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yvielcastillejos · Nov 13, 2020 · 2c10412 · 2c10412
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diff --git a/Neural Network/__pycache__/bot.cpython-38.pyc b/Neural Network/__pycache__/bot.cpython-38.pyc
diff --git a/Neural Network/__pycache__/model.cpython-38.pyc b/Neural Network/__pycache__/model.cpython-38.pyc
diff --git a/Neural Network/get_data.py b/Neural Network/get_data.py
@@ -0,0 +1,115 @@
+import random
+import json
+import torch
+import torch
+import torch.optim as optim
+import matplotlib.pyplot as plt
+import torchtext
+from torchtext import data
+import spacy
+import pandas as pd
+import numpy as np
+import time
+import torch.nn.utils.rnn as tnt
+import sklearn
+from sklearn.preprocessing import LabelEncoder, OneHotEncoder
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import confusion_matrix
+
+# Download spacy
+#!python -m spacy download en
+
+# Get the data in tsv form
+
+#device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+file = "/content/drive/My Drive/Colab Notebooks/Chat bot/intents.json"
+path  = "/content/drive/My Drive/Colab Notebooks/Chat bot"
+
+data2 = dict()
+
+def get_dataframe(filename):
+    global label
+    global tags
+    global labeldecode
+    with open(file, 'r') as json_data:
+        intents = json.load(json_data)
+
+    # Converting to dataframe
+    tags = []
+    patterns = []
+    patterns_full = []
+    for intent in intents['intents']:
+        tag = intent['tag']
+        # add to tag list
+        tags.append(tag)
+        for pattern in intent['patterns']:
+            patterns.append(pattern)
+        patterns_full.append(patterns)
+        patterns = []
+
+    label = []
+    # Label encode the labels
+    for i, tag in enumerate(tags):
+        label.append(i)
+
+    labeldecode = dict()
+    for i in range(len(label)):
+        labeldecode[label[i]] = tags[i]
+    print(labeldecode)
+
+    data1 = dict()
+    # Dictionary
+    print(len(patterns_full[0]))
+    for i in range(len(tags)):
+        for j in range(len(patterns_full[i])):
+            data1[str(patterns_full[i][j])] = int(label[i])
+
+    print(data1)
+    df = pd.DataFrame(list(data1.items()),columns = ['text','label'])
+    print(df)
+    print("=================================")
+    df.value_counts('label')
+    return df
+
+df = get_dataframe(file)
+print(label)
+print(labeldecode)
+
+train = df
+validation = train
+train.to_csv('/content/drive/My Drive/Colab Notebooks/Chat bot/train.tsv', sep='\t', index=False)
+validation.to_csv('/content/drive/My Drive/Colab Notebooks/Chat bot/valid.tsv', sep='\t', index=False)
+
+
+
+
+# Get data in tabular and bucket iterator form by tokenizing it 
+
+TEXT = data.Field(sequential=True, lower=True, tokenize='spacy', include_lengths=True)
+LABELS = data.Field(sequential=False, use_vocab=False)
+def Tokenize(path):
+    global TEXT 
+    TEXT = data.Field(sequential=True, lower=True, tokenize='spacy', include_lengths=True)
+    global LABELS 
+    LABELS = data.Field(sequential=False, use_vocab=False)
+    train_data, val_data = data.TabularDataset.splits( path=path, train='train.tsv',
+                                                              validation='valid.tsv', format='tsv', skip_header=True,
+                                                              fields=[('text', TEXT), ('label', LABELS)])
+
+    train_iter, val_iter = data.BucketIterator.splits((train_data, val_data), 
+                                                             batch_sizes=(9, 9), 
+                                                             sort_key=lambda x: len(x.text), 
+                                                             device=None, sort_within_batch=True, 
+                                                             repeat=False)
+
+    return train_iter, val_iter, train_data, val_data
+
+train_iter, val_iter, train_data, val_data = Tokenize(path)
+
+
+# Build the  vocab data
+
+TEXT.build_vocab(train_data,val_data)
+TEXT.vocab.load_vectors(torchtext.vocab.GloVe(name='6B', dim=100)) 
+vocab = TEXT.vocab
+print("Shape of Vocab:",TEXT.vocab.vectors.shape)
diff --git a/Neural Network/intents.json b/Neural Network/intents.json
@@ -0,0 +1,82 @@
+{
+  "intents": [
+    {
+      "tag": "greeting",
+      "patterns": [
+        "Hi",
+        "What's cracking",
+        "How've you been?",
+        "How have you been",
+        "Hey",
+        "How are you",
+        "Is anyone there?",
+        "Hello",
+        "Good day",
+        "Good afternoon",
+        "Good Morning",
+        "Hello",
+          "Good day",
+          "Howdy",
+          "What's up",
+          "Yo",
+          "Hey there",
+          "Sup",
+          "Good to see you",
+          "Happy to see you"
+
+      ],
+      "responses": [
+        "Hey :-)",
+        "Hello, thanks for visiting",
+        "Hi there, what can I do for you?",
+        "Hi there, how can I help?"
