TestApp / app.py
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import streamlit as st
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.feature_extraction.text import CountVectorizer
import seaborn as sns
import plotly.express as px
import plotly.io as pio
import plotly.graph_objects as go
# Set page configuration
st.set_page_config(layout="wide")
# Function to load and clean data
def load_and_clean_data():
df1 = pd.read_csv("data/reviewed_social_media_english.csv")
df2 = pd.read_csv("data/reviewed_news_english.csv")
df3 = pd.read_csv("data/tamil_social_media.csv")
df4 = pd.read_csv("data/tamil_news.csv")
# Concatenate dataframes and clean data
df_combined = pd.concat([df1, df2, df3, df4])
# Replace 'nan' and 'None' with numpy NaN for removal
df_combined['Domain'] = df_combined['Domain'].replace({"MUSLIM": "Muslim", "nan": pd.NA, "None": pd.NA, "Other-Ethnic": "Other-Ethnicity"})
# Specific replacements for 'Sentiment' and 'Discrimination'
df_combined['Sentiment'] = df_combined['Sentiment'].replace({"nan": pd.NA, "None": pd.NA, "No": pd.NA})
df_combined['Discrimination'] = df_combined['Discrimination'].replace({"nan": pd.NA, "None": pd.NA, "No": pd.NA})
# Drop rows with NA values in 'Domain', 'Sentiment', and 'Discrimination'
df_combined.dropna(subset=['Domain', 'Sentiment', 'Discrimination'], inplace=True)
return df_combined
df = load_and_clean_data()
# Page navigation setup
page_names = [" GESI Overview", "Sentiment Analysis", "Discrimination Analysis", "Channel Analysis"]
page = st.sidebar.selectbox("Choose a page", page_names)
# Sidebar Filters
domain_options = df['Domain'].dropna().unique()
channel_options = df['Channel'].dropna().unique()
sentiment_options = df['Sentiment'].dropna().unique()
discrimination_options = df['Discrimination'].dropna().unique()
domain_filter = st.sidebar.multiselect('Select Domain', options=domain_options, default=domain_options)
channel_filter = st.sidebar.multiselect('Select Channel', options=channel_options, default=channel_options)
sentiment_filter = st.sidebar.multiselect('Select Sentiment', options=sentiment_options, default=sentiment_options)
discrimination_filter = st.sidebar.multiselect('Select Discrimination', options=discrimination_options, default=discrimination_options)
# Apply filters
df_filtered = df[(df['Domain'].isin(domain_filter)) &
(df['Channel'].isin(channel_filter)) &
(df['Sentiment'].isin(sentiment_filter)) &
(df['Discrimination'].isin(discrimination_filter))]
# Define a color palette for consistent visualization styles
color_palette = px.colors.sequential.Viridis
# Visualisation for Domain Distribution
def create_pie_chart(df, column, title):
fig = px.pie(df, names=column, title=title, hole=0.35)
fig.update_layout(margin=dict(l=20, r=20, t=50, b=20), legend=dict(x=0.1, y=1), font=dict(size=10), title_x=0.5)
fig.update_traces(marker=dict(colors=color_palette))
return fig
# Visualization for Distribution of Gender versus Ethnicity
def create_gender_ethnicity_distribution_chart(df):
df['GenderOrEthnicity'] = df['Domain'].apply(lambda x: "Gender: Women & LGBTQIA+" if x in ["Women", "LGBTQIA+"] else "Ethnicity")
fig = px.pie(df, names='GenderOrEthnicity', title='Distribution of Gender versus Ethnicity', hole=0.35)
fig.update_layout(margin=dict(l=20, r=20, t=50, b=20), legend=dict(x=0.1, y=1), font=dict(size=10), title_x=0.5)
return fig
# Visualization for Sentiment Distribution Across Domains
def create_sentiment_distribution_chart(df):
df['Discrimination'] = df['Discrimination'].replace({"Non Discriminative": "Non-Discriminative"}) # Assuming typo in the original script
domain_counts = df.groupby(['Domain', 'Sentiment']).size().reset_index(name='counts')
