Update app.py

app.py

@@ -1,6 +1,17 @@
 import gradio as gr
 import joblib
 
+# Define the class names
+class_names = [
+    'Family Issues', 
+    'Relationship Conflicts', 
+    'Work Dynamics',
+    'Financial and Legal Disagreements', 
+    'Personal Boundaries',
+    'Cultural and Identity-Based Issues', 
+    'Other'
+]
+
 # Define the custom pipeline
 class CustomSVMTextClassificationPipeline:
     def __init__(self, model_path, vectorizer_path):
@@ -18,9 +29,12 @@ class CustomSVMTextClassificationPipeline:
         # Predict using the model
         predictions = self.model.predict(preprocessed_texts)
 
-        # Convert predictions into a readable format
-        results = [{"text": text, "predictions": list(pred)} for text, pred in zip(texts, predictions)]
-        return results
+        # Convert predictions into readable format (class names)
+        results = []
+        for pred in predictions:
+            predicted_classes = [class_names[i] for i, value in enumerate(pred) if value == 1]
+            results.append(predicted_classes)
+        return results if len(results) > 1 else results[0]  # Return a single result for single input
 
 # Load the model and vectorizer
 model_path = "svm_multi_output_model.pkl"  # Replace with your model file path