Api created

AI_ChatBot_Using_Sentiment_Analysis/ChatBotBE/.idea/.gitignore

+# Default ignored files
+/shelf/
+/workspace.xml

AI_ChatBot_Using_Sentiment_Analysis/ChatBotBE/.idea/.name

+views.py
\ No newline at end of file

AI_ChatBot_Using_Sentiment_Analysis/ChatBotBE/.idea/ChatbotBE.iml

+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$">
+      <excludeFolder url="file://$MODULE_DIR$/venv" />
+    </content>
+    <orderEntry type="jdk" jdkName="Python 3.10" jdkType="Python SDK" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+  <component name="PyDocumentationSettings">
+    <option name="format" value="PLAIN" />
+    <option name="myDocStringFormat" value="Plain" />
+  </component>
+</module>
\ No newline at end of file

AI_ChatBot_Using_Sentiment_Analysis/ChatBotBE/.idea/inspectionProfiles/profiles_settings.xml

+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>
\ No newline at end of file

AI_ChatBot_Using_Sentiment_Analysis/ChatBotBE/.idea/misc.xml

+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.10" project-jdk-type="Python SDK" />
+  <component name="PyCharmProfessionalAdvertiser">
+    <option name="shown" value="true" />
+  </component>
+</project>
\ No newline at end of file

AI_ChatBot_Using_Sentiment_Analysis/ChatBotBE/.idea/modules.xml

+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/ChatbotBE.iml" filepath="$PROJECT_DIR$/.idea/ChatbotBE.iml" />
+    </modules>
+  </component>
+</project>
\ No newline at end of file

AI_ChatBot_Using_Sentiment_Analysis/ChatBotBE/.idea/vcs.xml

+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="VcsDirectoryMappings">
+    <mapping directory="$PROJECT_DIR$" vcs="Git" />
+  </component>
+</project>
\ No newline at end of file

AI_ChatBot_Using_Sentiment_Analysis/ChatBotBE/Procfile

+web gunicorn mysite.wsgi:application --log-file -
\ No newline at end of file

AI_ChatBot_Using_Sentiment_Analysis/ChatBotBE/api/admin.py

+from django.contrib import admin
+
+from .models import Chat
+
+admin.site.register(Chat)

AI_ChatBot_Using_Sentiment_Analysis/ChatBotBE/api/apps.py

+from django.apps import AppConfig
+
+
+class ApiConfig(AppConfig):
+    default_auto_field = 'django.db.models.BigAutoField'
+    name = 'api'

AI_ChatBot_Using_Sentiment_Analysis/ChatBotBE/api/migrations/0001_initial.py

+# Generated by Django 4.1 on 2022-08-25 06:14
+
+from django.db import migrations, models
+
+
+class Migration(migrations.Migration):
+
+    initial = True
+
+    dependencies = [
+    ]
+
+    operations = [
+        migrations.CreateModel(
+            name='Note',
+            fields=[
+                ('id', models.BigAutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),
+                ('body', models.TextField()),
+                ('updated', models.DateTimeField(auto_now=True)),
+                ('created', models.DateTimeField(auto_now_add=True)),
+            ],
+            options={
+                'ordering': ['-updated'],
+            },
+        ),
+    ]

AI_ChatBot_Using_Sentiment_Analysis/ChatBotBE/api/models.py

+from django.db import models
+
+
+class Chat(models.Model):
+    body = models.TextField()
+    updated = models.DateTimeField(auto_now=True)
+    created = models.DateTimeField(auto_now_add=True)
+
+    def __str__(self):
+        return self.body[0:50]
+
+    class Meta:
+        ordering = ['-updated']

AI_ChatBot_Using_Sentiment_Analysis/ChatBotBE/api/serializers.py

+from rest_framework.serializers import ModelSerializer
+from .models import Chat
+
+
+class ChatSerializer(ModelSerializer):
+    class Meta:
+        model = Chat
+        fields = '__all__'

AI_ChatBot_Using_Sentiment_Analysis/ChatBotBE/api/tests.py

+from django.test import TestCase
+
+# Create your tests here.

