Update app.py

app.py

@@ -4,41 +4,25 @@ from transformers import pipeline
 classifier = pipeline("zero-shot-classification", model="MoritzLaurer/mDeBERTa-v3-base-xnli-multilingual-nli-2mil7")
 
 def classify(text, *labels):
-    labels = [label for label in labels if label]
+    labels = list(labels)
     if not text or not labels:
         return []
     result = classifier(text, candidate_labels=labels)
     return list(zip(result['labels'], result['scores']))
 
-def dynamic_ui(labels):
-    inputs = [gr.Textbox(label=f"Class {i+1}", value=label) for i, label in enumerate(labels)]
-    return inputs
-
 with gr.Blocks() as demo:
     gr.Markdown("## Zero-Shot Text Classification")
-
-    text_input = gr.Textbox(label="Text for classification")
-    
-    labels = [""]
-    classes_container = gr.Column(dynamic_ui(labels))
-    
-    def add_class():
-        labels.append("")
-        return dynamic_ui(labels)
     
-    def remove_class():
-        if labels:
-            labels.pop()
-        return dynamic_ui(labels)
-    
-    add_button = gr.Button("Add Class")
-    remove_button = gr.Button("Remove Class")
-    add_button.click(add_class, [], classes_container)
-    remove_button.click(remove_class, [], classes_container)
+    text_input = gr.Textbox(label="Text for classification")
+    labels_input = gr.Textbox(label="Classes (comma separated)")
     
     output = gr.Dataframe(headers=["Class", "Probability"], label="Classification Results")
+
+    def update_classes(classes):
+        return classes.split(',')
+
     button = gr.Button("Classify")
     
-    button.click(classify, inputs=[text_input] + classes_container.children, outputs=output)
+    button.click(classify, inputs=[text_input, labels_input], outputs=output)
     
 demo.launch()