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PoskoLog / sentiment_app.py

Update sentiment_app.py

dccfab0 verified 9 months ago

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import gradio as gr
import torch
from transformers import pipeline
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from datetime import datetime

# --- SETUP MODEL ---
# Menggunakan model RoBERTa Bahasa Indonesia untuk analisis sentimen
MODEL_NAME = "w11wo/indonesian-roberta-base-sentiment-classifier"
device = 0 if torch.cuda.is_available() else -1
sentiment_pipeline = pipeline("sentiment-analysis", model=MODEL_NAME, device=device)

# --- DATABASE SEDERHANA (In-Memory) ---
all_messages = [] 

# Mapping Label untuk Tampilan UI agar lebih mudah dipahami manusia
label_map = {
    "POSITIVE": "Pujian/Apresiasi", 
    "NEGATIVE": "Keluhan/Kritik", 
    "NEUTRAL": "Pertanyaan/Info"
}

def process_submission(text):
    if not text or text.strip() == "":
        return "⚠️ Mohon isi komentar Anda terlebih dahulu.", gr.update()
    
    # 1. Analisis Sentimen menggunakan Pipeline Hugging Face
    result = sentiment_pipeline(text)[0]
    label = result['label'].upper()
    
    # 2. Simpan ke Database Lokal dengan Timestamp
    new_entry = {
        "Waktu": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
        "Pesan": text.strip(),
        "Sentimen": label
    }
    all_messages.append(new_entry)
    
    # 3. Respons untuk User
    thanks_msg = "Terima kasih atas pesan Anda!\nMasukan Anda telah kami terima dan akan segera ditinjau oleh tim admin posko."
    return thanks_msg, gr.update(value="")

def get_admin_dashboard(filter_val):
    if not all_messages:
        return None, pd.DataFrame(columns=["Waktu", "Pesan", "Sentimen"]), "Belum ada data."
    
    df_all = pd.DataFrame(all_messages)
    
    # --- LOGIKA FILTER ---
    if filter_val != "SEMUA":
        # Balik mapping untuk mencari label asli (POSITIVE/NEGATIVE/NEUTRAL)
        rev_map = {v: k for k, v in label_map.items()}
        target = rev_map.get(filter_val)
        df_filtered = df_all[df_all['Sentimen'] == target]
    else:
        df_filtered = df_all

    if df_filtered.empty:
        return None, pd.DataFrame(columns=["Waktu", "Pesan", "Sentimen"]), f"Tidak ada data untuk kategori: {filter_val}"

    # --- VISUALISASI TOTAL (Pie Chart atau Bar Plot) ---
    fig, ax = plt.subplots(figsize=(8, 5))
    counts = df_all['Sentimen'].value_counts()
    # Mengubah index angka/label asli ke label buatan kita (Pujian/Keluhan/dll)
    counts.index = [label_map.get(i, i) for i in counts.index]
    
    sns.barplot(x=counts.index, y=counts.values, palette="viridis", ax=ax)
    ax.set_title("Proporsi Pesan Masuk (Total)", fontsize=12, fontweight='bold')
    ax.set_ylabel("Jumlah Pesan")

    # --- TABEL DENGAN KOLOM WAKTU/TANGGAL ---
    display_df = df_filtered[["Waktu", "Pesan", "Sentimen"]].copy()
    display_df['Sentimen'] = display_df['Sentimen'].map(label_map) # Percantik label di tabel
    display_df = display_df.sort_values(by="Waktu", ascending=False) # Urutkan: Terbaru di atas

    return fig, display_df, f"Menampilkan {len(df_filtered)} pesan ({filter_val})"

# --- INTERFACE GRADIO ---
with gr.Blocks(theme=gr.themes.Soft(primary_hue="emerald"), title="PoskoLog Dashboard") as demo:
    gr.Markdown("# 📦 PoskoLog: Suara Pengungsi")
    gr.Markdown("Sistem analisis sentimen otomatis untuk memprioritaskan laporan darurat pasca-bencana.")
    
