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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"id": "yowZ_FwQ53s6"
},
"outputs": [],
"source": [
"!pip install -q seaborn plotly sentence-transformers prince gradio==3.41.2"
]
},
{
"cell_type": "code",
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import pandas as pd\n",
"import os\n",
"import tensorflow as tf\n",
"from tensorflow import keras\n",
"import seaborn as sns\n",
"\n",
"from sklearn.metrics import accuracy_score, precision_score, recall_score, roc_auc_score\n",
"from sklearn.metrics import f1_score, confusion_matrix, precision_recall_curve, roc_curve\n",
"from sklearn.metrics import ConfusionMatrixDisplay\n",
"\n",
"from sklearn.model_selection import train_test_split\n",
"from tensorflow.keras import layers, losses\n",
"from tensorflow.keras.datasets import fashion_mnist\n",
"from tensorflow.keras.models import Model\n",
"\n",
"from plotly.subplots import make_subplots\n",
"import plotly.graph_objects as go\n",
"\n",
"from sklearn.decomposition import PCA\n",
"\n",
"import plotly.express as px\n",
"from scipy.interpolate import griddata\n",
"import sklearn\n",
"from sklearn.tree import DecisionTreeClassifier\n",
"from sklearn.metrics import confusion_matrix, precision_score, roc_auc_score, precision_recall_curve\n",
"from sklearn.model_selection import train_test_split, cross_val_score, GridSearchCV, cross_val_predict, StratifiedKFold\n",
"from sentence_transformers import SentenceTransformer\n",
"\n",
"from sklearn import tree\n",
"\n",
"\n",
"import gradio as gr\n",
"import os\n",
"import json\n",
"from datetime import datetime, timedelta\n",
"import shutil\n",
"import random\n",
"import plotly.io as pio\n",
"\n",
"import joblib\n",
"\n",
"\n",
"\n",
"#load models\n",
"autoencoder = keras.models.load_model('models/autoencoder')\n",
"classifier = keras.models.load_model('models/classifier')\n",
"decision_tree = joblib.load(\"models/decision_tree_model.pkl\")\n",
"llm_model = SentenceTransformer(r\"sentence-transformers/paraphrase-MiniLM-L6-v2\")\n",
"\n",
"pca_2d_llm_clusters = joblib.load('models/pca_llm_model.pkl')\n",
"\n",
"print(\"models loaded\")\n",
"\n",
"\n",
"\n",
"#compute training dataset constant (min and max) for data normalization\n",
"\n",
"dataframe = pd.read_csv('ecg.csv', header=None)\n",
"dataframe[140] = dataframe[140].apply(lambda x: 1 if x==0 else 0)\n",
"\n",
"df_ecg = dataframe[[i for i in range(140)]]\n",
"ecg_raw_data = df_ecg.values\n",
"labels = dataframe.values[:, -1]\n",
"ecg_data = ecg_raw_data[:, :]\n",
"train_data, test_data, train_labels, test_labels = train_test_split(\n",
" ecg_data, labels, test_size=0.2, random_state=21)\n",
"\n",
"min_val = tf.reduce_min(train_data)\n",
"max_val = tf.reduce_max(train_data)\n",
"\n",
"print(\"constant computing: OK\")\n",
"\n",
"\n",
"#compute PCA for latent space representation\n",
"\n",
"ecg_data = (ecg_data - min_val) / (max_val - min_val)\n",
"\n",
"ecg_data = tf.cast(ecg_data, tf.float32)\n",
"\n",
"print(ecg_data.shape)\n",
"X = autoencoder.encoder(ecg_data).numpy()\n",
"\n",
"n_components=2\n",
"pca = PCA(n_components=n_components)\n",
"X_compressed = pca.fit_transform(X)\n",
"\n",
"\n",
