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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}")
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