disable hover information
339
+ # fig.update_traces(mode="markers",
340
+ # hovertemplate = None,
341
+ # hoverinfo = "skip")
342
+
343
+ # Customize the plot layout
344
+ fig.update_layout(
345
+ title='Latent space 2D (PCA reduction)',
346
+ xaxis_title='component 1',
347
+ yaxis_title='component 2'
348
+ )
349
+
350
+ # add new point
351
+ new_point_compressed = pca.transform(p_encoder_preds.reshape(1,-1))
352
+
353
+ new_point = {'X':[new_point_compressed[0][0]] , 'Y':[new_point_compressed[0][1]] } # Target value 2 for the new point
354
+
355
+ new_point_df = pd.DataFrame(new_point)
356
+
357
+ #fig.add_trace(px.scatter(new_point_df, x='X', y='Y').data[0])
358
+ fig.add_trace(go.Scatter(
359
+ x=new_point_df['X'],
360
+ y=new_point_df['Y'],
361
+ mode='markers',
362
+ marker=dict(symbol='star', color='black', size=15),
363
+ name='actual patient'
364
+ ))
365
+
366
+ d = fig.to_dict()
367
+ d["data"][0]["type"] = "scatter"
368
+
369
+ fig=go.Figure(d)
370
+
371
+
372
+
373
+ # DECODER RECONSTRUCTION PLOT
374
+
375
+ fig_reconstruction = plt.figure(figsize=(10,8))
376
+ sns.set(font_scale = 2)
377
+ sns.set_style("white")
378
+ plt.plot(df_data[0], 'black',linewidth=2)
379
+ plt.plot(heartbeat_decoder_preds[0], 'red',linewidth=2)
380
+ plt.fill_between(np.arange(140), heartbeat_decoder_preds[0], df_data[0], color='lightcoral')
381
+ plt.legend(labels=["Input", "Reconstruction", "Error"])
382
+
383
+ #classification probability
384
+
385
+ # ----------DECISION TREE ANALYSIS---------------------------------
386
+
387
+
388
+ # Define the desired column order
389
+ encoded_features = ['ST_Slope_Up', 'ST_Slope_Flat', 'ST_Slope_Down', 'ChestPainType_ASY', 'ChestPainType_ATA', 'ChestPainType_NAP', 'ChestPainType_TA'] #il modello vuole le colonne in un determinato ordine
390
+
391
+ X_plot = pd.DataFrame(columns=encoded_features)
392
+
393
+ for k in range(len(df_tree['ST_Slope'])):
394
+ X_plot.loc[k] = 0
395
+ if df_tree['ST_Slope'][k] == 'Up':
396
+ X_plot['ST_Slope_Up'][k] = 1
397
+ if df_tree['ST_Slope'][k] == 'Flat':
398
+ X_plot['ST_Slope_Flat'][k] = 1
399
+ if df_tree['ST_Slope'][k] == 'Down':
400
+ X_plot['ST_Slope_Down'][k] = 1
401
+ if df_tree['ChestPainType'][k] == 'ASY':
402
+ X_plot['ChestPainType_ASY'][k] = 1
403
+ if df_tree['ChestPainType'][k] == 'ATA':
404
+ X_plot['ChestPainType_ATA'][k] = 1
405
+ if df_tree['ChestPainType'][k] == 'NAP':
406
+ X_plot['ChestPainType_NAP'][k] = 1
407
+ if df_tree['ChestPainType'][k] == 'TA':
408
+ X_plot['ChestPainType_TA'][k] = 1
409
+
410
+
411
+ #model prediction
412
+ y_score = decision_tree.predict_proba(X_plot)[:,1]
413
+
414
+ chest_pain = []
415
+ slop = []
416
+
417
+ for k in range(len(X_plot)):
418
+ if X_plot['ChestPainType_ASY'][k] == 1 and X_plot['ChestPainType_ATA'][k] == 0 and X_plot['ChestPainType_NAP'][k] == 0 and X_plot['ChestPainType_TA'][k] == 0:
419
+ chest_pain.append(0)
420