+      ]
+    },
+    {
+      "tag": "goodbye",
+      "patterns": ["I’m out of here","See ya", "I will see you then","Peace","Peace out","Take Care", "I look forward to our next meeting","Until Then","Have a nice day","I’m off"," I’ve got to get going",  "I must be going","Bye","Bye Bye", "See you later", "See you soon",  "Talk to you later", "See you later", "Goodbye"],
+      "responses": [
+        "See you later, thanks for visiting",
+        "Have a nice day",
+        "Bye! Come back again soon."
+      ]
+    },
+    {
+      "tag": "thanks",
+      "patterns": ["Thanks", "Thank you", "Thank you so much","That's helpful", "Thank's a lot!", "Okay", "Thank thee", "Thank you, you're amazing!", "Thank you kindly", "I’m so thankful for everything you bring to the table", " Thanks a million", "I truly appreciate your hard work", "I truly appreciate you", " From the bottom of my heart, thank you", " Please accept my deepest thanks"
+                    ,"Grateful for your support", " I appreciate your thoughtfulness, you’ve made my day!", "Thanks for being in my corner", " Please accept my deepest thanks"],
+      "responses": ["Happy to help!", "Any time!", "My pleasure"]
+    },
+    {
+      "tag": "bad",
+      "patterns": [
+        "You suck so much ",
+        " I hate you so much",
+        "You are so bad", "Suck my Suck my","Why are you so bad at this", "You don't help", "You make me want to die", "You are just awful","You are ugly", "F You so much", "You are a retard", "You are very stupid",
+          "You are lame", "Shut up bastard", "You tick me off", "You are an idiot", "You are lame", "Shut up bastard", "You tick me off", "You are an idiot", "You are a fool", "You are not funny"
+      ],
+      "responses": [
+        "Oh Okay...",
+        "I'm sorry",
+          "Sorry",
+          "I didn't mean to be bad, sorry"
+      ]
+    },
+    {
+      "tag": "funny",
+      "patterns": [
+        "Tell me a joke!",
+        "Tell me something funny!",
+        "Do you know a joke?",
+          "joke joke joke", "Give me a joke", "Joke to me", "What is funny", "How about a joke",
+          "Tell me something random", "Give me something funny", "Tell me a random joke",
+          "What is really funny", "Tell me an anecdote", "Tell me a funny story", "make me laugh",
+          "I want to laugh", "Can you make me laugh?", "What can make me laugh"
+      ],
+      "responses": [
+        "Why did the hipster burn his mouth? He drank the coffee before it was cool.",
+        "What did the buffalo say when his son left for college? Bison.", "joke joke joke", "What did the Cannibal get when he arrived to the party late? A cold shoulder", "What does a mermaid wear to her math class? An Algae Bra"
+      ]
+    }
+  ]
+}
diff --git a/Neural Network/model_cnn.pt b/Neural Network/model_cnn.pt
diff --git a/Neural Network/train.tsv b/Neural Network/train.tsv
@@ -0,0 +1,91 @@
+text	label
+Hi	0
+What's cracking	0
+How've you been?	0
+How have you been	0
+Hey	0
+How are you	0
+Is anyone there?	0
+Hello	0
+Good day	0
+Good afternoon	0
+Good Morning	0
+Howdy	0
+What's up	0
+Yo	0
+Hey there	0
+Sup	0
+Good to see you	0
+Happy to see you	0
+I’m out of here	1
+See ya	1
+I will see you then	1
+Peace	1
+Peace out	1
+Take Care	1
+I look forward to our next meeting	1
