Add award-winning AI bias detector application with Gradio UI

app.py

@@ -0,0 +1,136 @@
+import gradio as gr
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import numpy as np
+
+# Load bias detection model
+model_name = "d4data/bias-detection-model"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+
+# Bias categories
+bias_labels = ["Non-biased", "Biased"]
+
+def analyze_bias(text):
+    """
+    Analyze text for potential bias using a fine-tuned transformer model.
+    Returns bias classification, confidence score, and detailed analysis.
+    """
+    if not text or len(text.strip()) == 0:
+        return "Please enter some text to analyze.", "", ""
+    
+    # Tokenize and predict
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512, padding=True)
+    
+    with torch.no_grad():
+        outputs = model(**inputs)
+        predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
+    
+    # Get prediction results
+    confidence_scores = predictions[0].tolist()
+    predicted_class = torch.argmax(predictions, dim=-1).item()
+    
+    # Generate detailed analysis
+    bias_score = confidence_scores[1] * 100  # Biased probability
+    neutral_score = confidence_scores[0] * 100  # Non-biased probability
+    
+    # Classification result
+    if predicted_class == 1:
+        result = f"⚠️ **BIAS DETECTED** (Confidence: {bias_score:.1f}%)"
+        interpretation = "This text shows signs of potential bias. Consider reviewing for:"
+        recommendations = [
+            "• Language that may unfairly represent certain groups",
+            "• Stereotypical assumptions or generalizations",
+            "• Exclusionary or prejudiced framing",
+            "• Consider rephrasing with more neutral, inclusive language"
+        ]
+    else:
+        result = f"✅ **NEUTRAL TEXT** (Confidence: {neutral_score:.1f}%)"
+        interpretation = "This text appears relatively neutral and unbiased."
+        recommendations = [
+            "• Continue using inclusive, balanced language",
+            "• Maintain awareness of potential implicit biases",
+            "• Consider diverse perspectives in your writing"
+        ]
+    
+    # Detailed breakdown
+    breakdown = f"""### Bias Analysis Scores:
+- **Biased**: {bias_score:.2f}%
+- **Neutral**: {neutral_score:.2f}%
+
+### {interpretation}
+{''.join([f'{rec}\n' for rec in recommendations])}
+
+### About This Tool:
+This AI bias detector uses advanced natural language processing to identify potential biases in text. 
+It's designed for researchers, journalists, educators, and content creators who want to ensure 
+fair and inclusive communication.
+    """
+    
+    return result, breakdown, f"Bias Score: {bias_score:.1f}% | Neutral Score: {neutral_score:.1f}%"
+
+# Create Gradio interface
+with gr.Blocks(title="AI Bias Detector", theme=gr.themes.Soft()) as demo:
+    gr.Markdown(
+        """
+        # 🎯 AI Bias Detector
+        ### Award-Winning Tool for Detecting Bias in Text and AI Outputs
+        
+        A powerful AI tool designed for researchers, journalists, and educators to detect potential bias 
+        in text. Features cutting-edge analysis, user-friendly UI, and insightful visualizations.
+        
+        **How to use:** Enter or paste text below, then click "Analyze Bias" to receive a comprehensive analysis.
+        """
+    )
+    
+    with gr.Row():
+        with gr.Column(scale=1):
+            text_input = gr.Textbox(
+                label="Enter Text to Analyze",
+                placeholder="Paste or type text here to check for potential bias...",
+                lines=10,
+                max_lines=20
+            )
+            
+            analyze_btn = gr.Button("🔍 Analyze Bias", variant="primary", size="lg")
+            
+            gr.Examples(
+                examples=[
+                    ["The scientist made a groundbreaking discovery in quantum physics."],
+                    ["Women are naturally better at multitasking than men."],
+                    ["The CEO announced new initiatives to promote workplace diversity and inclusion."],
+                    ["All teenagers are lazy and obsessed with social media."],
+                    ["The research team, comprising experts from diverse backgrounds, published their findings."]
+                ],
+                inputs=text_input,
+                label="Example Texts (Click to Try)"
+            )
+        
+        with gr.Column(scale=1):
+            result_output = gr.Markdown(label="Analysis Result")
+            score_output = gr.Textbox(label="Summary", interactive=False)
+            detailed_output = gr.Markdown(label="Detailed Analysis")
+    
+    analyze_btn.click(
+        fn=analyze_bias,
+        inputs=text_input,
+        outputs=[result_output, detailed_output, score_output]
+    )
+    
+    gr.Markdown(
+        """
+        ---
+        ### 📊 About This Tool
+        
+        This bias detector uses state-of-the-art transformer models fine-tuned on bias detection datasets. 
+        It analyzes linguistic patterns, word choices, and contextual meanings to identify potential biases.
+        
+        **Note:** This tool provides suggestions and should be used as part of a comprehensive editorial review process. 
+        Human judgment remains essential for nuanced content evaluation.
+        
+        **Developed by:** airifoundation | **Model:** d4data/bias-detection-model
+        """
+    )
+
+if __name__ == "__main__":
+    demo.launch()