From f0cd2d20f64c5d93953efcebebc795f1f6e13faa Mon Sep 17 00:00:00 2001 From: Aniket Date: Mon, 11 Oct 2021 11:01:45 +0530 Subject: [PATCH] added chatbot project --- Projects/chatbot/chatbot.ipynb | 1 + Projects/chatbot/intents.json | 109 +++++++++++++++++++++++++++++++++ 2 files changed, 110 insertions(+) create mode 100644 Projects/chatbot/chatbot.ipynb create mode 100644 Projects/chatbot/intents.json diff --git a/Projects/chatbot/chatbot.ipynb b/Projects/chatbot/chatbot.ipynb new file mode 100644 index 0000000..852a880 --- /dev/null +++ b/Projects/chatbot/chatbot.ipynb @@ -0,0 +1 @@ +{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"Untitled1.ipynb","provenance":[],"collapsed_sections":[],"authorship_tag":"ABX9TyO2tVrMsJy5J1S56XCgNiGx"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","metadata":{"id":"7zf6ypYb1S1n","executionInfo":{"status":"ok","timestamp":1633091909645,"user_tz":-330,"elapsed":382,"user":{"displayName":"Aniket Rathod","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10938003875329210391"}}},"source":["import tensorflow as tf\n","import pandas as pd\n","import numpy as np\n","import json\n","import nltk\n","from tensorflow.keras.preprocessing.text import Tokenizer\n","import matplotlib.pyplot as plt\n","from tensorflow.keras.layers import Input,Flatten,LSTM,Dense,Embedding,GlobalMaxPooling1D\n","from tensorflow.keras.models import Model"],"execution_count":2,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"lhxW-i1z2Ta-","executionInfo":{"status":"ok","timestamp":1633092354878,"user_tz":-330,"elapsed":393,"user":{"displayName":"Aniket Rathod","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10938003875329210391"}},"outputId":"47b56ec7-2b2b-48fd-8d4a-fda8a5baf012"},"source":["%%writefile intents.json\n","{\n"," \"intents\": [\n"," {\n"," \"tag\": \"intro\",\n"," \"patterns\": [\n"," \"What is your name\",\n"," \"What can you do?\",\n"," \"Glad to meet you\",\n"," \"Bye bye\"\n"," ],\n"," \"responses\": [\n"," \"Hello,Sir,I am Natalie\",\n"," \"I can perform various AI tasks\",\n"," \"Me too\",\n"," \"Sayonara\"\n"," ]\n"," }, {\n"," \"tag\": \"greeting\",\n"," \"patterns\": [\n"," \"Hi\",\n"," \"Hey\",\n"," \"How are you\",\n"," \"Is anyone there?\",\n"," \"Hello\",\n"," \"Good day\"\n"," ],\n"," \"responses\": [\n"," \"Hey :-)\",\n"," \"Hello, thanks for visiting\",\n"," \"Hi there, what can I do for you?\",\n"," \"Hi there, how can I help?\"\n"," ]\n"," },\n"," {\n"," \"tag\": \"goodbye\",\n"," \"patterns\": [\"Bye\", \"See you later\", \"Goodbye\"],\n"," \"responses\": [\n"," \"See you later, thanks for visiting\",\n"," \"Have a nice day\",\n"," \"Bye! Come back again soon.\"\n"," ]\n"," },\n"," {\n"," \"tag\": \"thanks\",\n"," \"patterns\": [\"Thanks\", \"Thank you\", \"That's helpful\", \"Thank's a lot!\"],\n"," \"responses\": [\"Happy to help!\", \"Any time!\", \"My pleasure\"]\n"," },\n"," {\n"," \"tag\": \"items\",\n"," \"patterns\": [\n"," \"Which items do you have?\",\n"," \"What kinds of items are there?\",\n"," \"What do you sell?\"\n"," ],\n"," \"responses\": [\n"," \"We sell coffee and tea\",\n"," \"We have coffee and tea\"\n"," ]\n"," },\n"," {\n"," \"tag\": \"payments\",\n"," \"patterns\": [\n"," \"Do you take credit cards?\",\n"," \"Do you accept Mastercard?\",\n"," \"Can I pay with Paypal?\",\n"," \"Are you cash only?\"\n"," ],\n"," \"responses\": [\n"," \"We accept VISA, Mastercard and Paypal\",\n"," \"We accept most major credit cards, and Paypal\"\n"," ]\n"," },\n"," {\n"," \"tag\": \"delivery\",\n"," \"patterns\": [\n"," \"How long does delivery take?\",\n"," \"How long does shipping take?\",\n"," \"When do I get my delivery?\"\n"," ],\n"," \"responses\": [\n"," \"Delivery takes 2-4 days\",\n"," \"Shipping takes 2-4 days\"\n"," ]\n"," },{\n"," \"tag\": \"food\",\n"," \"patterns\": [\n"," \"Which is your favourite food?\",\n"," \"I like it too much\",\n"," \"And it's speciality of our store also\"\n"," ],\n"," \"responses\": [\n"," \"I like panipuri\",\n"," \"Same pinch\"\n"," ]\n"," },\n"," {\n"," \"tag\": \"funny\",\n"," \"patterns\": [\n"," \"Tell me a joke!\",\n"," \"Tell me something funny!\",\n"," \"Do you know a joke?\"\n"," ],\n"," \"responses\": [\n"," \"Why did the hipster burn his mouth? He drank the coffee before it was cool.\",\n"," \"What did the buffalo say when his son left for college? Bison.