updated sentence input change

app.py

@@ -1,3 +1,91 @@
+# # -*- coding: utf-8 -*-
+# """usemodel.ipynb
+
+# Automatically generated by Colaboratory.
+
+# Original file is located at
+#     https://colab.research.google.com/drive/1c8Qtf9TWr3apElEv2uDgCD_MQwHnmw0B
+# """
+
+# 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)
+
+
+
+#     return vec1
+# def loadModel():
+#     neuron1weightsbias = []
+#     with open(f'weights.pkl', '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
+#     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):
+#     input = input.split(',')
+#     inputs = [int(x.strip()) for x in input]
+#     # for i in input:
+#     #     if i.isalphanumeric():
+#     #         inputs.append(int(input))
+#     #     else:
+#     #         st.write('Invalid Input')
+#     return inputs
+# st.title('Chunk tagging')
+# input = st.text_input('Input the pos tags')
+# inputs = input_(input)
+# output = predictsentence(inputs)
+# st.write(output)
+
+# # import pandas as pd
+# # data = output
+# # df = pd.DataFrame.from_dict(data)
+# # st.dataframe(df)
 # -*- coding: utf-8 -*-
 """usemodel.ipynb
 
@@ -68,16 +156,27 @@ def predictsentence(postagsOfSentence):
             chunks.append(0)
     return chunks
 def input_(input):
-    input = input.split(',')
-    inputs = [int(x.strip()) for x in input]
-    # for i in input:
-    #     if i.isalphanumeric():
-    #         inputs.append(int(input))
-    #     else:
-    #         st.write('Invalid Input')
-    return inputs
+    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)