Update app.py

app.py

@@ -9,10 +9,10 @@ def sent_tokenize(text):
     return [s.strip() for s in sentences if s.strip()]
 
 # Initialize the classifiers
-zero_shot_classifier = pipeline("zero-shot-classification", model="tasksource/ModernBERT-base-nli")
-nli_classifier = pipeline("text-classification", model="tasksource/ModernBERT-base-nli")
+zero_shot_classifier = pipeline("zero-shot-classification", model="tasksource/ModernBERT-base-nli", device="cpu")
+nli_classifier = pipeline("text-classification", model="tasksource/ModernBERT-base-nli", device="cpu")
 
-# Define examples (including new long context example)
+# [Previous example definitions remain the same]
 zero_shot_examples = [
     ["I absolutely love this product, it's amazing!", "positive, negative, neutral"],
     ["I need to buy groceries", "shopping, urgent tasks, leisure, philosophy"],
@@ -131,7 +131,43 @@ def process_input(text_input, labels_or_premise, mode):
         analysis_html = create_analysis_html(sentence_results, global_label)
         return global_results, analysis_html
 
-# [Previous interface code remains the same until the outputs definition]
+def update_interface(mode):
+    if mode == "Zero-Shot Classification":
+        return (
+            gr.update(
+                label="🏷️ Categories", 
+                placeholder="Enter comma-separated categories...",
+                value=zero_shot_examples[0][1]
+            ),
+            gr.update(value=zero_shot_examples[0][0])
+        )
+    elif mode == "Natural Language Inference":
+        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])
+        )
+    else:  # Long Context NLI
+        return (
+            gr.update(
+                label="🔎 Global Hypothesis",
+                placeholder="Enter a hypothesis to test against the full context...",
+                value=long_context_examples[0][1]
+            ),
+            gr.update(value=long_context_examples[0][0])
+        )
+
+def update_visibility(mode):
+    return (
+        gr.update(visible=(mode == "Zero-Shot Classification")),
+        gr.update(visible=(mode == "Natural Language Inference")),
+        gr.update(visible=(mode == "Long Context NLI"))
+    )
+
+# Now define the Blocks interface
 with gr.Blocks() as demo:
     gr.Markdown("""
     # tasksource/ModernBERT-nli demonstration
@@ -167,7 +203,7 @@ with gr.Blocks() as demo:
         
         outputs = [
             gr.Label(label="📊 Results"),
-            gr.HTML(label="📈 Sentence Analysis")  # Changed from Markdown to HTML
+            gr.HTML(label="📈 Sentence Analysis")
         ]
 
         with gr.Column(variant="panel") as zero_shot_examples_panel:
@@ -191,13 +227,6 @@ with gr.Blocks() as demo:
                 label="Long Context NLI Examples",
             )
 
-    def update_visibility(mode):
-        return (
-            gr.update(visible=(mode == "Zero-Shot Classification")),
-            gr.update(visible=(mode == "Natural Language Inference")),
-            gr.update(visible=(mode == "Long Context NLI"))
-        )
-
     mode.change(
         fn=update_interface,
         inputs=[mode],