\"\n"," ]\n"," }\n"," ]\n","}\n"],"execution_count":3,"outputs":[{"output_type":"stream","name":"stdout","text":["Writing intents.json\n"]}]},{"cell_type":"code","metadata":{"id":"XwgHFAnQ478p","executionInfo":{"status":"ok","timestamp":1633092472462,"user_tz":-330,"elapsed":371,"user":{"displayName":"Aniket Rathod","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10938003875329210391"}}},"source":["with open(\"intents.json\") as f:\n"," data=json.load(f)"],"execution_count":4,"outputs":[]},{"cell_type":"code","metadata":{"id":"PJD1rzHc5Yl4","executionInfo":{"status":"ok","timestamp":1633092857050,"user_tz":-330,"elapsed":380,"user":{"displayName":"Aniket Rathod","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10938003875329210391"}}},"source":["tags=[]\n","patterns=[]\n","responses={}\n","for intent in data['intents']:\n"," responses[intent['tag']]=intent['responses']\n"," for line in intent['patterns']:\n"," patterns.append(line)\n"," tags.append(intent['tag'])"],"execution_count":6,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"2kT11U4c6nCg","executionInfo":{"status":"ok","timestamp":1633092946453,"user_tz":-330,"elapsed":373,"user":{"displayName":"Aniket Rathod","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10938003875329210391"}},"outputId":"78f3076b-b672-4758-b218-be32cc98ac2f"},"source":["df=pd.DataFrame({\"patterns\":patterns,\"tags\":tags})\n","df"],"execution_count":7,"outputs":[{"output_type":"execute_result","data":{"text/html":["
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patternstags
0What is your nameintro
1What can you do?intro
2Glad to meet youintro
3Bye byeintro
4Higreeting
5Heygreeting
6How are yougreeting
7Is anyone there?greeting
8Hellogreeting
9Good daygreeting
10Byegoodbye
11See you latergoodbye
12Goodbyegoodbye
13Thanksthanks
14Thank youthanks
15That's helpfulthanks
16Thank's a lot!thanks
17Which items do you have?items
18What kinds of items are there?items
19What do you sell?items
20Do you take credit cards?payments
21Do you accept Mastercard?payments
22Can I pay with Paypal?payments
23Are you cash only?payments
24How long does delivery take?delivery
25How long does shipping take?delivery
26When do I get my delivery?delivery
27Which is your favourite food?food
28I like it too muchfood
29And it's speciality of our store alsofood
30Tell me a joke!funny
31Tell me something funny!funny
32Do you know a joke?funny
\n","
"],"text/plain":[" patterns tags\n","0 What is your name intro\n","1 What can you do? intro\n","2 Glad to meet you intro\n","3 Bye bye intro\n","4 Hi greeting\n","5 Hey greeting\n","6 How are you greeting\n","7 Is anyone there? greeting\n","8 Hello greeting\n","9 Good day greeting\n","10 Bye goodbye\n","11 See you later goodbye\n","12 Goodbye goodbye\n","13 Thanks thanks\n","14 Thank you thanks\n","15 That's helpful thanks\n","16 Thank's a lot! thanks\n","17 Which items do you have? items\n","18 What kinds of items are there? items\n","19 What do you sell? items\n","20 Do you take credit cards? payments\n","21 Do you accept Mastercard? payments\n","22 Can I pay with Paypal? payments\n","23 Are you cash only? payments\n","24 How long does delivery take? delivery\n","25 How long does shipping take? delivery\n","26 When do I get my delivery? delivery\n","27 Which is your favourite food? food\n","28 I like it too much food\n","29 And it's speciality of our store also food\n","30 Tell me a joke! funny\n","31 Tell me something funny! funny\n","32 Do you know a joke? funny"]},"metadata":{},"execution_count":7}]},{"cell_type":"markdown","metadata":{"id":"pVH6brSQ8MkQ"},"source":["Tokenizing the sentences "]},{"cell_type":"code","metadata":{"id":"RGO97eve7MMh","executionInfo":{"status":"ok","timestamp":1633093835737,"user_tz":-330,"elapsed":369,"user":{"displayName":"Aniket Rathod","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10938003875329210391"}}},"source":["tokenizer=Tokenizer()\n","tokenizer.fit_on_texts(df['patterns'])\n","#text_to_sequences Transforms each text in texts to a sequence of integers. So it basically takes each word in the text and replaces it with its corresponding integer value from the word_index dictionary\n","texts=tokenizer.texts_to_sequences(df['patterns'])\n","#sequence padding\n","from tensorflow.keras.preprocessing.sequence import pad_sequences\n","X_train=pad_sequences(texts)\n","\n","#encoding\n","from sklearn.preprocessing import LabelEncoder\n","le=LabelEncoder()\n","#fit_transform() is used on the training data so that we can scale the training data and also learn the scaling parameters of that data\n","y_train=le.fit_transform(df['tags'])"],"execution_count":10,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"FmEYMvV8-af4","executionInfo":{"status":"ok","timestamp":1633093894638,"user_tz":-330,"elapsed":382,"user":{"displayName":"Aniket Rathod","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10938003875329210391"}},"outputId":"ab61ee94-3653-46b3-e31e-a4090a3a1d31"},"source":["y_train"],"execution_count":11,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([5, 5, 5, 5, 4, 4, 4, 4, 4, 4, 3, 3, 3, 8, 8, 8, 8, 6, 6, 6, 7, 7,\n"," 7, 7, 0, 0, 0, 1, 1, 1, 2, 2, 2])"]},"metadata":{},"execution_count":11}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"A8wzm_Se-z4E","executionInfo":{"status":"ok","timestamp":1633093920477,"user_tz":-330,"elapsed":385,"user":{"displayName":"Aniket Rathod","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10938003875329210391"}},"outputId":"0cf961af-dac5-4a92-fe24-8a099d7fa9a4"},"source":["X_train"],"execution_count":12,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[ 0, 0, 0, 3, 4, 11, 23],\n"," [ 0, 0, 0, 3, 12, 1, 2],\n"," [ 0, 0, 0, 24, 25, 26, 1],\n"," [ 0, 0, 0, 0, 0, 5, 5],\n"," [ 0, 0, 0, 0, 0, 0, 27],\n"," [ 0, 0, 0, 0, 0, 0, 28],\n"," [ 