Create app.py

app.py

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+import gradio as gr
+from transformers import pipeline
+
+# Load both models
+bias_detector = pipeline("text-classification", model="himel7/bias-detector")
+bias_type_classifier = pipeline("text-classification", model="maximuspowers/bias-type-classifier")
+
+def detect_bias_and_type(text):
+    detection_result = bias_detector(text)[0]
+    label = detection_result['label']
+    score = detection_result['score']
+
+    if label == "LABEL_1":  # Biased
+        type_result = bias_type_classifier(text)[0]
+        bias_type = type_result['label']
+        type_score = type_result['score']
+        return (f"🧐 **Bias Detected!**\n"
+                f"- **Bias Probability:** {score:.2%}\n"
+                f"- **Bias Type:** {bias_type} (Confidence: {type_score:.2%})")
+    else:
+        return f"✅ **Unbiased** (Confidence: {score:.2%})"
+
+# Gradio UI
+iface = gr.Interface(
+    fn=detect_bias_and_type,
+    inputs=gr.Textbox(lines=3, placeholder="Enter a sentence..."),
+    outputs="markdown",
+    title="Bias Detector + Bias Type Classifier",
+    description=(
+        "This tool detects whether a text is biased and classifies the type of bias.\n"
+        "Models: `himel7/bias-detector` and `maximuspowers/bias-type-classifier`"
+    ),
+    examples=[
+        ["The brilliant leader saved the country from disaster."],
+        ["The government announced new tax reforms."],
+        ["The selfish billionaire hoarded his wealth."]
+    ]
+)
+
+if __name__ == "__main__":
+    iface.launch()