Upload app.py

app.py

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+import torch
+import torch.nn as nn
+from transformers import AutoTokenizer, AutoModel
+import gradio as gr
+
+# Model multitask (Topik & Sentimen)
+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)
+
+    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
+
+# Load tokenizer & model
+tokenizer = AutoTokenizer.from_pretrained("tokenizer")
+model = MultiTaskModel("indobenchmark/indobert-base-p1", num_topic_classes=4, num_sentiment_classes=3)
+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"]
+
+# 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()
+
+    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}
+
+# Gradio UI
+demo = gr.Interface(fn=klasifikasi, inputs="text", outputs="json", title="Klasifikasi Topik dan Sentimen Pelanggan")
+demo.launch()