Update app.py

app.py

@@ -1,59 +1,44 @@
 import gradio as gr
 from transformers import pipeline
 
-pipe = pipeline("zero-shot-classification",model='MoritzLaurer/mDeBERTa-v3-base-xnli-multilingual-nli-2mil7')
+classifier = pipeline("zero-shot-classification", model="MoritzLaurer/mDeBERTa-v3-base-xnli-multilingual-nli-2mil7")
 
-with gr.Blocks() as demo:
-    txt = gr.Textbox('Введите текст', label='Текст для классификации', interactive=True)
-    with gr.Row():
-        labels = gr.DataFrame(
-            headers=['Labels'], 
-            row_count=(2, 'static'), 
-            col_count=(1, 'fixed'),
-            datatype='str', 
-            interactive=True, 
-            scale=4,
-        )
-        submit = gr.Button('Обработать', scale=1)
-    with gr.Group():
-        with gr.Row():
-            checkbox = gr.Checkbox(
-                label='Множественная положительная классификация', 
-                interactive=True, 
-                info='',
-            )
-            dropdown = gr.Dropdown(
-                label='Number of Labels to predict', 
-                multiselect=False, 
-                value=1, 
-                choices=list(range(1,6),),
-                interactive=False,
-            )
-    result = gr.Label(
-        label='Результат классификации', 
-        visible=False,
-    )
+def classify(text, *labels):
+    labels = [label for label in labels if label]
+    if not text or not labels:
+        return []
+    result = classifier(text, candidate_labels=labels)
+    return list(zip(result['labels'], result['scores']))
 
-    def activate_dropdown(ob):
-        if not ob:
-            return gr.Dropdown(
-                interactive=ob, 
-                value=1,
-            )
-        return gr.Dropdown(interactive=ob)
+def dynamic_ui(labels):
+    inputs = [gr.Textbox(label=f"Class {i+1}", value=label) for i, label in enumerate(labels)]
+    return inputs
 
-    def submit_btn(text, df, label_no):
-        output = pipe(
-            text, 
-            list(df['Labels']), 
-            multi_label=True,
-        )
-        return gr.Label(
-            visible=True, 
-            num_top_classes=int(label_no),
-            value={i: j for i, j in zip(output['labels'], output['scores'])}
-        )
+with gr.Blocks() as demo:
+    gr.Markdown("## Zero-Shot Text Classification")
 
-    checkbox.change(activate_dropdown, inputs=[checkbox], outputs=[dropdown])
-    submit.click(submit_btn, inputs=[txt, labels, dropdown], outputs=[result])
-demo.launch()
+    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)
+    
+    output = gr.Dataframe(headers=["Class", "Probability"], label="Classification Results")
+    button = gr.Button("Classify")
+    
+    button.click(classify, inputs=[text_input] + classes_container.children, outputs=output)
+    
+demo.launch()