Update app.py

app.py

@@ -3,8 +3,8 @@ from transformers import pipeline
 
 # Load the models
 MODEL_PATHS = {
-    "Unitary Toxic BERT": "unitary/toxic-bert",
-    "Martin HA Toxic Comment": "martin-ha/toxic-comment-model"
+    "Toxic Bert-based model": "unitary/toxic-bert",
+    "Martin-HA-toxic-comment-model": "martin-ha/toxic-comment-model"
 }
 
 classifiers = {name: pipeline("text-classification", model=path, tokenizer=path) for name, path in MODEL_PATHS.items()}
@@ -26,17 +26,17 @@ iface = gr.Interface(
     fn=predict_toxicity,
     inputs=[
         gr.Textbox(lines=5, label="Enter text to analyze"),
-        gr.Radio(choices=list(MODEL_PATHS.keys()), label="Choose a model", value="Toxic BERT")
+        gr.Radio(choices=list(MODEL_PATHS.keys()), label="Choose a model", value="Toxic Bert-based model")
     ],
     outputs=gr.Label(num_top_classes=6, label="Toxicity Scores"),
     title="Toxicity Prediction",
-    description="This POC uses trained&pre-trained models to predict toxicity in text. Choose between two models: 'Toxic BERT' based and 'Martin HA Toxic Comment'.",
+    description="This POC uses trained & pre-trained models to predict toxicity in text. Choose between two models: 'Toxic Bert-based model' for multi-class labeling and 'Martin-HA-toxic-comment-model' for binary clasification.",
     examples=[
-        ["Great game everyone!", "Toxic BERT"],
-        ["You're such a noob, uninstall please.", "Martin HA Toxic Comment"],
-        ["I hope you die in real life, loser.", "Toxic BERT"],
-        ["Nice move! How did you do that?", "Martin HA Toxic Comment"],
-        ["Go back to the kitchen where you belong.", "Toxic BERT"],
+        ["Great game everyone!", "Toxic Bert-based model"],
+        ["You're such a noob, uninstall please.", "Martin-HA-toxic-comment-model"],
+        ["I hope you die in real life, loser.", "Toxic Bert-based model"],
+        ["Nice move! How did you do that?", "Martin-HA-toxic-comment-model"],
+        ["Go back to the kitchen where you belong.", "Toxic Bert-based model"],
     ]
 )