new update example

app.py

@@ -51,26 +51,27 @@ def classify(text):
     probs_task1 = F.softmax(logits_task1, dim=1).cpu().numpy()[0]  # Extract the first batch item
     probs_task2 = F.softmax(logits_task2, dim=1).cpu().numpy()[0]  # Extract the first batch item
 
-    # Predict label with highest probability
-    pred_task1 = label_mapping_task1[probs_task1.argmax()]
-    pred_task2 = label_mapping_task2[probs_task2.argmax()]
-
-    # Format probabilities as percentages
-    probs_task1_str = ", ".join([f"{label}: {prob*100:.2f}%" for label, prob in zip(label_mapping_task1, probs_task1)])
-    probs_task2_str = ", ".join([f"{label}: {prob*100:.2f}%" for label, prob in zip(label_mapping_task2, probs_task2)])
-
-    # Combine label predictions with their probabilities
-    result_task1 = f"{pred_task1} ({probs_task1_str})"
-    result_task2 = f"{pred_task2} ({probs_task2_str})"
+    # Map probabilities to their corresponding labels
+    result_task1 = {label: prob for label, prob in zip(label_mapping_task1, probs_task1)}
+    result_task2 = {label: prob for label, prob in zip(label_mapping_task2, probs_task2)}
 
     return result_task1, result_task2
 
-# Gradio Interface
-iface = gr.Interface(fn=classify, 
-                     inputs="text", 
-                     outputs=[gr.Label(label="Fake Review Detection"), 
-                              gr.Label(label="Sentiment Classification")],
-                     title="Multitask IndoBERT: Fake Review & Sentiment Classification",
-                     description="Enter a skincare product review in Indonesian and the model will classify it as fake or trusted, and determine the sentiment.")
+# Gradio Interface with percentage bars
+iface = gr.Interface(
+    fn=classify, 
+    inputs="text", 
+    outputs=[
+        gr.outputs.Label(label="Fake Review Detection"), 
+        gr.outputs.Label(label="Sentiment Classification")
+    ],
+    title="Multitask IndoBERT: Fake Review & Sentiment Classification",
+    description="Enter a skincare product review in Indonesian and the model will classify it as fake or trusted, and determine the sentiment.",
+    examples=[
+        ["Jokowi sangat kecewa dengan POLRI atas kerusuhan yang terjadi di Malang"],
+        ["Lesti marah terhadap perlakuan KDRT yang dilakukan oleh Bilar"],
+        ["Ungkapan rasa bahagia diutarakan oleh Coki Pardede karena kebebasannya dari penjara"]
+    ]
+)
 
 iface.launch()