Update src/streamlit_app.py

src/streamlit_app.py

@@ -2,39 +2,40 @@ import streamlit as st
 import torch
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
 
-# ==========================
-# 1. Daftar Dua Model
-# ==========================
-MODEL_1 = "Dimsralf/indobert_no_ros"                   # Model Sentimen 1
-MODEL_2 = "Dimsralf/indobert_ros"       # Model Sentimen 2 (ganti sesuai model Anda)
+MODEL_1 = "Dimsralf/indobert_no_ros"
+MODEL_2 = "Dimsralf/indobert_ros"
 
 label_map = {0: "NEGATIF", 1: "POSITIF"}
 
-# ==========================
-# 2. Loader Model
-# ==========================
 @st.cache_resource
 def load_model(model_name):
-    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    # Coba load tokenizer fast terlebih dahulu
+    try:
+        tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=True)
+    except Exception as e_fast:
+        st.warning(f"Gagal load tokenizer fast untuk {model_name}: {e_fast}")
+        try:
+            tokenizer = AutoTokenizer.from_pretrained(model_name, use_fast=False)
+        except Exception as e_slow:
+            st.error(f"Gagal load tokenizer slow untuk {model_name}: {e_slow}")
+            # fallback ke tokenizer yang lebih umum
+            tokenizer = AutoTokenizer.from_pretrained("bert-base-multilingual-uncased", use_fast=False)
+            st.info(f"Gunakan fallback tokenizer bert-base-multilingual-uncased untuk {model_name}")
+
+    # Load model klasifikasi (asumsi model kamu untuk classification)
     model = AutoModelForSequenceClassification.from_pretrained(model_name)
     model.eval()
     return tokenizer, model
 
-# Load kedua model
 tokenizer1, model1 = load_model(MODEL_1)
 tokenizer2, model2 = load_model(MODEL_2)
 
-# ==========================
-# 3. UI
-# ==========================
-st.title("🔍 Demo Analisis Sentimen dengan 2 Model Sekaligus")
-st.write("Masukkan kalimat, dan kedua model akan memberikan prediksi masing-masing.")
+st.title("Analisis Sentimen dengan 2 Model IndoBERT")
+st.write("Model 1:", MODEL_1)  
+st.write("Model 2:", MODEL_2)
 
 text = st.text_input("Masukkan kalimat:")
 
-# ==========================
-# 4. Fungsi Prediksi
-# ==========================
 def predict(text, tokenizer, model):
     inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
     with torch.no_grad():
@@ -42,29 +43,23 @@ def predict(text, tokenizer, model):
         logits = outputs.logits
         probs = torch.softmax(logits, dim=1)
     pred_id = torch.argmax(probs, dim=1).item()
-    label = label_map[pred_id]
+    label = label_map.get(pred_id, "UNKNOWN")
     prob = probs[0][pred_id].item()
     return label, prob
 
-# ==========================
-# 5. Tampilkan Hasil Kedua Model
-# ==========================
 if text:
     label1, prob1 = predict(text, tokenizer1, model1)
     label2, prob2 = predict(text, tokenizer2, model2)
 
-    st.write("## 🧠 Hasil Prediksi dari Kedua Model")
-
+    st.write("## Hasil Prediksi")
     col1, col2 = st.columns(2)
 
     with col1:
-        st.subheader("Model Tanpa ROS")
-        st.write(f"**Nama Model:** `{MODEL_1}`")
-        st.write(f"**Prediksi:** {label1}")
-        st.write(f"**Probabilitas:** {prob1:.4f}")
+        st.subheader("Model 1 (no_ros)")
+        st.write(f"Prediksi: {label1}")
+        st.write(f"Probabilitas: {prob1:.4f}")
 
     with col2:
-        st.subheader("Model Dengan ROS")
-        st.write(f"**Nama Model:** `{MODEL_2}`")
-        st.write(f"**Prediksi:** {label2}")
-        st.write(f"**Probabilitas:** {prob2:.4f}")
+        st.subheader("Model 2 (ros)")
+        st.write(f"Prediksi: {label2}")
+        st.write(f"Probabilitas: {prob2:.4f}")