hidevscommunity commited on
Commit
7aae2a7
·
verified ·
1 Parent(s): 87a8e8c

Update app.py

Browse files
Files changed (1) hide show
  1. app.py +11 -11
app.py CHANGED
@@ -5,35 +5,35 @@ import streamlit.components.v1 as components
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  # Load the pickled model
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  def load_model():
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- return pickle.load(open('currency-exchange-rate-prediction_DTR.pkl', 'rb'))#change
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  # Function for model prediction
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  def model_prediction(model, features):
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- predicted = str(list(model.predict(features)[0]))
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  return predicted
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  def app_design():
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  # Add input fields for High, Open, and Low values
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- image = '10.png' #change
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  st.image(image, use_column_width=True)
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- st.subheader("Enter the following values:") #change
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-
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  Open = st.number_input("Open")
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  High = st.number_input("High")
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  Low = st.number_input("Low")
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-
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- # Create a feature list from the user inputs
 
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  features = [[Open,High,Low]]
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  # Load the model
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  model = load_model()
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  # Make a prediction when the user clicks the "Predict" button
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- if st.button('Predict Price'):
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  predicted_value = model_prediction(model, features)
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-
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- st.success(f"The Currency Exchange Rate price is: {predicted_value}")
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  def about_hidevs():
@@ -72,4 +72,4 @@ def main():
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  about_hidevs()
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  if __name__ == '__main__':
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- main()
 
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  # Load the pickled model
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  def load_model():
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+ return pickle.load(open('currency-exchange-rate-prediction_DTR.pkl', 'rb'))
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  # Function for model prediction
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  def model_prediction(model, features):
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+ predicted = str(model.predict(features)[0])
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  return predicted
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  def app_design():
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  # Add input fields for High, Open, and Low values
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+ image = '10.png'
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  st.image(image, use_column_width=True)
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+ st.subheader("Enter the following values:")
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+
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  Open = st.number_input("Open")
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  High = st.number_input("High")
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  Low = st.number_input("Low")
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+
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+
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+ # Create a feature list from the user inputs
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  features = [[Open,High,Low]]
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  # Load the model
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  model = load_model()
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  # Make a prediction when the user clicks the "Predict" button
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+ if st.button('Predict Amount'):
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  predicted_value = model_prediction(model, features)
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+ st.success(f'The Closing amount is: {predicted_value}')
 
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  def about_hidevs():
 
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  about_hidevs()
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  if __name__ == '__main__':
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+ main()