Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,123 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+import pandas as pd
+import matplotlib.pyplot as plt
+from wordcloud import WordCloud
+import requests
+import re
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+from scipy.special import softmax
+
+# ---------------------- Setup Awal ----------------------
+st.set_page_config(page_title="Analisis Opini Twitter Indonesia", layout="wide")
+
+# Load model IndoBERT Sentiment
+@st.cache_resource
+def load_model():
+    tokenizer = AutoTokenizer.from_pretrained("indobenchmark/indobert-base-p1")
+    model = AutoModelForSequenceClassification.from_pretrained("mdhugol/indonesia-bert-sentiment-classification")
+    return tokenizer, model
+
+tokenizer, model = load_model()
+label_map = {0: 'Negatif', 1: 'Netral', 2: 'Positif'}
+
+# Header 
+st.title("🇮🇩 Dashboard Analisis Sentimen X")
+st.markdown("""
+Aplikasi ini menggunakan model IndoBERT untuk menganalisis sentimen tweet berbahasa Indonesia dari Twitter atau file CSV.
+""")
+
+# Sidebar 
+st.sidebar.header("Konfigurasi")
+twitter_auth_token = st.sidebar.text_input("Twitter Bearer Token:", type="password")
+mode = st.sidebar.radio("Pilih Mode Data:", ["Ambil dari Twitter", "Upload File CSV"])
+
+# Fungsi Analisis 
+@st.cache_data
+def analisis_sentimen_indober(df):
+    results = []
+    for text in df['content']:
+        inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
+        with torch.no_grad():
+            logits = model(**inputs).logits
+        probs = softmax(logits.numpy()[0])
+        label_idx = probs.argmax()
+        results.append(label_map[label_idx])
+    df['label'] = results
+    return df
+
+def tampilkan_hasil(df):
+    st.write("### Contoh komentar", df.head())
+    st.write("### Distribusi Sentimen")
+    st.bar_chart(df['label'].value_counts())
+
+    st.write("### WordCloud")
+    all_text = " ".join(df['content'])
+    wordcloud = WordCloud(width=800, height=400, background_color='white').generate(all_text)
+    fig, ax = plt.subplots()
+    ax.imshow(wordcloud, interpolation='bilinear')
+    ax.axis('off')
+    st.pyplot(fig)
+
+    csv = df.to_csv(index=False).encode('utf-8')
+    st.download_button(
+        label="📥 Download Hasil CSV",
+        data=csv,
+        file_name='hasil_sentimen_indonesia.csv',
+        mime='text/csv',
+    )
+
+# Mode Twitter API 
+if mode == "Ambil dari Twitter":
+    keyword = st.text_input("Masukkan Kata Kunci:")
+    jumlah = st.slider("Jumlah Tweet:", 10, 100, 30)
+
+    if st.button("Ambil dan Analisis Tweet"):
+        if not twitter_auth_token:
+            st.error("Silakan masukkan Bearer Token.")
+        elif not keyword:
+            st.warning("Masukkan kata kunci terlebih dahulu.")
+        elif any(ord(c) > 127 for c in keyword):
+            st.error("Kata kunci tidak boleh mengandung emoji atau karakter non-ASCII.")
+        else:
+            with st.spinner("Mengambil tweet..."):
+                headers = {
+                    "Authorization": f"Bearer {twitter_auth_token}",
+                    "User-Agent": "StreamlitApp"
+                }
+                query = re.sub(r'[^\w\s]', '', keyword)
+                url = f"https://api.twitter.com/2/tweets/search/recent?query={query} lang:id&max_results={jumlah}&tweet.fields=created_at,text"
+
+                try:
+                    response = requests.get(url, headers=headers)
+                    if response.status_code == 200:
+                        tweets = response.json().get("data", [])
+                        if tweets:
+                            df = pd.DataFrame(tweets)
+                            df.rename(columns={"created_at": "date", "text": "content"}, inplace=True)
+                            df = analisis_sentimen_indober(df)
+                            tampilkan_hasil(df)
+                        else:
+                            st.warning("Tidak ada tweet ditemukan.")
+                    elif response.status_code == 401:
+                        st.error("Token tidak valid atau tidak memiliki izin.")
+                    elif response.status_code == 429:
+                        st.error("Terlalu banyak permintaan. Tunggu beberapa saat dan coba lagi.")
+                    else:
+                        st.error(f"Gagal mengambil data. Status: {response.status_code}")
+                except Exception as e:
+                    st.error(f"Terjadi kesalahan: {e}")
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+# ---------------------- Mode Upload CSV ----------------------
+elif mode == "Upload File CSV":
+    uploaded_file = st.file_uploader("Unggah file CSV dengan kolom 'content'", type="csv")
+    if uploaded_file:
+        try:
+            df = pd.read_csv(uploaded_file)
+            if 'content' not in df.columns:
+                st.error("Kolom 'content' tidak ditemukan.")
+            else:
+                df = analisis_sentimen_indober(df)
+                tampilkan_hasil(df)
+        except Exception as e:
+            st.error(f"Gagal membaca file: {e}")