import streamlit as st
st.title("Neural Network Prediction")
# from mygrad import Layer, Value
# import pickle

# # Define the predict function
# def predict(x):
#     x1 = hiddenLayer1(x)    
#     final = outputLayer([x1] + x)
#     return final.data

# # Load model
# def loadModel():
#     neuron1weightsbias, outputneuronweightsbias = [], []
#     with open(f'parameters/neuron1weightsbias_fn_reLu.pckl', 'rb') as file:
#         neuron1weightsbias = pickle.load(file)
#     with open('parameters/outputneuronweightsbias2.pckl', 'rb') as file:
#         outputneuronweightsbias = pickle.load(file)
#     hiddenLayer1_ = Layer(10, 1, 'reLu')
#     outputLayer_ = Layer(11, 1, 'sigmoid')

#     hiddenLayer1_.neurons[0].w = [Value(i) for i in neuron1weightsbias[:-1]]
#     hiddenLayer1_.neurons[0].b = Value(neuron1weightsbias[-1])

#     outputLayer_.neurons[0].w = [Value(i) for i in outputneuronweightsbias[:-1]]
#     outputLayer_.neurons[0].b = Value(outputneuronweightsbias[-1])
#     return hiddenLayer1_, outputLayer_

# hiddenLayer1, outputLayer = loadModel()

# st.title("Neural Network Prediction")

# st.header("Input")
# inputs = st.text_input("Input 10 digits Binary no")
# input = []
# flag = 0
# if len(inputs)!=10:
#     st.write("Error: Input not equal to 10 bits")
#     flag =1
# for i in inputs:
#     if i!='0' and i!='1':
#         st.write("Please input Binary number only")
#         flag = 1
#     else:
#         input.append(int(i))

# # Prediction
# if st.button("Predict"):
#     if flag:
#         st.stop()
#     try:
#         result = predict(input)
#         st.success(f"The prediction is: {result}")
#     except Exception as e:
#         st.error(f"An error occurred: {e}")