Update app.py

app.py

@@ -1,74 +1,74 @@
-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')
-# Display shopping tips
-shopping_tips = """
-### Shopping Tips:
-- Tip 1: Consider the 4Cs: Cut, Color, Clarity, and Carat weight when choosing a diamond.
-- Tip 2: Use filters like price range, carat range,cut, color and clarity to narrow down options and find the perfect diamond.
-  
-  Wishing you a joyful shopping experience! Find your dream diamond and make it a cherished symbol of love and happiness.
-"""
-st.sidebar.markdown(shopping_tips)
-
-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, max_price = st.slider('Select Price Range', int(df['price'].min()), int(df['price'].max()), (int(df['price'].min()), int(df['price'].max())))
-# users set the carat
-min_carat, max_carat = st.slider('Select Carat Range', float(df['carat'].min()), float(df['carat'].max()), (float(df['carat'].min()), float(df['carat'].max())))
-#users select
-selected_cuts = st.multiselect('choosing cut (Fair, Good, Very Good, Premium, Ideal)', df['cut'].unique())
-selected_colors = st.multiselect('choosing color, J (worst), I, H, G, F, E, D (best)', df['color'].unique())
-# Display diamond color image
-st.image('color.png', caption='Diamond Color', use_column_width=True)
-
-selected_clarities = st.multiselect('choosing clarity, I1 (worst), SI2, SI1, VS2, VS1, VVS2, VVS1, IF (best)', df['clarity'].unique())
-# Display diamond cut image
-st.image('cut.png', caption='Diamond Clarity', use_column_width=True)
-
-# Check if any cuts are selected, if not, consider all cuts
-if not selected_cuts:
-    selected_cuts = df['cut'].unique()
-# Check if any colors are selected, if not, consider all colors
-if not selected_colors:
-    selected_colors = df['color'].unique()
-# Check if any clarities are selected, if not, consider all clarities
-if not selected_clarities:
-    selected_clarities = df['clarity'].unique()
-# Filter diamonds based on selected criteria
-filtered_diamonds = df[(df['price'] >= min_price) & (df['price'] <= max_price) &
-                       (df['carat'] >= min_carat) & (df['carat'] <= max_carat) &
-                       (df['cut'].isin(selected_cuts)) &
-                       (df['color'].isin(selected_colors)) &
-                       (df['clarity'].isin(selected_clarities))]
-# 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)
-# Recommended top ten diamonds
-    st.write("Top ten diamonds with the best value for money:")
-    recommended_diamonds = filtered_diamonds.nsmallest(10, 'price')
-    st.dataframe(recommended_diamonds)
-
-else:
+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')
+# Display shopping tips
+shopping_tips = """
+### Shopping Tips:
+- Tip 1: Consider the 4Cs: Cut, Color, Clarity, and Carat weight when choosing a diamond.
+- Tip 2: Use filters like price range, carat range,cut, color and clarity to narrow down options and find the perfect diamond.
+  
+  Wishing you a joyful shopping experience! Find your dream diamond and make it a cherished symbol of love and happiness.
+"""
+st.sidebar.markdown(shopping_tips)
+
+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, max_price = st.slider('Select Price Range', int(df['price'].min()), int(df['price'].max()), (int(df['price'].min()), int(df['price'].max())))
+# users set the carat
+min_carat, max_carat = st.slider('Select Carat Range', float(df['carat'].min()), float(df['carat'].max()), (float(df['carat'].min()), float(df['carat'].max())))
+#users select
+selected_cuts = st.multiselect('choosing cut (Fair, Good, Very Good, Premium, Ideal)', df['cut'].unique())
+selected_colors = st.multiselect('choosing color, J (worst), I, H, G, F, E, D (best)', df['color'].unique())
+# Display diamond color image
+st.image('color.png', caption='Diamond Color', use_column_width=True)
+
+selected_clarities = st.multiselect('choosing clarity, I1 (worst), SI2, SI1, VS2, VS1, VVS2, VVS1, IF (best)', df['clarity'].unique())
+# Display diamond clarity image
+st.image('clarity.png', caption='Diamond Clarity', use_column_width=True)
+
+# Check if any cuts are selected, if not, consider all cuts
+if not selected_cuts:
+    selected_cuts = df['cut'].unique()
+# Check if any colors are selected, if not, consider all colors
+if not selected_colors:
+    selected_colors = df['color'].unique()
+# Check if any clarities are selected, if not, consider all clarities
+if not selected_clarities:
+    selected_clarities = df['clarity'].unique()
+# Filter diamonds based on selected criteria
+filtered_diamonds = df[(df['price'] >= min_price) & (df['price'] <= max_price) &
+                       (df['carat'] >= min_carat) & (df['carat'] <= max_carat) &
+                       (df['cut'].isin(selected_cuts)) &
+                       (df['color'].isin(selected_colors)) &
+                       (df['clarity'].isin(selected_clarities))]
+# 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)
+# Recommended top ten diamonds
+    st.write("Top ten diamonds with the best value for money:")
+    recommended_diamonds = filtered_diamonds.nsmallest(10, 'price')
+    st.dataframe(recommended_diamonds)
+
+else:
     st.write("No diamonds fit you")