customerrates / src /streamlit_app.py
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import streamlit as st
import numpy as np
import pandas as pd
from statistics import mode, multimode
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
import plotly.graph_objects as go
from collections import Counter
import re
import math
from google import genai
import os
from dotenv import load_dotenv
# Set a custom cache directory (inside your project)
os.environ["TRANSFORMERS_CACHE"] = "./hf_cache"
os.environ["HF_HOME"] = "./hf_home"
load_dotenv()
client = genai.Client(api_key=os.getenv("GOOGLE_API_KEY"))
def sentiment_to_rating(score, label):
if label == "positive":
scaled = 6 + score * 4 # 6–10 range
elif label == "neutral":
scaled = 4 + (score - 0.5) * 2 # 4–6 range
else: # negative
scaled = 1 + (1 - score) * 4 # 1–5 range
return round(max(1, min(10, scaled)), 2)
# --- PAGE SETUP ---
st.set_page_config(page_title="Warung Sentiment Dashboard", page_icon="🍜", layout="centered")
st.title("Analisis Kepuasan Pelanggan - Dari Profitly")
st.markdown("Masukkan hingga *15 ulasan pelanggan* untuk mengetahui tingkat kepuasan mereka secara mudah dan visual.")
sentiment_model = pipeline(
"sentiment-analysis",
model="w11wo/indonesian-roberta-base-sentiment-classifier"
)
if "num_inputs" not in st.session_state:
st.session_state.num_inputs = 1
def add_input():
if st.session_state.num_inputs < 15:
st.session_state.num_inputs += 1
else:
st.warning("⚠ Maksimum 15 ulasan sudah tercapai!")
st.header("πŸ’¬ Input Ulasan Pelanggan")
comments = []
with st.form("customer_reviews"):
for i in range(st.session_state.num_inputs):
comment = st.text_area(f"Ulasan ke-{i+1}", height=80, key=f"comment_{i}")
if comment.strip():
comments.append(comment.strip())
col1, col2 = st.columns([1, 2])
with col1:
st.form_submit_button("Tambah Ulasan Baru", on_click=add_input)
with col2:
submitted = st.form_submit_button("Analisis Semua Ulasan")
# --- ANALYSIS ---
if submitted and len(comments) > 0:
st.header("πŸ“Š Hasil Analisis Kepuasan")
data = []
for text in comments:
res = sentiment_model(text)[0]
label = res["label"].lower()
score = max(1e-6, min(res["score"], 1))
rating = sentiment_to_rating(score, label)
data.append({
"Ulasan": text,
"Sentimen": label.capitalize(),
"Nilai (1-10)": rating,
"Kepercayaan Model": round(score * 100, 2)
})
df = pd.DataFrame(data)
df = df[["Ulasan", "Sentimen", "Nilai (1-10)", "Kepercayaan Model"]]
st.subheader("πŸ“‹ Tabel Hasil Ulasan")
st.dataframe(df.style.format({
"Kepercayaan Model": "{:.2f}%",
"Nilai (1-10)": "{:.2f}"
}), use_container_width=True)
ratings = df["Nilai (1-10)"].tolist()
avg_rating = round(np.mean(ratings), 2)
sorted_ratings = sorted(ratings)
q1, q2, q3 = np.percentile(sorted_ratings, [25, 50, 75])
try:
mode_rating = round(mode([round(r) for r in ratings]), 2)
except:
mode_rating = ", ".join(map(str, multimode([round(r) for r in ratings])))
# Ringkasan cui
st.subheader("Ringkasan Kepuasan")
st.write(f"*Rata-rata kepuasan pelanggan:* {avg_rating}/10")
st.write(f"*Nilai yang paling sering muncul:* {mode_rating}")
st.write(f"*Sebagian besar pelanggan memberi nilai antara:* {q1:.2f} – {q3:.2f}")
if avg_rating >= 8:
st.success("Pelanggan sangat puas! Pertahankan kekuatan produk Anda.")
elif avg_rating >= 6:
st.info("Pelanggan cukup puas, namun masih ada ruang untuk perbaikan.")
else:
st.warning("Banyak pelanggan kurang puas. Evaluasi produk Anda.")
# Quartile
st.subheader("πŸ“¦ Persebaran Nilai Kepuasan")
fig_quartile = go.Figure()
# Background full bar
fig_quartile.add_trace(go.Bar(
x=[10],
y=["Distribusi Kepuasan"],
orientation='h',
marker_color='rgba(220, 220, 220, 0.3)',
hoverinfo='none',
showlegend=False
))
# Add vertical lines for Q1, Median, Q3, and Avg
quartiles = [
("Q1", q1, "#63b3ed"),
("Median", q2, "#48bb78"),
("Q3", q3, "#38a169"),
("Rata-rata", avg_rating, "black")
]
for label, value, color in quartiles:
fig_quartile.add_shape(
type="line",
x0=value,
x1=value,
y0=-0.4,
y1=0.4,
line=dict(color=color, width=3, dash="dot" if label=="Rata-rata" else "solid")
)
fig_quartile.add_annotation(
x=value,
y=0,
text=label,
showarrow=True,
arrowhead=2,
ax=40,
ay=-30
)
# Determine dynamic range
min_rating = min(ratings)
max_rating = max(ratings)
padding = (max_rating - min_rating) * 0.1 # 10% margin
x_min = max(1, min_rating - padding)
x_max = min(10, max_rating + padding)
fig_quartile.update_layout(
xaxis=dict(
title="Nilai Kepuasan (1–10)",
range=[x_min, x_max],
tickmode="linear",
dtick=0.5
),
yaxis=dict(showticklabels=False),
template="plotly_white",
height=300,
margin=dict(l=30, r=30, t=40, b=30)
)
st.plotly_chart(fig_quartile, use_container_width=True)
# FEEDBACK SUMMARIZER
st.subheader("🧠 Ringkasan Feedback Otomatis")
# Prepare textual summary for Gemini
summary_prompt = f"""
Kamu adalah seorang konsultan bisnis.
Jawablah secara runtun dan jelas.
Diberikan beberapa ulasan berikut (dalam bahasa Indonesia):
{comments}
Tugasmu:
1. Sebutkan secara SINGKAT TIGA hal utama yang menjadi *kekuatan bisnis* (berdasarkan ulasan positif).
2. Sebutkan secara SINGKAT TIGA hal utama yang menjadi *kelemahan atau masalah utama* (berdasarkan ulasan negatif).
3. Tentukan TIGA cara terelaborasi untuk memperkuatan keunggulan dan mencegah kelemahan bisnis terjadi secara optimal:
*Kekuatan Utama:* (isian singkat)
*Kelemahan Utama:* (isian singkat)
*Saran :* (sedikit penjelasan)
Jawab dalam format markdown terstruktur
"""
try:
with st.spinner("πŸ” Menganalisis kekuatan dan kelemahan bisnis..."):
response = client.models.generate_content(
model="gemini-2.5-flash",
contents=summary_prompt
)
summary_text = response.text.strip()
st.markdown(summary_text)
except Exception as e:
st.error("Gagal menghasilkan ringkasan otomatis. Coba refresh.")
st.exception(e)
elif submitted:
st.warning("⚠ Silakan isi minimal satu ulasan pelanggan terlebih dahulu.")