upload 21 files

PATIENT/Elisabeth Smith/January_01_00_00_00.csv

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+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
+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

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+{}

PATIENT/Michael Mims/January_01_00_00_00.csv

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+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
+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

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+{}

current_ecg/January_01_00_00_00.csv

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+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
+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

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+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import os
+import tensorflow as tf
+from tensorflow import keras
+import seaborn as sns
+
+from sklearn.metrics import accuracy_score, precision_score, recall_score, roc_auc_score
+from sklearn.metrics import f1_score, confusion_matrix, precision_recall_curve, roc_curve
+from sklearn.metrics import ConfusionMatrixDisplay
+
+from sklearn.model_selection import train_test_split
+from tensorflow.keras import layers, losses
+from tensorflow.keras.datasets import fashion_mnist
+from tensorflow.keras.models import Model
+
+from plotly.subplots import make_subplots
+import plotly.graph_objects as go
+
+from sklearn.decomposition import PCA
+
+import plotly.express as px
+from scipy.interpolate import griddata
+import sklearn
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.metrics import confusion_matrix, precision_score, roc_auc_score, precision_recall_curve
+from sklearn.model_selection import train_test_split, cross_val_score, GridSearchCV, cross_val_predict, StratifiedKFold
+from sentence_transformers import SentenceTransformer
+
+from sklearn import tree
+
+
+import gradio as gr
+import os
+import json
+from datetime import datetime, timedelta
+import shutil
+import random
+import plotly.io as pio
+
+import joblib
+
+
+
+#load models
+autoencoder = keras.models.load_model('models/autoencoder')
+classifier = keras.models.load_model('models/classifier')
+decision_tree = joblib.load("models/decision_tree_model.pkl")
+llm_model = SentenceTransformer(r"sentence-transformers/paraphrase-MiniLM-L6-v2")
+
+pca_2d_llm_clusters = joblib.load('models/pca_llm_model.pkl')
+
+print("models loaded")
+
+
+
+#compute training dataset constant (min and max) for data normalization
+
+dataframe = pd.read_csv('ecg.csv', header=None)
+dataframe[140] = dataframe[140].apply(lambda x: 1 if x==0 else 0)
+
+df_ecg = dataframe[[i for i in range(140)]]
+ecg_raw_data = df_ecg.values
+labels = dataframe.values[:, -1]
+ecg_data = ecg_raw_data[:, :]
+train_data, test_data, train_labels, test_labels = train_test_split(
+    ecg_data, labels, test_size=0.2, random_state=21)
+
+min_val = tf.reduce_min(train_data)
+max_val = tf.reduce_max(train_data)
+
+print("constant computing: OK")
+
+
+#compute PCA for latent space representation
+
+ecg_data = (ecg_data - min_val) / (max_val - min_val)
+
+ecg_data = tf.cast(ecg_data, tf.float32)
+
+print(ecg_data.shape)
+X = autoencoder.encoder(ecg_data).numpy()
+
+n_components=2
+pca = PCA(n_components=n_components)
+X_compressed = pca.fit_transform(X)
+
+
+column_names = [f"Feature{i + 1}" for i in range(n_components)]
+categories = ["normal","heart disease"]
+target_categorical = pd.Categorical.from_codes(labels.astype(int), categories=categories)
+df_compressed = pd.DataFrame(X_compressed, columns=column_names)
+df_compressed["target"] = target_categorical
+
+print("PCA: done")
+
+
+#load dataset for decision tree map plot
+df_plot = pd.read_csv("df_mappa.csv", sep=",", header=0)
+print("df map for decision tree loaded.")
+
+#load dataset form llm pca
+df_pca_llm = pd.read_csv("df_PCA_llm.csv",sep=",",header=0)