+Until Then	1
+Have a nice day	1
+I’m off	1
+ I’ve got to get going	1
+I must be going	1
+Bye	1
+Bye Bye	1
+See you later	1
+See you soon	1
+Talk to you later	1
+Goodbye	1
+Thanks	2
+Thank you	2
+Thank you so much	2
+That's helpful	2
+Thank's a lot!	2
+Okay	2
+Thank thee	2
+Thank you, you're amazing!	2
+Thank you kindly	2
+I’m so thankful for everything you bring to the table	2
+ Thanks a million	2
+I truly appreciate your hard work	2
+I truly appreciate you	2
+ From the bottom of my heart, thank you	2
+ Please accept my deepest thanks	2
+Grateful for your support	2
+ I appreciate your thoughtfulness, you’ve made my day!	2
+Thanks for being in my corner	2
+You suck so much 	3
+ I hate you so much	3
+You are so bad	3
+Suck my Suck my	3
+Why are you so bad at this	3
+You don't help	3
+You make me want to die	3
+You are just awful	3
+You are ugly	3
+F You so much	3
+You are a retard	3
+You are very stupid	3
+You are lame	3
+Shut up bastard	3
+You tick me off	3
+You are an idiot	3
+You are a fool	3
+You are not funny	3
+Tell me a joke!	4
+Tell me something funny!	4
+Do you know a joke?	4
+joke joke joke	4
+Give me a joke	4
+Joke to me	4
+What is funny	4
+How about a joke	4
+Tell me something random	4
+Give me something funny	4
+Tell me a random joke	4
+What is really funny	4
+Tell me an anecdote	4
+Tell me a funny story	4
+make me laugh	4
+I want to laugh	4
+Can you make me laugh?	4
+What can make me laugh	4
diff --git a/Neural Network/valid.tsv b/Neural Network/valid.tsv
@@ -0,0 +1,91 @@
+text	label
+Hi	0
+What's cracking	0
+How've you been?	0
+How have you been	0
+Hey	0
+How are you	0
+Is anyone there?	0
+Hello	0
+Good day	0
+Good afternoon	0
+Good Morning	0
+Howdy	0
+What's up	0
+Yo	0
+Hey there	0
+Sup	0
+Good to see you	0
+Happy to see you	0
+I’m out of here	1
+See ya	1
+I will see you then	1
+Peace	1
+Peace out	1
+Take Care	1
+I look forward to our next meeting	1
+Until Then	1
+Have a nice day	1
+I’m off	1
+ I’ve got to get going	1
+I must be going	1
+Bye	1
+Bye Bye	1
+See you later	1
+See you soon	1
+Talk to you later	1
+Goodbye	1
+Thanks	2
+Thank you	2
+Thank you so much	2
+That's helpful	2
+Thank's a lot!	2
+Okay	2
+Thank thee	2
+Thank you, you're amazing!	2
+Thank you kindly	2
+I’m so thankful for everything you bring to the table	2
+ Thanks a million	2
+I truly appreciate your hard work	2
+I truly appreciate you	2
+ From the bottom of my heart, thank you	2
+ Please accept my deepest thanks	2
+Grateful for your support	2
+ I appreciate your thoughtfulness, you’ve made my day!	2
+Thanks for being in my corner	2
+You suck so much 	3
+ I hate you so much	3
+You are so bad	3
+Suck my Suck my	3
+Why are you so bad at this	3
+You don't help	3
+You make me want to die	3
+You are just awful	3
+You are ugly	3
+F You so much	3
+You are a retard	3
+You are very stupid	3
+You are lame	3
+Shut up bastard	3
+You tick me off	3
+You are an idiot	3
+You are a fool	3
+You are not funny	3
+Tell me a joke!	4
+Tell me something funny!	4
+Do you know a joke?	4
+joke joke joke	4
+Give me a joke	4
+Joke to me	4
+What is funny	4
+How about a joke	4
+Tell me something random	4
+Give me something funny	4
+Tell me a random joke	4
+What is really funny	4
+Tell me an anecdote	4
+Tell me a funny story	4
+make me laugh	4
+I want to laugh	4
+Can you make me laugh?	4
+What can make me laugh	4