Create app.py

app.py

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+import streamlit as st
+import plotly.express as px
+import pandas as pd
+
+
+
+st.set_page_config(
+    page_title = 'Streamlit Sample Dashboard Template',
+    page_icon = '✅',
+    layout = 'wide'
+)
+
+
+pie_new_color_discrete_sequence = [ 'royalblue', 'tomato', 'gold']
+bar_new_color_discrete_sequence = [ ' royalblue', 'royalblue', 'tomato', 'gold']
+
+
+def rating_to_sentiment(rating: float):
+    if rating >= 4:
+        sentiment = 'positive'
+    elif rating == 3:
+        sentiment = 'neutral'
+    elif rating <= 2:
+        sentiment = 'negative'
+    
+    return sentiment
+
+
+s = {'a': "rgb(235, 69, 95)", 
+     'b': "rgb(255, 184, 76)",
+     'c':'rgb(43, 52, 103)'}
+
+color_map = {'positive' : "royalblue",
+                      'neutral': 'gold',
+                      'negative': 'tomato'}
+
+
+df = pd.read_csv('20230220_selected_df.csv',
+                 index_col=0)
+
+st.write("""
+         # Distribution of topics discussed from *Trustadvisor.com* on **Carrefour**
+         """)
+
+clean_superclass = ['clean_BE', 'clean_PD', 'clean_DM', 'clean_AS']
+group_df  = df.loc[: , ['ratings'] + clean_superclass ]
+group_df['sentiment'] = group_df['ratings'].apply(lambda x: rating_to_sentiment(x))
+group_df['topic_count'] = group_df.iloc[ :, 1:5].sum(axis= 1)
+heamap_data = group_df.groupby('sentiment').sum().reset_index().iloc[: , 2:6].to_numpy()
+
+
+pie_fig = px.pie(data_frame= group_df,
+       names = group_df.sentiment,
+       color= 'sentiment',
+       color_discrete_map = color_map,
+       category_orders = {"sentiment": ['positive' ,'neutral' 'negative']},
+       hole= 0.5)
+
+pie_fig.update_layout(legend=dict(
+    orientation="h",
+    yanchor="middle",
+    y= 1.15,
+    xanchor="center",
+    x= 0.5
+))
+
+
+bar_fig = px.histogram(data_frame=group_df,
+             x = 'topic_count',
+             color= 'sentiment',
+             color_discrete_map = color_map,
+             text_auto =True,
+             category_orders = {"sentiment": ['positive' ,'negative' 'neutral']})
+            #  color_discrete_sequence = bar_new_color_discrete_sequence,
+            
+
+heatmap_fig = px.imshow(heamap_data,
+                        labels=dict(x="4 Super Classes", y="Sentiment"),
+                        x=['Buying Experience', 'Product', 'Delivery', 'After Sales'],
+                        y=['Negative', 'Neutral', 'Positive'],
+                        color_continuous_scale=['royalblue', 'gold', 'tomato'],
+                        text_auto=True)
+
+
+
+class_1_fig = px.pie(data_frame= group_df[group_df['clean_BE'] == 1],
+       names = group_df[group_df['clean_BE'] == 1].sentiment,
+       color = 'sentiment',
+       color_discrete_map = color_map,
+       category_orders = {"sentiment": ['positive' ,'negative' 'neutral']},
+       hole= 0.5)
+
+class_2_fig = px.pie(data_frame= group_df[group_df['clean_PD'] == 1],
+       names = group_df[group_df['clean_PD'] == 1].sentiment,
+       color = 'sentiment',
+       color_discrete_map = color_map,
+       category_orders = {"sentiment": ['positive' ,'negative' 'neutral']},
+       hole= 0.5)
+
+class_3_fig = px.pie(data_frame= group_df[group_df['clean_DM'] == 1],
+       names = group_df[group_df['clean_DM'] == 1].sentiment,
+       color = 'sentiment',
+       color_discrete_map = color_map,
+       category_orders = {"sentiment": ['positive' ,'negative' 'neutral']},
+       hole= 0.5)
+
+class_4_fig = px.pie(data_frame= group_df[group_df['clean_AS'] == 1],
+       names = group_df[group_df['clean_AS'] == 1].sentiment,
+       color = 'sentiment',
+       color_discrete_map = color_map,
+       category_orders = {"sentiment": ['positive' ,'negative' 'neutral']},
+       hole= 0.5)
+
+
+
+
+kpi1, kpi2, kpi3 = st.columns(3)
+
+with kpi1:
+    st.markdown("**All reviewsf**")
+    st.plotly_chart(pie_fig, use_container_width=True)
+
+
+with kpi2:
+    
+    with st.expander("Sentiment Count"):
+        st.dataframe(data=group_df['sentiment'].value_counts().rename_axis('unique_values').reset_index(name='counts'),
+             use_container_width=True)
+    st.plotly_chart(heatmap_fig, use_container_width=True)
+    
+with kpi3:
+    st.markdown("Looking at the how many topics each review is talking about")
+    st.plotly_chart(bar_fig, use_container_width=True)
+    
+
+st.markdown("<hr/>",unsafe_allow_html=True)
+
+
+st.markdown("## Distribution broken down into 4 super classes")
+
+class1, class2, class3, class4 = st.columns(4)
+
+with class1:
+    st.markdown("#### Buying Experience")
+    st.plotly_chart(class_1_fig, use_container_width=True)
+
+with class2:
+    st.markdown("#### Product")
+    st.plotly_chart(class_2_fig, use_container_width=True)
+    
+with class3:
+    st.markdown("#### Delivery Mode")
+    st.plotly_chart(class_3_fig, use_container_width=True)
+    
+with class4:
+    st.markdown("#### After Sales")
+    st.plotly_chart(class_4_fig, use_container_width=True)