diff --git a/bot/ML/NLPChatbot.ipynb b/bot/ML/NLPChatbot.ipynb
index f2bf308..9ccb4dd 100644
--- a/bot/ML/NLPChatbot.ipynb
+++ b/bot/ML/NLPChatbot.ipynb
@@ -9,7 +9,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 50,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -27,7 +27,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 51,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -43,7 +43,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 52,
    "metadata": {},
    "outputs": [
     {
@@ -67,7 +67,7 @@
        "True"
       ]
      },
-     "execution_count": 58,
+     "execution_count": 52,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -87,7 +87,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 53,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -106,16 +106,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 54,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "'{\\n  \"intent\": [\\n    {\\n      \"tag\": \"greeting\",\\n      \"patterns\": [\\n        \"Hi\",\\n        \"How are you?\",\\n        \"Hey there, how have you been?\",\\n        \"Is anyone there?\",\\n        \"Hello\",\\n        \"Good day\",\\n        \"Hiii\",\\n        \"Hii\"\\n      ],\\n      \"responses\": [\\n        \"Hi there, what can I do for you?\",\\n        \"Hey there, how can I help?\"\\n      ]\\n    },\\n    {\\n      \"tag\": \"goodbye\",\\n      \"patterns\": [\"Bye\", \"See you there\", \"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\", \"Nice\", \"Good\"],\\n      \"responses\": [\"Happy to help!\", \"Any time!\", \"My pleasure\"]\\n    },\\n    {\\n      \"tag\": \"about\",\\n      \"patterns\": [\\n        \"about you\",\\n        \"Who are you?\",\\n        \"What are you?\",\\n        \"Who you are?\",\\n        \"what you can do\",\\n        \"What is the Purpose of the project\"\\n      ],\\n      \"responses\": [\\n        \"I am AyurBot. I can help determine your prakriti type. Prakriti is our inherent nature, with which we are born. It is the nature of our body in terms of dosha. It dies not change during the whole lifetime. It is also responsible for the physical and mental characteristics.\"\\n      ]\\n    },\\n    {\\n      \"tag\": \"name\",\\n      \"patterns\": [\\n        \"what is your name\",\\n        \"what should I call you\",\\n        \"whats your name?\"\\n      ],\\n      \"responses\": [\\n        \"You can call me AyurBot.\",\\n        \"I\\'m AyurBot!\",\\n        \"Just call me as AyurBot\"\\n      ]\\n    },\\n    {\\n      \"tag\": \"help\",\\n      \"patterns\": [\\n        \"Could you help me?\",\\n        \"give me a hand please\",\\n        \"Can you help?\",\\n        \"What can you do for me?\",\\n        \"I need a support\",\\n        \"I need a help\",\\n        \"support me please\"\\n      ],\\n      \"responses\": [\\n        \"Tell me how can I assist you\",\\n        \"Tell me your problem to assist you\",\\n        \"Yes Sure, How can I help you?\"\\n      ]\\n    },\\n    {\\n      \"tag\": \"noanswer\",\\n      \"patterns\": [\\n        \"Complete the work\",\\n        \"My name\",\\n        \"user name\",\\n        \"you are not Good\",\\n        \"Nothing\",\\n        \"You are Bad\",\\n        \"Noting to worry\",\\n        \"Chatgpt is Best\",\\n        \"Irrelevant\"\\n      ],\\n      \"responses\": [\\n        \"Sorry, can\\'t understand you\",\\n        \"Please give me more info\",\\n        \"Not sure I understand\"\\n      ]\\n    },\\n    {\\n      \"tag\": \"prakriti\",\\n      \"patterns\": [\\n        \"Can you help me find my prakriti\",\\n        \"what is my prakriti?