AI_ChatBot_Using_Sentiment_Analysis/ChatBotBE/api/urls.py

+from django.urls import path
+from . import views
+
+urlpatterns = [
+    path('chat/', views.getChatBot),
+    path('predict/', views.getPrecdict),
+
+]

AI_ChatBot_Using_Sentiment_Analysis/ChatBotBE/api/views.py

+from django.shortcuts import render
+from rest_framework.decorators import api_view
+from rest_framework.response import Response
+
+from chat import get_response, get_sa
+
+
+@api_view(['GET'])
+def getChatBot(request):
+    return Response("Welcome to Canis care Vet bot!")
+
+
+@api_view(['POST'])
+def getPrecdict(request):
+    data = request.data
+    message = data['message']
+    output = get_response(message)
+    sa = get_sa(message)
+    result = output+","+str(sa)
+    return Response(result)
+
+
+

AI_ChatBot_Using_Sentiment_Analysis/ChatBotBE/chat.py

+import random
+import json
+import torch
+
+from textblob import TextBlob
+from model import NeuralNet
+from nltk_utils import bag_of_words, tokenize
+
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+with open('intents.json', 'r') as json_data:
+    intents = json.load(json_data)
+
+FILE = "model.h5"
+data = torch.load(FILE)
+
+input_size = data["input_size"]
+hidden_size = data["hidden_size"]
+output_size = data["output_size"]
+all_words = data['all_words']
+tags = data['tags']
+model_state = data["model_state"]
+
+model = NeuralNet(input_size, hidden_size, output_size).to(device)
+model.load_state_dict(model_state)
+model.eval()
+
+bot_name = "VetBot"
+
+
+def get_response(msg):
+    sentence = tokenize(msg)
+    X = bag_of_words(sentence, all_words)
+    X = X.reshape(1, X.shape[0])
+    X = torch.from_numpy(X).to(device)
+
+    output = model(X)
+    _, predicted = torch.max(output, dim=1)
+
+    tag = tags[predicted.item()]
+
+    probs = torch.softmax(output, dim=1)
+    prob = probs[0][predicted.item()]
+    if prob.item() > 0.75:
+        for intent in intents['intents']:
+            if tag == intent["tag"]:
+                return random.choice(intent['bot_response'])
+
+    return "I didn't get that, try again."
+
+def get_sa(msg):
+    edu = TextBlob(msg)
+    sa = edu.sentiment.polarity
+
+    return sa
+
+
+if __name__ == "__main__":
+    print("Welcome to Canis care Vet bot!")
+    while True:
+
+        sentence = input("You: ")
+        if sentence == "quit":
+            break
+
+        # getting sentiment analysis value
+
+
+
+        resp = get_response(sentence)
+        print(resp)

AI_ChatBot_Using_Sentiment_Analysis/intents.json → AI_ChatBot_Using_Sentiment_Analysis/ChatBotBE/intents.json

@@ -111,8 +111,8 @@
         "What are the precautions for Dandruff?",
         "What are the Dandruff precautions?",
         "What are some Dandruff preventative measures?",
-        "How can I prevent my dog ​​from Dandruff?",
-        "How can I keep my dog from getting Dandruff?",
+        "How could I prevent my dog ​​from Dandruff?",
+        "How could I keep my dog from getting Dandruff?",
         "Precautions for Dandruff?"
       ],
       "bot_response": [
@@ -486,6 +486,41 @@
         "Flea allergy, Food allergy, Inhalant or contact allergy",
         "Allergy to the normal bacterial flora and Yeast organisms of the skin"
       ]
+    },
+    {
+      "tag": "identify1",
+      "patterns": [
+        "Itchiness"
+      ],
+      "bot_response": ["May be Ringworm or Dandruff or Lupus or Canine Atopic Dermatitis or Mange or Folliculitis"]
+    },
+    {
+      "tag": "identify2",
+      "patterns": [
+        "Hair loss"
+      ],
+      "bot_response": ["May be Ringworm or Dandruff or Yeast Infections or Mange or Folliculitis"]
+    },
+    {
+      "tag": "identify3",
+      "patterns": [
+        "Scratching"
+      ],
+      "bot_response": ["May be Ringworm or Ticks and Fleas or Yeast Infections or Lupus or Canine Atopic Dermatitis"]
+    },
+    {
+      "tag": "identify4",
+      "patterns": [
+        "Redness"
+      ],
+      "bot_response": ["May be Canine Atopic Dermatitis or Mange or Folliculitis or Lupus or Canine Atopic Dermatitis"]
+    },
+    {
+      "tag": "identify5",
+      "patterns": [
+        "Papules"
+      ],
+      "bot_response": ["May be Impetigo or Folliculitis"]
     }
   ]
 }