"column_names = [f\"Feature{i + 1}\" for i in range(n_components)]\n",
"categories = [\"normal\",\"heart disease\"]\n",
"target_categorical = pd.Categorical.from_codes(labels.astype(int), categories=categories)\n",
"df_compressed = pd.DataFrame(X_compressed, columns=column_names)\n",
"df_compressed[\"target\"] = target_categorical\n",
"\n",
"print(\"PCA: done\")\n",
"\n",
"\n",
"#load dataset for decision tree map plot\n",
"df_plot = pd.read_csv(\"df_mappa.csv\", sep=\",\", header=0)\n",
"print(\"df map for decision tree loaded.\")\n",
"\n",
"#load dataset form llm pca\n",
"df_pca_llm = pd.read_csv(\"df_PCA_llm.csv\",sep=\",\",header=0)\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"#useful functions\n",
"\n",
"def df_encoding(df):\n",
" df.ExerciseAngina.replace(\n",
" {\n",
" 'N' : 'No',\n",
" 'Y' : 'exercise-induced angina'\n",
" },\n",
" inplace = True\n",
" )\n",
" df.FastingBS.replace(\n",
" {\n",
" 0 : 'Not Diabetic',\n",
" 1 : 'High fasting blood sugar'\n",
" },\n",
" inplace = True\n",
" )\n",
" df.Sex.replace(\n",
" {\n",
" 'M' : 'Man',\n",
" 'F' : 'Female'\n",
" },\n",
" inplace = True\n",
" )\n",
" df.ChestPainType.replace(\n",
" {\n",
" 'ATA' : 'Atypical',\n",
" 'NAP' : 'Non-Anginal Pain',\n",
" 'ASY' : 'Asymptomatic',\n",
" 'TA' : 'Typical Angina'\n",
" },\n",
" inplace = True\n",
" )\n",
" df.RestingECG.replace(\n",
" {\n",
" 'Normal' : 'Normal',\n",
" 'ST' : 'ST-T wave abnormality',\n",
" 'LVH' : 'Probable left ventricular hypertrophy'\n",
" },\n",
" inplace = True\n",
" )\n",
" df.ST_Slope.replace(\n",
" {\n",
" 'Up' : 'Up',\n",
" 'Flat' : 'Flat',\n",
" 'Down' : 'Downsloping'\n",
" },\n",
" inplace = True\n",
" )\n",
"\n",
" return df\n",
"\n",
"\n",
"\n",
"def compile_text_no_target(x):\n",
"\n",
"\n",
" text = f\"\"\"Age: {x['Age']},\n",
" Sex: {x['Sex']},\n",
" Chest Pain Type: {x['ChestPainType']},\n",
" RestingBP: {x['RestingBP']},\n",
" Cholesterol: {x['Cholesterol']},\n",
" FastingBS: {x['FastingBS']},\n",
" RestingECG: {x['RestingECG']},\n",
" MaxHR: {x['MaxHR']}\n",
" Exercise Angina: {x['ExerciseAngina']},\n",
" Old peak: {x['Oldpeak']},\n",
" ST_Slope: {x['ST_Slope']}\n",
" \"\"\"\n",
"\n",
" return text\n",
"\n",
"def LLM_transform(df , model = llm_model):\n",
" sentences = df.apply(lambda x: compile_text_no_target(x), axis=1).tolist()\n",
"\n",
"\n",
"\n",
" #model = SentenceTransformer(r\"sentence-transformers/paraphrase-MiniLM-L6-v2\")\n",
"\n",
" output = model.encode(sentences=sentences, show_progress_bar= True, normalize_embeddings = True)\n",
"\n",
" df_embedding = pd.DataFrame(output)\n",
"\n",
" return df_embedding\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"def upload_ecg(file):\n",
"\n",
"\n",
"\n",
" if len(os.listdir(\"current_ecg\"))>0: # se ci sono file nella cartella, eliminali\n",
"\n",
" try:\n",
" for filename in os.listdir(\"current_ecg\"):\n",
" file_path = os.path.join(\"current_ecg\", filename)\n",
" if os.path.isfile(file_path):\n",
" os.remove(file_path)\n",
" print(f\"I file nella cartella 'current_ecg' sono stati eliminati.\")\n",
"\n",
" except Exception as e:\n",
" print(f\"Errore nell'eliminazione dei file: {str(e)}\")\n",
"\n",
"\n",
"\n",
" df = pd.read_csv(file.name,header=None) #file.name è il path temporaneo del file caricato\n",