\",\\n        \"figure out my prakriti\",\\n        \"identify my prakriti\",\\n        \"Determine my Prakriti\",\\n        \"Determine\"\\n      ],\\n      \"responses\": [\\n        \"Absolutely, just need you to answer a few questions. Answer yes to get started.\",\\n        \"I can definitely do that with your help in answering some questions. Answer yes to get started.\"\\n      ]\\n    },\\n    {\\n      \"tag\": \"creators\",\\n      \"patterns\": [\\n        \"Who are the creators\",\\n        \"who made these project\",\\n        \"Project Creators\",\\n        \"project is made by\",\\n        \"Project\",\\n        \"Which project\",\\n        \"who created you\"\\n      ],\\n      \"responses\": [\\n        \"These project is created by Prathamesh Dhande, Chaitanya Mestry, Lonith Shetty & Goutam Thakur\",\\n        \"This project was crafted by a talented team: Prathamesh Dhande, Chaitanya Mestry, Lonith Shetty, and Goutam Thakur.\",\\n        \"Meet the brilliant minds behind this project: Prathamesh Dhande, Chaitanya Mestry, Lonith Shetty, and Goutam Thakur.\",\\n        \"The masterminds responsible for this project are Prathamesh Dhande, Chaitanya Mestry, Lonith Shetty, and Goutam Thakur.\",\\n        \"Curious about the brains behind the project? It\\'s the collaborative effort of Prathamesh Dhande, Chaitanya Mestry, Lonith Shetty, and Goutam Thakur.\",\\n        \"This project was brought to life by Prathamesh Dhande, Chaitanya Mestry, Lonith Shetty, and Goutam Thakur.\"\\n      ]\\n    },\\n    {\\n      \"tag\": \"badwords\",\\n      \"patterns\": [\\n        \"fuck you\",\\n        \"Shut Up\",\\n        \"i hate you\",\\n        \"hate\",\\n        \"You\\'re so annoying\",\\n        \"I can\\'t stand you\",\\n        \"Go away\",\\n        \"You\\'re worthless\",\\n        \"You make me sick\",\\n        \"I despise you\"\\n      ],\\n      \"responses\": [\\n        \"I\\'m here to help, let\\'s keep it respectful.\",\\n        \"Please refrain from using offensive language.\",\\n        \"Let\\'s keep the conversation positive.\",\\n        \"I\\'m here to assist you, no need for rude language.\",\\n        \"Sorry to hear you\\'re upset. Let\\'s talk calmly.\",\\n        \"Let\\'s focus on constructive communication.\",\\n        \"I\\'m here to provide support, no need for negativity.\",\\n        \"Please choose a more respectful way to express your feelings.\",\\n        \"I understand you might be frustrated, let\\'s find a solution.\",\\n        \"Respectful communication makes the conversation more productive.\"\\n      ]\\n    },\\n    {\\n      \"tag\": \"acknowledge\",\\n      \"patterns\": [\"ok\", \"yeah\", \"This should be best\", \"Now I Understand\"],\\n      \"responses\": [\\n        \"Great!\",\\n        \"Got it!\",\\n        \"Perfect!\",\\n        \"Awesome!\",\\n        \"Excellent!\",\\n        \"Fantastic!\",\\n        \"Terrific!\"\\n      ]\\n    },\\n    {\\n      \"tag\": \"age_inquiry\",\\n      \"patterns\": [\"How old are you?\", \"What\\'s your age?\"],\\n      \"responses\": [\"I\\'m a virtual assistant, so I don\\'t have an age!\"]\\n    },\\n    {\\n      \"tag\": \"positive_feedback\",\\n      \"patterns\": [\\n        \"I love this chatbot\",\\n        \"This is great!\",\\n        \"You\\'re doing a good job\",\\n        \"I\\'m impressed!\"\\n      ],\\n      \"responses\": [\\n        \"Thank you! I\\'m glad you\\'re enjoying it.\",\\n        \"Appreciate the positive feedback!