Upload app.py

app.py

@@ -1,43 +1,49 @@
-import streamlit as st
-import pandas as pd
-import matplotlib.pyplot as plt
-import seaborn as sns
-
-# read the dataset
-df = pd.read_csv('diamonds.csv')
-
-st.title('Diamond APP sorting festivity')
-
-df = df.drop(["Unnamed: 0"], axis=1)
-
-#Potential diamonds with a impossible dimensions are dropped, for example a diamond that has a 0 in x, y or z should not be possible.
-df = df.drop(df[df["x"]==0].index)
-df = df.drop(df[df["y"]==0].index)
-df = df.drop(df[df["z"]==0].index)
-st.image('diamonds.jpg')
-st.write(f"the price range is {326} to {18823}")
-# users set the price range
-min_price = st.number_input('Input minimum price', min_value=int(df['price'].min()), max_value=int(df['price'].max()), value=int(df['price'].min()))
-max_price = st.number_input('Input maximum price', min_value=int(df['price'].min()), max_value=int(df['price'].max()), value=int(df['price'].max()))
-
-#users select
-selected_cuts = st.multiselect('choosing cut', df['cut'].unique())
-selected_cuts = st.multiselect('choosing color', df['color'].unique())
-selected_cuts = st.multiselect('choosing clarity', df['clarity'].unique())
-selected_cuts = st.multiselect('choosing carat', df['carat'].unique())
-
-
-# select the diamonds
-filtered_diamonds = df[(df['price'] >= min_price) & (df['price'] <= max_price)]
-
-# show the results
-if not filtered_diamonds.empty:
-    st.write(f"find {len(filtered_diamonds)} diamonds for you.")
-    st.dataframe(filtered_diamonds)
-# Create a scatterplot based on the selected criteria
-    fig, ax = plt.subplots()
-    sns.scatterplot(x='carat', y='price', data=filtered_diamonds, ax=ax)
-    ax.set_title('Prices by Carat')
-    st.pyplot(fig)
-else:
-    st.write("No diamonds fit you")
+import streamlit as st
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+df_diamonds = pd.read_csv('diamonds.csv')
+df_diamonds = df_diamonds.drop(["Unnamed: 0"], axis=1)
+
+st.title('Diamond selecting APP')
+
+# Users set the price range
+min_price = st.number_input('Input minimum price', min_value=int(df_diamonds['price'].min()), max_value=int(df_diamonds['price'].max()), value=int(df_diamonds['price'].min()))
+max_price = st.number_input('Input maximum price', min_value=min_price, max_value=int(df_diamonds['price'].max()), value=int(df_diamonds['price'].max()))
+
+# Select the diamonds
+cuts = st.multiselect('Select cut', df_diamonds['cut'].unique())
+colors = st.multiselect('Select color', df_diamonds['color'].unique())
+clarities = st.multiselect('Select clarity', df_diamonds['clarity'].unique())
+
+if st.button('Reset'):
+    cuts = []
+    colors = []
+    clarities = []
+    min_price = int(df_diamonds['price'].min())
+    max_price = int(df_diamonds['price'].max())
+
+# Filter the diamonds
+filtered_diamonds = df_diamonds[(df_diamonds['price'] >= min_price) & (df_diamonds['price'] <= max_price)]
+
+if cuts:
+    filtered_diamonds = filtered_diamonds[filtered_diamonds['cut'].isin(cuts)]
+if colors:
+    filtered_diamonds = filtered_diamonds[filtered_diamonds['color'].isin(colors)]
+if clarities:
+    filtered_diamonds = filtered_diamonds[filtered_diamonds['clarity'].isin(clarities)]
+
+# Check if there is data to plot
+if not filtered_diamonds.empty:
+    # Create a scatterplot based on the selected criteria
+    fig, ax = plt.subplots()
+    sns.scatterplot(x='carat', y='price', data=filtered_diamonds, ax=ax)
+    ax.set_title('Prices by Carat')
+    st.pyplot(fig)
+else:
+    st.write("No diamonds fit the selected criteria.")
+
+# Display a scrollable table with the total information based on the selected criteria
+st.write("Total Information:")
+st.dataframe(filtered_diamonds)