Update app.py

app.py

@@ -28,7 +28,7 @@ label_map = {
 def detect_bias(text):
     # convert user input into tensors using the tokenizer
     inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
-    
+
     # disable gradient tracking — we’re only doing prediction, not training
     with torch.no_grad():
         outputs = model(**inputs)  # pass inputs through the model
@@ -37,22 +37,25 @@ def detect_bias(text):
         pred_label = torch.argmax(probs).item()  # get the predicted label (0 or 1)
         confidence = round(probs[pred_label].item(), 2)  # grab the confidence score of that prediction
 
-    # now we decide the final label based on confidence score and predicted class
+    # classify the result based on thresholds
     if confidence > 0.75 and pred_label == 1:
         final_label = "Biased"
         explanation = (
             "⚠️ This text is likely biased. The model is highly confident that it reflects gender stereotypes or role bias."
         )
-    elif 0.56 <= confidence <= 0.75 and pred_label == 1:
+    elif 0.56 < confidence <= 0.75 and pred_label == 1:
         final_label = "Possibly Biased"
         explanation = (
             "🤔 This text might contain some gender bias, but the model is not entirely sure. Review it carefully."
         )
-    else:
+    elif confidence <= 0.56:
         final_label = "Unbiased"
         explanation = (
             "✅ This text appears neutral with no strong signs of gender bias based on the model's prediction."
         )
+    else:
+        final_label = label_map[pred_label]
+        explanation = "Prediction complete."
 
     # send the results back to the UI
     return {
@@ -64,7 +67,7 @@ def detect_bias(text):
 # build the Gradio web interface
 with gr.Blocks() as demo:
     # title and description at the top
-    gr.Markdown("Bias Bin – Fine-Tuned BERT Version by Aryan, Gowtham & Manoj")
+    gr.Markdown("## Bias Bin – Fine-Tuned BERT Version by Aryan, Gowtham & Manoj")
     gr.Markdown("This tool detects **gender bias** in narrative text using a BERT model fine-tuned on custom counterfactual data.")
 
     # text input box for user
@@ -87,4 +90,4 @@ with gr.Blocks() as demo:
     gr.Markdown("⚠️ **Disclaimer:** This model is trained on a small, augmented dataset and may not always be accurate. Interpret results carefully and consider human review where needed.")
 
 # launch the app (runs on HF Spaces)
-demo.launch()
+demo.launch()