+
+
+
+
+
+
+#useful functions
+
+def df_encoding(df):
+    df.ExerciseAngina.replace(
+    {
+       'N' : 'No',
+       'Y' : 'exercise-induced angina'
+    }, 
+    inplace = True
+    )
+    df.FastingBS.replace(
+        {
+           0 : 'Not Diabetic',
+           1 : 'High fasting blood sugar'
+        }, 
+        inplace = True
+    )
+    df.Sex.replace(
+        {
+           'M' : 'Man',
+           'F' : 'Female'
+        }, 
+        inplace = True
+    )
+    df.ChestPainType.replace(
+        {
+           'ATA' : 'Atypical',
+           'NAP' : 'Non-Anginal Pain',
+           'ASY' : 'Asymptomatic',
+            'TA' : 'Typical Angina'
+        }, 
+        inplace = True
+    )
+    df.RestingECG.replace(
+        {
+           'Normal' : 'Normal',
+               'ST' : 'ST-T wave abnormality',
+              'LVH' : 'Probable left ventricular hypertrophy'
+        }, 
+        inplace = True
+    )
+    df.ST_Slope.replace(
+        {
+              'Up' : 'Up',
+            'Flat' : 'Flat',
+            'Down' : 'Downsloping'
+        }, 
+        inplace = True
+    )
+
+    return df
+
+
+
+def compile_text_no_target(x):
+
+
+    text =  f"""Age: {x['Age']},  
+                Sex: {x['Sex']}, 
+                Chest Pain Type: {x['ChestPainType']}, 
+                RestingBP: {x['RestingBP']}, 
+                Cholesterol: {x['Cholesterol']}, 
+                FastingBS: {x['FastingBS']}, 
+                RestingECG: {x['RestingECG']}, 
+                MaxHR: {x['MaxHR']}
+                Exercise Angina: {x['ExerciseAngina']}, 
+                Old peak: {x['Oldpeak']}, 
+                ST_Slope: {x['ST_Slope']}
+                """
+
+    return text
+
+def LLM_transform(df , model = llm_model):
+    sentences = df.apply(lambda x: compile_text_no_target(x), axis=1).tolist()
+    
+    
+    
+    #model = SentenceTransformer(r"sentence-transformers/paraphrase-MiniLM-L6-v2")
+    
+    output = model.encode(sentences=sentences, show_progress_bar= True, normalize_embeddings  = True)
+    
+    df_embedding = pd.DataFrame(output)
+
+    return df_embedding
+
+
+
+
+
+
+
+
+
+def upload_ecg(file):
+
+   
+    
+    if len(os.listdir("current_ecg"))>0: # se ci sono file nella cartella, eliminali
+
+        try:
+            for filename in os.listdir("current_ecg"):
+                file_path = os.path.join("current_ecg", filename)
+                if os.path.isfile(file_path):
+                    os.remove(file_path)
+            print(f"I file nella cartella 'current_ecg' sono stati eliminati.")
+
+        except Exception as e:
+            print(f"Errore nell'eliminazione dei file: {str(e)}")
+
+
+    
+    df = pd.read_csv(file.name,header=None) #file.name è il path temporaneo del file caricato
+    
+    
+    source_directory = os.path.dirname(file.name)  # Replace with the source directory path
+    destination_directory = 'current_ecg'  # Replace with the destination directory path
+    
+
+    # Specify the filename (including the extension) of the CSV file you want to copy
+    file_to_copy = os.path.basename(file.name) # Replace with the actual filename
+    
+
+    # Construct the full source and destination file paths
+    source_file_path = f"{source_directory}/{file_to_copy}"
+    destination_file_path = f"{destination_directory}/{file_to_copy}"
+
+    # Copy the file from the source directory to the destination directory
+    shutil.copy(source_file_path, destination_file_path)
+    
+
+    return "Your ECG is ready, you can analyze it!"
+
+  
+
+    
+    
+    
+    
+    
+    
+
+def ecg_availability(patient_name):
+
+    folder_path = os.path.join("PATIENT",patient_name)
+    status_file_path = os.path.join(folder_path, "status.json")
+    
+    # Check if the "status.json" file exists
+    if not os.path.isfile(status_file_path):
+        return None  # If the file doesn't exist, return None