+ if X_plot['ChestPainType_ASY'][k] == 0 and X_plot['ChestPainType_ATA'][k] == 1 and X_plot['ChestPainType_NAP'][k] == 0 and X_plot['ChestPainType_TA'][k] == 0:
421
+ chest_pain.append(1)
422
+ if X_plot['ChestPainType_ASY'][k] == 0 and X_plot['ChestPainType_ATA'][k] == 0 and X_plot['ChestPainType_NAP'][k] == 1 and X_plot['ChestPainType_TA'][k] == 0:
423
+ chest_pain.append(2)
424
+ if X_plot['ChestPainType_ASY'][k] == 0 and X_plot['ChestPainType_ATA'][k] == 0 and X_plot['ChestPainType_NAP'][k] == 0 and X_plot['ChestPainType_TA'][k] == 1:
425
+ chest_pain.append(3)
426
+ if X_plot['ST_Slope_Up'][k] == 1 and X_plot['ST_Slope_Flat'][k] == 0 and X_plot['ST_Slope_Down'][k] == 0:
427
+ slop.append(0)
428
+ if X_plot['ST_Slope_Up'][k] == 0 and X_plot['ST_Slope_Flat'][k] == 1 and X_plot['ST_Slope_Down'][k] == 0:
429
+ slop.append(1)
430
+ if X_plot['ST_Slope_Up'][k] == 0 and X_plot['ST_Slope_Flat'][k] == 0 and X_plot['ST_Slope_Down'][k] == 1:
431
+ slop.append(2)
432
+
433
+
434
+ # Create a structured grid
435
+ fig_tree = plt.figure()
436
+ x1 = np.linspace(df_plot['ST_Slope'].min()-0.5, df_plot['ST_Slope'].max()+0.5)
437
+ x2 = np.linspace(df_plot['ChestPainType'].min()-0.5, df_plot['ChestPainType'].max()+0.5)
438
+ X1, X2 = np.meshgrid(x1, x2)
439
+
440
+ # Interpolate the 'Prob' values onto the grid
441
+ points = df_plot[['ST_Slope', 'ChestPainType']].values
442
+ values = df_plot['Prob'].values
443
+ Z = griddata(points, values, (X1, X2), method='nearest')
444
+
445
+ # Create the contour plot with regions colored by interpolated 'Prob'
446
+ plt.contourf(X1, X2, Z, cmap='coolwarm', levels=10)
447
+ plt.colorbar(label='Predicted Probability')
448
+
449
+ # Add data points if needed
450
+ plt.scatter(slop[:1], chest_pain[:1], c="k", cmap='coolwarm', edgecolor='k', marker='o', label=f'prob={y_score[:1].round(3)}')
451
+
452
+ # Remove the numerical labels from the x and y axes
453
+ plt.xticks([])
454
+ plt.yticks([])
455
+
456
+ # Add custom labels "0" and "1" near the center of the axis
457
+ plt.text(0.0, -0.7, "Up", ha='center',fontsize=15)
458
+ plt.text(1.00, -0.7, "Flat", ha='center',fontsize=15)
459
+ plt.text(2.00, -0.7, "Down", ha='center',fontsize=15)
460
+ plt.text(-0.62, 0.0, "ASY", rotation='vertical', va='center',fontsize=15)
461
+ plt.text(-0.62, 1.00, "ATA", rotation='vertical', va='center',fontsize=15)
462
+ plt.text(-0.62, 2.0, "NAP", rotation='vertical', va='center',fontsize=15)
463
+ plt.text(-0.62, 3.0, "TA", rotation='vertical', va='center',fontsize=15)
464
+
465
+ # Add labels and title
466
+ plt.xlabel('ST_Slope', fontsize=15, labelpad=20)
467
+ plt.ylabel('ChestPainType', fontsize=15, labelpad=20)
468
+ #plt.legend()
469
+
470
+
471
+
472
+ # ------------LLM ANALYSIS------------------------------------
473
+
474
+ df_llm_encoding = df_encoding(df_llm)
475
+ df_point_LLM = LLM_transform(df_llm_encoding)
476
+
477
+ df_point_LLM.columns = [str(column) for column in df_point_LLM.columns]
