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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}") | |