Upload 9 files

app.py

@@ -0,0 +1,92 @@
+import streamlit as st # type: ignore
+import numpy as np
+import pandas as pd
+import seaborn as sns # type: ignore
+import matplotlib.pyplot as plt
+import base64
+import pickle
+import time
+
+@st.cache_data
+def load_data(dataset):
+    df = pd.read_csv(dataset)
+    return df
+def filedownload(df):
+    csv = df.to_csv(index=False)
+    b64 = base64.b64encode(csv.encode()).decode()  # strings <-> bytes conversions
+    href = f'<a href="data:file/csv;base64,{b64}" download="diabete_predictions.csv">Download CSV File</a>'
+    href2 = f'<a href="https://huggingface.co/spaces/Jasonntone/Brain_Stroke"></a>'
+    return href,href2
+data = load_data('dataset/brain_stroke.csv')
+meanGlucose = data['avg_glucose_level'].mean()
+meanBmi = data['bmi'].mean()
+meanAge = data['age'].mean()
+st.sidebar.image('images/ahat.png',width=280)
+
+def main():
+    st.markdown("<h1 style='text-align:center;color: red;'>Streamlit Brain Stroke Prediction App</h1>",unsafe_allow_html=True)
+    st.markdown("<h2 style='text-align:center;color: grey;'>Brain Stroke study in USA</h2>",unsafe_allow_html=True)
+    menu = ['Home','Analysis','Data Visualization','Machine Learning']
+    choice = st.sidebar.selectbox('Select Menu', menu)
+    if choice == 'Home':
+        left,middle,right = st.columns((2,3,2))
+        with middle:
+            col4, col5, col6 = st.columns(3)
+            with col4:
+                temp = st.metric(label="Average Glucose Level", value=meanGlucose, delta="From 5010 To 2012")
+            with col5:
+                temp = st.metric(label="Average BMI", value=meanBmi, delta="From 2010 To 2012")
+            with col6:
+                temp = st.metric(label="Average Age", value=meanAge, delta="From 2010 To 2012")
+            st.image('images/ahat.png',width=280)
+    if choice == 'Analysis':
+        st.subheader('Brain Stroke Dataset')
+        st.write(data.head())
+        if st.checkbox('Summary'):
+            st.write(data.describe())
+        elif st.checkbox('Correlation'):
+            fig = plt.figure(figsize=(15,5))
+            st.write(sns.heatmap(data.corr(),annot=True))
+            st.pyplot(fig)
+    elif choice == 'Data Visualization':
+        if st.checkbox('Countplot'):
+            fig = plt.figure(figsize=(15,5))
+            sns.countplot(x='age',data=data)
+            st.pyplot(fig)
+        elif st.checkbox('Scatterplot'):
+            fig = plt.figure(figsize=(15,5))
+            sns.scatterplot(x='avg_glucose_level',y='age',data=data,hue='stroke')
+            st.pyplot(fig)
+    elif choice == 'Machine Learning':
+        tab1, tab2, tab3 = st.tabs([":clipboard: Data",":bar_chart:✅ Visualisation", "📈🎯 Prediction"])
+        uploaded_file = st.sidebar.file_uploader('Upload your Dataset(.csv file)',
+                                                 type=['csv'])
+        if uploaded_file:
+            df = load_data(uploaded_file)
+            with tab1:
+                st.subheader('Loaded Dataset')
+                st.write(df)
+            with tab2:
+                st.subheader("Heart Disease's Histogram")
+                fig = plt.figure(figsize=(8,8))
+                sns.histplot(x='heart_disease',data=data)
+                st.pyplot(fig)
+            with tab3:
+                model = pickle.load(open('./models/gbc_dump.pkl', 'rb'))  # Load the trained model from disk
+                prediction = model.predict(df)
+                pp = pd.DataFrame(prediction, columns=['Prediction'])
+                ndf = pd.concat([df, pp], axis=1)
+                ndf['Prediction'].replace(0, 'No Stroke Risk', inplace=True)
+                ndf['Prediction'].replace(1, 'Stroke Risk', inplace=True)
+                st.header("📈🎯 Stroke Risk Prediction")
+                st.subheader("Predictions")
+                st.write(ndf)
+    
+                if st.button(' 💾 Download csv file'):
+                    st.markdown(filedownload(ndf), unsafe_allow_html=True)
+                
+
+
+
+if __name__ == '__main__':
+    main()

models/ada_dump.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:65e1911a9f5efcdc68af77ffc1d7fd18b05501d40d6167eae0610a31631fe18e
+size 28671

models/gbc_dump.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:18713af9f1cd67969b431b0626850fd8982a629795d746ad4a3a9eb806b05beb
+size 136292

models/rf_dump.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b94fc17908bdf1f7d63b6e9dc91a4175bca025b1fba1da23cdd90505ae147999
+size 5636386