Updated GUI

app.py

@@ -1,8 +1,11 @@
 import gradio as gr
 from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification
 
+# Models included within the interface
 models = ["bert-base-uncased", "roberta-base"]
-datasets = ["None", 
+
+# Datasets included within the interface
+datasets = ["None",
             "vedantgaur/GPTOutputs-MWP - AI Data Only",
             "vedantgaur/GPTOutputs-MWP - Human Data Only",
             "vedantgaur/GPTOutputs-MWP - Both AI and Human Data",
@@ -22,6 +25,7 @@ model_mapping = {
     ("roberta-base", "dmitva/human_ai_generated_text - Both AI and Human Data"): "SkwarczynskiP/roberta-base-finetuned-dmitva-AI-and-human-generated"
 }
 
+
 def detect_ai_generated_text(model: str, dataset: str, text: str) -> str:
     # Get the fine-tuned model using mapping
     finetuned_model = model_mapping.get((model, dataset))
@@ -33,7 +37,21 @@ def detect_ai_generated_text(model: str, dataset: str, text: str) -> str:
     # Classify the input based on the fine-tuned model
     classifier = pipeline('text-classification', model=model, tokenizer=tokenizer)
     result = classifier(text)
-    return "AI-generated" if result[0]['label'] == 'LABEL_1' else "Not AI-generated"
+
+    # Get the label and score
+    label = "AI-generated" if result[0]['label'] == 'LABEL_1' else "Human-written"
+    score = result[0]['score']
+
+    return f"{label} with confidence {score * 100:.2f}%"
+
+
+# Examples included within the interface
+examples = [
+    ["AI Generated", ""],
+    ["Human Written", ""],
+    ["AI Generated", ""],
+    ["Human Written", ""]
+]
 
 interface = gr.Interface(
     fn=detect_ai_generated_text,
@@ -42,7 +60,9 @@ interface = gr.Interface(
         gr.Dropdown(choices=datasets, label="Dataset"),
         gr.Textbox(lines=5, label="Input Text")
     ],
-    outputs=gr.Textbox(label="Output")
+    outputs=gr.Textbox(label="Output"),
+    examples=examples,
+    title="AI Generated Text Detection",
 )
 
 if __name__ == "__main__":