478
+
479
+ pca_llm_point = pca_2d_llm_clusters.transform(df_point_LLM)
480
+ pca_llm_point.columns = ["comp1", "comp2"]
481
+
482
+
483
+ #clusters
484
+
485
+ fig_llm_cluster = plt.figure()
486
+ x = df_pca_llm['comp1']
487
+ y = df_pca_llm['comp2']
488
+
489
+ labels = ['Cluster 0', 'Cluster 1', 'Cluster 2', 'Cluster 3']
490
+
491
+ # Create a dictionary to map 'RestingECG' values to colors
492
+ color_mapping = {0: 'r', 1: 'b', 2: 'g', 3: 'y'}
493
+
494
+ for i in df_pca_llm['cluster'].unique():
495
+ color = color_mapping.get(i, 'k') # Use 'k' (black) for undefined values
496
+ plt.scatter(x[df_pca_llm['cluster'] == i], y[df_pca_llm['cluster'] == i], c=color, label=labels[i])
497
+
498
+ plt.scatter(pca_llm_point['comp1'], pca_llm_point['comp1'], c='k', marker='D')
499
+
500
+ # Remove the numerical labels from the x and y axes
501
+ plt.xticks([])
502
+ plt.yticks([])
503
+
504
+ plt.xlabel('Principal Component 1')
505
+ plt.ylabel('Principal Component 2')
506
+ plt.legend()
507
+ plt.grid(False)
508
+
509
+
510
+
511
+
512
+
513
+
514
+
515
+
516
+ return fig, fig_reconstruction , f"Heart disease probability: {int(p_class_res[0]*100)} %" , fig_tree , f"Heart disease probability: {int(y_score[0]*100)} %" , fig_llm_cluster
517
+
518
+
519
+
520
+
521
+
522
+
523
+
524
+
525
+ #demo app
526
+
527
+ with gr.Blocks(title="TIQUE - AI DEMO CAPABILITIES") as demo:
528
+
529
+ gr.Markdown("<h1><center>TIQUE: AI DEMO CAPABILITIES<center><h1>")
530
+
531
+
532
+ with gr.Row():
533
+
534
+ pazienti = ["Elisabeth Smith","Michael Mims"]
535
+ menu_pazienti = gr.Dropdown(choices=pazienti,label="patients")
536
+
537
+ available_ecg_result = gr.Textbox()
538
+
539
+
540
+ menu_pazienti.input(ecg_availability, inputs=[menu_pazienti], outputs=[available_ecg_result])
541
+
542
+ with gr.Row():
543
+
544
+ input_file = gr.UploadButton("Click to Upload an ECG 📁")
545
+ text_upload_results = gr.Textbox()
546
+
547
+ input_file.upload(upload_ecg,inputs=[input_file],outputs=text_upload_results)
548
+
549
+ with gr.Row():
550
+ ecg_start_analysis_button = gr.Button(value="Start ECG analysis",scale=1)
551
+
552
+
553
+ gr.Markdown("## Large Language Model clustering")
554
+
555
+ with gr.Row():
556
+
557
+ llm_cluster = gr.Plot()
558
+
559
+
560
+ gr.Markdown("## Autoencoder results:")
561
+
562
+ with gr.Row():
563
+
564
+ with gr.Column():
565
+
566
+ latent_space_representation = gr.Plot()
567
+
568
+ with gr.Column():
569
+
570
+ autoencoder_ecg_reconstruction = gr.Plot()
571
+
572
+ classifier_nn_prediction = gr.Textbox()
573
+
574
+ gr.Markdown("## Decision Tree results:")
575
+
576
+ with gr.Row():
577
+
578
+ decision_tree_plot = gr.Plot()
579
+
580
+ decision_tree_proba = gr.Textbox()
581
+
582
+
583
+
584
+
585
+ ecg_start_analysis_button.click(fn=ecg_analysis, inputs=None, outputs=[latent_space_representation,
586
+ autoencoder_ecg_reconstruction,
587
