Create app.py

app.py

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+import gradio as gr
+from transformers import BertTokenizer, BertForSequenceClassification
+import torch
+import torch.nn.functional as F
+
+# Load the tokenizer and model
+tokenizer = BertTokenizer.from_pretrained('indobenchmark/indobert-large-p1')
+model = BertForSequenceClassification.from_pretrained("hendri/nergrit")
+
+labels = ["LABEL_0", "LABEL_1", "LABEL_2", "LABEL_3", "LABEL_4", "LABEL_5" , "LABEL_6"]
+
+# Map these to your actual labels:
+label_mapping = {
+    "LABEL_0": "I-PERSON",
+    "LABEL_1": "B-ORGANISATION",
+    "LABEL_2": "I-ORGANISATION",
+    "LABEL_3": "B-PLACE",
+    "LABEL_4": "I-PLACE",
+    "LABEL_5": "O",
+    "LABEL_6": "B-PERSON"
+}
+
+# Define a function to process user input and return predictions
+def classify_emotion(text):
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=128)
+    with torch.no_grad():
+        outputs = model(**inputs)
+        logits = outputs.logits
+        probabilities = F.softmax(logits, dim=-1)
+        predictions = {label_mapping[labels[i]]: round(float(prob), 4) for i, prob in enumerate(probabilities[0])}
+    return predictions
+
+# Create the Gradio interface
+interface = gr.Interface(
+    fn=classify_emotion,
+    inputs=gr.Textbox(label="Enter Text for NER"),
+    outputs=gr.Label(label="Predicted NER"),
+    title="Emotion Classification",
+    description="This application uses an IndoBERT model fine-tuned for NER. Enter a sentence (bahasa Indonesia) to see the predicted NER and their probabilities."
+)
+
+# Launch the Gradio interface
+interface.launch()