"\n",
"\n",
" source_directory = os.path.dirname(file.name) # Replace with the source directory path\n",
" destination_directory = 'current_ecg' # Replace with the destination directory path\n",
"\n",
"\n",
" # Specify the filename (including the extension) of the CSV file you want to copy\n",
" file_to_copy = os.path.basename(file.name) # Replace with the actual filename\n",
"\n",
"\n",
" # Construct the full source and destination file paths\n",
" source_file_path = f\"{source_directory}/{file_to_copy}\"\n",
" destination_file_path = f\"{destination_directory}/{file_to_copy}\"\n",
"\n",
" # Copy the file from the source directory to the destination directory\n",
" shutil.copy(source_file_path, destination_file_path)\n",
"\n",
"\n",
" return \"Your ECG is ready, you can analyze it!\"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"def ecg_availability(patient_name):\n",
"\n",
" folder_path = os.path.join(\"PATIENT\",patient_name)\n",
" status_file_path = os.path.join(folder_path, \"status.json\")\n",
"\n",
" # Check if the \"status.json\" file exists\n",
" if not os.path.isfile(status_file_path):\n",
" return None # If the file doesn't exist, return None\n",
"\n",
" # Load the JSON data from the \"status.json\" file\n",
" with open(status_file_path, 'r') as status_file:\n",
" status_data = json.load(status_file)\n",
"\n",
" # Extract the last datetime from the status JSON (if available)\n",
" last_datetime_str = status_data.get(\"last_datetime\", None)\n",
"\n",
" # Get the list of CSV files in the folder\n",
" csv_files = [f for f in os.listdir(folder_path) if f.endswith(\".csv\")]\n",
"\n",
" if last_datetime_str is None:\n",
" return f\"New ECG available\" # If the JSON is empty, return all CSV files\n",
"\n",
" last_datetime = datetime.strptime(last_datetime_str, \"%B_%d_%H_%M_%S\")\n",
"\n",
" # Find successive CSV files\n",
" successive_csv_files = []\n",
" for csv_file in csv_files:\n",
" csv_datetime_str = csv_file.split('.')[0]\n",
" csv_datetime = datetime.strptime(csv_datetime_str, \"%B_%d_%H_%M_%S\")\n",
"\n",
" # Check if the CSV datetime is successive to the last saved datetime\n",
" if csv_datetime > last_datetime:\n",
" successive_csv_files.append(csv_file)\n",
"\n",
" if len(successive_csv_file)>0:\n",
" return f\"New ECG available (last ECG: {last_datetime})\"\n",
"\n",
" else:\n",
" return f\"No ECG available (last ECG: {last_datetime})\"\n",
"\n",
"\n",
"\n",
"\n",
"def ecg_analysis():\n",
"\n",
" df = pd.read_csv(os.path.join(\"current_ecg\",os.listdir(\"current_ecg\")[0]))\n",
"\n",
"\n",
" df_ecg = df[[str(i) for i in range(140)]] #ecg data columns\n",
" df_data = df_ecg.values #raw data. shape: (n_rows , 140)\n",
" df_data = (df_data - min_val) / (max_val - min_val)\n",
" df_data = tf.cast(df_data, tf.float32) #raw data. shape: (n_rows , 140)\n",
"\n",
"\n",
" df_tree = df[[\"ChestPainType\",\"ST_Slope\"]].copy() #dataset for decision tree\n",
"\n",
" df_llm = df[[\"Age\",\"Sex\",\"ChestPainType\",\"RestingBP\",\"Cholesterol\",\"FastingBS\",\"RestingECG\",\"MaxHR\",\"ExerciseAngina\",\"Oldpeak\",\"ST_Slope\"]].copy() # dataset for LLM\n",
"\n",
" true_label = df.values[:,-1]\n",
"\n",