+ classifier_nn_prediction,decision_tree_plot, decision_tree_proba,
588
+ llm_cluster])
589
+ demo.launch()
590
+
591
+
592
+
593
+
594
+
595
+
596
+
597
+
598
+
599
+
600
+
601
+
602
+
603
+
604
+
605
+
606
+
607
+
608
+
609
+
610
+
611
+
612
+
613
+
df_PCA_llm.csv ADDED
@@ -0,0 +1,827 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ comp1,comp2,cluster,RestingECG,Sex,ChestPainType,ST_Slope,target
2
+ 9.619010181431264,-1.4144743036148473,1,0,0,0,0,0
3
+ 14.920675572310769,-9.216852095155183,0,0,1,1,1,1
4
+ 7.034229655606625,2.966583681240097,1,1,0,0,0,0
5
+ -6.690527912916168,-13.873548275079354,0,0,1,2,1,1
6
+ 13.1987102413112,1.2694878087680175,1,0,0,1,0,0
7
+ 13.156938557351047,0.4928063398175892,1,0,0,1,0,0
8
+ 11.6728597082073,-10.678031315447239,0,0,1,0,0,0
9
+ 10.000981156529411,-0.1680697123771176,1,0,0,0,0,0
10
+ -6.379667502189462,-4.663452992791154,2,0,0,2,1,1
11
+ 10.92903217727642,-11.20083414698684,0,0,1,0,0,0
12
+ 15.644827798526846,-9.152984193299105,0,0,1,1,0,0
13
+ 4.6274507150961455,2.696578438779159,1,1,0,0,1,1
14
+ 9.609454249782356,-1.5090657347282685,1,0,0,0,0,0
15
+ -6.658434811681424,-4.306993372292747,2,0,0,2,1,1
16
+ 12.074229897768646,-6.036142985971734,0,1,1,1,0,0
17
+ 12.088213700502688,-10.984326818011825,0,0,1,0,1,0
18
+ -3.426723534143882,-3.975019820150812,2,0,0,2,1,1
19
+ 10.385499168309583,-10.670317626589295,0,0,1,0,0,0
20
+ -3.2764853945916514,-2.701422744036913,2,0,0,2,1,1
21
+ 10.448364305665452,-0.03524665284813394,1,0,0,0,1,1
22
+ 10.186941001454922,-0.7174321511148423,1,0,0,0,1,0
23
+ 11.78538012869047,-10.904113550180812,0,0,1,0,0,0
24
+ 7.228228619826089,-2.0122086253263562,1,0,0,0,1,1
25
+ 13.958880254159176,0.838329046829511,1,0,0,1,0,0
26
+ 13.269072592880445,1.4277220474622379,1,0,0,1,0,0
27
+ -8.584350658571871,-0.6391206228704797,2,1,0,2,1,0
28
+ 9.634314660970409,-0.988343282449797,1,0,0,0,0,0
29
+ 11.615293130490855,-10.3081869121009,0,0,1,0,0,0
30
+ 9.558672802229555,-0.6313793140601567,1,0,0,0,0,0
31
+ 13.844171084023305,1.1962433619443165,1,0,0,1,1,1
32
+ 13.531155588699049,1.6193543247458964,1,0,0,1,0,0
33
+ -3.5962718061278416,-2.8569141416623216,2,0,0,2,1,1
34
+ -6.737308990828911,-0.6541322180965294,2,1,0,2,1,1
35
+ 10.987910992291562,-10.711398093411095,0,0,1,0,0,0
36
+ 9.578707105607478,-1.4735952214173975,1,0,0,0,0,0
37
+ -10.491573184684182,-3.218390609798486,2,0,0,2,1,1
38
+ -6.636975793682202,-14.665658807629125,0,0,1,2,1,0
39
+ 11.495479738125582,-11.196662223497308,0,0,1,0,0,0
40
+ 9.685143266688783,-2.3664322763196575,1,0,0,0,0,0
41
+ 9.970236639126234,2.874062605427239,1,1,0,1,0,0
42
+ -7.968455603926181,-3.994740407548555,2,0,0,2,1,1
43
+ 10.36829107180509,0.5250322507310498,1,0,0,1,1,0
44
+ -3.520931317049324,-4.3718147848889615,2,0,0,2,0,0
45
+ 10.436457122373229,-0.817392727888405,1,0,0,0,0,0
46
+ 13.975809680711532,1.3248901671052875,1,0,0,1,1,0
47
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