Update app.py

app.py

@@ -4,21 +4,21 @@ from transformers import BertTokenizer, BertForSequenceClassification
 import zipfile
 import os
 
-# ✅ Unzip the fine-tuned model if it's not already extracted
+#Unzip model if needed
 if not os.path.exists("fine_tuned_model"):
     with zipfile.ZipFile("fine_tuned_model.zip", 'r') as zip_ref:
         zip_ref.extractall("fine_tuned_model")
 
-# ✅ Load your fine-tuned model and tokenizer
+#Load model and tokenizer
 model_path = "./fine_tuned_model"
 tokenizer = BertTokenizer.from_pretrained(model_path)
 model = BertForSequenceClassification.from_pretrained(model_path)
 model.eval()
 
-# ✅ Define label mapping (adjust based on your labels)
-label_map = {0: "Original-like", 1: "Swapped-like"}
+#Define output labels
+label_map = {0: "Unbiased", 1: "Biased"}
 
-# ✅ Inference function
+#Bias classification function
 def detect_bias(text):
     inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
     with torch.no_grad():
@@ -27,21 +27,44 @@ def detect_bias(text):
         probs = torch.softmax(logits, dim=1).squeeze()
         pred_label = torch.argmax(probs).item()
         confidence = round(probs[pred_label].item(), 2)
-    
+
+    explanation = (
+        "⚠️ This text may contain stereotypical gender associations or role biases. "
+        "Consider rephrasing to ensure neutrality and inclusiveness."
+        if pred_label == 1
+        else "✅ This text appears neutral with no obvious gender bias based on the model's understanding."
+    )
+
     return {
-        "Predicted Class": label_map[pred_label],
-        "Confidence": confidence
+        "Bias Classification": label_map[pred_label],
+        "Confidence Score": confidence,
+        "Explanation": explanation
     }
 
-# ✅ Gradio UI
+#Gradio Interface
 with gr.Blocks() as demo:
-    gr.Markdown("# Bias Bin – Fine-Tuned BERT Version")
-    gr.Markdown("This interface uses a fine-tuned BERT model to classify gender bias in narrative text.")
-    
-    text_input = gr.Textbox(label="Enter Narrative Text", lines=4, placeholder="Type here...")
+    gr.Markdown(
+        "# Bias Bin – Fine-Tuned BERT Version by Aryan, Gowtham & Manoj\n"
+        "This tool detects **gender bias** in narrative text using a BERT model fine-tuned on custom counterfactual data."
+    )
+
+    text_input = gr.Textbox(
+        label="Enter Narrative Text",
+        placeholder="E.g., 'She is a great leader and he takes care of the house.'",
+        lines=4
+    )
+
     submit_btn = gr.Button("Detect Bias")
-    output = gr.JSON(label="Output")
+    output = gr.JSON(label="Prediction Output")
 
     submit_btn.click(fn=detect_bias, inputs=text_input, outputs=output)
 
+    #Disclaimer
+    gr.Markdown(
+        "___\n"
+        "<span style='color: gray; font-style: italic;'>⚠️ Disclaimer: This model is trained on a small, synthetic dataset. "
+        "Its predictions may not always be accurate or generalizable. Use with caution and consider human review when necessary.</span>",
+        unsafe_allow_html=True
+    )
+
 demo.launch()