" # ----------------ECG ANALYSIS WITH AUTOENCODER-------------------------------\n",
" heartbeat_encoder_preds = autoencoder.encoder(df_data).numpy() #encoder data representation. shape: (n_rows , 8)\n",
" heartbeat_decoder_preds = autoencoder.decoder(heartbeat_encoder_preds).numpy() #decoder data reconstruction. shape: (n_rows , 140)\n",
"\n",
" classification_res = classifier.predict(df_data) #shape: (n_rows , 1)\n",
"\n",
"\n",
" print(\"shapes of: encoder preds, decoder preds, classification preds/n\",heartbeat_encoder_preds.shape,heartbeat_decoder_preds.shape,classification_res.shape)\n",
"\n",
" #heartbeat_indexes = [i for i, pred in enumerate(classification_res) if pred == 0]\n",
"\n",
" p_encoder_preds = heartbeat_encoder_preds[0,:] #encoder representation of the chosen row\n",
" p_decoder_preds = heartbeat_decoder_preds[0,:] #decoder reconstruction of the chosen row\n",
" p_class_res = classification_res[0,:] # classification res of the chosen row\n",
" p_true = true_label[0]\n",
"\n",
"\n",
"\n",
"\n",
" #LATENT SPACE PLOT\n",
"\n",
" # Create the scatter plot\n",
" fig = px.scatter(df_compressed, x='Feature1', y='Feature2', color='target', color_discrete_map={0: 'red', 1: 'blue'},\n",
" labels={'Target': 'Binary Target'},size_max=18)\n",
"\n",
"\n",
" # Disable hover information\n",
" # fig.update_traces(mode=\"markers\",\n",
" # hovertemplate = None,\n",
" # hoverinfo = \"skip\")\n",
"\n",
" # Customize the plot layout\n",
" fig.update_layout(\n",
" title='Latent space 2D (PCA reduction)',\n",
" xaxis_title='component 1',\n",
" yaxis_title='component 2'\n",
" )\n",
"\n",
" # add new point\n",
" new_point_compressed = pca.transform(p_encoder_preds.reshape(1,-1))\n",
"\n",
" new_point = {'X':[new_point_compressed[0][0]] , 'Y':[new_point_compressed[0][1]] } # Target value 2 for the new point\n",
"\n",
" new_point_df = pd.DataFrame(new_point)\n",
"\n",
" #fig.add_trace(px.scatter(new_point_df, x='X', y='Y').data[0])\n",
" fig.add_trace(go.Scatter(\n",
" x=new_point_df['X'],\n",
" y=new_point_df['Y'],\n",
" mode='markers',\n",
" marker=dict(symbol='star', color='black', size=15),\n",
" name='actual patient'\n",
" ))\n",
"\n",
" d = fig.to_dict()\n",
" d[\"data\"][0][\"type\"] = \"scatter\"\n",
"\n",
" fig=go.Figure(d)\n",
"\n",
"\n",
"\n",
" # DECODER RECONSTRUCTION PLOT\n",
"\n",
" fig_reconstruction = plt.figure(figsize=(10,8))\n",
" sns.set(font_scale = 2)\n",
" sns.set_style(\"white\")\n",
" plt.plot(df_data[0], 'black',linewidth=2)\n",
" plt.plot(heartbeat_decoder_preds[0], 'red',linewidth=2)\n",
" plt.fill_between(np.arange(140), heartbeat_decoder_preds[0], df_data[0], color='lightcoral')\n",
" plt.legend(labels=[\"Input\", \"Reconstruction\", \"Error\"])\n",
"\n",
" #classification probability\n",
"\n",
" # ----------DECISION TREE ANALYSIS---------------------------------\n",
"\n",
"\n",
" # Define the desired column order\n",
" 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\n",
"\n",
" X_plot = pd.DataFrame(columns=encoded_features)\n",
"\n",
" for k in range(len(df_tree['ST_Slope'])):\n",
" X_plot.loc[k] = 0\n",
" if df_tree['ST_Slope'][k] == 'Up':\n",
" X_plot['ST_Slope_Up'][k] = 1\n",
" if df_tree['ST_Slope'][k] == 'Flat':\n",
" X_plot['ST_Slope_Flat'][k] = 1\n",
