Update app.py

app.py

@@ -8,6 +8,23 @@ nli_classifier = pipeline("text-classification", model="tasksource/ModernBERT-ba
 if False:
     gr.load("models/answerdotai/ModernBERT-base").launch()
 
+# Define examples
+zero_shot_examples = [
+    ["I absolutely love this product, it's amazing!", "positive, negative, neutral, angry"],
+    ["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"]
+]
+
+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"]
+]
+
 def process_input(text_input, labels_or_premise, mode):
     if mode == "Zero-Shot Classification":
         labels = [label.strip() for label in labels_or_premise.split(',')]
@@ -16,7 +33,6 @@ def process_input(text_input, labels_or_premise, mode):
         return results, ''
     else:  # NLI mode
         prediction = nli_classifier([{"text": text_input, "text_pair": labels_or_premise}])[0]
-        # Force showing all three labels
         results = {
             "entailment": prediction.get("score", 0) if prediction.get("label") == "entailment" else 0,
             "contradiction": prediction.get("score", 0) if prediction.get("label") == "contradiction" else 0,
@@ -26,13 +42,33 @@ def process_input(text_input, labels_or_premise, mode):
 
 def update_interface(mode):
     if mode == "Zero-Shot Classification":
-        return gr.update(label="🏷️ Categories", placeholder="Enter comma-separated categories...")
+        return (
+            gr.update(
+                label="🏷️ Categories", 
+                placeholder="Enter comma-separated categories...",
+                value=zero_shot_examples[0][1]
+            ),
+            gr.update(value=zero_shot_examples[0][0])
+        )
     else:
-        return gr.update(label="🔎 Hypothesis", placeholder="Enter a hypothesis to compare with the premise...")
+        return (
+            gr.update(
+                label="🔎 Hypothesis", 
+                placeholder="Enter a hypothesis to compare with the premise...",
+                value=nli_examples[0][1]
+            ),
+            gr.update(value=nli_examples[0][0])
+        )
 
 with gr.Blocks() as demo:
-    gr.Markdown("# 🤖 ModernBERT Text Analysis")
+    gr.Markdown("""
+    # 🤖 ModernBERT Text Analysis, fine-tuned from Answer.ai
     
+    Using [tasksource/ModernBERT-base-nli](https://huggingface.co/tasksource/ModernBERT-base-nli), 
+    fine-tuned from [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base) 
+    on large scale tasksource classification tasks. The tuned model achieves high accuracy on reasoning and long-context NLI, outperforming Llama 3 8B on ConTRoL and FOLIO.
+    """)
+
     mode = gr.Radio(
         ["Zero-Shot Classification", "Natural Language Inference"],
         label="Select Mode",
@@ -43,13 +79,15 @@ with gr.Blocks() as demo:
         text_input = gr.Textbox(
             label="✍️ Input Text",
             placeholder="Enter your text...",
-            lines=3
+            lines=3,
+            value=zero_shot_examples[0][0]  # Initial value
         )
         
         labels_or_premise = gr.Textbox(
             label="🏷️ Categories",
             placeholder="Enter comma-separated categories...",
-            lines=2
+            lines=2,
+            value=zero_shot_examples[0][1]  # Initial value
         )
         
         submit_btn = gr.Button("Submit")
@@ -61,26 +99,14 @@ with gr.Blocks() as demo:
 
         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"]
-                ],
+                examples=zero_shot_examples,
                 inputs=[text_input, labels_or_premise],
                 label="Zero-Shot Classification Examples"
             )
 
         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"]
-                ],
+                examples=nli_examples,
                 inputs=[text_input, labels_or_premise],
                 label="Natural Language Inference Examples"
             )
@@ -94,7 +120,7 @@ with gr.Blocks() as demo:
     mode.change(
         fn=update_interface,
         inputs=[mode],
-        outputs=[labels_or_premise]
+        outputs=[labels_or_premise, text_input]
     )
     
     mode.change(