Update app.py

app.py

@@ -4,6 +4,7 @@ import streamlit as st
 import pandas as pd
 import numpy as np
 from prediction import predict
+from function import filter_dataframe
 from sklearn.datasets import load_iris
 from ydata_profiling.utils.cache import cache_file
 
@@ -16,9 +17,9 @@ setosa, versicolor, virginica')
 st.image('https://machinelearninghd.com/wp-content/uploads/2021/03/iris-dataset.png')
 st.image('https://www.integratedots.com/wp-content/uploads/2019/06/iris_petal-sepal-e1560211020463.png')
 
-# Load Dataset
-#iris = load_iris(as_frame=True)
+st.write("---")
 
+# Load Dataset
 @st.cache_data
 def load_data(url):
     df = pd.read_csv(url)
@@ -31,14 +32,17 @@ iris = cache_file(
 
 df = load_data(iris)
 
-# Create a DataFrame from the iris data
-#df = pd.DataFrame(iris.data, columns=iris.feature_names)
+if st.checkbox('Open Iris Dataset'):
+  fd = filter_dataframe(df)
+  st.dataframe(fd, use_container_width=True)
+
+st.write("---")
 
-# Add a target column to the DataFrame
-#df['Target'] = iris['target']
+if st.checkbox('Open EDA Report'):
+  pr = ProfileReport(df)
+  st_profile_report(pr)
 
-# Translate the target
-#df['Target'] = df['Target'].apply(lambda x: iris['target_names'][x])
+st.write("---")
 
 st.header('Plant Features')
 col1, col2 = st.columns(2)
@@ -54,12 +58,4 @@ with col2:
 
 if st.button('Predict type of Iris'):
   result = predict(np.array([[sepal_l, sepal_w, petal_l, petal_w]]))
-  st.text(result[0])
-
-st.write("---")
-if st.checkbox("Sample preview the Iris Dataset"):
-    #st.write(df.sample(10))  # Same as st.write(df)
-    pr = ProfileReport(df,title="Dataset Report")
-    st_profile_report(pr)
-
-st.write("---")
+  st.text(result[0])