" if df_tree['ST_Slope'][k] == 'Down':\n",
" X_plot['ST_Slope_Down'][k] = 1\n",
" if df_tree['ChestPainType'][k] == 'ASY':\n",
" X_plot['ChestPainType_ASY'][k] = 1\n",
" if df_tree['ChestPainType'][k] == 'ATA':\n",
" X_plot['ChestPainType_ATA'][k] = 1\n",
" if df_tree['ChestPainType'][k] == 'NAP':\n",
" X_plot['ChestPainType_NAP'][k] = 1\n",
" if df_tree['ChestPainType'][k] == 'TA':\n",
" X_plot['ChestPainType_TA'][k] = 1\n",
"\n",
"\n",
" #model prediction\n",
" y_score = decision_tree.predict_proba(X_plot)[:,1]\n",
"\n",
" chest_pain = []\n",
" slop = []\n",
"\n",
" for k in range(len(X_plot)):\n",
" 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:\n",
" chest_pain.append(0)\n",
" 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:\n",
" chest_pain.append(1)\n",
" 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:\n",
" chest_pain.append(2)\n",
" 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:\n",
" chest_pain.append(3)\n",
" if X_plot['ST_Slope_Up'][k] == 1 and X_plot['ST_Slope_Flat'][k] == 0 and X_plot['ST_Slope_Down'][k] == 0:\n",
" slop.append(0)\n",
" if X_plot['ST_Slope_Up'][k] == 0 and X_plot['ST_Slope_Flat'][k] == 1 and X_plot['ST_Slope_Down'][k] == 0:\n",
" slop.append(1)\n",
" if X_plot['ST_Slope_Up'][k] == 0 and X_plot['ST_Slope_Flat'][k] == 0 and X_plot['ST_Slope_Down'][k] == 1:\n",
" slop.append(2)\n",
"\n",
"\n",
" # Create a structured grid\n",
" fig_tree = plt.figure()\n",
" x1 = np.linspace(df_plot['ST_Slope'].min()-0.5, df_plot['ST_Slope'].max()+0.5)\n",
" x2 = np.linspace(df_plot['ChestPainType'].min()-0.5, df_plot['ChestPainType'].max()+0.5)\n",
" X1, X2 = np.meshgrid(x1, x2)\n",
"\n",
" # Interpolate the 'Prob' values onto the grid\n",
" points = df_plot[['ST_Slope', 'ChestPainType']].values\n",
" values = df_plot['Prob'].values\n",
" Z = griddata(points, values, (X1, X2), method='nearest')\n",
"\n",
" # Create the contour plot with regions colored by interpolated 'Prob'\n",
" plt.contourf(X1, X2, Z, cmap='coolwarm', levels=10)\n",
" plt.colorbar(label='Predicted Probability')\n",
"\n",
" # Add data points if needed\n",
" plt.scatter(slop[:1], chest_pain[:1], c=\"k\", cmap='coolwarm', edgecolor='k', marker='o', label=f'prob={y_score[:1].round(3)}')\n",
"\n",
" # Remove the numerical labels from the x and y axes\n",
" plt.xticks([])\n",
" plt.yticks([])\n",
"\n",
" # Add custom labels \"0\" and \"1\" near the center of the axis\n",
" plt.text(0.0, -0.7, \"Up\", ha='center',fontsize=15)\n",
" plt.text(1.00, -0.7, \"Flat\", ha='center',fontsize=15)\n",
" plt.text(2.00, -0.7, \"Down\", ha='center',fontsize=15)\n",
" plt.text(-0.62, 0.0, \"ASY\", rotation='vertical', va='center',fontsize=15)\n",
" plt.text(-0.62, 1.00, \"ATA\", rotation='vertical', va='center',fontsize=15)\n",
" plt.text(-0.62, 2.0, \"NAP\", rotation='vertical', va='center',fontsize=15)\n",
" plt.text(-0.62, 3.0, \"TA\", rotation='vertical', va='center',fontsize=15)\n",
"\n",
" # Add labels and title\n",
" plt.xlabel('ST_Slope', fontsize=15, labelpad=20)\n",
" plt.ylabel('ChestPainType', fontsize=15, labelpad=20)\n",
" #plt.legend()\n",
"\n",
"\n",
"\n",
" # ------------LLM ANALYSIS------------------------------------\n",
"\n",
" df_llm_encoding = df_encoding(df_llm)\n",