AI_ChatBot_Using_Sentiment_Analysis/ChatBotBE/manage.py

+#!/usr/bin/env python
+"""Django's command-line utility for administrative tasks."""
+import os
+import sys
+
+
+def main():
+    """Run administrative tasks."""
+    os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'mysite.settings')
+    try:
+        from django.core.management import execute_from_command_line
+    except ImportError as exc:
+        raise ImportError(
+            "Couldn't import Django. Are you sure it's installed and "
+            "available on your PYTHONPATH environment variable? Did you "
+            "forget to activate a virtual environment?"
+        ) from exc
+    execute_from_command_line(sys.argv)
+
+
+if __name__ == '__main__':
+    main()

AI_ChatBot_Using_Sentiment_Analysis/ChatBotBE/model.py

+import torch
+import torch.nn as nn
+
+
+class NeuralNet(nn.Module):
+    def __init__(self, input_size, hidden_size, num_classes):
+        super(NeuralNet, self).__init__()
+        self.l1 = nn.Linear(input_size, hidden_size)
+        self.l2 = nn.Linear(hidden_size, hidden_size)
+        self.l3 = nn.Linear(hidden_size, num_classes)
+        self.relu = nn.ReLU()
+
+    def forward(self, x):
+        out = self.l1(x)
+        out = self.relu(out)
+        out = self.l2(out)
+        out = self.relu(out)
+        out = self.l3(out)
+        # no activation and no softmax at the end
+        return out

AI_ChatBot_Using_Sentiment_Analysis/ChatBotBE/mysite/asgi.py

+"""
+ASGI config for mysite project.
+
+It exposes the ASGI callable as a module-level variable named ``application``.
+
+For more information on this file, see
+https://docs.djangoproject.com/en/3.2/howto/deployment/asgi/
+"""
+
+import os
+
+from django.core.asgi import get_asgi_application
+
+os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'mysite.settings')
+
+application = get_asgi_application()

AI_ChatBot_Using_Sentiment_Analysis/ChatBotBE/mysite/settings.py

+"""
+Django settings for mysite project.
+
+Generated by 'django-admin startproject' using Django 3.2.15.
+
+For more information on this file, see
+https://docs.djangoproject.com/en/3.2/topics/settings/
+
+For the full list of settings and their values, see
+https://docs.djangoproject.com/en/3.2/ref/settings/
+"""
+import os
+from pathlib import Path
+import django_heroku
+import dj_database_url
+
+# Build paths inside the project like this: BASE_DIR / 'subdir'.
+BASE_DIR = Path(__file__).resolve().parent.parent
+
+
+# Quick-start development settings - unsuitable for production
+# See https://docs.djangoproject.com/en/3.2/howto/deployment/checklist/
+
+# SECURITY WARNING: keep the secret key used in production secret!
+SECRET_KEY = 'django-insecure-0gurly7=gyq2pso@wuq_t&*&=268k=0e&ro8n4t49(e#rtt7l$'
+
+# SECURITY WARNING: don't run with debug turned on in production!
+DEBUG = True
+
+ALLOWED_HOSTS = ['*']
+
+
+# Application definition
+
+INSTALLED_APPS = [
+    'django.contrib.admin',
+    'django.contrib.auth',
+    'django.contrib.contenttypes',
+    'django.contrib.sessions',
+    'django.contrib.messages',
+    'django.contrib.staticfiles',
+
+    'api.apps.ApiConfig',
+
+    'rest_framework',
+
+    'corsheaders'
+]
+
+MIDDLEWARE = [
+    'django.middleware.security.SecurityMiddleware',
+
+    "corsheaders.middleware.CorsMiddleware",
+
+    'django.contrib.sessions.middleware.SessionMiddleware',
+    'django.middleware.common.CommonMiddleware',
+    'django.middleware.csrf.CsrfViewMiddleware',
+    'django.contrib.auth.middleware.AuthenticationMiddleware',
+    'django.contrib.messages.middleware.MessageMiddleware',
+    'django.middleware.clickjacking.XFrameOptionsMiddleware',
+]
+
+ROOT_URLCONF = 'mysite.urls'
+
+TEMPLATES = [
+    {
+        'BACKEND': 'django.template.backends.django.DjangoTemplates',
+        'DIRS': [BASE_DIR / 'templates'],
+        'APP_DIRS': True,
+        'OPTIONS': {
+            'context_processors': [
+                'django.template.context_processors.debug',
+                'django.template.context_processors.request',
+                'django.contrib.auth.context_processors.auth',
+                'django.contrib.messages.context_processors.messages',
+            ],
+        },
+    },
+]
+
+WSGI_APPLICATION = 'mysite.wsgi.application'
+
+
+# Database
+# https://docs.djangoproject.com/en/3.2/ref/settings/#databases
+
+DATABASES = {
+    'default': {
+        'ENGINE': 'django.db.backends.sqlite3',
+        'NAME': BASE_DIR / 'db.sqlite3',
+    }
+}
+
+
+# Password validation
+# https://docs.djangoproject.com/en/3.2/ref/settings/#auth-password-validators
+
+AUTH_PASSWORD_VALIDATORS = [
+    {
+        'NAME': 'django.contrib.auth.password_validation.UserAttributeSimilarityValidator',
+    },
+    {
+        'NAME': 'django.contrib.auth.password_validation.MinimumLengthValidator',
+    },
+    {
+        'NAME': 'django.contrib.auth.password_validation.CommonPasswordValidator',
+    },
+    {
+        'NAME': 'django.contrib.auth.password_validation.NumericPasswordValidator',
+    },
+]
+
+
+# Internationalization
+# https://docs.djangoproject.com/en/3.2/topics/i18n/
+
+LANGUAGE_CODE = 'en-us'
+
+TIME_ZONE = 'UTC'
+
+USE_I18N = True
+
+USE_L10N = True
+
+USE_TZ = True
+
+
+# Static files (CSS, JavaScript, Images)
+# https://docs.djangoproject.com/en/3.2/howto/static-files/
+
+STATIC_URL = '/static/'
+STATIC_ROOT = os.path.join(BASE_DIR, 'staticfiles')
+STATICFILES_DIRS = (os.path.join(BASE_DIR, 'static'),)
+django_heroku.settings(locals())
+
+# Default primary key field type
+# https://docs.djangoproject.com/en/3.2/ref/settings/#default-auto-field
+
+DEFAULT_AUTO_FIELD = 'django.db.models.BigAutoField'
+
+CORS_ALLOW_ALL_ORIGINS = True
\ No newline at end of file