+    
+    # Load the JSON data from the "status.json" file
+    with open(status_file_path, 'r') as status_file:
+        status_data = json.load(status_file)
+    
+    # Extract the last datetime from the status JSON (if available)
+    last_datetime_str = status_data.get("last_datetime", None)
+    
+    # Get the list of CSV files in the folder
+    csv_files = [f for f in os.listdir(folder_path) if f.endswith(".csv")]
+    
+    if last_datetime_str is None:
+        return f"New ECG available"  # If the JSON is empty, return all CSV files
+    
+    last_datetime = datetime.strptime(last_datetime_str, "%B_%d_%H_%M_%S")
+    
+    # Find successive CSV files
+    successive_csv_files = []
+    for csv_file in csv_files:
+        csv_datetime_str = csv_file.split('.')[0]
+        csv_datetime = datetime.strptime(csv_datetime_str, "%B_%d_%H_%M_%S")
+        
+        # Check if the CSV datetime is successive to the last saved datetime
+        if csv_datetime > last_datetime:
+            successive_csv_files.append(csv_file)
+            
+    if len(successive_csv_file)>0:
+        return f"New ECG available (last ECG: {last_datetime})"
+    
+    else:
+        return f"No ECG available (last ECG: {last_datetime})"
+    
+
+
+    
+def ecg_analysis():
+    
+    df = pd.read_csv(os.path.join("current_ecg",os.listdir("current_ecg")[0]))
+
+
+    df_ecg = df[[str(i) for i in range(140)]] #ecg data columns
+    df_data = df_ecg.values #raw data. shape: (n_rows , 140)
+    df_data = (df_data - min_val) / (max_val - min_val)
+    df_data = tf.cast(df_data, tf.float32) #raw data. shape: (n_rows , 140)
+
+
+    df_tree = df[["ChestPainType","ST_Slope"]].copy() #dataset for decision tree
+
+    df_llm = df[["Age","Sex","ChestPainType","RestingBP","Cholesterol","FastingBS","RestingECG","MaxHR","ExerciseAngina","Oldpeak","ST_Slope"]].copy() # dataset for LLM
+    
+    true_label = df.values[:,-1]
+    
+    # ----------------ECG ANALYSIS WITH AUTOENCODER-------------------------------
+    heartbeat_encoder_preds = autoencoder.encoder(df_data).numpy() #encoder  data representation. shape: (n_rows , 8)
+    heartbeat_decoder_preds = autoencoder.decoder(heartbeat_encoder_preds).numpy() #decoder data reconstruction. shape: (n_rows , 140)
+    
+    classification_res = classifier.predict(df_data) #shape: (n_rows , 1)
+    
+    
+    print("shapes of: encoder preds, decoder preds, classification preds/n",heartbeat_encoder_preds.shape,heartbeat_decoder_preds.shape,classification_res.shape)
+    
+    #heartbeat_indexes = [i for i, pred in enumerate(classification_res) if pred == 0]
+    
+    p_encoder_preds = heartbeat_encoder_preds[0,:] #encoder representation of the chosen row
+    p_decoder_preds = heartbeat_decoder_preds[0,:] #decoder reconstruction of the chosen row
+    p_class_res = classification_res[0,:] # classification res of the chosen row
+    p_true = true_label[0]
+    
+    
+    
+
+    #LATENT SPACE PLOT
+    
+    # Create the scatter plot
+    fig = px.scatter(df_compressed, x='Feature1', y='Feature2', color='target', color_discrete_map={0: 'red', 1: 'blue'},
+                     labels={'Target': 'Binary Target'},size_max=18)
+
+
+    # Disable hover information
+    # fig.update_traces(mode="markers",
+    #                   hovertemplate = None,
+    #                   hoverinfo = "skip")
+
+    # Customize the plot layout
+    fig.update_layout(
+        title='Latent space 2D (PCA reduction)',
+        xaxis_title='component 1',
+        yaxis_title='component 2'
+    )
+    
+    # add new point
+    new_point_compressed = pca.transform(p_encoder_preds.reshape(1,-1))
+
+    new_point = {'X':[new_point_compressed[0][0]] , 'Y':[new_point_compressed[0][1]] }  # Target value 2 for the new point
+
+    new_point_df = pd.DataFrame(new_point)
+
+    #fig.add_trace(px.scatter(new_point_df, x='X', y='Y').data[0])
+    fig.add_trace(go.Scatter(
