Update app.py

app.py

@@ -7,23 +7,39 @@ models = {
     "ModernBERT Large (gender)": "breadlicker45/ModernBERT-large-gender"
 }
 
+# Define the mapping for user-friendly labels
+# Note: Transformers pipelines often output 'LABEL_0', 'LABEL_1'.
+# We handle potential variations like just '0', '1'.
+label_map = {
+    "LABEL_0": "Male (0)",
+    "0": "Male (0)",
+    "LABEL_1": "Female (1)",
+    "1": "Female (1)"
+}
+
 # Function to load the selected model and classify text
 def classify_text(model_name, text):
-    classifier = pipeline("text-classification", model=models[model_name], top_k=None)
-    predictions = classifier(text)
-
-    # Map the numerical labels to human-readable labels
-    label_mapping = {"0": "Male", "1": "Female"}
+    try:
+        classifier = pipeline("text-classification", model=models[model_name], top_k=None)
+        predictions = classifier(text)
 
-    # Construct the output dictionary with human-readable labels
-    output_predictions = {}
-    for pred in predictions[0]:
-        # Ensure the label is treated as a string for dictionary lookup
-        numerical_label_str = str(pred["label"])
-        human_readable_label = label_mapping.get(numerical_label_str, numerical_label_str) # Use fallback if label not in mapping
-        output_predictions[human_readable_label] = pred["score"]
+        # Process predictions to use friendly labels
+        processed_results = {}
+        if predictions and isinstance(predictions, list) and predictions[0]:
+             # predictions[0] should be a list of label dicts like [{'label': 'LABEL_1', 'score': 0.9...}, ...]
+            for pred in predictions[0]:
+                raw_label = pred["label"]
+                score = pred["score"]
+                # Use the map to get a friendly name, fallback to the raw label if not found
+                friendly_label = label_map.get(raw_label, raw_label)
+                processed_results[friendly_label] = score
+        return processed_results
+    except Exception as e:
+        # Handle potential errors during model loading or inference
+        print(f"Error: {e}")
+        # Return an error message suitable for gr.Label
+        return {"Error": f"Failed to process: {e}"}
 
-    return output_predictions
 
 # Create the Gradio interface
 interface = gr.Interface(
@@ -32,18 +48,20 @@ interface = gr.Interface(
         gr.Dropdown(
             list(models.keys()),
             label="Select Model",
-            value="ModernBERT Base (gender)"
+            value="ModernBERT Base (gender)" # Default model
         ),
         gr.Textbox(
             lines=2,
-            placeholder="Enter text to analyze emotions...",
-            value="I am thrilled to be a part of this amazing journey!"
+            placeholder="Enter text to classify for perceived gender...", # Corrected placeholder
+            value="This is an example sentence." # Changed example text
         )
     ],
-    outputs=gr.Label(num_top_classes=5),
-    title="ModernBERT gender Classifier",
-    description="Select a model and enter a sentence to see its associated gender and confidence scores.",
+    # The gr.Label component works well for showing classification scores
+    outputs=gr.Label(num_top_classes=2), # Show both classes explicitly
+    title="ModernBERT Gender Classifier",
+    description="Select a model and enter a sentence to see the perceived gender classification (Male=0, Female=1) and confidence scores. Note: Text-based gender classification can be unreliable and reflect societal biases.", # Updated description
 )
 
 # Launch the app
-interface.launch()
+if __name__ == "__main__":
+    interface.launch()