AI_ChatBot_Using_Sentiment_Analysis/ChatBotBE/mysite/urls.py

+"""mysite URL Configuration
+
+The `urlpatterns` list routes URLs to views. For more information please see:
+    https://docs.djangoproject.com/en/3.2/topics/http/urls/
+Examples:
+Function views
+    1. Add an import:  from my_app import views
+    2. Add a URL to urlpatterns:  path('', views.home, name='home')
+Class-based views
+    1. Add an import:  from other_app.views import Home
+    2. Add a URL to urlpatterns:  path('', Home.as_view(), name='home')
+Including another URLconf
+    1. Import the include() function: from django.urls import include, path
+    2. Add a URL to urlpatterns:  path('blog/', include('blog.urls'))
+"""
+from django.contrib import admin
+from django.urls import path, include
+
+urlpatterns = [
+    path('admin/', admin.site.urls),
+    path('', include('api.urls'))
+]

AI_ChatBot_Using_Sentiment_Analysis/ChatBotBE/mysite/wsgi.py

+"""
+WSGI config for mysite project.
+
+It exposes the WSGI callable as a module-level variable named ``application``.
+
+For more information on this file, see
+https://docs.djangoproject.com/en/3.2/howto/deployment/wsgi/
+"""
+
+import os
+
+from django.core.wsgi import get_wsgi_application
+
+os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'mysite.settings')
+
+application = get_wsgi_application()

AI_ChatBot_Using_Sentiment_Analysis/ChatBotBE/nltk.txt

+stem.porter
+utils
+tokenize

AI_ChatBot_Using_Sentiment_Analysis/ChatBotBE/nltk_utils.py

+import numpy as np
+import nltk
+# nltk.download('punkt')
+from nltk.stem.porter import PorterStemmer
+
+stemmer = PorterStemmer()
+
+
+def tokenize(sentence):
+    return nltk.word_tokenize(sentence)
+
+
+def stem(word):
+    return stemmer.stem(word.lower())
+
+
+def bag_of_words(tokenized_sentence, words):
+    # stem each word
+    sentence_words = [stem(word) for word in tokenized_sentence]
+    # initialize bag with 0 for each word
+    bag = np.zeros(len(words), dtype=np.float32)
+    for idx, w in enumerate(words):
+        if w in sentence_words:
+            bag[idx] = 1
+
+    return bag

