Update app.py

app.py

@@ -7,21 +7,21 @@ nli_classifier = pipeline("text-classification", model="tasksource/ModernBERT-ba
 
 def process_input(text_input, labels_or_premise, mode):
     if mode == "Zero-Shot Classification":
-        # Clean and process the labels
         labels = [label.strip() for label in labels_or_premise.split(',')]
-        
-        # Get predictions
         prediction = zero_shot_classifier(text_input, labels)
         results = {label: score for label, score in zip(prediction['labels'], prediction['scores'])}
         return results, ''
-    
     else:  # NLI mode
-        # Process as premise-hypothesis pair
         prediction = nli_classifier([{"text": text_input, "text_pair": labels_or_premise}])
         results = {pred['label']: pred['score'] for pred in prediction}
         return results, ''
 
-# Create the interface
+def update_interface(mode):
+    if mode == "Zero-Shot Classification":
+        return gr.update(label="🏷️ Categories", placeholder="Enter comma-separated categories...")
+    else:
+        return gr.update(label="Hypothesis", placeholder="Enter a hypothesis to compare with the premise...")
+
 with gr.Blocks() as demo:
     gr.Markdown("# 🤖 ModernBERT Text Analysis")
     
@@ -39,8 +39,8 @@ with gr.Blocks() as demo:
         )
         
         labels_or_premise = gr.Textbox(
-            label="🏷️ Categories / Premise",
-            placeholder="Enter comma-separated categories or premise text...",
+            label="🏷️ Categories",
+            placeholder="Enter comma-separated categories...",
             lines=2
         )
         
@@ -51,38 +51,48 @@ with gr.Blocks() as demo:
             gr.Markdown(label="📈 Analysis", visible=False)
         ]
 
-    # Different examples for each mode
-    zero_shot_examples = [
-        ["I need to buy groceries", "shopping, urgent tasks, leisure, philosophy"],
-        ["The sun is very bright today", "weather, astronomy, complaints, poetry"],
-        ["I love playing video games", "entertainment, sports, education, business"],
-        ["The car won't start", "transportation, art, cooking, literature"],
-        ["She wrote a beautiful poem", "creativity, finance, exercise, technology"]
-    ]
-
-    nli_examples = [
-        ["A man is sleeping on a couch", "The man is awake"],
-        ["The restaurant is full of people", "The place is empty"],
-        ["The child is playing with toys", "The kid is having fun"],
-        ["It's raining outside", "The weather is wet"],
-        ["The dog is barking at the mailman", "There is a cat"]
-    ]
+        with gr.Column(variant="panel") as zero_shot_examples_panel:
+            gr.Examples(
+                examples=[
+                    ["I need to buy groceries", "shopping, urgent tasks, leisure, philosophy"],
+                    ["The sun is very bright today", "weather, astronomy, complaints, poetry"],
+                    ["I love playing video games", "entertainment, sports, education, business"],
+                    ["The car won't start", "transportation, art, cooking, literature"],
+                    ["She wrote a beautiful poem", "creativity, finance, exercise, technology"]
+                ],
+                inputs=[text_input, labels_or_premise],
+                label="Zero-Shot Classification Examples"
+            )
 
-    examples = gr.Examples(
-        examples=zero_shot_examples,
-        inputs=[text_input, labels_or_premise]
-    )
+        with gr.Column(variant="panel") as nli_examples_panel:
+            gr.Examples(
+                examples=[
+                    ["A man is sleeping on a couch", "The man is awake"],
+                    ["The restaurant is full of people", "The place is empty"],
+                    ["The child is playing with toys", "The kid is having fun"],
+                    ["It's raining outside", "The weather is wet"],
+                    ["The dog is barking at the mailman", "There is a cat"]
+                ],
+                inputs=[text_input, labels_or_premise],
+                label="Natural Language Inference Examples"
+            )
 
-    def update_examples(mode_value):
-        return gr.Examples(
-            examples=zero_shot_examples if mode_value == "Zero-Shot Classification" else nli_examples,
-            inputs=[text_input, labels_or_premise]
-        ).update()
+    def update_visibility(mode):
+        return (
+            gr.update(visible=(mode == "Zero-Shot Classification")),
+            gr.update(visible=(mode == "Natural Language Inference"))
+        )
 
     mode.change(
-        fn=update_examples,
+        fn=update_interface,
+        inputs=[mode],
+        outputs=[labels_or_premise]
+    )
+    
+    mode.change(
+        fn=update_visibility,
         inputs=[mode],
-        outputs=[examples]
+        outputs=[zero_shot_examples_panel, nli_examples_panel]
     )
     
     submit_btn.click(
@@ -91,6 +101,5 @@ with gr.Blocks() as demo:
         outputs=outputs
     )
 
-# Launch the app
 if __name__ == "__main__":
     demo.launch()