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import streamlit as st
import plotly.express as px
import plotly.graph_objects as go
import pandas as pd
from plotly.subplots import make_subplots
import plotly.graph_objects as go
from wordcloud import WordCloud
from underthesea import word_tokenize
import matplotlib.pyplot as plt
def sentence_topic_plot(result):
labels = ["Quality", "Serve", "Pack", "Shipping", "Price", "Other"][::-1]
values = result.detach().numpy()[0][::-1]
combined = {labels[i] : values[i] for i in range(len(labels))}
sorted_data = dict(sorted(combined.items(), key=lambda item: item[1]))
labels = list(sorted_data.keys())
values = list(sorted_data.values())
filtered_data = {key: value for key, value in combined.items() if value >= 0.5}
fig = go.Figure()
fig.add_trace(go.Bar(
y=labels,
x=values,
orientation='h'
))
fig.update_layout(xaxis_title="Probability", yaxis_title="Topics")
message = ", ".join(i for i in filtered_data.keys())
st.header(f"Your review is related to :blue[{message}]" )
st.plotly_chart(fig, use_container_width=True)
def KPI_card(name = "Total Reviews", value = 1000, box_color = (123,167,212), font_color = (0, 0, 0), icon = "fa-list"):
wch_colour_box = box_color
wch_colour_font = font_color
fontsize = 20
valign = "left"
iconname = icon
sline = name # kpi name
i = value # kpi value
htmlstr = f"""<p style='background-color:rgb({wch_colour_box[0]}, {wch_colour_box[1]}, {wch_colour_box[2]});
color: rgb({wch_colour_font[0]}, {wch_colour_font[1]}, {wch_colour_font[2]});
font-size: {fontsize + 10}px;
font-family: "Source Sans Pro", sans-serif;
font-weight: 600;
border-radius: 20px;
padding-left: 20px;
padding-top: 30px;
padding-bottom: 30px;
line-height:30px; text-align: center;'>
<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.0.0/css/all.min.css">
<i class="fa-solid fas {iconname} fa-2x fa-pull-left "></i>
<span style='font-size: {fontsize}px;
margin-top: 0'>{sline}</span><BR>
{i}
</style>
</p>"""
return htmlstr
def rating_distribution_pie_chart(classification_df):
pie_data = classification_df["rating"].value_counts().sort_index()
fig_pie = px.pie(pie_data,height=150, values=pie_data.values, names=pie_data.index, color=pie_data.values, color_discrete_sequence=px.colors.sequential.Blues)
fig_pie.update_traces(sort=False)
fig_pie.update_layout(margin=dict(t=0, b=0, l=0, r=0))
st.plotly_chart(fig_pie, use_container_width=True,height=100)
def kpi_total_reviews(classification_df):
total = str(len(classification_df))
total_reviews_card = KPI_card(name = "Total Reviews", value = total, icon = "fa-list")
st.markdown(total_reviews_card, unsafe_allow_html=True)
def kpi_average_rating(classification_df):
average_rating = round(classification_df["rating"].mean(), 1)
avg_rating_card = KPI_card(name = "Average Rating", value = average_rating, icon = "fa-star")
st.markdown(avg_rating_card, unsafe_allow_html=True)
def time_series_comments(classification_df, freq = "D", metric = "Count Reviews"):
labels = ["Quality", "Serve", "Pack", "Shipping", "Price"]
ts_plot_data = pd.DataFrame()
grouped_by_day = classification_df.groupby(pd.Grouper(key='time', freq = freq))
if metric == "Count Reviews":
ts_plot_data[labels] = grouped_by_day[labels].sum()
ts_plot_data["Total"] = grouped_by_day["comment"].count()
ts_plot_data["time"]= pd.to_datetime(ts_plot_data.index)
elif metric == "Average Rating":
for i in labels:
ts_plot_data[i] = classification_df[classification_df[i] == 1].groupby(pd.Grouper(key='time', freq = freq))["rating"].mean()
ts_plot_data["Total"] = grouped_by_day["rating"].mean()
ts_plot_data["time"]= pd.to_datetime(ts_plot_data.index)
ts_plot_data = ts_plot_data.ffill()
fig = px.line(ts_plot_data, x = "time", y = ts_plot_data.columns, height=300, title = f"{metric} on time")
fig.update_layout(
title=dict(font=dict(size=20), y=1, x=0),
legend=dict(
orientation="h",
entrywidth=90,
yanchor="bottom",
y= 0,
xanchor="right",
x=1, title = None, traceorder = "normal", yref = "container"
), yaxis_title = metric, xaxis = dict(showgrid = False), yaxis = dict(showgrid = False), margin=dict(r=5, l=5, t=50, b=5))
st.plotly_chart(fig, use_container_width = True)
def hor_barchart(classification_df, metric = "Count Reviews"):
labels = ["Quality", "Serve", "Pack", "Shipping", "Price"]