0, 0, 0, 0, 6, 7, 1],\n"," [ 0, 0, 0, 0, 4, 29, 13],\n"," [ 0, 0, 0, 0, 0, 0, 30],\n"," [ 0, 0, 0, 0, 0, 31, 32],\n"," [ 0, 0, 0, 0, 0, 0, 5],\n"," [ 0, 0, 0, 0, 33, 1, 34],\n"," [ 0, 0, 0, 0, 0, 0, 35],\n"," [ 0, 0, 0, 0, 0, 0, 36],\n"," [ 0, 0, 0, 0, 0, 37, 1],\n"," [ 0, 0, 0, 0, 0, 38, 39],\n"," [ 0, 0, 0, 0, 40, 8, 41],\n"," [ 0, 0, 14, 15, 2, 1, 42],\n"," [ 0, 3, 43, 16, 15, 7, 13],\n"," [ 0, 0, 0, 3, 2, 1, 44],\n"," [ 0, 0, 2, 1, 9, 45, 46],\n"," [ 0, 0, 0, 2, 1, 47, 48],\n"," [ 0, 0, 12, 10, 49, 50, 51],\n"," [ 0, 0, 0, 7, 1, 52, 53],\n"," [ 0, 0, 6, 17, 18, 19, 9],\n"," [ 0, 0, 6, 17, 18, 54, 9],\n"," [ 0, 55, 2, 10, 56, 57, 19],\n"," [ 0, 0, 14, 4, 11, 58, 59],\n"," [ 0, 0, 10, 60, 61, 62, 63],\n"," [64, 65, 66, 16, 67, 68, 69],\n"," [ 0, 0, 0, 20, 21, 8, 22],\n"," [ 0, 0, 0, 20, 21, 70, 71],\n"," [ 0, 0, 2, 1, 72, 8, 22]], dtype=int32)"]},"metadata":{},"execution_count":12}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"TsEvLIj_DQXy","executionInfo":{"status":"ok","timestamp":1633095112896,"user_tz":-330,"elapsed":371,"user":{"displayName":"Aniket Rathod","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10938003875329210391"}},"outputId":"6bc4e851-4173-44fc-bee0-985c1b716526"},"source":["input_shape=X_train.shape[1]\n","print(input_shape)"],"execution_count":23,"outputs":[{"output_type":"stream","name":"stdout","text":["7\n"]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"5StMnuYR-6HI","executionInfo":{"status":"ok","timestamp":1633094052341,"user_tz":-330,"elapsed":365,"user":{"displayName":"Aniket Rathod","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10938003875329210391"}},"outputId":"d25e78ab-10ca-452f-d91d-c70704138330"},"source":["vocalbary=len(tokenizer.word_index)\n","vocalbary"],"execution_count":14,"outputs":[{"output_type":"execute_result","data":{"text/plain":["72"]},"metadata":{},"execution_count":14}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Z1Io8G3x_XxA","executionInfo":{"status":"ok","timestamp":1633094150064,"user_tz":-330,"elapsed":379,"user":{"displayName":"Aniket Rathod","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10938003875329210391"}},"outputId":"ad9dd97e-8402-4649-8c3b-aa8c499914ea"},"source":["token=tokenizer.word_index\n","token"],"execution_count":17,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'a': 8,\n"," 'accept': 47,\n"," 'also': 69,\n"," 'and': 64,\n"," 'anyone': 29,\n"," 'are': 7,\n"," 'bye': 5,\n"," 'can': 12,\n"," 'cards': 46,\n"," 'cash': 52,\n"," 'credit': 45,\n"," 'day': 32,\n"," 'delivery': 19,\n"," 'do': 2,\n"," 'does': 18,\n"," 'favourite': 58,\n"," 'food': 59,\n"," 'funny': 71,\n"," 'get': 56,\n"," 'glad': 24,\n"," 'good': 31,\n"," 'goodbye': 35,\n"," 'have': 42,\n"," 'hello': 30,\n"," 'helpful': 39,\n"," 'hey': 28,\n"," 'hi': 27,\n"," 'how': 6,\n"," 'i': 10,\n"," 'is': 4,\n"," 'it': 61,\n"," \"it's\": 65,\n"," 'items': 15,\n"," 'joke': 22,\n"," 'kinds': 43,\n"," 'know': 72,\n"," 'later': 34,\n"," 'like': 60,\n"," 'long': 17,\n"," 'lot': 41,\n"," 'mastercard': 48,\n"," 'me': 21,\n"," 'meet': 26,\n"," 'much': 63,\n"," 'my': 57,\n"," 'name': 23,\n"," 'of': 16,\n"," 'only': 53,\n"," 'our': 67,\n"," 'pay': 49,\n"," 'paypal': 51,\n"," 'see': 33,\n"," 'sell': 44,\n"," 'shipping': 54,\n"," 'something': 70,\n"," 'speciality': 66,\n"," 'store': 68,\n"," 'take': 9,\n"," 'tell': 20,\n"," 'thank': 37,\n"," \"thank's\": 40,\n"," 'thanks': 36,\n"," \"that's\": 38,\n"," 'there': 13,\n"," 'to': 25,\n"," 'too': 62,\n"," 'what': 3,\n"," 'when': 55,\n"," 'which': 14,\n"," 'with': 50,\n"," 'you': 1,\n"," 'your': 11}"]},"metadata":{},"execution_count":17}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"UhZ8PkjK_i39","executionInfo":{"status":"ok","timestamp":1633094205871,"user_tz":-330,"elapsed":408,"user":{"displayName":"Aniket Rathod","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10938003875329210391"}},"outputId":"f48f9f27-8039-4ed2-9072-8f0eb5a24a5d"},"source":["output=le.classes_\n","output"],"execution_count":19,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array(['delivery', 'food', 'funny', 'goodbye', 'greeting', 'intro',\n"," 'items', 'payments', 'thanks'], dtype=object)"]},"metadata":{},"execution_count":19}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"2XD2F5M6Cg2m","executionInfo":{"status":"ok","timestamp":1633094894469,"user_tz":-330,"elapsed":509,"user":{"displayName":"Aniket Rathod","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10938003875329210391"}},"outputId":"6a9f314c-aecc-4aed-c3cf-eac9e3b1c482"},"source":["output_len=len(le.classes_)\n","output_len"],"execution_count":20,"outputs":[{"output_type":"execute_result","data":{"text/plain":["9"]},"metadata":{},"execution_count":20}]},{"cell_type":"code","metadata":{"id":"C0lp4GxR_9HV","executionInfo":{"status":"ok","timestamp":1633095117954,"user_tz":-330,"elapsed":1070,"user":{"displayName":"Aniket