Update app.py | Separate results

app.py

@@ -8,49 +8,95 @@ model = AutoModelForSequenceClassification.from_pretrained(model_name)
 
 def predict_sentiment(text):
     if not text.strip():
-        return "Please enter text."
+        return "Please enter text.", "—", "—"
+
     inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=128)
     outputs = model(**inputs)
     predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
     predicted_class = torch.argmax(predictions, dim=1).item()
+    
     sentiment_map = {0: "NEGATIVE", 1: "NEUTRAL", 2: "POSITIVE"}
     confidence = predictions[0][predicted_class].item()
-    result = f"Predicted Sentiment: \n{sentiment_map[predicted_class]}\n"
-    result += f"Confidence: \n{confidence:.3f}\n"
-    result += f"\nRaw Probabilities:\nNegative: {predictions[0][0]:.3f}\nNeutral: {predictions[0][1]:.3f}\nPositive: {predictions[0][2]:.3f}"
-    return result
-
-demo = gr.Interface(
-    fn=predict_sentiment,
-    inputs=gr.Textbox(
-        placeholder="Type a Taglish sentence, e.g., 'Maganda pero expensive tlga'",
-        label="Input Tagalog–English (Taglish) Text",
-        lines=10,
-        max_lines=20
-    ),
-    outputs=gr.Textbox(
-        label="Sentiment Prediction",
-        lines=15,
-        max_lines=30
-    ),
-    title="🔍 LexCAT: Taglish Sentiment Analysis",
-    description="""
-    LexCAT is a lexicon-enhanced transformer model for sentiment analysis of Tagalog–English code-switched text (Taglish).
-
-    • Developed by Glenn Marcus D. Cinco for his BS/MS thesis at Mapúa University.
-    • Trained on the FiReCS dataset.
-    • Enhanced with LexiLiksik to detect intra-sentential shifts (e.g., “Maganda pero expensive” → ❌ Negative).
-    """,
-    examples=[
-        ["sobrang lambot ng burger pero expensive tlga"],
-        ["Ang ganda ng service, one star!"],
-        ["Super duper late delivery umabot ng 2 weeks metro manila area lang naman"],
-        ["Salamat sa nyo nagana nmn po sya kaya super thank you ako"],
-        ["Ganda legit, kumpleto... problema lang nainit ng sobra..."]
-    ],
-    theme="soft",
-    allow_flagging="never"
-)
+    
+    # Format each output separately
+    sentiment_output = f"{sentiment_map[predicted_class]}"
+    confidence_output = f"{confidence:.3f}"
+    probabilities_output = (
+        f"Negative: {predictions[0][0]:.3f}\n"
+        f"Neutral: {predictions[0][1]:.3f}\n"
+        f"Positive: {predictions[0][2]:.3f}"
+    )
+
+    return sentiment_output, confidence_output, probabilities_output
+
+# Define Gradio Interface with 3 separate outputs
+with gr.Blocks(theme="soft") as demo:
+    gr.Markdown("# 🔍 LexCAT: Taglish Sentiment Analysis")
+    gr.Markdown("""
+    LexCAT is a lexicon-enhanced transformer model for sentiment analysis of Tagalog–English code-switched text (Taglish). \n\n
+
+    • Developed by Glenn Marcus D. Cinco for his BS/MS thesis at Mapúa University. \n
+    • Trained on the FiReCS dataset. \n
+    • Enhanced with LexiLiksik to detect intra-sentential shifts (e.g., “Maganda pero expensive” → Negative).
+    """)
+
+    with gr.Row():
+        with gr.Column(scale=1):
+            input_box = gr.Textbox(
+                placeholder="Type a Taglish sentence, e.g., 'Maganda pero expensive tlga'",
+                label="Input Tagalog–English (Taglish) Text",
+                lines=10,
+                max_lines=20
+            )
+            submit_btn = gr.Button("Submit", variant="primary")
+            clear_btn = gr.Button("Clear")
+
+        with gr.Column(scale=1):
+            sentiment_box = gr.Textbox(
+                label="Predicted Sentiment",
+                lines=3,
+                max_lines=5,
+                interactive=False
+            )
+            confidence_box = gr.Textbox(
+                label="Confidence",
+                lines=3,
+                max_lines=5,
+                interactive=False
+            )
+            probabilities_box = gr.Textbox(
+                label="Raw Probabilities",
+                lines=6,
+                max_lines=10,
+                interactive=False
+            )
+
+    # Set up event listeners
+    submit_btn.click(
+        fn=predict_sentiment,
+        inputs=input_box,
+        outputs=[sentiment_box, confidence_box, probabilities_box]
+    )
+
+    clear_btn.click(
+        fn=lambda: ("", "", ""),
+        inputs=None,
+        outputs=[input_box, sentiment_box, confidence_box, probabilities_box]
+    )
+
+    # Add examples below
+    gr.Examples(
+        examples=[
+            ["sobrang lambot ng burger pero expensive tlga"],
+            ["Ang ganda ng service, one star!"],
+            ["Super duper late delivery umabot ng 2 weeks metro manila area lang naman"],
+            ["Salamat sa nyo nagana nmn po sya kaya super thank you ako"],
+            ["Ganda legit, kumpleto... problema lang nainit ng sobra..."]
+        ],
+        inputs=input_box,
+        outputs=[sentiment_box, confidence_box, probabilities_box],
+        label="Example Sentences"
+    )
 
 if __name__ == "__main__":
     demo.launch()