data = {}
if metric == "Average Rating":
for i in labels:
data[i] = classification_df[classification_df[i] == 1]["rating"].mean()
data = pd.Series(data)
elif metric == "Count Reviews":
data = classification_df[labels].sum().sort_values(ascending = True)
fig = px.bar(data, orientation = "h", width = 350, title = "Most commented topic")
fig.update_layout(yaxis_title=None, height=400, xaxis_visible = False, showlegend = False, title=dict(font=dict(size=20), y=0.85,
x=0))
st.plotly_chart(fig, use_container_width=True)
def print_reviews(classification_df):
col_1, col_2 = st.columns([1, 3], gap="large")
with col_1:
labels = ["Quality", "Serve", "Pack", "Shipping", "Price"]
viewing_method = st.selectbox("$$ \\bold{Viewing \: method: } $$", ["Individual Comments", "Wordcloud"])
filter_by = st.multiselect("$$ \\bold{Select \: reviews \: related \: to: } $$", labels)
if filter_by:
selected_labels = ", ".join(filter_by)
else:
selected_labels = "All topics"
df_filter = classification_df.copy()
if filter_by:
df_filter = df_filter[df_filter[filter_by].all(axis=1)]
if viewing_method == "Individual Comments":
with col_1:
filter_rating = st.slider("$$ \\bold{Rating \: range: \:} $$", min_value = 1, max_value= 5, value = (1, 5), step= 1)
df_filter = df_filter[(df_filter["rating"] >= filter_rating[0]) & (df_filter["rating"] <= filter_rating[1])]
df_filter = df_filter.sort_values("time", ascending = False)
if not df_filter.empty:
top_n = st.slider("$$ \\bold{Print \: top: \:} $$", min_value = 1, max_value= len(df_filter), value = int(len(df_filter) / 10), step= 1)
else:
top_n = 0
with col_2:
st.header(f"Displaying {top_n} most recent reviews related to :red[{selected_labels}]")
if not df_filter.empty:
comment_container = st.container(height=300)
with comment_container:
for i in range(top_n):
st.markdown(f"**Reviews** **{i + 1}:**")
comment = df_filter["comment"].iloc[i]
time = df_filter["time"].iloc[i]
rating = ":star:" * df_filter["rating"].iloc[i]
sender = df_filter["username"].iloc[i]
topics = [topic for topic in labels if df_filter[topic].iloc[i] == 1]
topics_str = ", ".join(topics)
col_1, col_2 = st.columns([1, 2])
col_1.markdown(f"From: {sender} \n Time: {time} \n Rating: {rating}")
col_1.markdown(f"Topics: {topics_str}")
col_2.markdown(comment)
st.markdown("---")
else:
st.markdown("No comment satisfy the condition")
else:
with col_2:
st.header(f"Wordcloud for reviews related to :red[{selected_labels}]")
text = " ".join(comment for comment in df_filter["comment"].str.lower().values)
text = word_tokenize(text, format = "text")
word_cloud = WordCloud(collocations = False, background_color = 'white').generate(text)
fig, ax = plt.subplots()
# Plot the word cloud on the axes
ax.imshow(word_cloud)
ax.axis("off")
st.pyplot(fig)
def tornado_chart(df):
labels = ["Quality", "Serve", "Pack", "Shipping", "Price"]
avg_rating = {}
for i in labels:
avg_rating[i] = df[df[i] == 1]["rating"].mean()
count_reviews = df[labels].sum().sort_values(ascending = True)
avg_rating = pd.Series(avg_rating).reindex(index = count_reviews.index)
fig = make_subplots(
rows=1
,cols=2
,vertical_spacing=0
)
fig_add = fig.add_trace(
go.Histogram(
x= count_reviews.values
,y= count_reviews.index
,histfunc='sum'
,orientation='h'
,opacity=0.6, name='Count Reviews')
,row=1
,col=1
)
fig_add = fig.add_trace(
go.Histogram(
x= avg_rating.values
,y= avg_rating.index
,histfunc='sum'
,orientation='h'
,opacity=0.6, name='Average Rating')
,row=1
,col=2
)
fig_add = fig.update_xaxes(
autorange="reversed"
,row=1
,col=1)
fig_add = fig.update_xaxes(
tickmode='linear'
,dtick=1
,row=1
,col=2)
fig_add = fig.update_layout(
title="Review Count and Average Rating by Topic",
)
fig_add = fig.update_yaxes(
visible=False
,row=1
,col=2)
fig.update_layout(xaxis=dict(domain=[0.0, 0.45]), xaxis2=dict(domain=[0.45, 0.90]))
fig.update_layout(legend=dict(orientation='h', xanchor='center', x=0.45))
fig.update_layout(
width=500,
height=300
)
fig.update_layout(
title=dict(font=dict(size=18), y=1, x=0),
legend=dict(
orientation="h",
entrywidth=90,
yanchor="bottom",
y= 0,
xanchor="right",
x=1, title = None, traceorder = "normal", yref = "container"
))
fig.update_layout(margin=dict(r=5, l=5, t=50, b=0))
st.plotly_chart(fig, use_container_width = True) |