+        x=new_point_df['X'],
+        y=new_point_df['Y'],
+        mode='markers',
+        marker=dict(symbol='star', color='black', size=15),
+        name='actual patient'
+    ))
+
+    d = fig.to_dict()
+    d["data"][0]["type"] = "scatter"
+
+    fig=go.Figure(d)
+
+    
+    
+    # DECODER RECONSTRUCTION PLOT
+    
+    fig_reconstruction = plt.figure(figsize=(10,8))
+    sns.set(font_scale = 2)
+    sns.set_style("white")
+    plt.plot(df_data[0], 'black',linewidth=2)
+    plt.plot(heartbeat_decoder_preds[0], 'red',linewidth=2)
+    plt.fill_between(np.arange(140), heartbeat_decoder_preds[0], df_data[0], color='lightcoral')
+    plt.legend(labels=["Input", "Reconstruction", "Error"])
+    
+    #classification probability
+
+    # ----------DECISION TREE ANALYSIS---------------------------------
+
+
+    # Define the desired column order
+    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
+
+    X_plot = pd.DataFrame(columns=encoded_features)
+    
+    for k in range(len(df_tree['ST_Slope'])):
+        X_plot.loc[k] = 0
+        if df_tree['ST_Slope'][k] == 'Up':
+            X_plot['ST_Slope_Up'][k] = 1
+        if df_tree['ST_Slope'][k] == 'Flat':
+            X_plot['ST_Slope_Flat'][k] = 1
+        if df_tree['ST_Slope'][k] == 'Down':
+            X_plot['ST_Slope_Down'][k] = 1
+        if df_tree['ChestPainType'][k] == 'ASY':
+            X_plot['ChestPainType_ASY'][k] = 1
+        if df_tree['ChestPainType'][k] == 'ATA':
+            X_plot['ChestPainType_ATA'][k] = 1
+        if df_tree['ChestPainType'][k] == 'NAP':
+            X_plot['ChestPainType_NAP'][k] = 1
+        if df_tree['ChestPainType'][k] == 'TA':
+            X_plot['ChestPainType_TA'][k] = 1
+    
+
+    #model prediction
+    y_score = decision_tree.predict_proba(X_plot)[:,1]
+
+    chest_pain = []
+    slop = []
+
+    for k in range(len(X_plot)):
+        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:
+            chest_pain.append(0)
+        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:
+            chest_pain.append(1)
+        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:
+            chest_pain.append(2)
+        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:
+            chest_pain.append(3)
+        if X_plot['ST_Slope_Up'][k] == 1 and X_plot['ST_Slope_Flat'][k] == 0 and X_plot['ST_Slope_Down'][k] == 0:
+            slop.append(0)
+        if X_plot['ST_Slope_Up'][k] == 0 and X_plot['ST_Slope_Flat'][k] == 1 and X_plot['ST_Slope_Down'][k] == 0:
+            slop.append(1)
+        if X_plot['ST_Slope_Up'][k] == 0 and X_plot['ST_Slope_Flat'][k] == 0 and X_plot['ST_Slope_Down'][k] == 1:
+            slop.append(2)
+
+
+    # Create a structured grid
+    fig_tree = plt.figure()
+    x1 = np.linspace(df_plot['ST_Slope'].min()-0.5, df_plot['ST_Slope'].max()+0.5)  
+    x2 = np.linspace(df_plot['ChestPainType'].min()-0.5, df_plot['ChestPainType'].max()+0.5)  
+    X1, X2 = np.meshgrid(x1, x2)
+
+    # Interpolate the 'Prob' values onto the grid
+    points = df_plot[['ST_Slope', 'ChestPainType']].values
+    values = df_plot['Prob'].values
+    Z = griddata(points, values, (X1, X2), method='nearest')
+
+    # Create the contour plot with regions colored by interpolated 'Prob'
+    plt.contourf(X1, X2, Z, cmap='coolwarm', levels=10)
+    plt.colorbar(label='Predicted Probability')
+
+    # Add data points if needed
+    plt.scatter(slop[:1], chest_pain[:1], c="k", cmap='coolwarm', edgecolor='k', marker='o', label=f'prob={y_score[:1].round(3)}')
+
+    # Remove the numerical labels from the x and y axes
+    plt.xticks([])
+    plt.yticks([])
+
+    # Add custom labels "0" and "1" near the center of the axis
+    plt.text(0.0, -0.7, "Up", ha='center',fontsize=15)
+    plt.text(1.00, -0.7, "Flat", ha='center',fontsize=15)
+    plt.text(2.00, -0.7, "Down", ha='center',fontsize=15)
