187 lines
7.5 KiB
Python
187 lines
7.5 KiB
Python
import streamlit as st
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import plotly.express as px
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import pandas as pd
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from plotly.subplots import make_subplots
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import matplotlib.pyplot as plt
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from wordcloud import WordCloud
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import plotly.graph_objects as go
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import os
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from backend import get_pivot_sentiment, get_label_counts, get_keyword_sentiment_distribution, get_avg_metrics, generate_wordclouds
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from datetime import timedelta
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def inject_css():
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css_path = os.path.join(os.path.dirname(__file__), "style.css")
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with open(css_path) as f:
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st.markdown(f"<style>{f.read()}</style>", unsafe_allow_html=True)
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def app():
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inject_css()
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# Load semua data dengan format tanggal yang benar
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sentiment_data = pd.read_csv(
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'https://raw.githubusercontent.com/potaHere/Tugas-Akhir/refs/heads/main/dashboard/datasets/datasets-keyword-label.csv',
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parse_dates=['created_at'],
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date_format="%d %b %Y" # Format yang sesuai dengan "30 Dec 2024"
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)
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# Tidak perlu normalisasi timezone karena sudah menggunakan format tanpa jam
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# sentiment_data['created_at'] = pd.to_datetime(sentiment_data['created_at']).dt.tz_localize(None)
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# Tambahkan filter di sidebar
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with st.sidebar:
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st.markdown("## 🔍 Filter Dashboard")
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# Mendapatkan tanggal minimum dan maksimum dari dataset
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min_date = sentiment_data['created_at'].min().date()
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max_date = sentiment_data['created_at'].max().date()
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# Menampilkan opsi filter waktu
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st.markdown("### ⏱️ Filter Waktu")
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# Filter waktu menggunakan slider tanggal
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date_range = st.slider(
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"Pilih Rentang Waktu:",
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min_value=min_date,
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max_value=max_date,
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value=(min_date, max_date),
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format="DD-MMM-YYYY"
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)
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# Convert date range to datetime for filtering
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start_datetime = pd.Timestamp(date_range[0])
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end_datetime = pd.Timestamp(date_range[1]) + timedelta(days=1) - timedelta(seconds=1) # end of day
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# Menampilkan periode waktu yang dipilih
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st.info(f"Menampilkan data dari: {start_datetime.strftime('%d %B %Y')} hingga {end_datetime.strftime('%d %B %Y')}")
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# Filter keyword
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st.markdown("### 🏷️ Filter Keyword")
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keywords = sorted(sentiment_data['keyword'].unique())
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selected_keywords = st.multiselect(
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"Pilih Keyword",
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options=keywords,
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default=keywords
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)
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# # Filter sentimen
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# st.markdown("### 😊 Filter Sentimen")
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# sentiments = ['Positif', 'Negatif', 'Netral']
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# selected_sentiments = st.multiselect(
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# "Pilih Sentimen",
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# options=sentiments,
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# default=sentiments
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# )
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# Terapkan filter
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filtered_data = sentiment_data[
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(sentiment_data['created_at'] >= start_datetime) &
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(sentiment_data['created_at'] <= end_datetime) &
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(sentiment_data['keyword'].isin(selected_keywords)) #&
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# (sentiment_data['label'].isin(selected_sentiments))
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]
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# # Tampilkan jumlah data yang ditampilkan
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# st.write(f"Menampilkan {len(filtered_data)} dari {len(sentiment_data)} data")
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# Data wordcloud - tetap menggunakan semua data
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wordcloud_data = pd.read_csv('https://raw.githubusercontent.com/potaHere/Tugas-Akhir/refs/heads/main/dashboard/datasets/word_count_labeled.csv')
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# Performance data - tetap menggunakan semua data
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performance_data = pd.read_csv('https://raw.githubusercontent.com/potaHere/Tugas-Akhir/refs/heads/main/dashboard/datasets/evaluation_results_combine.csv')
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# Row 1: Pie Chart
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col1, col2 = st.columns(2)
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with col1:
