| import torch | |
| import torch.nn as nn | |
| from transformers import AutoTokenizer, AutoModel | |
| import gradio as gr | |
| 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.topik_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, token_type_ids=None): | |
| outputs = self.encoder( | |
| input_ids=input_ids, | |
| attention_mask=attention_mask, | |
| token_type_ids=token_type_ids | |
| ) | |
| pooled_output = outputs.last_hidden_state[:, 0] | |
| topik_logits = self.topik_classifier(pooled_output) | |
| sentimen_logits = self.sentiment_classifier(pooled_output) | |
| return topik_logits, sentimen_logits | |
| tokenizer = AutoTokenizer.from_pretrained("tokenizer") | |
| model = MultiTaskModel("indobenchmark/indobert-base-p1", num_topic_classes=5, num_sentiment_classes=3) | |
| model.load_state_dict(torch.load("model.pt", map_location=torch.device("cpu"))) | |
| model.eval() | |
| topik_labels = ["Produk", "Layanan", "Pengiriman", "Pembatalan", "Lainnya"] | |
| sentimen_labels = ["Negatif", "Netral", "Positif"] | |
| def klasifikasi(text): | |
| inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True) | |
| with torch.no_grad(): | |
| topik_logits, sentimen_logits = model(**inputs) | |
| topik_idx = torch.argmax(topik_logits, dim=-1).item() | |
| sentimen_idx = torch.argmax(sentimen_logits, dim=-1).item() | |
| topik = topik_labels[topik_idx] | |
| sentimen = sentimen_labels[sentimen_idx] | |
| ringkasan = f"Pelanggan: {text}\nCS: Kami senang dapat membantu Anda." | |
| return f"HASIL ANALISIS\nTopik: {topik}\nSentimen: {sentimen}\nRingkasan: {ringkasan}" | |
| demo = gr.Interface(fn=klasifikasi, inputs="text", outputs="text", title="Klasifikasi Topik dan Sentimen Pelanggan") | |
| demo.launch() | |