    with gr.Tabs():
        # --- TAB USER ---
        with gr.Tab("📝 Sampaikan Pesan"):
            with gr.Column(variant="panel"):
                gr.Markdown("### Laporkan kondisi atau berikan masukan Anda")
                user_input = gr.Textbox(
                    label="Komentar Anda", 
                    placeholder="Contoh: Bantuan air bersih belum sampai di tenda C...", 
                    lines=4
                )
                submit_btn = gr.Button("Kirim Pesan", variant="primary")
                user_feedback = gr.Markdown("")

        # --- TAB ADMIN ---
        with gr.Tab("📊 Dashboard Admin"):
            with gr.Row():
                sentiment_filter = gr.Dropdown(
                    choices=["SEMUA"] + list(label_map.values()), 
                    value="SEMUA", 
                    label="Filter Sentimen"
                )
                refresh_btn = gr.Button("🔄 Refresh & Filter Data", variant="secondary")
            
            with gr.Row():
                with gr.Column(scale=1):
                    plot_output = gr.Plot(label="Grafik Distribusi")
                with gr.Column(scale=2):
                    gr.Markdown("#### Daftar Laporan Masuk")
                    # Tabel diperbarui dengan kolom Waktu
                    table_output = gr.Dataframe(
                        headers=["Waktu", "Pesan", "Sentimen"], 
                        interactive=False,
                        wrap=True
                    )
            
            status_txt = gr.Markdown("Klik 'Refresh' untuk memuat data terbaru.")

    # --- BINDING EVENTS ---
    # Saat klik kirim: proses teks, beri feedback, dan kosongkan textbox
    submit_btn.click(
        fn=process_submission, 
        inputs=user_input, 
        outputs=[user_feedback, user_input]
    )
    
    # Saat klik refresh: update grafik dan tabel berdasarkan filter
    refresh_btn.click(
        fn=get_admin_dashboard, 
        inputs=sentiment_filter, 
        outputs=[plot_output, table_output, status_txt]
    )

if __name__ == "__main__":
    demo.launch(server_name="0.0.0.0", server_port=7860)

	import gradio as gr
	import torch
	from transformers import pipeline
	import pandas as pd
	import matplotlib.pyplot as plt
	import seaborn as sns
	from datetime import datetime

	# --- SETUP MODEL ---
	# Menggunakan model RoBERTa Bahasa Indonesia untuk analisis sentimen
	MODEL_NAME = "w11wo/indonesian-roberta-base-sentiment-classifier"
	device = 0 if torch.cuda.is_available() else -1
	sentiment_pipeline = pipeline("sentiment-analysis", model=MODEL_NAME, device=device)

	# --- DATABASE SEDERHANA (In-Memory) ---
	all_messages = []

	# Mapping Label untuk Tampilan UI agar lebih mudah dipahami manusia
	label_map = {
	"POSITIVE": "Pujian/Apresiasi",
	"NEGATIVE": "Keluhan/Kritik",
	"NEUTRAL": "Pertanyaan/Info"
	}

	def process_submission(text):
	if not text or text.strip() == "":
	return "⚠️ Mohon isi komentar Anda terlebih dahulu.", gr.update()

	# 1. Analisis Sentimen menggunakan Pipeline Hugging Face
	result = sentiment_pipeline(text)[0]
	label = result['label'].upper()

	# 2. Simpan ke Database Lokal dengan Timestamp
	new_entry = {
	"Waktu": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
	"Pesan": text.strip(),
	"Sentimen": label
	}
	all_messages.append(new_entry)

	# 3. Respons untuk User
	thanks_msg = "Terima kasih atas pesan Anda!\nMasukan Anda telah kami terima dan akan segera ditinjau oleh tim admin posko."
	return thanks_msg, gr.update(value="")

	def get_admin_dashboard(filter_val):
	if not all_messages:
	return None, pd.DataFrame(columns=["Waktu", "Pesan", "Sentimen"]), "Belum ada data."

	df_all = pd.DataFrame(all_messages)