\"\\n      ]\\n    },\\n    {\\n      \"tag\": \"negative_feedback\",\\n      \"patterns\": [\\n        \"I\\'m not satisfied\",\\n        \"This isn\\'t helpful\",\\n        \"I\\'m disappointed\"\\n      ],\\n      \"responses\": [\\n        \"I\\'m sorry to hear that. How can I improve?\",\\n        \"Apologies if I fell short. Let me know how I can assist you better.\"\\n      ]\\n    },\\n    {\\n      \"tag\": \"information_request\",\\n      \"patterns\": [\\n        \"Can you provide more information?\",\\n        \"Tell me more\",\\n        \"I need more details\"\\n      ],\\n      \"responses\": [\\n        \"Sure, what specific information are you looking for?\",\\n        \"Of course, what do you want to know more about?\"\\n      ]\\n    },\\n    {\\n      \"tag\": \"contact_support\",\\n      \"patterns\": [\\n        \"I need to contact support\",\\n        \"How can I reach customer service?\",\\n        \"Help me get in touch with support\"\\n      ],\\n      \"responses\": [\\n        \"You can reach our support team at support@example.com\",\\n        \"For assistance, please contact our customer support team.\"\\n      ]\\n    },\\n    {\\n      \"tag\": \"diet_recommendation\",\\n      \"patterns\": [\\n        \"Can you recommend a diet for me?\",\\n        \"What foods should I eat?\",\\n        \"I need a diet plan\"\\n      ],\\n      \"responses\": [\\n        \"Sure, I can provide personalized diet recommendations based on your Prakriti. Please answer a few questions to get started.\",\\n        \"I\\'d be happy to recommend a diet plan tailored to your Ayurvedic constitution. Let\\'s begin by understanding your Prakriti.\"\\n      ]\\n    },\\n    {\\n      \"tag\": \"exercise_suggestions\",\\n      \"patterns\": [\\n        \"What exercises should I do?\",\\n        \"Can you suggest some workouts?\",\\n        \"I need exercise tips\"\\n      ],\\n      \"responses\": [\\n        \"Certainly! I can recommend exercises based on your Prakriti and fitness goals. Let\\'s get started!\",\\n        \"I can provide personalized exercise suggestions to help you stay healthy and balanced.\"\\n      ]\\n    },\\n    {\\n      \"tag\": \"stress_management\",\\n      \"patterns\": [\\n        \"How can I manage stress?\",\\n        \"Give me stress-relief tips\",\\n        \"I\\'m feeling stressed\"\\n      ],\\n      \"responses\": [\\n        \"Managing stress is crucial for overall well-being. I can offer tips and techniques to help you relax and unwind.\",\\n        \"I can provide stress management strategies, including mindfulness practices, deep breathing exercises, and relaxation techniques.\"\\n      ]\\n    },\\n    {\\n      \"tag\": \"sleep_tips\",\\n      \"patterns\": [\\n        \"How can I improve my sleep?\",\\n        \"Give me sleep tips\",\\n        \"I have trouble sleeping\"\\n      ],\\n      \"responses\": [\\n        \"Improving sleep quality is essential for health. I can suggest tips and practices to promote better sleep hygiene.\",\\n        \"I can offer advice on enhancing sleep quality, including establishing a bedtime routine, creating a comfortable sleep environment, and practicing relaxation techniques.\"\\n      ]\\n    },\\n    {\\n      \"tag\": \"nutrition_information\",\\n      \"patterns\": [\\n        \"Tell me about nutrition\",\\n        \"Give me nutrition facts\",\\n        \"I want to learn about healthy eating\"\\n      ],\\n      \"responses\": [\\n        \"Nutrition plays a vital role in overall health. I can provide information on balanced diets, essential nutrients, and healthy eating habits.\",\\n        \"I can offer insights into nutrition and dietary principles to help you make informed choices for optimal health and wellness.\"\\n      ]\\n    },\\n    {\\n      \"tag\": \"exit\",\\n      \"patterns\": [\"exit\", \"quit\", \"over\"],\\n      \"responses\": [\"Thank you for using me. Come back Later\"]\\n    }\\n  ]\\n}\\n'"
