import streamlit as st
import pandas as pd
import plotly.express as px
from wordcloud import WordCloud, STOPWORDS
import matplotlib.pyplot as plt
# Caching data loading
@st.cache_data
def load_data():
df = pd.read_csv("mafindo_mix_llm.csv")
return df
# Caching WordCloud generation
@st.cache_resource
def generate_wordcloud(text, colormap, stopwords):
wordcloud = WordCloud(width=500, height=200, background_color='white', colormap=colormap, stopwords=stopwords).generate(text)
return wordcloud
def show_home():
# Load the dataset
df = load_data()
# Convert 'Tanggal' to datetime
df['Tanggal'] = pd.to_datetime(df['Tanggal'], format='%d/%m/%Y')
df['Year'] = df['Tanggal'].dt.year
# Convert text columns to string to avoid type errors
df['Content'] = df['Content'].astype(str)
# Define additional stopwords
additional_stopwords = {"dan", "di", "yang", "ke", "dari", "untuk", "pada", "adalah", "sebuah", "dengan", "tersebut", "ini", "itu", "atau", "dalam", "juga", "adalah", "yg", "tapi"}
# Combine default stopwords with additional stopwords
combined_stopwords = set(STOPWORDS).union(additional_stopwords)
# Row with 4 visualizations
col1, col2, col3, col4 = st.columns([1.5, 2.5, 1.5, 2.5])
# Visualization 1: Bar chart for Hoax vs Non-Hoax using Plotly
with col1:
st.markdown("Hoax vs Non-Hoax
", unsafe_allow_html=True)
df_label_counts = df['Label'].value_counts().reset_index()
df_label_counts.columns = ['Label', 'Jumlah']
bar_chart_label = px.bar(df_label_counts, x='Label', y='Jumlah', color='Label',
color_discrete_map={'HOAX': 'red', 'NON-HOAX': 'green'})
bar_chart_label.update_layout(
width=200, height=150, xaxis_title='Label', yaxis_title='Jumlah',
xaxis_title_font_size=10, yaxis_title_font_size=10,
xaxis_tickfont_size=8, yaxis_tickfont_size=8, margin=dict(t=10, b=10, l=10, r=10),
showlegend=False
)
st.plotly_chart(bar_chart_label, use_container_width=False)
# Visualization 2: Bar chart for Hoax vs Non-Hoax per Data Source using Plotly
with col2:
st.markdown("Hoax vs Non-Hoax per Data Source
", unsafe_allow_html=True)
datasource_label_counts = df.groupby(['Datasource', 'Label']).size().reset_index(name='counts')
fig_datasource = px.bar(datasource_label_counts, x='Datasource', y='counts', color='Label', barmode='group',
color_discrete_map={'HOAX': 'red', 'NON-HOAX': 'green'})
fig_datasource.update_layout(
width=500, height=150, xaxis_title='Datasource', yaxis_title='Jumlah',
xaxis_title_font_size=10, yaxis_title_font_size=10,
xaxis_tickfont_size=6, yaxis_tickfont_size=8, xaxis_tickangle=0,
margin=dict(t=10, b=10, l=10, r=50),
legend=dict(
font=dict(size=8), # Smaller font size for the legend
traceorder='normal',
orientation='v', # Vertical orientation of the legend
title_text='Label', # Title for the legend
yanchor='top', y=1, xanchor='left', x=1.05, # Adjust position of the legend
bgcolor='rgba(255, 255, 255, 0)', # Transparent background for legend
bordercolor='rgba(0, 0, 0, 0)' # No border color
),
showlegend=True
)
st.plotly_chart(fig_datasource, use_container_width=False)
# Visualization 3: Line chart for Hoax per Year using Plotly
with col3:
st.markdown("Hoax per Tahun
", unsafe_allow_html=True)
# Filter data to include only years up to 2023
hoax_per_year = df[(df['Label'] == 'HOAX') & (df['Year'] <= 2023)].groupby('Year').size().reset_index(name='count')
line_chart_hoax = px.line(hoax_per_year, x='Year', y='count', line_shape='linear',
color_discrete_sequence=['red'])
line_chart_hoax.update_layout(
width=200, height=150, xaxis_title='Tahun', yaxis_title='Jumlah Hoax',
xaxis_title_font_size=10, yaxis_title_font_size=10,
xaxis_tickfont_size=8, yaxis_tickfont_size=8, margin=dict(t=10, b=10, l=10, r=10),
showlegend=False
)
st.plotly_chart(line_chart_hoax, use_container_width=False)
# Visualization 4: Bar chart for Topics per Year using Plotly
with col4:
st.markdown("Topik per Tahun
", unsafe_allow_html=True)
df['Tanggal'] = pd.to_datetime(df['Tanggal'], format='%d/%m/%Y')
df['Year'] = df['Tanggal'].dt.year
# Filter the data to include only years up to 2023
df_mafindo_filtered = df[df['Year'] <= 2023]
topics_per_year = df_mafindo_filtered.groupby(['Year', 'Topic']).size().reset_index(name='count')
# Create the vertical bar chart
bar_chart_topics = px.bar(topics_per_year, x='Year', y='count', color='Topic',
color_continuous_scale=px.colors.sequential.Viridis)
# Update layout to adjust the legend
bar_chart_topics.update_layout(
width=600, height=150, xaxis_title='Tahun', yaxis_title='Jumlah Topik',
xaxis_title_font_size=10, yaxis_title_font_size=10,
xaxis_tickfont_size=8, yaxis_tickfont_size=8, margin=dict(t=10, b=10, l=10, r=10),
showlegend=True,
legend=dict(
yanchor="top", y=1, xanchor="left", x=1.02, # Adjust position of the legend