" df_point_LLM = LLM_transform(df_llm_encoding)\n",
"\n",
" df_point_LLM.columns = [str(column) for column in df_point_LLM.columns]\n",
"\n",
" pca_llm_point = pca_2d_llm_clusters.transform(df_point_LLM)\n",
" pca_llm_point.columns = [\"comp1\", \"comp2\"]\n",
"\n",
"\n",
" #clusters\n",
"\n",
" fig_llm_cluster = plt.figure()\n",
" x = df_pca_llm['comp1']\n",
" y = df_pca_llm['comp2']\n",
"\n",
" labels = ['Cluster 0', 'Cluster 1', 'Cluster 2', 'Cluster 3']\n",
"\n",
" # Create a dictionary to map 'RestingECG' values to colors\n",
" color_mapping = {0: 'r', 1: 'b', 2: 'g', 3: 'y'}\n",
"\n",
" for i in df_pca_llm['cluster'].unique():\n",
" color = color_mapping.get(i, 'k') # Use 'k' (black) for undefined values\n",
" plt.scatter(x[df_pca_llm['cluster'] == i], y[df_pca_llm['cluster'] == i], c=color, label=labels[i])\n",
"\n",
" plt.scatter(pca_llm_point['comp1'], pca_llm_point['comp1'], c='k', marker='D')\n",
"\n",
" # Remove the numerical labels from the x and y axes\n",
" plt.xticks([])\n",
" plt.yticks([])\n",
"\n",
" plt.xlabel('Principal Component 1')\n",
" plt.ylabel('Principal Component 2')\n",
" plt.legend()\n",
" plt.grid(False)\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
" 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\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"#demo app\n",
"\n",
"with gr.Blocks(title=\"TIQUE - AI DEMO CAPABILITIES\") as demo:\n",
"\n",
" gr.Markdown(\"<h1><center>TIQUE: AI DEMO CAPABILITIES<center><h1>\")\n",
"\n",
"\n",
" with gr.Row():\n",
"\n",
" pazienti = [\"Elisabeth Smith\",\"Michael Mims\"]\n",
" menu_pazienti = gr.Dropdown(choices=pazienti,label=\"patients\")\n",
"\n",
" available_ecg_result = gr.Textbox()\n",
"\n",
"\n",
" menu_pazienti.input(ecg_availability, inputs=[menu_pazienti], outputs=[available_ecg_result])\n",
"\n",
" with gr.Row():\n",
"\n",
" input_file = gr.UploadButton(\"Click to Upload an ECG 📁\")\n",
" text_upload_results = gr.Textbox()\n",
"\n",
" input_file.upload(upload_ecg,inputs=[input_file],outputs=text_upload_results)\n",
"\n",
" with gr.Row():\n",
" ecg_start_analysis_button = gr.Button(value=\"Start ECG analysis\",scale=1)\n",
"\n",
"\n",
" gr.Markdown(\"## Large Language Model clustering\")\n",
"\n",
" with gr.Row():\n",
"\n",
" llm_cluster = gr.Plot()\n",
"\n",
"\n",
" gr.Markdown(\"## Autoencoder results:\")\n",
"\n",
" with gr.Row():\n",
"\n",
" with gr.Column():\n",
"\n",
" latent_space_representation = gr.Plot()\n",
"\n",
" with gr.Column():\n",
"\n",
" autoencoder_ecg_reconstruction = gr.Plot()\n",
"\n",
" classifier_nn_prediction = gr.Textbox()\n",
"\n",
" gr.Markdown(\"## Decision Tree results:\")\n",
"\n",
" with gr.Row():\n",
"\n",
" decision_tree_plot = gr.Plot()\n",
"\n",
" decision_tree_proba = gr.Textbox()\n",
"\n",
"\n",
"\n",
"\n",
" ecg_start_analysis_button.click(fn=ecg_analysis, inputs=None, outputs=[latent_space_representation,\n",
" autoencoder_ecg_reconstruction,\n",
" classifier_nn_prediction,decision_tree_plot, decision_tree_proba,\n",
" llm_cluster])\n",
"if __name__ == \"__main__\":\n",
" demo.launch()\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n"
],
"metadata": {
"id": "bVSujh5-677-"
},
"execution_count": null,
"outputs": []
}
]
} |