AI_ChatBot_Using_Sentiment_Analysis/ChatBotBE/requirements.txt

+asgiref==3.5.2
+click==8.1.3
+colorama==0.4.5
+dj-database-url==1.0.0
+Django==4.1.2
+django-heroku==0.3.1
+gunicorn==20.1.0
+joblib==1.2.0
+nltk==3.7
+numpy==1.23.3
+psycopg2==2.9.4
+regex==2022.9.13
+sqlparse==0.4.3
+textblob==0.17.1
+torch==1.12.1
+tqdm==4.64.1
+typing_extensions==4.4.0
+tzdata==2022.4
+whitenoise==6.2.0

AI_ChatBot_Using_Sentiment_Analysis/ChatBotBE/runtime.txt

+python-3.10.7

AI_ChatBot_Using_Sentiment_Analysis/ChatBotBE/train.py

+import numpy as np
+import random
+import json
+import torch
+import torch.nn as nn
+
+from torch.utils.data import Dataset, DataLoader
+from nltk_utils import bag_of_words, tokenize, stem
+from model import NeuralNet
+
+with open('intents.json', 'r') as f:
+    intents = json.load(f)
+
+all_words = []
+tags = []
+xy = []
+# loop through each sentence in our intents patterns
+for intent in intents['intents']:
+    tag = intent['tag']
+    # add to tag list
+    tags.append(tag)
+    for pattern in intent['patterns']:
+        # tokenize each word in the sentence
+        w = tokenize(pattern)
+        # add to our words list
+        all_words.extend(w)
+        # add to xy pair
+        xy.append((w, tag))
+
+# stem and lower each word
+ignore_words = ['?', '.', '!']
+all_words = [stem(w) for w in all_words if w not in ignore_words]
+# remove duplicates and sort
+all_words = sorted(set(all_words))
+tags = sorted(set(tags))
+
+print(len(xy), "patterns")
+print(len(tags), "tags:", tags)
+print(len(all_words), "unique stemmed words:", all_words)
+
+# create training data
+X_train = []
+y_train = []
+for (pattern_sentence, tag) in xy:
+    # X: bag of words for each pattern_sentence
+    bag = bag_of_words(pattern_sentence, all_words)
+    X_train.append(bag)
+    # y: PyTorch CrossEntropyLoss needs only class labels, not one-hot
+    label = tags.index(tag)
+    y_train.append(label)
+
+X_train = np.array(X_train)
+y_train = np.array(y_train)
+
+# Hyper-parameters 
+num_epochs = 2000
+batch_size = 8
+learning_rate = 0.001
+input_size = len(X_train[0])
+hidden_size = 8
+output_size = len(tags)
+print(input_size, output_size)
+
+
+class ChatDataset(Dataset):
+
+    def __init__(self):
+        self.n_samples = len(X_train)
+        self.x_data = X_train
+        self.y_data = y_train
+
+    # support indexing such that dataset[i] can be used to get i-th sample
+    def __getitem__(self, index):
+        return self.x_data[index], self.y_data[index]
+
+    # we can call len(dataset) to return the size
+    def __len__(self):
+        return self.n_samples
+
+
+dataset = ChatDataset()
+train_loader = DataLoader(dataset=dataset,
+                          batch_size=batch_size,
+                          shuffle=True,
+                          num_workers=0)
+
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+model = NeuralNet(input_size, hidden_size, output_size).to(device)
+
+# Loss and optimizer
+criterion = nn.CrossEntropyLoss()
+optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
+
+# Train the model
+for epoch in range(num_epochs):
+    for (words, labels) in train_loader:
+        words = words.to(device)
+        labels = labels.to(dtype=torch.long).to(device)
+
+        # Forward pass
+        outputs = model(words)
+        # if y would be one-hot, we must apply
+        # labels = torch.max(labels, 1)[1]
+        loss = criterion(outputs, labels)
+
+        # Backward and optimize
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+
+    if (epoch + 1) % 100 == 0:
+        print(f'Epoch [{epoch + 1}/{num_epochs}], Loss: {loss.item():.4f}')
+
+print(f'final loss: {loss.item():.4f}')
+
+data = {
+    "model_state": model.state_dict(),
+    "input_size": input_size,
+    "hidden_size": hidden_size,
+    "output_size": output_size,
+    "all_words": all_words,
+    "tags": tags
+}
+
+FILE = "model.h5"
+torch.save(data, FILE)
+
+print(f'training complete. file saved to {FILE}')

