Update app.py

app.py

@@ -1,70 +1,90 @@
-import gradio as gr
-import torch
-from transformers import BertTokenizer, BertForSequenceClassification
-import zipfile
-import os
+import gradio as gr  # gradio makes it super easy to build a web UI
+import torch  # torch is used to run the model and handle tensors
+from transformers import BertTokenizer, BertForSequenceClassification  # for loading our fine-tuned BERT model and tokenizer
+import zipfile  # we use this to unzip the uploaded model
+import os  # lets us check if the model folder already exists
 
-#Unzip model if needed
+# check if model folder is already extracted
 if not os.path.exists("fine_tuned_model"):
+    # if not, unzip it
     with zipfile.ZipFile("fine_tuned_model.zip", 'r') as zip_ref:
         zip_ref.extractall("fine_tuned_model")
 
-#Load model and tokenizer
+# path to our model directory
 model_path = "./fine_tuned_model"
-tokenizer = BertTokenizer.from_pretrained(model_path)
-model = BertForSequenceClassification.from_pretrained(model_path)
-model.eval()
 
-#Define output labels
-label_map = {0: "Unbiased", 1: "Biased"}
+# load the tokenizer and model from the directory
+tokenizer = BertTokenizer.from_pretrained(model_path)  # tokenizer breaks text into model-friendly tokens
+model = BertForSequenceClassification.from_pretrained(model_path)  # load the actual fine-tuned BERT model
+model.eval()  # set it to eval mode so it doesn’t try to learn during predictions
 
-#Bias classification function
+# define labels in case we want to refer back to them
+label_map = {
+    0: "Unbiased",
+    1: "Biased"
+}
+
+# the main function that runs when user submits text
 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)
-        logits = outputs.logits
-        probs = torch.softmax(logits, dim=1).squeeze()
-        pred_label = torch.argmax(probs).item()
-        confidence = round(probs[pred_label].item(), 2)
+        outputs = model(**inputs)  # pass inputs through the model
+        logits = outputs.logits  # raw prediction scores
+        probs = torch.softmax(logits, dim=1).squeeze()  # turn scores into probabilities
+        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
 
-    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."
-    )
+    # now we decide the final label based on confidence score and predicted class
+    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 0.50 <= 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:
+        final_label = "Unbiased"
+        explanation = (
+            "✅ This text appears neutral with no strong signs of gender bias based on the model's prediction."
+        )
 
+    # send the results back to the UI
     return {
-        "Bias Classification": label_map[pred_label],
+        "Bias Classification": final_label,
         "Confidence Score": confidence,
         "Explanation": explanation
     }
 
-#Gradio Interface
+# build the Gradio web interface
 with gr.Blocks() as demo:
-    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."
-    )
+    # title and description at the top
+    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
     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
+        lines=4,
+        placeholder="E.g., 'The woman stayed at home while the man went to work.'"
     )
 
+    # button to trigger prediction
     submit_btn = gr.Button("Detect Bias")
+
+    # box to show the output (bias label + confidence + explanation)
     output = gr.JSON(label="Prediction Output")
 
+    # link the button to the function
     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
-    )
+    # show a simple disclaimer at the bottom for transparency
+    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()