+       "'{\\n  \"intent\": [\\n    {\\n      \"tag\": \"greeting\",\\n      \"patterns\": [\\n        \"Hi\",\\n        \"How are you?\",\\n        \"Hey there, how have you been?\",\\n        \"Is anyone there?\",\\n        \"Hello\",\\n        \"Good day\",\\n        \"Hiii\",\\n        \"Hii\",\\n        \"AyurBot\"\\n      ],\\n      \"responses\": [\\n        \"Hi there, what can I do for you?\",\\n        \"Hey there, how can I help?\"\\n      ]\\n    },\\n    {\\n      \"tag\": \"goodbye\",\\n      \"patterns\": [\"Bye\", \"See you there\", \"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\": [\\n        \"Thanks\",\\n        \"Thank you\",\\n        \"That\\'s helpful\",\\n        \"Nice\",\\n        \"Good\",\\n        \"Thank you Bot\",\\n        \"Thank you soo much\",\\n        \"You are Beautiful Bot\",\\n        \"You are Great\"\\n      ],\\n      \"responses\": [\"Happy to help!\", \"Any time!\", \"My pleasure\"]\\n    },\\n    {\\n      \"tag\": \"about\",\\n      \"patterns\": [\\n        \"about you\",\\n        \"Who are you?\",\\n        \"What are you?\",\\n        \"Who you are?\",\\n        \"what you can do\",\\n        \"What is the Purpose of the project\"\\n      ],\\n      \"responses\": [\\n        \"I am AyurBot. I can help determine your prakriti type. Prakriti is our inherent nature, with which we are born. It is the nature of our body in terms of dosha. It dies not change during the whole lifetime. It is also responsible for the physical and mental characteristics.\"\\n      ]\\n    },\\n    {\\n      \"tag\": \"name\",\\n      \"patterns\": [\\n        \"what is your name\",\\n        \"what should I call you\",\\n        \"whats your name?\"\\n      ],\\n      \"responses\": [\\n        \"You can call me AyurBot.\",\\n        \"I\\'m AyurBot!\",\\n        \"Just call me as AyurBot\"\\n      ]\\n    },\\n    {\\n      \"tag\": \"help\",\\n      \"patterns\": [\\n        \"Could you help me?\",\\n        \"give me a hand please\",\\n        \"Can you help?\",\\n        \"What can you do for me?\",\\n        \"I need a support\",\\n        \"I need a help\",\\n        \"support me please\"\\n      ],\\n      \"responses\": [\\n        \"Tell me how can I assist you\",\\n        \"Tell me your problem to assist you\",\\n        \"Yes Sure, How can I help you?\"\\n      ]\\n    },\\n    {\\n      \"tag\": \"noanswer\",\\n      \"patterns\": [\\n        \"Complete the work\",\\n        \"My name\",\\n        \"user name\",\\n        \"you are not Good\",\\n        \"Nothing\",\\n        \"You are Bad\",\\n        \"Noting to worry\",\\n        \"Chatgpt is Best\",\\n        \"Irrelevant\"\\n      ],\\n      \"responses\": [\\n        \"Sorry, can\\'t understand you\",\\n        \"Please give me more info\",\\n        \"Not sure I understand\"\\n      ]\\n    },\\n    {\\n      \"tag\": \"prakriti\",\\n      \"patterns\": [\\n        \"Can you help me find my prakriti\",\\n        \"what is my prakriti?\",\\n        \"figure out my prakriti\",\\n        \"identify my prakriti\",\\n        \"Determine my Prakriti\",\\n        \"Determine\"\\n      ],\\n      \"responses\": [\\n        \"Absolutely, just need you to answer a few questions.\",\\n        \"I can definitely do that with your help in answering some questions.