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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")
# Read data into dataframes
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")
# Normalize Text
df1['Domain'].replace("MUSLIM", "Muslim", inplace=True)
df2['Domain'].replace("MUSLIM", "Muslim", inplace=True)
df3['Domain'].replace("MUSLIM", "Muslim", inplace=True)
df4['Domain'].replace("MUSLIM", "Muslim", inplace=True)
# Drop irrelevant data
frames = [df1, df2, df3, df4]
for df in frames:
df = df[df['Domain'] != 'Not relevant']
df = df[df['Domain'] != 'None']
df = df[df['Discrimination'] != 'None']
df = df[df['Sentiment'] != 'None']
# Concatenate/merge dataframes
df = pd.concat(frames)
# Visualization function
def create_visualizations(df):
# [Existing visualization code]
pass
# Page navigation
page = st.sidebar.selectbox("Choose a page", ["Overview", "Sentiment Analysis", "Discrimination Analysis", "Channel Analysis"])
if page == "Overview":
create_visualizations(df) # Placeholder for overview visualizations
elif page == "Sentiment Analysis":
create_visualizations(df) # Placeholder for sentiment analysis visualizations
elif page == "Discrimination Analysis":
create_visualizations(df) # Placeholder for discrimination analysis visualizations
elif page == "Channel Analysis":
create_visualizations(df) # Placeholder for channel analysis visualizations
# [Place the rest of the code for the visualizations here]
# Define a color palette for consistent visualization styles
color_palette = px.colors.sequential.Viridis
# Function for Domain Distribution Chart
def create_domain_distribution_chart(df):
fig = px.pie(df, names='Domain', title='Distribution of Domains', hole=0.35)
fig.update_layout(title_x=0.5, margin=dict(l=20, r=20, t=30, b=20), legend=dict(x=0.1, y=1))
fig.update_traces(marker=dict(colors=color_palette))
return fig
# Function for Sentiment Distribution Across Domains Chart
def create_sentiment_distribution_chart(df):
# ... [Include the existing code for the Sentiment Distribution chart]
fig.update_layout(margin=dict(l=20, r=20, t=40, b=20))
return fig
# ... [Define other chart functions following the same pattern]
# Function for Channel-wise Sentiment Over Time Chart
def create_channel_sentiment_over_time_chart(df):
df['Date'] = pd.to_datetime(df['Date'])
timeline = df.groupby([df['Date'].dt.to_period('M'), 'Channel', 'Sentiment']).size().unstack(fill_value=0)
fig = px.line(timeline, x=timeline.index.levels[1].to_timestamp(), y=['Positive', 'Negative', 'Neutral'], color='Channel')
fig.update_layout(title='Channel-wise Sentiment Over Time', margin=dict(l=20, r=20, t=40, b=20))
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=40, b=20))
return fig
# Dashboard Layout
def render_dashboard():
# Overview page layout
if page == "Overview":
st.header("Overview of Domains and Sentiments")
col1, col2 = st.beta_columns(2)
with col1:
st.plotly_chart(create_domain_distribution_chart(df))
with col2:
st.plotly_chart(create_sentiment_distribution_chart(df))
# ... [Additional overview charts]
# ... [Other pages]
# Sidebar Filters
domain_filter = st.sidebar.multiselect('Select Domain', options=df['Domain'].unique(), default=df['Domain'].unique())
channel_filter = st.sidebar.multiselect('Select Channel', options=df['Channel'].unique(), default=df['Channel'].unique())
sentiment_filter = st.sidebar.multiselect('Select Sentiment', options=df['Sentiment'].unique(), default=df['Sentiment'].unique())
discrimination_filter = st.sidebar.multiselect('Select Discrimination', options=df['Discrimination'].unique(), default=df['Discrimination'].unique())
# Apply the filters
df_filtered = df[df['Domain'].isin(domain_filter) & df['Channel'].isin(channel_filter) & df['Sentiment'].isin(sentiment_filter) & df['Discrimination'].isin(discrimination_filter)]
# Render the dashboard with filtered data
render_dashboard(df_filtered)
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