Rathod","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10938003875329210391"}}},"source":["i=Input(shape=(input_shape,))\n","x=Embedding(vocalbary+1,20)(i)\n","x=LSTM(20,return_sequences=True)(x)\n","x=Flatten()(x)\n","x=Dense(output_len,activation=\"softmax\")(x)\n","model=Model(i,x)"],"execution_count":24,"outputs":[]},{"cell_type":"code","metadata":{"id":"TNvN-ayFC9X9","executionInfo":{"status":"ok","timestamp":1633095260381,"user_tz":-330,"elapsed":1611,"user":{"displayName":"Aniket Rathod","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10938003875329210391"}}},"source":["model.compile(loss=\"sparse_categorical_crossentropy\",optimizer=\"adam\",metrics=[\"accuracy\"])"],"execution_count":25,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"-yD1KFsxEAwy","executionInfo":{"status":"ok","timestamp":1633095427486,"user_tz":-330,"elapsed":5698,"user":{"displayName":"Aniket Rathod","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"10938003875329210391"}},"outputId":"160e1f63-5731-402c-a7ab-d629bb9d55e0"},"source":["model.fit(X_train,y_train,epochs=200)"],"execution_count":27,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/200\n","2/2 [==============================] - 0s 9ms/step - loss: 1.9426 - accuracy: 0.2424\n","Epoch 2/200\n","2/2 [==============================] - 0s 9ms/step - loss: 1.9322 - accuracy: 0.2424\n","Epoch 3/200\n","2/2 [==============================] - 0s 10ms/step - loss: 1.9215 - accuracy: 0.2424\n","Epoch 4/200\n","2/2 [==============================] - 0s 9ms/step - loss: 1.9103 - accuracy: 0.2424\n","Epoch 5/200\n","2/2 [==============================] - 0s 9ms/step - loss: 1.9000 - accuracy: 0.2424\n","Epoch 6/200\n","2/2 [==============================] - 0s 10ms/step - loss: 1.8888 - accuracy: 0.2424\n","Epoch 7/200\n","2/2 [==============================] - 0s 10ms/step - loss: 1.8781 - accuracy: 0.2424\n","Epoch 8/200\n","2/2 [==============================] - 0s 10ms/step - loss: 1.8678 - accuracy: 0.2424\n","Epoch 9/200\n","2/2 [==============================] - 0s 10ms/step - loss: 1.8575 - accuracy: 0.2727\n","Epoch 10/200\n","2/2 [==============================] - 0s 13ms/step - loss: 1.8481 - accuracy: 0.2727\n","Epoch 11/200\n","2/2 [==============================] - 0s 12ms/step - loss: 1.8390 - accuracy: 0.2727\n","Epoch 12/200\n","2/2 [==============================] - 0s 9ms/step - loss: 1.8272 - accuracy: 0.3030\n","Epoch 13/200\n","2/2 [==============================] - 0s 10ms/step - loss: 1.8150 - accuracy: 0.3030\n","Epoch 14/200\n","2/2 [==============================] - 0s 12ms/step - loss: 1.8024 - accuracy: 0.3030\n","Epoch 15/200\n","2/2 [==============================] - 0s 11ms/step - loss: 1.7890 - accuracy: 0.3030\n","Epoch 16/200\n","2/2 [==============================] - 0s 10ms/step - loss: 1.7750 - accuracy: 0.3333\n","Epoch 17/200\n","2/2 [==============================] - 0s 8ms/step - loss: 1.7625 - accuracy: 0.3636\n","Epoch 18/200\n","2/2 [==============================] - 0s 9ms/step - loss: 1.7499 - accuracy: 0.3636\n","Epoch 19/200\n","2/2 [==============================] - 0s 8ms/step - loss: 1.7378 - accuracy: 0.3333\n","Epoch 20/200\n","2/2 [==============================] - 0s 12ms/step - loss: 1.7251 - accuracy: 0.3333\n","Epoch 21/200\n","2/2 [==============================] - 0s 10ms/step - loss: 1.7130 - accuracy: 0.3333\n","Epoch 22/200\n","2/2 [==============================] - 0s 11ms/step - loss: 1.7010 - accuracy: 0.3333\n","Epoch 23/200\n","2/2 [==============================] - 0s 13ms/step - loss: 1.6912 - accuracy: 0.3333\n","Epoch 24/200\n","2/2 [==============================] - 0s 12ms/step - loss: 1.6802 - accuracy: 0.3333\n","Epoch 25/200\n","2/2 [==============================] - 0s 12ms/step - loss: 1.6686 - accuracy: 0.3636\n","Epoch 26/200\n","2/2 [==============================] - 0s 10ms/step - loss: 1.6562 - accuracy: 0.3939\n","Epoch 27/200\n","2/2 [==============================] - 0s 11ms/step - loss: 1.6422 - accuracy: 0.3636\n","Epoch 28/200\n","2/2 [==============================] - 0s 12ms/step - loss: 1.6293 - accuracy: 0.3636\n","Epoch 29/200\n","2/2 [==============================] - 0s 10ms/step - loss: 1.6153 - accuracy: 0.3636\n","Epoch 30/200\n","2/2 [==============================] - 0s 9ms/step - loss: 1.6034 - accuracy: 0.4242\n","Epoch 31/200\n","2/2 [==============================] - 0s 12ms/step - loss: 1.5956 - accuracy: 0.4545\n","Epoch 32/200\n","2/2 [==============================] - 0s 11ms/step - loss: 1.5884 - accuracy: 0.3939\n","Epoch 33/200\n","2/2 [==============================] - 0s 12ms/step - loss: 1.5873 - accuracy: 0.3636\n","Epoch 34/200\n","2/2 [==============================] - 0s 15ms/step - loss: 1.5855 - accuracy: 0.3939\n","Epoch 35/200\n","2/2 [==============================] - 0s 12ms/step - loss: 1.5844 - accuracy: 0.3636\n","Epoch 36/200\n","2/2 [==============================] - 0s 12ms/step - loss: 1.5870 - accuracy: 0.3636\n","Epoch 37/200\n","2/2 [==============================] - 0s 9ms/step - loss: 1.5883 - accuracy: 0.3030\n","Epoch 38/200\n","2/2 [==============================] - 0s 12ms/step - loss: 1.5806 - accuracy: 0.3636\n","Epoch 39/200\n","2/2 [==============================] - 0s 12ms/step - loss: 