\"\\n      ]\\n    },\\n    {\\n      \"tag\": \"creators\",\\n      \"patterns\": [\\n        \"Who are the creators\",\\n        \"who made these project\",\\n        \"Project Creators\",\\n        \"project is made by\",\\n        \"Project\",\\n        \"Which project\",\\n        \"who created you\"\\n      ],\\n      \"responses\": [\\n        \"These project is created by Prathamesh Dhande, Chaitanya Mestry, Lonith Shetty & Goutam Thakur\",\\n        \"This project was crafted by a talented team: Prathamesh Dhande, Chaitanya Mestry, Lonith Shetty, and Goutam Thakur.\",\\n        \"Meet the brilliant minds behind this project: Prathamesh Dhande, Chaitanya Mestry, Lonith Shetty, and Goutam Thakur.\",\\n        \"The masterminds responsible for this project are Prathamesh Dhande, Chaitanya Mestry, Lonith Shetty, and Goutam Thakur.\",\\n        \"Curious about the brains behind the project? It\\'s the collaborative effort of Prathamesh Dhande, Chaitanya Mestry, Lonith Shetty, and Goutam Thakur.\",\\n        \"This project was brought to life by Prathamesh Dhande, Chaitanya Mestry, Lonith Shetty, and Goutam Thakur.\"\\n      ]\\n    },\\n    {\\n      \"tag\": \"badwords\",\\n      \"patterns\": [\\n        \"fuck you\",\\n        \"Shut Up\",\\n        \"i hate you\",\\n        \"You\\'re so annoying\",\\n        \"I can\\'t stand you\",\\n        \"Go away\",\\n        \"You\\'re worthless\",\\n        \"You make me sick\",\\n        \"I despise you\"\\n      ],\\n      \"responses\": [\\n        \"I\\'m here to help, let\\'s keep it respectful.\",\\n        \"Please refrain from using offensive language.\",\\n        \"Let\\'s keep the conversation positive.\",\\n        \"I\\'m here to assist you, no need for rude language.\",\\n        \"Sorry to hear you\\'re upset. Let\\'s talk calmly.\",\\n        \"Let\\'s focus on constructive communication.\",\\n        \"I\\'m here to provide support, no need for negativity.\",\\n        \"Please choose a more respectful way to express your feelings.\",\\n        \"I understand you might be frustrated, let\\'s find a solution.\",\\n        \"Respectful communication makes the conversation more productive.\"\\n      ]\\n    },\\n    {\\n      \"tag\": \"acknowledge\",\\n      \"patterns\": [\"ok\", \"yeah\", \"This should be best\", \"Now I Understand\"],\\n      \"responses\": [\"Great!\", \"Got it!\", \"Perfect!\", \"Awesome!\"]\\n    },\\n    {\\n      \"tag\": \"age_inquiry\",\\n      \"patterns\": [\"How old are you?\", \"What\\'s your age?\"],\\n      \"responses\": [\"I\\'m a Ayurbot, so I don\\'t have an age!\"]\\n    },\\n    {\\n      \"tag\": \"positive_feedback\",\\n      \"patterns\": [\\n        \"I love this chatbot\",\\n        \"This is great!\",\\n        \"You\\'re doing a good job\",\\n        \"I\\'m impressed!\",\\n        \"You are Great\"\\n      ],\\n      \"responses\": [\\n        \"Thank you! I\\'m glad you\\'re enjoying it.\",\\n        \"Appreciate the positive feedback!\"\\n      ]\\n    },\\n    {\\n      \"tag\": \"negative_feedback\",\\n      \"patterns\": [\\n        \"I\\'m not satisfied\",\\n        \"This isn\\'t helpful\",\\n        \"I\\'m disappointed\",\\n        \"Very bad bot.\"\\n      ],\\n      \"responses\": [\\n        \"I\\'m sorry to hear that. How can I improve?\",\\n        \"Apologies if I fell short. Let me know how I can assist you better.\"\\n      ]\\n    },\\n    {\\n      \"tag\": \"information_request\",\\n      \"patterns\": [\\n        \"Can you provide more information?\",\\n        \"Tell me more\",\\n        \"I need more details\",\\n        \"give me more information\",\\n        \"guide me\"\\n      ],\\n      \"responses\": [\\n        \"Sure, what specific information are you looking for?