AI_ChatBot_Using_Sentiment_Analysis/Vet_Bot.ipynb

-{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":173470,"status":"ok","timestamp":1660462996396,"user":{"displayName":"Sachindu Gimhana","userId":"13341145685503871291"},"user_tz":-330},"id":"0V8EjOkXGTFb","outputId":"5524a27d-8f30-4905-dc82-b49d25e19bda"},"outputs":[],"source":["!pip install tflearn\n","!pip install textblob\n","\n","import nltk\n","nltk.download('punkt')\n","from nltk.stem.lancaster import LancasterStemmer\n","stemmer = LancasterStemmer()\n","from textblob import TextBlob\n","import numpy\n","import tflearn\n","import tensorflow\n","import random\n","import json\n","import pickle\n","import matplotlib.pyplot as plt\n","\n","with open(\"intents.json\") as file:\n","    data = json.load(file)\n","\n","try:\n","    with open(\"data.pickle\", \"rb\") as f:\n","        words, labels, training, output = pickle.load(f)\n","except:\n","    words = []\n","    labels = []\n","    docs_x = []\n","    docs_y = []\n","\n","    for intent in data[\"intents\"]:\n","        for pattern in intent[\"patterns\"]:\n","            wrds = nltk.word_tokenize(pattern)\n","            words.extend(wrds)\n","            docs_x.append(wrds)\n","            docs_y.append(intent[\"tag\"])\n","\n","        if intent[\"tag\"] not in labels:\n","            labels.append(intent[\"tag\"])\n","\n","    words = [stemmer.stem(w.lower()) for w in words if w != \"?\"]\n","    words = sorted(list(set(words)))\n","\n","    labels = sorted(labels)\n","\n","    training = []\n","    output = []\n","\n","    out_empty = [0 for _ in range(len(labels))]\n","\n","    for x, doc in enumerate(docs_x):\n","        bag = []\n","\n","        wrds = [stemmer.stem(w.lower()) for w in doc]\n","\n","        for w in words:\n","            if w in wrds:\n","                bag.append(1)\n","            else:\n","                bag.append(0)\n","\n","        output_row = out_empty[:]\n","        output_row[labels.index(docs_y[x])] = 1\n","\n","        training.append(bag)\n","        output.append(output_row)\n","\n","\n","    training = numpy.array(training)\n","    output = numpy.array(output)\n","\n","    with open(\"data.pickle\", \"wb\") as f:\n","        pickle.dump((words, labels, training, output), f)\n","\n","from tensorflow.python.framework import ops\n","ops.reset_default_graph()\n","\n","net = tflearn.input_data(shape=[None, len(training[0])])\n","net = tflearn.fully_connected(net, 8)\n","net = tflearn.fully_connected(net, 8)\n","net = tflearn.fully_connected(net, len(output[0]), activation=\"softmax\")\n","net = tflearn.regression(net)\n","\n","model = tflearn.DNN(net)\n","\n","try:\n","    model.load(\"model.tflearn\")\n","except:\n","    train = model.fit(training, output, n_epoch=2000, batch_size=8, show_metric=True)\n","    model.save(\"model.tflearn\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"o5kZkho2SH1n"},"outputs":[],"source":["def bag_of_words(s, words):\n","    bag = [0 for _ in range(len(words))]\n","\n","    s_words = nltk.word_tokenize(s)\n","    s_words = [stemmer.stem(word.lower()) for word in s_words]\n","\n","    for se in s_words:\n","        for i, w in enumerate(words):\n","            if w == se:\n","                bag[i] = 1\n","            \n","    return numpy.array(bag)\n","    \n","def chat():\n","    print(\"Welcome to Canis Care VetBot (type quit to stop)!\")\n","    while True:\n","        inp = input(\"You: \")\n","        if inp.lower() == \"quit\":\n","            break\n","            \n","        #getting sentiment analysis value\n","        edu=TextBlob(inp)\n","        sa=edu.sentiment.polarity\n","        print(\"Sentiment Value is : \",sa)    \n","\n","        results = model.predict([bag_of_words(inp, words)])[0]\n","        results_index = numpy.argmax(results)\n","        tag = labels[results_index]\n","\n","        if results[results_index] > 0.7:\n","            for tg in data[\"intents\"]:\n","              if tg['tag'] == tag:\n","                  responses = tg['bot_response']\n","\n","            print(random.choice(responses))\n","\n","        else:\n","            print(\"I didn't get that, try again.\")\n","\n","chat()"]}],"metadata":{"colab":{"authorship_tag":"ABX9TyMO0aMAMOJF7Y+oufnfsl+s","collapsed_sections":[],"name":"Vet_Bot.ipynb","provenance":[]},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0}