Create app.py

app.py

@@ -0,0 +1,31 @@
+import gradio as gr
+from transformers import pipeline
+
+pipe = pipeline("zero-shot-classification",model='MoritzLaurer/mDeBERTa-v3-base-xnli-multilingual-nli-2mil7')
+
+with gr.Blocks() as demo:
+    txt = gr.Textbox('Input Text', label='Text to classify', interactive=True)
+    with gr.Row():
+        labels = gr.DataFrame(headers=['Labels'], row_count=(2, 'dynamic'), col_count=(1, 'fixed'),
+                              datatype='str', interactive=True, scale=4)
+        submit = gr.Button('Submit', scale=1)
+    with gr.Group():
+        with gr.Row():
+            checkbox = gr.Checkbox(label='Multi-Label Classification', interactive=True, info='Showing the score for more than one label')
+            dropdown = gr.Dropdown(label='Number of Labels to predict', multiselect=False, value=1, choices=list(range(1,6)),
+                        interactive=False)
+    result = gr.Label(label='Classification Result', visible=False)
+
+    def activate_dropdown(ob):
+        if not ob:
+            return gr.Dropdown(interactive=ob, value=1)
+        return gr.Dropdown(interactive=ob)
+
+    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'])})
+
+    checkbox.change(activate_dropdown, inputs=[checkbox], outputs=[dropdown])
+    submit.click(submit_btn, inputs=[txt, labels, dropdown], outputs=[result])
+demo.launch()