added titles to interfaces

interfaces/cap.py

@@ -101,8 +101,9 @@ def predict_cap(text, language, domain):
     return predict(text, model_id, tokenizer_id)
 
 demo = gr.Interface(
+    title="CAP Babel Demo",
     fn=predict_cap,
     inputs=[gr.Textbox(lines=6, label="Input"),
             gr.Dropdown(languages, label="Language"),
             gr.Dropdown(domains.keys(), label="Domain")],
-    outputs=[gr.Label(num_top_classes=5, label="Output"), gr.Markdown()])
+    outputs=[gr.Label(num_top_classes=5, label="Output"), gr.Markdown()])

interfaces/emotion.py

@@ -50,8 +50,9 @@ def predict_cap(text, language, domain):
     return predict(text, model_id, tokenizer_id)
 
 demo = gr.Interface(
+    title="Emotion Babel Demo",
     fn=predict_cap,
     inputs=[gr.Textbox(lines=6, label="Input"),
             gr.Dropdown(languages, label="Language"),
             gr.Dropdown(domains.keys(), label="Domain")],
-    outputs=[gr.Label(num_top_classes=5, label="Output"), gr.Markdown()])
+    outputs=[gr.Label(num_top_classes=5, label="Output"), gr.Markdown()])

interfaces/emotion9.py

@@ -50,8 +50,9 @@ def predict_e6(text, language, domain):
     return predict(text, model_id, tokenizer_id)
 
 demo = gr.Interface(
+    title="Emotions (9) Babel Demo",
     fn=predict_e6,
     inputs=[gr.Textbox(lines=6, label="Input"),
             gr.Dropdown(languages, label="Language"),
             gr.Dropdown(domains.keys(), label="Domain")],
-    outputs=[gr.Label(num_top_classes=5, label="Output"), gr.Markdown()])
+    outputs=[gr.Label(num_top_classes=5, label="Output"), gr.Markdown()])

interfaces/illframes.py

@@ -102,8 +102,9 @@ def predict_illframes(text, language, domain):
     return predict(text, model_id, tokenizer_id, label_names)
 
 demo = gr.Interface(
+    title="ILLFRAMES Babel Demo",
     fn=predict_illframes,
     inputs=[gr.Textbox(lines=6, label="Input"),
             gr.Dropdown(languages, label="Language"),
             gr.Dropdown(domains.keys(), label="Domain")],
-    outputs=[gr.Label(num_top_classes=5, label="Output"), gr.Markdown()])
+    outputs=[gr.Label(num_top_classes=5, label="Output"), gr.Markdown()])

interfaces/manifesto.py

@@ -48,7 +48,8 @@ def predict_cap(text, language):
     return predict(text, model_id, tokenizer_id)
 
 demo = gr.Interface(
+    title="Manifesto Babel Demo",
     fn=predict_cap,
     inputs=[gr.Textbox(lines=6, label="Input"),
             gr.Dropdown(languages, label="Language")],
-    outputs=[gr.Label(num_top_classes=5, label="Output"), gr.Markdown()])
+    outputs=[gr.Label(num_top_classes=5, label="Output"), gr.Markdown()])

interfaces/ner.py

@@ -44,7 +44,8 @@ def named_entity_recognition(text, language):
     return output, output_info
 
 demo = gr.Interface(
+    title="NER Babel Demo",
     fn=named_entity_recognition,
     inputs=[gr.Textbox(lines=6, label="Input"),
             gr.Dropdown(languages, label="Language")],
-    outputs=[gr.HighlightedText(label='Output'), gr.Markdown()])
+    outputs=[gr.HighlightedText(label='Output'), gr.Markdown()])

interfaces/ontolisst.py

@@ -82,7 +82,8 @@ def predict_cap(text, language):
     return predict(text, model_id, tokenizer_id)
 
 demo = gr.Interface(
+    title="ONTOLISST Babel Demo",
     fn=predict_cap,
     inputs=[gr.Textbox(lines=6, label="Input"),
             gr.Dropdown(languages, label="Language")],
-    outputs=[gr.Label(num_top_classes=3, label="Output"), gr.Markdown()])
+    outputs=[gr.Label(num_top_classes=3, label="Output"), gr.Markdown()])

interfaces/sentiment.py

@@ -59,8 +59,9 @@ def predict_cap(text, language, domain):
     return predict(text, model_id, tokenizer_id)
 
 demo = gr.Interface(
+    title="Sentiment (3) Babel Demo",
     fn=predict_cap,
     inputs=[gr.Textbox(lines=6, label="Input"),
             gr.Dropdown(languages, label="Language"),
             gr.Dropdown(domains.keys(), label="Domain")],
-    outputs=[gr.Label(num_top_classes=3, label="Output"), gr.Markdown()])
+    outputs=[gr.Label(num_top_classes=3, label="Output"), gr.Markdown()])