Update app.py

app.py

@@ -1,25 +1,63 @@
 import gradio as gr
 from transformers import pipeline
 
-# Load FLAN-T5 pipeline
-classifier = pipeline("text2text-generation", model="google/flan-t5-base", max_length=32)
+# Load zero-shot classifier for bias detection using Facebook's BART MNLI.
+# This model will classify the input text into "biased" or "neutral".
+classifier = pipeline(
+    "zero-shot-classification",
+    model="facebook/bart-large-mnli"
+)
 
-# Prompt template
-def classify_bias(headline):
-    prompt = f"Classify the political bias of the following news headline into one of these three categories: Left, Center, or Right.\n\nHeadline: {headline}"
-    output = classifier(prompt)[0]['generated_text'].strip()
-    return output
+# Load FLAN-T5 for rewriting text.
+# This model will generate a neutral version of biased text.
+rewriter = pipeline(
+    "text2text-generation",
+    model="google/flan-t5-base",
+    max_length=128
+)
 
-# Gradio UI
-with gr.Blocks() as demo:
-    gr.Markdown("# 🧠 BiasLens – Political Bias Detector")
-    gr.Markdown("Enter a news headline to detect whether it's Left, Center, or Right biased.")
-
-    headline_input = gr.Textbox(label="📰 Enter News Headline", placeholder="e.g. Biden signs climate bill")
-    result_output = gr.Textbox(label="🧭 Predicted Bias")
-
-    classify_btn = gr.Button("Classify Bias")
-
-    classify_btn.click(fn=classify_bias, inputs=[headline_input], outputs=[result_output])
+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]
+    
+    # If the text is detected as biased, generate a neutral rewrite.
+    if detected_bias == "biased":
+        prompt = (
+            f"Rewrite the following text to remove gender stereotypes and "
+            f"make it neutral:\n\n{text}"
+        )
+        rewritten_text = rewriter(prompt)[0]["generated_text"]
+    else:
+        rewritten_text = "The text is already neutral."
+    
+    # Return results in a dictionary.
+    return {
+        "Detected Bias": detected_bias,
+        "Confidence": confidence,
+        "Rewritten Text": rewritten_text
+    }
 
+# Build the Gradio UI.
+with gr.Blocks() as demo:
+    gr.Markdown("# BiasNarrator")
+    gr.Markdown(
+        "Detect and neutralize gender stereotypes in narrative text. "
+        "Enter a story or sentence, and the model will check for biased language. "
+        "If bias is detected, it will generate a neutral version."
+    )
+    
+    text_input = gr.Textbox(
+        label="Enter Story Text",
+        placeholder="Type a story or sentence here...",
+        lines=5
+    )
+    result_output = gr.JSON(label="Output")
+    
+    text_input.submit(process_text, inputs=[text_input], outputs=[result_output])
+    
 demo.launch()