1.5614 - accuracy: 0.3939\n","Epoch 40/200\n","2/2 [==============================] - 0s 10ms/step - loss: 1.5367 - accuracy: 0.4242\n","Epoch 41/200\n","2/2 [==============================] - 0s 11ms/step - loss: 1.5107 - accuracy: 0.4242\n","Epoch 42/200\n","2/2 [==============================] - 0s 13ms/step - loss: 1.4863 - accuracy: 0.4242\n","Epoch 43/200\n","2/2 [==============================] - 0s 8ms/step - loss: 1.4622 - accuracy: 0.5455\n","Epoch 44/200\n","2/2 [==============================] - 0s 12ms/step - loss: 1.4397 - accuracy: 0.5455\n","Epoch 45/200\n","2/2 [==============================] - 0s 12ms/step - loss: 1.4209 - accuracy: 0.6364\n","Epoch 46/200\n","2/2 [==============================] - 0s 9ms/step - loss: 1.4063 - accuracy: 0.6667\n","Epoch 47/200\n","2/2 [==============================] - 0s 11ms/step - loss: 1.3920 - accuracy: 0.6667\n","Epoch 48/200\n","2/2 [==============================] - 0s 13ms/step - loss: 1.3802 - accuracy: 0.6364\n","Epoch 49/200\n","2/2 [==============================] - 0s 15ms/step - loss: 1.3680 - accuracy: 0.5758\n","Epoch 50/200\n","2/2 [==============================] - 0s 10ms/step - loss: 1.3552 - accuracy: 0.5758\n","Epoch 51/200\n","2/2 [==============================] - 0s 7ms/step - loss: 1.3407 - accuracy: 0.5758\n","Epoch 52/200\n","2/2 [==============================] - 0s 10ms/step - loss: 1.3253 - accuracy: 0.5758\n","Epoch 53/200\n","2/2 [==============================] - 0s 12ms/step - loss: 1.3105 - accuracy: 0.5758\n","Epoch 54/200\n","2/2 [==============================] - 0s 11ms/step - loss: 1.2956 - accuracy: 0.6364\n","Epoch 55/200\n","2/2 [==============================] - 0s 16ms/step - loss: 1.2798 - accuracy: 0.6364\n","Epoch 56/200\n","2/2 [==============================] - 0s 11ms/step - loss: 1.2643 - accuracy: 0.6970\n","Epoch 57/200\n","2/2 [==============================] - 0s 13ms/step - loss: 1.2501 - accuracy: 0.6970\n","Epoch 58/200\n","2/2 [==============================] - 0s 8ms/step - loss: 1.2354 - accuracy: 0.6970\n","Epoch 59/200\n","2/2 [==============================] - 0s 9ms/step - loss: 1.2209 - accuracy: 0.6970\n","Epoch 60/200\n","2/2 [==============================] - 0s 8ms/step - loss: 1.2057 - accuracy: 0.7273\n","Epoch 61/200\n","2/2 [==============================] - 0s 14ms/step - loss: 1.1905 - accuracy: 0.7273\n","Epoch 62/200\n","2/2 [==============================] - 0s 11ms/step - loss: 1.1794 - accuracy: 0.7273\n","Epoch 63/200\n","2/2 [==============================] - 0s 10ms/step - loss: 1.1675 - accuracy: 0.7273\n","Epoch 64/200\n","2/2 [==============================] - 0s 11ms/step - loss: 1.1550 - accuracy: 0.7273\n","Epoch 65/200\n","2/2 [==============================] - 0s 11ms/step - loss: 1.1406 - accuracy: 0.7273\n","Epoch 66/200\n","2/2 [==============================] - 0s 12ms/step - loss: 1.1254 - accuracy: 0.7273\n","Epoch 67/200\n","2/2 [==============================] - 0s 11ms/step - loss: 1.1090 - accuracy: 0.7273\n","Epoch 68/200\n","2/2 [==============================] - 0s 16ms/step - loss: 1.0909 - accuracy: 0.7576\n","Epoch 69/200\n","2/2 [==============================] - 0s 12ms/step - loss: 1.0768 - accuracy: 0.7273\n","Epoch 70/200\n","2/2 [==============================] - 0s 14ms/step - loss: 1.0643 - accuracy: 0.7273\n","Epoch 71/200\n","2/2 [==============================] - 0s 10ms/step - loss: 1.0541 - accuracy: 0.7273\n","Epoch 72/200\n","2/2 [==============================] - 0s 11ms/step - loss: 1.0480 - accuracy: 0.6970\n","Epoch 73/200\n","2/2 [==============================] - 0s 10ms/step - loss: 1.0496 - accuracy: 0.6970\n","Epoch 74/200\n","2/2 [==============================] - 0s 12ms/step - loss: 1.0483 - accuracy: 0.6970\n","Epoch 75/200\n","2/2 [==============================] - 0s 13ms/step - loss: 1.0373 - accuracy: 0.7273\n","Epoch 76/200\n","2/2 [==============================] - 0s 9ms/step - loss: 1.0207 - accuracy: 0.7273\n","Epoch 77/200\n","2/2 [==============================] - 0s 11ms/step - loss: 1.0029 - accuracy: 0.6970\n","Epoch 78/200\n","2/2 [==============================] - 0s 9ms/step - loss: 0.9891 - accuracy: 0.6970\n","Epoch 79/200\n","2/2 [==============================] - 0s 12ms/step - loss: 0.9818 - accuracy: 0.7273\n","Epoch 80/200\n","2/2 [==============================] - 0s 12ms/step - loss: 0.9750 - accuracy: 0.7273\n","Epoch 81/200\n","2/2 [==============================] - 0s 8ms/step - loss: 0.9721 - accuracy: 0.6970\n","Epoch 82/200\n","2/2 [==============================] - 0s 11ms/step - loss: 0.9706 - accuracy: 0.6364\n","Epoch 83/200\n","2/2 [==============================] - 0s 11ms/step - loss: 0.9714 - accuracy: 0.6364\n","Epoch 84/200\n","2/2 [==============================] - 0s 12ms/step - loss: 0.9585 - accuracy: 0.6364\n","Epoch 85/200\n","2/2 [==============================] - 0s 15ms/step - loss: 0.9322 - accuracy: 0.6667\n","Epoch 86/200\n","2/2 [==============================] - 0s 11ms/step - loss: 0.9053 - accuracy: 0.6970\n","Epoch 87/200\n","2/2 [==============================] - 0s 12ms/step - loss: 0.8797 - accuracy: 0.7879\n","Epoch 88/200\n","2/2 [==============================] - 0s 9ms/step - loss: 0.8577 - accuracy: 0.7879\n","Epoch 89/200\n","2/2 [==============================] - 0s 10ms/step - loss: 0.8387 - accuracy: 0.7879\n","Epoch 90/200\n","2/2 [==============================] - 0s 13ms/step - loss: 0.8268 - accuracy: 0.8182\n","Epoch 91/200\n","2/2 [==============================] - 0s 15ms/step - loss: 0.8162 - accuracy: 0.8485\n","Epoch 92/200\n","2/2 [==============================] - 0s 11ms/step - loss: 0.8080 - accuracy: 0.8182\n","Epoch 93/200\n","2/2 [==============================] - 0s 11ms/step - loss: 0.8012 - accuracy: 0.8182\n","Epoch 94/200\n","2/2 [==============================] - 0s 12ms/step - loss: 0.7974 - accuracy: 0.8182\n","Epoch 95/200\n","2/2 [==============================] - 0s 10ms/step - loss: 0.7909 - accuracy: 0.8182\n","Epoch 96/200\n","2/2 [==============================] - 0s 17ms/step - loss: 0.7847 - accuracy: 0.8182\n","Epoch 97/200\n","2/2 [==============================] - 0s 10ms/step - loss: 0.7806 - accuracy: 0.7879\n","Epoch 98/200\n","2/2 [==============================] - 0s 10ms/step - loss: 0.7738 - accuracy: 0.7879\n","Epoch 99/200\n","2/2 [==============================] - 0s 8ms/step - loss: 0.7649 - accuracy: 0.8182\n","Epoch 100/200\n","2/2 [==============================] - 0s 10ms/step - loss: 0.7569 - accuracy: 0.8182\n","Epoch 101/200\n","2/2 [==============================] - 0s 11ms/step - loss: 0.7490 - accuracy: 0.8182\n","Epoch 102/200\n","2/2 [==============================] - 0s 12ms/step - loss: 0.7329 - accuracy: 0.8182\n","Epoch 103/200\n","2/2 [==============================] - 0s 12ms/step - loss: 0.7213 - accuracy: 0.8485\n","Epoch 104/200\n","2/2 [==============================] - 0s 12ms/step - loss: 0.7126 - accuracy: 0.8485\n","Epoch 105/200\n","2/2 [==============================] - 0s 14ms/step - loss: 0.7034 - accuracy: 0.8485\n","Epoch 106/200\n","2/2 [==============================] - 0s 10ms/step - loss: 0.6951 - accuracy: 0.8788\n","Epoch 107/200\n","2/2 [==============================] - 0s 11ms/step - loss: 0.6871 - accuracy: 0.8788\n","Epoch 108/200\n","2/2 [==============================] - 0s 10ms/step - loss: 0.6812 - accuracy: 0.8788\n","Epoch 109/200\n","2/2 [==============================] - 0s 12ms/step - loss: 0.6742 - accuracy: 0.8788\n","Epoch 110/200\n","2/2 [==============================] - 0s 11ms/step - loss: 0.6661 - accuracy: 0.8788\n","Epoch 111/200\n","2/2 [==============================] - 0s 10ms/step - loss: 0.6614 - accuracy: 0.8788\n","Epoch 112/200\n","2/2 [==============================] - 0s 13ms/step - loss: 0.6625 - accuracy: 0.8788\n","Epoch 113/200\n","2/2 [==============================] - 0s 12ms/step - loss: 0.6582 - accuracy: 0.8788\n","Epoch 114/200\n","2/2 [==============================] - 0s 11ms/step - loss: 0.6465 - accuracy: 0.8788\n","Epoch 115/200\n","2/2 [==============================] - 0s 14ms/step - loss: 0.6366 - accuracy: 0.8788\n","Epoch 116/200\n","2/2 [==============================] - 0s 11ms/step - loss: 0.6283 - accuracy: 0.8788\n","Epoch 117/200\n","2/2 [==============================] - 0s 8ms/step - loss: 0.6184 - accuracy: 0.9091\n","Epoch 118/200\n","2/2 [==============================] - 0s 13ms/step - loss: 0.6095 - accuracy: 0.9091\n","Epoch 119/200\n","2/2 [==============================] - 0s 11ms/step - loss: 0.6033 - accuracy: 0.9091\n","Epoch 120/200\n","2/2 [==============================] - 0s 12ms/step - loss: 0.6016 - accuracy: 0.8485\n","Epoch 121/200\n","2/2 [==============================] - 0s 11ms/step - loss: 0.6030 - accuracy: 0.8485\n","Epoch 122/200\n","2/2 [==============================] - 0s 10ms/step - loss: 0.6071 - accuracy: 0.7879\n","Epoch 123/200\n","2/2 [==============================] - 0s 14ms/step - loss: 0.6122 - accuracy: 0.7879\n","Epoch 124/200\n","2/2 [==============================] - 0s 12ms/step - loss: 0.6083 - accuracy: 0.7879\n","Epoch 125/200\n","2/2 [==============================] - 0s 11ms/step - loss: 0.6039 - accuracy: 0.7879\n","Epoch 126/200\n","2/2 [==============================] - 0s 10ms/step - loss: 0.6040 - accuracy: 0.8485\n","Epoch 127/200\n","2/2 [==============================] - 0s 14ms/step - loss: 0.6026 - accuracy: 0.8485\n","Epoch 128/200\n","2/2 [==============================] - 0s 16ms/step - loss: 0.5964 - accuracy: 0.8485\n","Epoch 129/200\n","2/2 [==============================] - 0s 11ms/step - loss: 0.5887 - accuracy: 0.8485\n","Epoch 130/200\n","2/2 [==============================] - 0s 11ms/step - loss: 0.5769 - accuracy: 0.8485\n","Epoch 131/200\n","2/2 [==============================] - 0s 12ms/step - loss: 0.5577 - accuracy: 0.8485\n","Epoch 132/200\n","2/2 [==============================] - 0s 14ms/step - loss: 0.5332 - accuracy: 0.8485\n","Epoch 133/200\n","2/2 [==============================] - 0s 9ms/step - loss: 0.5154 - accuracy: 0.8788\n","Epoch 134/200\n","2/2 [==============================] - 