Update app.py

app.py

@@ -1,25 +1,19 @@
 import gradio as gr
 import tensorflow as tf
+import numpy as np
 
 # Load the pickled model
 model = tf.keras.models.load_model("census.h5")
 
 # Define the function for making predictions
 def salarybracket(age, workclass, education, education_num, marital_status, occupation, relationship, race, gender, capital_gain, capital_loss, hours_per_week, native_country):
-    inputs = [[age, workclass, education, education_num, marital_status, occupation, relationship, race, gender, capital_gain, capital_loss, hours_per_week, native_country]]
+    inputs = np.array([[age, workclass, education, education_num, marital_status, occupation, relationship, race, gender, capital_gain, capital_loss, hours_per_week, native_country]])
     prediction = model.predict(inputs)
-    prediction_value = prediction[0]
-   
-    # Categorize prediction_value
-    if prediction_value == 0:
-        result = "Income_bracket lesserthan or equal to 50K "
-    else:
-        result = "Income_bracket greater than to 50K"
-                         
+    prediction_value = prediction[0][0]  # Assuming the prediction is a scalar
+    result = "Income_bracket lesser than or equal to 50K" if prediction_value <= 0.5 else "Income_bracket greater than 50K"
     return f"Income_bracket Prediction: {prediction_value} \n\nResult: {result}"
 
-
-
+    
 # Create the Gradio interface
 salarybracket_ga = gr.Interface(fn=salarybracket, 
                                inputs = [