fig = px.bar(domain_counts, x='Domain', y='counts', color='Sentiment', title="Sentiment Distribution Across Domains", barmode='stack')
fig.update_layout(margin=dict(l=20, r=20, t=50, b=20), xaxis_title="Domain", yaxis_title="Counts", font=dict(size=10), title_x=0.5)
return fig
# Visualization for Correlation between Sentiment and Discrimination
def create_sentiment_discrimination_grouped_chart(df):
# Creating a crosstab of 'Sentiment' and 'Discrimination'
crosstab_df = pd.crosstab(df['Sentiment'], df['Discrimination'])
# Check if 'Yes' and 'No' are in the columns after the crosstab operation
value_vars = crosstab_df.columns.intersection(['Discriminative', 'Non Discriminative']).tolist()
# If 'No' is not in columns, it will not be included in melting
melted_df = pd.melt(crosstab_df.reset_index(), id_vars='Sentiment', value_vars=value_vars, var_name='Discrimination', value_name='Count')
# Proceeding to plot only if we have data to plot
if not melted_df.empty:
fig = px.bar(melted_df, x='Sentiment', y='Count', color='Discrimination', barmode='group', title="Sentiment vs. Discrimination")
fig.update_layout(margin=dict(l=20, r=20, t=50, b=20), xaxis_title="Sentiment", yaxis_title="Count", font=dict(size=10), title_x=0.5)
return fig
else:
return "No data to display for the selected filters."
# Function for Top Domains with Negative Sentiment Chart
def create_top_negative_sentiment_domains_chart(df):
domain_counts = df.groupby(['Domain', 'Sentiment']).size().unstack(fill_value=0)
domain_counts.sort_values(by='Negative', ascending=False, inplace=True)
domain_counts_subset = domain_counts.iloc[:3, [0]]
domain_counts_subset = domain_counts_subset.rename(columns={domain_counts_subset.columns[0]: 'Count'})
domain_counts_subset = domain_counts_subset.reset_index()
colors = ['limegreen', 'crimson', 'darkcyan']
fig = px.bar(domain_counts_subset, x='Count', y='Domain', title='Top Domains with Negative Sentiment', color='Domain',
orientation='h', color_discrete_sequence=colors)
fig.update_layout(margin=dict(l=20, r=20, t=50, b=20), xaxis_title="Negative sentiment content Count", yaxis_title="Domain", font=dict(size=10), title_x=0.5)
return fig
# Function for Key Phrases in Negative Sentiment Content Chart
def create_key_phrases_negative_sentiment_chart(df):
cv = CountVectorizer(ngram_range=(3,3), stop_words='english')
trigrams = cv.fit_transform(df['Content'][df['Sentiment'] == 'Negative'])
count_values = trigrams.toarray().sum(axis=0)
ngram_freq = pd.DataFrame(sorted([(count_values[i], k) for k, i in cv.vocabulary_.items()], reverse=True))
ngram_freq.columns = ['frequency', 'ngram']
fig = px.bar(ngram_freq.head(10), x='frequency', y='ngram', orientation='h', title='Key phrases in Negative Sentiment Content')
fig.update_layout(margin=dict(l=20, r=20, t=50, b=20), xaxis_title="Frequency", yaxis_title="Trigram", font=dict(size=10), title_x=0.5)
return fig
# Function for Key Phrases in Positive Sentiment Content Chart
def create_key_phrases_positive_sentiment_chart(df):
cv = CountVectorizer(ngram_range=(3, 3), stop_words='english')
trigrams = cv.fit_transform(df['Content'][df['Sentiment'] == 'Positive'])
count_values = trigrams.toarray().sum(axis=0)
ngram_freq = pd.DataFrame(sorted([(count_values[i], k) for k, i in cv.vocabulary_.items()], reverse=True))
ngram_freq.columns = ['frequency', 'ngram']
fig = px.bar(ngram_freq.head(10), x='frequency', y='ngram', orientation='h', title='Key phrases in Positive Sentiment Content')
fig.update_layout(margin=dict(l=20, r=20, t=50, b=20), xaxis_title="Frequency", yaxis_title="Trigram", font=dict(size=10), title_x=0.5)
return fig
# Function for Prevalence of Discriminatory Content Chart
def create_prevalence_discriminatory_content_chart(df):
domain_counts = df.groupby(['Domain', 'Discrimination']).size().unstack(fill_value=0)
