Update app.py

app.py

@@ -1,4 +1,5 @@
 import gradio as gr
+import random
 from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification
 
 # Models included within the interface
@@ -25,14 +26,26 @@ model_mapping = {
     ("roberta-base", "dmitva/human_ai_generated_text - Both AI and Human Data"): "SkwarczynskiP/roberta-base-finetuned-dmitva-AI-and-human-generated"
 }
 
-# Examples included within the interface
-examples = [
+# Example text included within the interface
+exampleText = [
     ["ex1"],
     ["ex2"],
     ["ex3"],
     ["ex4"]
 ]
 
+# Example models and datasets included within the interface
+exampleModels = ["bert-base-uncased", "roberta-base"]
+
+# Example datasets included within the interface
+exampleDatasets = ["No Dataset Finetuning",
+            "vedantgaur/GPTOutputs-MWP - AI Data Only",
+            "vedantgaur/GPTOutputs-MWP - Human Data Only",
+            "vedantgaur/GPTOutputs-MWP - Both AI and Human Data",
+            "dmitva/human_ai_generated_text - Both AI and Human Data"]
+
+examples = [[random.choice(exampleModels), random.choice(exampleDatasets), random.choice(exampleText)] for example in exampleText]
+
 
 def detect_ai_generated_text(model: str, dataset: str, text: str) -> str:
     # Get the fine-tuned model using mapping