+    plt.text(-0.62, 0.0, "ASY", rotation='vertical', va='center',fontsize=15)
+    plt.text(-0.62, 1.00, "ATA", rotation='vertical', va='center',fontsize=15)
+    plt.text(-0.62, 2.0, "NAP", rotation='vertical', va='center',fontsize=15)
+    plt.text(-0.62, 3.0, "TA", rotation='vertical', va='center',fontsize=15)
+
+    # Add labels and title
+    plt.xlabel('ST_Slope', fontsize=15, labelpad=20)
+    plt.ylabel('ChestPainType', fontsize=15, labelpad=20)
+    #plt.legend()
+
+
+
+    # ------------LLM ANALYSIS------------------------------------
+
+    df_llm_encoding = df_encoding(df_llm)
+    df_point_LLM = LLM_transform(df_llm_encoding)
+
+    df_point_LLM.columns = [str(column) for column in df_point_LLM.columns]
+
+    pca_llm_point = pca_2d_llm_clusters.transform(df_point_LLM)
+    pca_llm_point.columns = ["comp1", "comp2"]
+
+
+    #clusters
+
+    fig_llm_cluster = plt.figure()
+    x = df_pca_llm['comp1']
+    y = df_pca_llm['comp2']
+
+    labels = ['Cluster 0', 'Cluster 1', 'Cluster 2', 'Cluster 3']
+
+    # Create a dictionary to map 'RestingECG' values to colors
+    color_mapping = {0: 'r', 1: 'b', 2: 'g', 3: 'y'}
+
+    for i in df_pca_llm['cluster'].unique():
+        color = color_mapping.get(i, 'k')  # Use 'k' (black) for undefined values
+        plt.scatter(x[df_pca_llm['cluster'] == i], y[df_pca_llm['cluster'] == i], c=color, label=labels[i])
+
+    plt.scatter(pca_llm_point['comp1'], pca_llm_point['comp1'], c='k', marker='D')
+
+    # Remove the numerical labels from the x and y axes
+    plt.xticks([])
+    plt.yticks([])
+
+    plt.xlabel('Principal Component 1')
+    plt.ylabel('Principal Component 2')
+    plt.legend()
+    plt.grid(False)
+
+
+
+
+
+
+    
+    
+    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
+    
+    
+
+    
+    
+    
+    
+    
+#demo app
+
+with gr.Blocks(title="TIQUE - AI DEMO CAPABILITIES") as demo:
+    
+    gr.Markdown("<h1><center>TIQUE: AI DEMO CAPABILITIES<center><h1>")
+
+    
+    with gr.Row():
+    
+        pazienti = ["Elisabeth Smith","Michael Mims"]
+        menu_pazienti = gr.Dropdown(choices=pazienti,label="patients")
+        
+        available_ecg_result = gr.Textbox()
+        
+        
+        menu_pazienti.input(ecg_availability, inputs=[menu_pazienti], outputs=[available_ecg_result])
+    
+    with gr.Row():
+        
+        input_file = gr.UploadButton("Click to Upload an ECG 📁")
+        text_upload_results = gr.Textbox()
+        
+        input_file.upload(upload_ecg,inputs=[input_file],outputs=text_upload_results)
+        
+    with gr.Row():
+        ecg_start_analysis_button = gr.Button(value="Start ECG analysis",scale=1)
+
+
+    gr.Markdown("## Large Language Model clustering")
+
+    with gr.Row():
+
+        llm_cluster = gr.Plot()
+        
+        
+    gr.Markdown("## Autoencoder results:")
+    
+    with gr.Row():
+        
+        with gr.Column():
+            
+            latent_space_representation = gr.Plot()
+            
+        with gr.Column():
+            
+            autoencoder_ecg_reconstruction = gr.Plot()
+            
+            classifier_nn_prediction = gr.Textbox()
+
+    gr.Markdown("## Decision Tree results:")
+
+    with gr.Row():
+
+        decision_tree_plot = gr.Plot()
+
+        decision_tree_proba = gr.Textbox()
+        
+        
+        
+    
+    ecg_start_analysis_button.click(fn=ecg_analysis, inputs=None, outputs=[latent_space_representation,
+                                                                            autoencoder_ecg_reconstruction,
+                                                                            classifier_nn_prediction,decision_tree_plot, decision_tree_proba,
+                                                                           llm_cluster])
+demo.launch()
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+

df_PCA_llm.csv

@@ -0,0 +1,827 @@
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