Parthebhan commited on
Commit
7131760
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1 Parent(s): 99789b3

Update app.py

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Files changed (1) hide show
  1. app.py +13 -3
app.py CHANGED
@@ -19,7 +19,17 @@ mapping = {
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  # Define the function for making predictions
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  def salarybracket(age, workclass, education, marital_status, occupation, relationship, race, gender, native_country):
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- inputs = np.array([[age, workclass, education, marital_status, occupation, relationship, race, gender, native_country]])
 
 
 
 
 
 
 
 
 
 
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  prediction = model.predict(inputs)
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  prediction_value = prediction[0][0] # Assuming the prediction is a scalar
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  result = "Income_bracket lesser than or equal to 50K ⬇️" if prediction_value <= 0.5 else "Income_bracket greater than 50K ⬆️"
@@ -30,7 +40,7 @@ dropdown_options = {}
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  for column, values in mapping.items():
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  options = []
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  for label, value in values.items():
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- options.append({label})
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  dropdown_options[column] = options
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  # Create the Gradio interface
@@ -52,4 +62,4 @@ salarybracket_ga = gr.Interface(fn=salarybracket,
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  theme='dark'
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  )
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- salarybracket_ga.launch(share=True, debug=True)
 
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  # Define the function for making predictions
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  def salarybracket(age, workclass, education, marital_status, occupation, relationship, race, gender, native_country):
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+ # Get the value associated with the selected label
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+ workclass_value = next((v for k, v in mapping['workclass'].items() if k == workclass), None)
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+ education_value = next((v for k, v in mapping['education'].items() if k == education), None)
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+ marital_status_value = next((v for k, v in mapping['marital_status'].items() if k == marital_status), None)
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+ occupation_value = next((v for k, v in mapping['occupation'].items() if k == occupation), None)
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+ relationship_value = next((v for k, v in mapping['relationship'].items() if k == relationship), None)
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+ race_value = next((v for k, v in mapping['race'].items() if k == race), None)
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+ gender_value = next((v for k, v in mapping['gender'].items() if k == gender), None)
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+ native_country_value = next((v for k, v in mapping['native_country'].items() if k == native_country), None)
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+
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+ inputs = np.array([[age, workclass_value, education_value, marital_status_value, occupation_value, relationship_value, race_value, gender_value, native_country_value]])
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  prediction = model.predict(inputs)
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  prediction_value = prediction[0][0] # Assuming the prediction is a scalar
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  result = "Income_bracket lesser than or equal to 50K ⬇️" if prediction_value <= 0.5 else "Income_bracket greater than 50K ⬆️"
 
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  for column, values in mapping.items():
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  options = []
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  for label, value in values.items():
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+ options.append(label)
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  dropdown_options[column] = options
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  # Create the Gradio interface
 
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  theme='dark'
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  )
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+ salarybracket_ga.launch(share=True, debug=True)