Update app.py

app.py

@@ -3,21 +3,21 @@ import torch.nn as nn
 from transformers import AutoTokenizer, AutoModel
 import gradio as gr
 
-# Model multitask (Topik & Sentimen)
+# Model multitask (Topik & Sentimen) dengan nama layer sesuai model.pt
 class MultiTaskModel(nn.Module):
     def __init__(self, base_model_name, num_topic_classes, num_sentiment_classes):
         super(MultiTaskModel, self).__init__()
         self.encoder = AutoModel.from_pretrained(base_model_name)
         hidden_size = self.encoder.config.hidden_size
-        self.topic_classifier = nn.Linear(hidden_size, num_topic_classes)
-        self.sentiment_classifier = nn.Linear(hidden_size, num_sentiment_classes)
+        self.topik_classifier = nn.Linear(hidden_size, num_topic_classes)
+        self.sentimen_classifier = nn.Linear(hidden_size, num_sentiment_classes)
 
     def forward(self, input_ids, attention_mask):
         outputs = self.encoder(input_ids=input_ids, attention_mask=attention_mask)
         pooled_output = outputs.last_hidden_state[:, 0]
-        topic_logits = self.topic_classifier(pooled_output)
-        sentiment_logits = self.sentiment_classifier(pooled_output)
-        return topic_logits, sentiment_logits
+        topik_logits = self.topik_classifier(pooled_output)
+        sentimen_logits = self.sentimen_classifier(pooled_output)
+        return topik_logits, sentimen_logits
 
 # Load tokenizer & model
 tokenizer = AutoTokenizer.from_pretrained("tokenizer")
@@ -26,21 +26,19 @@ model.load_state_dict(torch.load("model.pt", map_location=torch.device("cpu")))
 model.eval()
 
 # Label mapping
-topic_labels = ["Produk", "Layanan", "Pengiriman", "Lainnya"]
-sentiment_labels = ["Negatif", "Netral", "Positif"]
+topik_labels = ["Produk", "Layanan", "Pengiriman", "Lainnya"]
+sentimen_labels = ["Negatif", "Netral", "Positif"]
 
-# Fungsi klasifikasi
 def klasifikasi(text):
     inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
     with torch.no_grad():
-        topic_logits, sentiment_logits = model(**inputs)
-        topic_probs = torch.softmax(topic_logits, dim=-1).squeeze()
-        sentiment_probs = torch.softmax(sentiment_logits, dim=-1).squeeze()
+        topik_logits, sentimen_logits = model(**inputs)
+        topik_probs = torch.softmax(topik_logits, dim=-1).squeeze()
+        sentimen_probs = torch.softmax(sentimen_logits, dim=-1).squeeze()
 
-    topic_result = {label: float(prob) for label, prob in zip(topic_labels, topic_probs)}
-    sentiment_result = {label: float(prob) for label, prob in zip(sentiment_labels, sentiment_probs)}
-    return {"Topik": topic_result, "Sentimen": sentiment_result}
+    topik_result = {label: float(prob) for label, prob in zip(topik_labels, topik_probs)}
+    sentimen_result = {label: float(prob) for label, prob in zip(sentimen_labels, sentimen_probs)}
+    return {"Topik": topik_result, "Sentimen": sentimen_result}
 
-# Gradio UI
 demo = gr.Interface(fn=klasifikasi, inputs="text", outputs="json", title="Klasifikasi Topik dan Sentimen Pelanggan")
 demo.launch()