Update app.py

app.py

@@ -1,56 +1,56 @@
 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")
+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}")
+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}")