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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) |