revert app.py

app.py

@@ -1,99 +1,51 @@
-# -*- coding: utf-8 -*-
-"""usemodel.ipynb
-
-Automatically generated by Colaboratory.
-
-Original file is located at
-    https://colab.research.google.com/drive/1c8Qtf9TWr3apElEv2uDgCD_MQwHnmw0B
-"""
-
+import streamlit as st
+from mygrad import Layer, Value
 import pickle
-from mygrad import Neuron, Value
-import streamlit as st 
-def convertToOneHotEncode(tags):
-    tag1 = tags[0]
-    tag2 = tags[1]
-    vec1 = [0]*5
-    vec2 = [0]*4
-    vec1[tag1] = 1
-    vec2[tag2-1] = 1
-    vec1.extend(vec2)
 
+# Define the predict function
+def predict(x):
+    x1 = hiddenLayer1(x)    
+    final = outputLayer([x1] + x)
+    return final.data
 
-
-    return vec1
+# Load model
 def loadModel():
-    neuron1weightsbias = []
-    with open(f'weights.pkl', 'rb') as file:
+    neuron1weightsbias, outputneuronweightsbias = [], []
+    with open(f'parameters/neuron1weightsbias_fn_reLu.pckl', 'rb') as file:
         neuron1weightsbias = pickle.load(file)
-    neuron = Neuron(10)
-
-    neuron.w = [Value(i) for i in neuron1weightsbias[:-1]]
-    neuron.b = Value(neuron1weightsbias[-1])
-    return neuron
-
-import json
-def loadjson(filepath):
-    data = []
-    with open(filepath, 'rb') as file:
-        for line in file:
-            data.append(json.loads(line))
-    return data
-
-data = loadjson('data/train.jsonl')
-data2 = loadjson('data/test.jsonl')
-X = [element['pos_tags'] for element in data] + [element['pos_tags'] for element in data2]
-Y = [element['chunk_tags'] for element in data] + [element['chunk_tags'] for element in data2]
-
-n = loadModel()
-
-def predictsentence(postagsOfSentence):
-    if postagsOfSentence:
-        postagsOfSentence = [0] + postagsOfSentence
+    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:
-        return
-    xnew = []
-    for ix in range(1, len(postagsOfSentence)):
-        xnew.append([ postagsOfSentence[ix-1], postagsOfSentence[ix]])
-    for ix, pair in enumerate(xnew):
-        xnew[ix] = convertToOneHotEncode(pair)
-    w = Value(0)
-    chunks = []
-    for ix2, wordpair in enumerate(xnew):
-        xinput = [w] + wordpair
-        w = n(xinput)
-        if w.data > 0.5:
-            chunks.append(1)
-        else:
-            chunks.append(0)
-    return chunks
-def input_(input):
-    if not input:
-        return
-    result = word_tokenize(input)
-    word_pos=  nltk.pos_tag(result)
-    pos = [ i[1] for i in word_pos]
-    for i in range(len(pos)):
-        if pos[i] =='NN':
-            pos[i] = 1
-        elif pos[i] =='DT':
-            pos[i] = 2
-        elif pos[i] =='JJ':
-            pos[i] = 3
-        else:
-            pos[i]= 4
-    return pos
-st.title('Chunk tagging')
-input = st.text_input('Input the pos tags')
-
-import nltk
-from nltk.tokenize import word_tokenize
-
-inputs = input_(input)
-output = predictsentence(inputs)
-st.write(output)
-
-# import pandas as pd
-# data = output
-# df = pd.DataFrame.from_dict(data)
-# st.dataframe(df)
+        input.append(int(i))
+
+# Prediction
+if st.button("Predict"):
+    if flag:
+        st.stop()
+    result = predict(input)
+    st.success(f"The prediction is: {result}")