Create app.py

app.py

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+import torch
+from transformers import AutoTokenizer, AutoModel
+from model import IndoBERTMultitask
+import gradio as gr
+
+# Load tokenizer
+tokenizer = AutoTokenizer.from_pretrained("plipustel/idxstockBERT")
+
+# Load model
+base_model = AutoModel.from_pretrained("indobenchmark/indobert-base-p1")
+model = IndoBERTMultitask(base_model=base_model, num_event_labels=43)
+model.load_state_dict(torch.load("pytorch_model.bin", map_location="cpu"))
+model.eval()
+
+# Label mapping
+sentiment_map = {0: "Negatif", 1: "Positif", 2: "Netral"}
+
+# Event mapping (ganti sesuai mapping di dataset Tuan)
+event_map = {
+    0: "corporate action", 1: "divestment", 2: "ipo", 3: "regulation", 4: "insider",
+    5: "market outlook", 6: "forex", 7: "commodity", 8: "finance report", 9: "debt",
+    10: "macroeconomic", 11: "acquisition", 12: "merger", 13: "product launch", 
+    14: "litigation", 15: "rating", 16: "investment", 17: "employment", 18: "governance",
+    19: "funding", 20: "competition", 21: "environment", 22: "infrastructure",
+    23: "trade", 24: "tax", 25: "technology", 26: "sector update", 27: "bankruptcy",
+    28: "security", 29: "restructuring", 30: "sustainability", 31: "ownership",
+    32: "inflation", 33: "interest rate", 34: "monetary policy", 35: "digital economy",
+    36: "consumer behavior", 37: "political", 38: "climate", 39: "energy",
+    40: "supply chain", 41: "geopolitical", 42: "misc"
+}
+
+# IDX sector labels
+idx_labels = [
+    "idx_energy", "idx_basic", "idx_indust", "idx_noncyc", "idx_cyclic",
+    "idx_health", "idx_finance", "idx_propert", "idx_techno", "idx_infra", "idx_trans"
+]
+
+# Inference function
+def predict(text):
+    with torch.no_grad():
+        encoded = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=64)
+        out_sent, out_event, out_idx = model(encoded["input_ids"], encoded["attention_mask"])
+
+        # Sentiment
+        sent_label = torch.argmax(out_sent, dim=1).item()
+        sent_result = sentiment_map[sent_label]
+
+        # Event
+        event_label = torch.argmax(out_event, dim=1).item()
+        event_result = event_map.get(event_label, "unknown")
+
+        # IDX (multi-label)
+        idx_probs = torch.sigmoid(out_idx).squeeze(0)
+        idx_result = [label for i, label in enumerate(idx_labels) if idx_probs[i] > 0.5]
+
+    return sent_result, event_result, ", ".join(idx_result)
+
+# Gradio UI
+iface = gr.Interface(
+    fn=predict,
+    inputs=gr.Textbox(lines=2, placeholder="Masukkan headline berita saham..."),
+    outputs=[
+        gr.Textbox(label="Sentiment"),
+        gr.Textbox(label="Event Type"),
+        gr.Textbox(label="IDX Sectors"),
+    ],
+    title="IndoBERT Multitask: Berita Saham",
+    description="Masukkan 1 headline berita saham. Model akan memprediksi sentimen, tipe event, dan sektor IDX terkait."
+)
+
+iface.launch()