Update app.py

app.py

@@ -1,33 +1,44 @@
 import gradio as gr
-from transformers import pipeline, BertTokenizer, BertForSequenceClassification
+from transformers import pipeline
 
-# Load the fine-tuned model and tokenizer from the saved directory.
-model_path = "fine_tuned_model"
-tokenizer = BertTokenizer.from_pretrained(model_path)
-model = BertForSequenceClassification.from_pretrained(model_path)
+# Load the zero-shot classifier for bias detection using Facebook's BART MNLI.
+classifier = pipeline(
+    "zero-shot-classification",
+    model="facebook/bart-large-mnli"
+)
 
-# Create an inference pipeline using the fine-tuned model.
-# For demonstration, we use the zero-shot pipeline with BART MNLI,
-# but here you can choose to use your fine-tuned model if it is adapted for inference.
-# In this example, we'll demonstrate with the fine-tuned BERT model for classification.
-def classify_text(text):
-    # Tokenize input text
-    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=128)
-    outputs = model(**inputs)
-    # Get the predicted label (0: original, 1: swapped)
-    predicted_label = torch.argmax(outputs.logits, dim=1).item()
-    # Map the label to a meaningful output
-    label_map = {0: "Original (potentially biased)", 1: "Gender-swapped (counterfactual)"}
-    return label_map[predicted_label]
+def process_text(text):
+    # Define candidate labels for bias classification.
+    candidate_labels = ["biased", "neutral"]
+    
+    # Run zero-shot classification.
+    classification = classifier(text, candidate_labels)
+    detected_bias = classification["labels"][0]
+    confidence = classification["scores"][0]
+    
+    # Return the results.
+    return {
+        "Detected Bias": detected_bias,
+        "Confidence": round(confidence, 2),
+    }
 
 # Build the Gradio UI.
 with gr.Blocks() as demo:
-    gr.Markdown("# Bias Bin Inference")
-    gr.Markdown("Enter a narrative text below to classify it based on bias (original vs. gender-swapped).")
-    text_input = gr.Textbox(label="Enter Narrative Text", placeholder="Type text here...", lines=5)
+    gr.Markdown("# Bias Bin")
+    gr.Markdown(
+        "Detect gender stereotypes in narrative text. "
+        "Enter a story or sentence below and click the **Submit** button."
+    )
+    
+    text_input = gr.Textbox(
+        label="Enter Story Text",
+        placeholder="Type a story or sentence here...",
+        lines=5
+    )
+    
     submit_btn = gr.Button("Submit")
-    result_output = gr.Textbox(label="Predicted Label")
+    result_output = gr.JSON(label="Output")
     
-    submit_btn.click(fn=classify_text, inputs=[text_input], outputs=[result_output])
-
-demo.launch()
+    submit_btn.click(fn=process_text, inputs=[text_input], outputs=[result_output])
+    
+demo.launch()