Update app.py

app.py

@@ -1,15 +1,44 @@
+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
+
+# 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")
+
+# path to our model directory
+model_path = "./fine_tuned_model"
+
+# 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
+
+# define labels just for reference (not used directly in decision now)
+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)
-
-    # logic checks both label AND confidence
-    if pred_label == 1:  # predicted as biased
+        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
+
+    # proper logic: evaluate both label and confidence
+    if pred_label == 1:  # model predicts "biased"
         if confidence > 0.75:
             final_label = "Biased"
             explanation = (
@@ -23,10 +52,10 @@ def detect_bias(text):
         else:
             final_label = "Uncertain"
             explanation = (
-                "😐 The model predicted 'biased' but with very low confidence. The result may not be reliable."
+                "😐 The model predicted 'biased' but with low confidence. The result may not be reliable."
             )
 
-    elif pred_label == 0:  # predicted as unbiased
+    elif pred_label == 0:  # model predicts "unbiased"
         if confidence > 0.75:
             final_label = "Unbiased"
             explanation = (
@@ -43,8 +72,37 @@ def detect_bias(text):
                 "😐 The model predicted 'unbiased' but with low confidence. The result is unclear."
             )
 
+    # send the results back to the UI
     return {
         "Bias Classification": final_label,
         "Confidence Score": confidence,
         "Explanation": explanation
-    }
+    }
+
+# 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("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",
+        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)
+
+    # 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()