Update app.py

app.py

@@ -8,17 +8,16 @@ vectorizer = joblib.load('count_vectorizer2.joblib')
 
 def predict(input_text):
     # Preprocess the input with your vectorizer and encoder as needed
-    # For example, if your model expects vectorized input:
     vectorized_text = vectorizer.transform([input_text])
     
     # Make a prediction
     prediction = model.predict(vectorized_text)
     
-    # If your model's output needs to be decoded (optional)
+    # Decode the prediction into a readable label
     decoded_prediction = encoder.inverse_transform(prediction)
     
-    # Return the prediction (you might want to convert it into a more readable form)
-    return prediction[0]  # Modify this according to your needs
+    # Return the decoded prediction
+    return decoded_prediction[0]  #
 
 # Setup the Gradio interface
 iface = gr.Interface(fn=predict,
@@ -38,4 +37,4 @@ def greet(name):
 
 iface = gr.Interface(fn=greet, inputs="text", outputs="text")
 iface.launch()
-"""
+"""