fig = px.bar(domain_counts, x=domain_counts.index, y=['Discriminative', 'Non-Discriminative'], barmode='group',
title='Prevalence of Discriminatory Content')
fig.update_layout(margin=dict(l=20, r=20, t=50, b=20), xaxis_title="Domain", yaxis_title="Count", font=dict(size=10), title_x=0.5)
return fig
# Function for Top Domains with Discriminatory Content Chart
def create_top_discriminatory_domains_chart(df):
domain_counts = df.groupby(['Domain', 'Discrimination']).size().unstack(fill_value=0)
domain_counts.sort_values(by='Discriminative', ascending=False, inplace=True)
domain_counts_subset = domain_counts.iloc[:3]
domain_counts_subset = domain_counts_subset.rename(columns={'Discriminative': 'Count'})
fig = px.bar(domain_counts_subset, x='Count', y=domain_counts_subset.index, orientation='h',
title='Top Domains with Discriminatory Content')
fig.update_layout(margin=dict(l=20, r=20, t=50, b=20), xaxis_title="Discriminatory Content Count", yaxis_title="Domain", font=dict(size=10), title_x=0.5)
return fig
# Function for Channel-wise Sentiment Over Time Chart
def create_sentiment_distribution_by_channel_chart(df):
sentiment_by_channel = df.groupby(['Channel', 'Sentiment']).size().reset_index(name='counts')
fig = px.bar(sentiment_by_channel, x='Channel', y='counts', color='Sentiment', title="Sentiment Distribution by Channel", barmode='group')
fig.update_layout(margin=dict(l=20, r=20, t=50, b=20), xaxis_title="Channel", yaxis_title="Counts", font=dict(size=10), title_x=0.5)
return fig
# Function for Channel-wise Distribution of Discriminative Content Chart
def create_channel_discrimination_chart(df):
channel_discrimination = df.groupby(['Channel', 'Discrimination']).size().unstack(fill_value=0)
fig = px.bar(channel_discrimination, x=channel_discrimination.index, y=['Discriminative', 'Non-Discriminative'], barmode='group')
fig.update_layout(title='Channel-wise Distribution of Discriminative Content', margin=dict(l=20, r=20, t=50, b=20), font=dict(size=10), title_x=0.5)
return fig
# Function for rendering dashboard
def render_dashboard(page, df_filtered):
if page == " GESI Overview":
st.title(" GESI Overview Dashboard")
col1, col2 = st.columns(2)
with col1:
st.plotly_chart(create_pie_chart(df_filtered, 'Domain', 'Distribution of Domains'))
with col2:
st.plotly_chart(create_gender_ethnicity_distribution_chart(df_filtered))
col3, col4 = st.columns(2)
with col3:
st.plotly_chart(create_sentiment_distribution_chart(df_filtered))
with col4:
chart = create_sentiment_discrimination_grouped_chart(df_filtered)
if isinstance(chart, str):
st.write(chart)
else:
st.plotly_chart(chart)
elif page == "Sentiment Analysis":
st.title("Sentiment Analysis Dashboard")
col1, col2 = st.columns(2)
with col1:
st.plotly_chart(create_sentiment_distribution_chart(df_filtered))
with col2:
st.plotly_chart(create_top_negative_sentiment_domains_chart(df_filtered))
col3, col4 = st.columns(2)
with col3:
st.plotly_chart(create_key_phrases_negative_sentiment_chart(df_filtered))
with col4:
st.plotly_chart(create_key_phrases_positive_sentiment_chart(df_filtered))
elif page == "Discrimination Analysis":
st.title("Discrimination Analysis Dashboard")
col1, col2 = st.columns(2)
with col1:
st.plotly_chart(create_prevalence_discriminatory_content_chart(df_filtered))
with col2:
st.plotly_chart(create_top_discriminatory_domains_chart(df_filtered))
elif page == "Channel Analysis":
st.title("Channel Analysis Dashboard")
col1, col2 = st.columns(2)
with col1:
st.plotly_chart(create_sentiment_distribution_by_channel_chart(df_filtered))
with col2:
st.plotly_chart(create_channel_discrimination_chart(df_filtered))
# Render the selected dashboard page
render_dashboard(page, df_filtered)