	# --- LOGIKA FILTER ---
	if filter_val != "SEMUA":
	# Balik mapping untuk mencari label asli (POSITIVE/NEGATIVE/NEUTRAL)
	rev_map = {v: k for k, v in label_map.items()}
	target = rev_map.get(filter_val)
	df_filtered = df_all[df_all['Sentimen'] == target]
	else:
	df_filtered = df_all

	if df_filtered.empty:
	return None, pd.DataFrame(columns=["Waktu", "Pesan", "Sentimen"]), f"Tidak ada data untuk kategori: {filter_val}"

	# --- VISUALISASI TOTAL (Pie Chart atau Bar Plot) ---
	fig, ax = plt.subplots(figsize=(8, 5))
	counts = df_all['Sentimen'].value_counts()
	# Mengubah index angka/label asli ke label buatan kita (Pujian/Keluhan/dll)
	counts.index = [label_map.get(i, i) for i in counts.index]

	sns.barplot(x=counts.index, y=counts.values, palette="viridis", ax=ax)
	ax.set_title("Proporsi Pesan Masuk (Total)", fontsize=12, fontweight='bold')
	ax.set_ylabel("Jumlah Pesan")

	# --- TABEL DENGAN KOLOM WAKTU/TANGGAL ---
	display_df = df_filtered[["Waktu", "Pesan", "Sentimen"]].copy()
	display_df['Sentimen'] = display_df['Sentimen'].map(label_map) # Percantik label di tabel
	display_df = display_df.sort_values(by="Waktu", ascending=False) # Urutkan: Terbaru di atas

	return fig, display_df, f"Menampilkan {len(df_filtered)} pesan ({filter_val})"

	# --- INTERFACE GRADIO ---
	with gr.Blocks(theme=gr.themes.Soft(primary_hue="emerald"), title="PoskoLog Dashboard") as demo:
	gr.Markdown("# 📦 PoskoLog: Suara Pengungsi")
	gr.Markdown("Sistem analisis sentimen otomatis untuk memprioritaskan laporan darurat pasca-bencana.")

	with gr.Tabs():
	# --- TAB USER ---
	with gr.Tab("📝 Sampaikan Pesan"):
	with gr.Column(variant="panel"):
	gr.Markdown("### Laporkan kondisi atau berikan masukan Anda")
	user_input = gr.Textbox(
	label="Komentar Anda",
	placeholder="Contoh: Bantuan air bersih belum sampai di tenda C...",
	lines=4
	)
	submit_btn = gr.Button("Kirim Pesan", variant="primary")
	user_feedback = gr.Markdown("")

	# --- TAB ADMIN ---
	with gr.Tab("📊 Dashboard Admin"):
	with gr.Row():
	sentiment_filter = gr.Dropdown(
	choices=["SEMUA"] + list(label_map.values()),
	value="SEMUA",
	label="Filter Sentimen"
	)
	refresh_btn = gr.Button("🔄 Refresh & Filter Data", variant="secondary")

	with gr.Row():
	with gr.Column(scale=1):
	plot_output = gr.Plot(label="Grafik Distribusi")
	with gr.Column(scale=2):
	gr.Markdown("#### Daftar Laporan Masuk")
	# Tabel diperbarui dengan kolom Waktu
	table_output = gr.Dataframe(
	headers=["Waktu", "Pesan", "Sentimen"],
	interactive=False,
	wrap=True
	)

	status_txt = gr.Markdown("Klik 'Refresh' untuk memuat data terbaru.")

	# --- BINDING EVENTS ---
	# Saat klik kirim: proses teks, beri feedback, dan kosongkan textbox
	submit_btn.click(
	fn=process_submission,
	inputs=user_input,
	outputs=[user_feedback, user_input]
	)

	# Saat klik refresh: update grafik dan tabel berdasarkan filter
	refresh_btn.click(
	fn=get_admin_dashboard,
	inputs=sentiment_filter,
	outputs=[plot_output, table_output, status_txt]
	)

	if __name__ == "__main__":
	demo.launch(server_name="0.0.0.0", server_port=7860)