0s 8ms/step - loss: 0.5014 - accuracy: 0.9091\n","Epoch 135/200\n","2/2 [==============================] - 0s 11ms/step - loss: 0.4951 - accuracy: 0.9091\n","Epoch 136/200\n","2/2 [==============================] - 0s 9ms/step - loss: 0.4949 - accuracy: 0.9091\n","Epoch 137/200\n","2/2 [==============================] - 0s 10ms/step - loss: 0.4916 - accuracy: 0.9091\n","Epoch 138/200\n","2/2 [==============================] - 0s 11ms/step - loss: 0.4865 - accuracy: 0.9394\n","Epoch 139/200\n","2/2 [==============================] - 0s 12ms/step - loss: 0.4797 - accuracy: 0.9394\n","Epoch 140/200\n","2/2 [==============================] - 0s 17ms/step - loss: 0.4725 - accuracy: 0.9091\n","Epoch 141/200\n","2/2 [==============================] - 0s 10ms/step - loss: 0.4653 - accuracy: 0.9091\n","Epoch 142/200\n","2/2 [==============================] - 0s 10ms/step - loss: 0.4586 - accuracy: 0.9091\n","Epoch 143/200\n","2/2 [==============================] - 0s 9ms/step - loss: 0.4524 - accuracy: 0.9091\n","Epoch 144/200\n","2/2 [==============================] - 0s 11ms/step - loss: 0.4480 - accuracy: 0.9091\n","Epoch 145/200\n","2/2 [==============================] - 0s 11ms/step - loss: 0.4436 - accuracy: 0.9091\n","Epoch 146/200\n","2/2 [==============================] - 0s 13ms/step - loss: 0.4494 - accuracy: 0.9091\n","Epoch 147/200\n","2/2 [==============================] - 0s 15ms/step - loss: 0.4664 - accuracy: 0.9091\n","Epoch 148/200\n","2/2 [==============================] - 0s 11ms/step - loss: 0.4848 - accuracy: 0.8788\n","Epoch 149/200\n","2/2 [==============================] - 0s 10ms/step - loss: 0.4977 - accuracy: 0.8788\n","Epoch 150/200\n","2/2 [==============================] - 0s 18ms/step - loss: 0.5008 - accuracy: 0.8788\n","Epoch 151/200\n","2/2 [==============================] - 0s 14ms/step - loss: 0.4814 - accuracy: 0.8788\n","Epoch 152/200\n","2/2 [==============================] - 0s 11ms/step - loss: 0.4539 - accuracy: 0.8788\n","Epoch 153/200\n","2/2 [==============================] - 0s 8ms/step - loss: 0.4359 - accuracy: 0.8788\n","Epoch 154/200\n","2/2 [==============================] - 0s 12ms/step - loss: 0.4257 - accuracy: 0.8788\n","Epoch 155/200\n","2/2 [==============================] - 0s 14ms/step - loss: 0.4186 - accuracy: 0.8788\n","Epoch 156/200\n","2/2 [==============================] - 0s 11ms/step - loss: 0.4124 - accuracy: 0.8788\n","Epoch 157/200\n","2/2 [==============================] - 0s 13ms/step - loss: 0.4106 - accuracy: 0.8788\n","Epoch 158/200\n","2/2 [==============================] - 0s 11ms/step - loss: 0.4164 - accuracy: 0.9091\n","Epoch 159/200\n","2/2 [==============================] - 0s 10ms/step - loss: 0.4238 - accuracy: 0.9091\n","Epoch 160/200\n","2/2 [==============================] - 0s 11ms/step - loss: 0.4283 - accuracy: 0.9091\n","Epoch 161/200\n","2/2 [==============================] - 0s 11ms/step - loss: 0.4325 - accuracy: 0.9091\n","Epoch 162/200\n","2/2 [==============================] - 0s 9ms/step - loss: 0.4300 - accuracy: 0.9091\n","Epoch 163/200\n","2/2 [==============================] - 0s 12ms/step - loss: 0.4180 - accuracy: 0.9091\n","Epoch 164/200\n","2/2 [==============================] - 0s 9ms/step - loss: 0.4047 - accuracy: 0.9091\n","Epoch 165/200\n","2/2 [==============================] - 0s 12ms/step - loss: 0.3928 - accuracy: 0.9091\n","Epoch 166/200\n","2/2 [==============================] - 0s 13ms/step - loss: 0.3830 - accuracy: 0.9394\n","Epoch 167/200\n","2/2 [==============================] - 0s 10ms/step - loss: 0.3759 - accuracy: 0.9091\n","Epoch 168/200\n","2/2 [==============================] - 0s 9ms/step - loss: 0.3711 - accuracy: 0.9091\n","Epoch 169/200\n","2/2 [==============================] - 0s 11ms/step - loss: 0.3688 - accuracy: 0.9091\n","Epoch 170/200\n","2/2 [==============================] - 0s 10ms/step - loss: 0.3662 - accuracy: 0.8788\n","Epoch 171/200\n","2/2 [==============================] - 0s 8ms/step - loss: 0.3630 - accuracy: 0.8788\n","Epoch 172/200\n","2/2 [==============================] - 0s 14ms/step - loss: 0.3601 - accuracy: 0.8788\n","Epoch 173/200\n","2/2 [==============================] - 0s 13ms/step - loss: 0.3541 - accuracy: 0.9091\n","Epoch 174/200\n","2/2 [==============================] - 0s 9ms/step - loss: 0.3448 - accuracy: 0.9091\n","Epoch 175/200\n","2/2 [==============================] - 0s 14ms/step - loss: 0.3383 - accuracy: 0.9091\n","Epoch 176/200\n","2/2 [==============================] - 0s 11ms/step - loss: 0.3340 - accuracy: 0.9394\n","Epoch 177/200\n","2/2 [==============================] - 0s 11ms/step - loss: 0.3307 - accuracy: 0.9394\n","Epoch 178/200\n","2/2 [==============================] - 0s 11ms/step - loss: 0.3276 - accuracy: 0.9394\n","Epoch 179/200\n","2/2 [==============================] - 0s 11ms/step - loss: 0.3247 - accuracy: 0.9394\n","Epoch 180/200\n","2/2 [==============================] - 0s 11ms/step - loss: 0.3218 - accuracy: 0.9394\n","Epoch 181/200\n","2/2 [==============================] - 0s 11ms/step - loss: 0.3190 - accuracy: 0.9394\n","Epoch 182/200\n","2/2 [==============================] - 0s 16ms/step - loss: 0.3132 - accuracy: 0.9394\n","Epoch 183/200\n","2/2 [==============================] - 0s 13ms/step - loss: 0.3132 - accuracy: 0.9394\n","Epoch 184/200\n","2/2 [==============================] - 0s 17ms/step - loss: 0.3203 - accuracy: 0.9091\n","Epoch 185/200\n","2/2 [==============================] - 0s 12ms/step - loss: 0.3276 - accuracy: 0.9091\n","Epoch 186/200\n","2/2 [==============================] - 0s 14ms/step - loss: 0.3302 - accuracy: 0.9091\n","Epoch 187/200\n","2/2 [==============================] - 0s 19ms/step - loss: 0.3273 - accuracy: 0.9091\n","Epoch 188/200\n","2/2 [==============================] - 0s 11ms/step - loss: 0.3250 - accuracy: 0.9091\n","Epoch 189/200\n","2/2 [==============================] - 0s 11ms/step - loss: 0.3237 - accuracy: 0.9394\n","Epoch 190/200\n","2/2 [==============================] - 0s 13ms/step - loss: 0.3180 - accuracy: 0.9394\n","Epoch 191/200\n","2/2 [==============================] - 0s 10ms/step - loss: 0.3037 - accuracy: 0.9394\n","Epoch 192/200\n","2/2 [==============================] - 0s 11ms/step - loss: 0.2879 - accuracy: 0.9394\n","Epoch 193/200\n","2/2 [==============================] - 0s 13ms/step - loss: 0.2793 - accuracy: 0.9394\n","Epoch 194/200\n","2/2 [==============================] - 0s 13ms/step - loss: 0.2749 - accuracy: 0.9394\n","Epoch 195/200\n","2/2 [==============================] - 0s 10ms/step - loss: 0.2735 - accuracy: 0.9394\n","Epoch 196/200\n","2/2 [==============================] - 0s 10ms/step - loss: 0.2737 - accuracy: 0.9394\n","Epoch 197/200\n","2/2 [==============================] - 0s 10ms/step - loss: 0.2719 - accuracy: 0.9394\n","Epoch 198/200\n","2/2 [==============================] - 0s 9ms/step - loss: 0.2690 - accuracy: 0.9394\n","Epoch 199/200\n","2/2 [==============================] - 0s 9ms/step - loss: 0.2674 - accuracy: 0.9394\n","Epoch 200/200\n","2/2 [==============================] - 0s 8ms/step - loss: 0.2685 - accuracy: 0.9394\n"]},{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{},"execution_count":27}]},{"cell_type":"code","metadata":{"id":"AcAGAehuEdA9"},"source":["import random\n","\n","while True:\n"," text=[]\n"," punctuation=['+','^','&',':',';','?','$']\n"," pred_input=input(\"User : \")\n","\n"," pred_input=[letter.lower() for letter in pred_input if letter not in punctuation]\n"," pred_input=''.join(pred_input)\n"," text.append(pred_input)\n","\n"," pred_input=tokenizer.texts_to_sequences(text)\n"," pred_input=np.array(pred_input).reshape(-1)\n"," pred_input=pad_sequences([pred_input],input_shape)\n","\n"," output=model.predict(pred_input)\n"," output=output.argmax()\n","\n"," response=le.inverse_transform([output])[0]\n"," print(\"Margreta:\"random.choice(responses[response]))\n"," if response==\"Sayonara\":\n"," break"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"I5YvSNNwIS8z"},"source":[""],"execution_count":null,"outputs":[]}]} \ No newline at end of file diff --git a/Projects/chatbot/intents.json b/Projects/chatbot/intents.json new file mode 100644 index 0000000..f0f1e3a --- /dev/null +++ b/Projects/chatbot/intents.json @@ -0,0 +1,109 @@ +{ + "intents": [ + { + "tag": "intro", + "patterns": [ + "What is your name", + "What can you do?", + "Glad to meet you", + "Bye bye" + ], + "responses": [ + "Hello,Sir,I am Natalie", + "I can perform various AI tasks", + "Me too", + "Sayonara" + ] + }, { + "tag": "greeting", + "patterns": [ + "Hi", + "Hey", + "How are you", + "Is anyone there?", + "Hello", + "Good day" + ], + "responses": [ + "Hey :-)", + "Hello, thanks for visiting", + "Hi there, what can I do for you?", + "Hi there, how can I help?" + ] + }, + { + "tag": "goodbye", + "patterns": ["Bye", "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", "That's helpful", "Thank's a lot!"], + "responses": ["Happy to help!", "Any time!", "My pleasure"] + }, + { + "tag": "items", + "patterns": [ + "Which items do you have?", + "What kinds of items are there?", + "What do you sell?" + ], + "responses": [ + "We sell coffee and tea", + "We have coffee and tea" + ] + }, + { + "tag": "payments", + "patterns": [ + "Do you take credit cards?", + "Do you accept Mastercard?", + "Can I pay with Paypal?", + "Are you cash only?" + ], + "responses": [ + "We accept VISA, Mastercard and Paypal", + "We accept most major credit cards, and Paypal" + ] + }, + { + "tag": "delivery", + "patterns": [ + "How long does delivery take?", + "How long does shipping take?", + "When do I get my delivery?" + ], + "responses": [ + "Delivery takes 2-4 days", + "Shipping takes 2-4 days" + ] + },{ + "tag": "food", + "patterns": [ + "Which is your favourite food?", + "I like it too much", + "And it's speciality of our store also" + ], + "responses": [ + "I like panipuri", + "Same pinch" + ] + }, + { + "tag": "funny", + "patterns": [ + "Tell me a joke!", + "Tell me something funny!", + "Do you know a joke?" + ], + "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." + ] + } + ] +}