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st.subheader('Sentiment Distribution')
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label_counts = get_label_counts(filtered_data)
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pie_fig = px.pie(label_counts, names='label', values='count')
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pie_fig.update_traces(textinfo='percent+label')
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pie_fig.update_layout(showlegend=False)
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st.plotly_chart(pie_fig, use_container_width=True)
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# Distribusi Sentimen bedasarkan Model
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with col2:
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st.subheader('Keyword Sentiment Distribution')
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keyword_sentiment_counts = get_keyword_sentiment_distribution(filtered_data)
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# Mengatur urutan kategori label di frontend
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keyword_sentiment_counts['label'] = pd.Categorical(keyword_sentiment_counts['label'],
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categories=['Negatif', 'Positif', 'Netral'],
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ordered=True)
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# Membuat bar chart dengan urutan label yang diatur
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bar_fig = px.bar(keyword_sentiment_counts, x='keyword', y='count', color='label', barmode='group',
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category_orders={'label': ['Negatif', 'Positif', 'Netral']})
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st.plotly_chart(bar_fig, use_container_width=True)
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# Row 2: Wordclouds (tidak difilter)
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label_colors = {
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'positif': 'green',
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'negatif': 'red',
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'netral': 'gray'
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}
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wordclouds = generate_wordclouds(wordcloud_data, label_colors)
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cols = st.columns(len(wordclouds))
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for col, (label, wordcloud) in zip(cols, wordclouds.items()):
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with col:
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st.subheader(f'Word Cloud {label}')
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plt.figure(figsize=(10, 6))
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plt.imshow(wordcloud, interpolation='bilinear')
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plt.axis('off')
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st.pyplot(plt)
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# cols = st.columns(len(wordclouds))
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# for col, (label, wordcloud) in zip(cols, wordclouds.items()):
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# with col:
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# st.subheader(f'Word Cloud {label}')
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# fig = px.imshow(wordcloud, binary_string=True)
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# fig.update_layout(
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# coloraxis_showscale=False,
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# xaxis=dict(visible=False),
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# yaxis=dict(visible=False),
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# margin=dict(l=0, r=0, t=0, b=0)
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# )
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# st.plotly_chart(fig, use_container_height=False)
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st.markdown('<div class="column-costum"></div>', unsafe_allow_html=True)
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# Row 3: Line Chart & Grouped Bar Chart
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col5, col6 = st.columns(2)
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with col5:
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st.subheader('Sentiment Trends Over Time')
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pivot_sentiment = get_pivot_sentiment(filtered_data)
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line_fig = px.line(pivot_sentiment, x='Year', y=['Negatif', 'Positif', 'Netral'], markers=True)
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st.plotly_chart(line_fig, use_container_width=True)
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with col6:
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st.subheader('Model Performance')
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avg_metrics_df = get_avg_metrics(performance_data)
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bar_group_fig = px.bar(avg_metrics_df.melt(id_vars='model', value_vars=['Accuracy', 'Precision', 'Recall', 'F1-score']),
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x='variable', y='value', color='model', barmode='group')
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st.plotly_chart(bar_group_fig, use_container_width=True)
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# Data table
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st.subheader('Data Tables')
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columns_to_display = ['created_at', 'cleanning_text', 'keyword', 'label']
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display_df = filtered_data[columns_to_display].copy()
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# Urutkan data berdasarkan tanggal (created_at) dari yang terbaru
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display_df = display_df.sort_values(by='created_at', ascending=False)
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# Format kolom created_at menjadi format DD-BULAN-YYYY (contoh: 30-December-2024)
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display_df['created_at'] = display_df['created_at'].dt.strftime('%d-%B-%Y')
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display_df = display_df.rename(columns={
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'created_at': 'Tanggal',
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'cleanning_text': 'Tweet',
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'keyword': 'Kata Kunci',
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'label': 'Sentimen'
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})
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st.dataframe(display_df, hide_index=True, use_container_width=True)
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if __name__ == "__main__":
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app() |