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streamlit_app.py
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streamlit_app.py
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import streamlit as st
import pandas as pd
from scipy.io import loadmat
import plotly.express as px
import utils
import tensorflow as tf
from tensorflow.keras.layers import (
Input,
Conv1D,
BatchNormalization,
Activation,
GlobalAveragePooling1D,
Dense,
)
from tensorflow.keras.models import Model
from sklearn.model_selection import train_test_split
from tensorflow.keras.callbacks import EarlyStopping
from sklearn.model_selection import StratifiedKFold, KFold
from tensorflow.keras.models import load_model
# Title
st.title("Cardio Scan Pro")
st.write("Welcome to Cardio Scan Pro. A tool to help you diagnose heart diseases.")
st.write("Please upload the ECG recording and header files below.")
# anomalies df
anomalies_df = pd.read_csv("Dx_map.csv")
details_df = pd.read_csv("Dx_map_with_details.csv")
# Function definitions
def read_hea_file(file):
header_info = file.readlines()
return header_info
# Table sketch
# File Uploaders
# collecting mat file from user
mat_file = st.file_uploader(
"ECG Recording file",
type=["mat"],
accept_multiple_files=False,
key=None,
help=None,
on_change=None,
args=None,
kwargs=None,
disabled=False,
label_visibility="visible",
)
# collecting hea file from user
hea_file = st.file_uploader(
"ECG Header file",
type=["hea"],
accept_multiple_files=False,
key=None,
help=None,
on_change=None,
args=None,
kwargs=None,
disabled=False,
label_visibility="visible",
)
# read the file and convert to dataframe
if mat_file is not None and hea_file is not None:
data = loadmat(mat_file)
df = pd.DataFrame(data["val"])
# st.write(df)
array = data["val"]
# the data contains 12 time series, each with 7500 data points
# plot the first 1000 data points of the first time series
st.write("First 1000 data points of Lead 1")
fig = px.line(df.iloc[0, 0:1000])
st.plotly_chart(fig)
st.write("First 1000 data points of Lead 2")
fig = px.line(df.iloc[1, 0:1000])
st.plotly_chart(fig)
st.write("First 1000 data points of Lead 3")
fig = px.line(df.iloc[2, 0:1000])
st.plotly_chart(fig)
st.write("First 1000 data points of Lead 4")
fig = px.line(df.iloc[3, 0:1000])
st.plotly_chart(fig)
st.write("First 1000 data points of Lead 5")
fig = px.line(df.iloc[4, 0:1000])
st.plotly_chart(fig)
st.write("First 1000 data points of Lead 6")
fig = px.line(df.iloc[5, 0:1000])
st.plotly_chart(fig)
st.write("First 1000 data points of Lead 7")
fig = px.line(df.iloc[6, 0:1000])
st.plotly_chart(fig)
st.write("First 1000 data points of Lead 8")
fig = px.line(df.iloc[7, 0:1000])
st.plotly_chart(fig)
st.write("First 1000 data points of Lead 9")
fig = px.line(df.iloc[8, 0:1000])
st.plotly_chart(fig)
st.write("First 1000 data points of Lead 10")
fig = px.line(df.iloc[9, 0:1000])
st.plotly_chart(fig)
st.write("First 1000 data points of Lead 11")
fig = px.line(df.iloc[10, 0:1000])
st.plotly_chart(fig)
st.write("First 1000 data points of Lead 12")
fig = px.line(df.iloc[11, 0:1000])
st.plotly_chart(fig)
# read the header file and print data
# st.write("Header file data")
header_info = read_hea_file(hea_file)
freq = int(header_info[0].split()[2])
age = int(header_info[13].split()[-1])
sex = str(header_info[14].split()[-1])
sex = sex[2:-1]
# st.write(header_info)
model = load_model("CardioScanPro_model_light_weight.h5")
processed_array = utils.process_input(array, freq)
res1 = model.predict(processed_array)
df_probs = utils.get_best_(list(res1[0]), anomalies_df)
# Patient details
output = "The patient is a " + str(age) + " year old " + sex
# Result heading
st.header("Results of the ECG scan")
# Display the table
st.table(df_probs)
# Insights
st.header("Insights")
st.write(output)
st.write(
"The patient is most likely suffering from suffering from "
+ str(df_probs.iloc[0, 0])
+ " ("
+ str(int(df_probs.iloc[0, 1]))
+ "/"
+ str(df_probs.iloc[0, 2])
+ ")"
+ "."
+ " The exact probability is about "
+ str(round((df_probs.iloc[0, 3] * 100), 2))
+ "%"
)
# Details
st.header("Details about the anomaly")
abbreviated_name = str(df_probs.iloc[0, 2])
details = details_df[details_df["Abbreviation"] == abbreviated_name]
st.write("Here are some additional detials about " + str(details.iloc[0, 0]))
st.write(details.iloc[0, 3])
st.write("For more detials visit " + str(details.iloc[0, 4]))
st.write("Made with ❤️ by Team 4")
st.write("© 2023 Team 4. All rights reserved.")
st.write("Amila, Isuru, Sulakshi")