bgcolor='rgba(255, 255, 255, 0)', # Transparent background for legend
bordercolor='rgba(0, 0, 0, 0)', # No border color
itemclick='toggleothers', # Allow toggling of legend items
itemsizing='constant', # Consistent sizing for legend items
font=dict(size=8),
traceorder='normal',
orientation='v', # Vertical orientation of legend
title_text='Topic'
)
)
st.plotly_chart(bar_chart_topics, use_container_width=True)
# Create a new row for WordCloud visualizations
col5, col6, col7 = st.columns([2, 2.5, 2.5])
# Wordcloud for Hoax
with col5:
st.markdown("Wordcloud for Hoax
", unsafe_allow_html=True)
hoax_text = ' '.join(df[df['Label'] == 'HOAX']['Content'])
wordcloud_hoax = generate_wordcloud(hoax_text, 'Reds', combined_stopwords)
fig_hoax = plt.figure(figsize=(5, 2.5))
plt.imshow(wordcloud_hoax, interpolation='bilinear')
plt.axis('off')
st.pyplot(fig_hoax)
with col6:
st.markdown("Klasifikasi
", unsafe_allow_html=True)
df_classification_counts = df['Classification'].value_counts().reset_index()
df_classification_counts.columns = ['Classification', 'Count']
# Create the donut chart
donut_chart_classification = px.pie(df_classification_counts, names='Classification', values='Count',
hole=0.3, color_discrete_sequence=px.colors.qualitative.Set2)
# Update layout to move the legend and adjust its size
donut_chart_classification.update_layout(
width=300, height=170, # Adjust the size of the chart
margin=dict(t=20, b=20, l=20, r=120), # Adjust margins to make room for the legend
legend=dict(
yanchor="top", y=1, xanchor="left", x=1.07, # Adjust position of the legend
bgcolor='rgba(255, 255, 255, 0)', # Transparent background for legend
bordercolor='rgba(0, 0, 0, 0)', # No border color
itemclick='toggleothers', # Allow toggling of legend items
itemsizing='constant', # Consistent sizing for legend items
font=dict(size=8), # Smaller font size for the legend
traceorder='normal',
orientation='v', # Vertical legend
title_text='Classification' # Title for the legend
)
)
st.plotly_chart(donut_chart_classification, use_container_width=True)
with col7:
st.markdown("Tone
", unsafe_allow_html=True)
df_tone_counts = df['Tone'].value_counts().reset_index()
df_tone_counts.columns = ['Tone', 'Count']
# Create the donut chart
donut_chart_tone = px.pie(df_tone_counts, names='Tone', values='Count',
hole=0.3, color_discrete_sequence=px.colors.qualitative.Set2)
# Update layout to move the legend and adjust its size
donut_chart_tone.update_layout(
width=250, height=170, # Adjust the size of the chart
margin=dict(t=20, b=20, l=20, r=100), # Adjust margins to make room for the legend
legend=dict(
yanchor="top", y=1, xanchor="left", x=1.07, # Adjust position of the legend
bgcolor='rgba(255, 255, 255, 0)', # Transparent background for legend
bordercolor='rgba(0, 0, 0, 0)', # No border color
itemclick='toggleothers', # Allow toggling of legend items
itemsizing='constant', # Consistent sizing for legend items
font=dict(size=8), # Smaller font size for the legend
traceorder='normal',
orientation='v', # Vertical legend
title_text='Tone' # Title for the legend
)
)
st.plotly_chart(donut_chart_tone, use_container_width=True)
col8, col9 = st.columns([5, 1.5])
# Evaluation Metrics Table
data = [
["indobenchmark/indobert-base-p2", 0.6898, 0.9793, 0.8094, 0.8400, 0.1981, 0.3206, 0.7023],
["cahya/bert-base-indonesian-522M", 0.7545, 0.8756, 0.8106, 0.6800, 0.4811, 0.5635, 0.7358],
["indolem/indobert-base-uncased", 0.7536, 0.8238, 0.7871, 0.6136, 0.5094, 0.5567, 0.7124],
["mdhugol/indonesia-bert-sentiment-classification", 0.7444, 0.8601, 0.7981, 0.6447, 0.4623, 0.5385, 0.7191]
]
highest_accuracy = max(data, key=lambda x: x[-1])
# Header Table
html_table = """
| Pre-trained Model |
NON-HOAX |
HOAX |
Accuracy |
| Precision |
Recall |
F1-Score |
Precision |
Recall |
F1-Score |
"""
# Isi Data
for row in data:
formatted_row = [f"{item:.4f}" if isinstance(item, float) else item for item in row]
if row == highest_accuracy:
html_table += ""
else:
html_table += "
"
# Left-align the first column (Pre-trained Model)
html_table += f"| {row[0]} | "
# Center-align the rest of the columns
for item in formatted_row[1:]:
html_table += f"{item} | "
html_table += "
"
# Add a border to the last row
html_table += "|
"
html_table += "
"
# Tampilkan Tabel di Streamlit
with col8:
st.markdown("Matriks Evaluasi
", unsafe_allow_html=True)
st.markdown(html_table, unsafe_allow_html=True)
html_table_col9 = """
| Label |
Train |
Test |
Dev |
| HOAX |
11.563 |
193 |
193 |
| NON-HOAX |
789 |
106 |
106 |
| TOTAL |
12,352 |
299 |
299 |
"""
# Display the table in col9 using HTML
with col9:
st.markdown("Statistik Data
", unsafe_allow_html=True)
st.markdown(html_table_col9, unsafe_allow_html=True)