\",\\n        \"Of course, what do you want to know more about?\"\\n      ]\\n    },\\n    {\\n      \"tag\": \"contact_support\",\\n      \"patterns\": [\\n        \"I need to contact support\",\\n        \"How can I reach customer service?\",\\n        \"Help me get in touch with support\",\\n        \"who to contact\"\\n      ],\\n      \"responses\": [\\n        \"You can reach our support team at support@example.com\",\\n        \"For assistance, please contact our customer support team.\"\\n      ]\\n    },\\n    {\\n      \"tag\": \"diet_recommendation\",\\n      \"patterns\": [\\n        \"Can you recommend a diet for me?\",\\n        \"What foods should I eat?\",\\n        \"I need a diet plan\",\\n        \"Recommend me Diet\"\\n      ],\\n      \"responses\": [\\n        \"Sure, I can provide personalized diet recommendations based on your Prakriti. Please answer a few questions to get started.\",\\n        \"I\\'d be happy to recommend a diet plan tailored to your Ayurvedic constitution. Let\\'s begin by understanding your Prakriti.\"\\n      ]\\n    },\\n    {\\n      \"tag\": \"exercise_suggestions\",\\n      \"patterns\": [\\n        \"What exercises should I do?\",\\n        \"Can you suggest some workouts?\",\\n        \"I need exercise tips\",\\n        \"Recommend Some Exercise.\"\\n      ],\\n      \"responses\": [\\n        \"Certainly! I can recommend exercises based on your Prakriti and fitness goals. Let\\'s get started!\",\\n        \"I can provide personalized exercise suggestions to help you stay healthy and balanced.\"\\n      ]\\n    },\\n    {\\n      \"tag\": \"stress_management\",\\n      \"patterns\": [\\n        \"How can I manage stress?\",\\n        \"Give me stress-relief tips\",\\n        \"I\\'m feeling stressed\"\\n      ],\\n      \"responses\": [\\n        \"Managing stress is crucial for overall well-being. I can offer tips and techniques to help you relax and unwind.\",\\n        \"I can provide stress management strategies, including mindfulness practices, deep breathing exercises, and relaxation techniques.\"\\n      ]\\n    },\\n    {\\n      \"tag\": \"sleep_tips\",\\n      \"patterns\": [\\n        \"How can I improve my sleep?\",\\n        \"Give me sleep tips\",\\n        \"I have trouble sleeping\"\\n      ],\\n      \"responses\": [\\n        \"Improving sleep quality is essential for health. I can suggest tips and practices to promote better sleep hygiene.\",\\n        \"I can offer advice on enhancing sleep quality, including establishing a bedtime routine, creating a comfortable sleep environment, and practicing relaxation techniques.\"\\n      ]\\n    },\\n    {\\n      \"tag\": \"nutrition_information\",\\n      \"patterns\": [\\n        \"Tell me about nutrition\",\\n        \"Give me nutrition facts\",\\n        \"I want to learn about healthy eating\"\\n      ],\\n      \"responses\": [\\n        \"Nutrition plays a vital role in overall health. I can provide information on balanced diets, essential nutrients, and healthy eating habits.\",\\n        \"I can offer insights into nutrition and dietary principles to help you make informed choices for optimal health and wellness.\"\\n      ]\\n    },\\n    {\\n      \"tag\": \"exit\",\\n      \"patterns\": [\"exit\", \"quit\", \"over\"],\\n      \"responses\": [\\n        \"Thank you for using me. Come back Later\",\\n        \"Hope you found the bot Helpful.\"\\n      ]\\n    },\\n    {\\n      \"tag\": \"info_prakriti\",\\n      \"patterns\": [\"what is prakriti\", \"tell me more about the prakriti\"],\\n      \"responses\": [\\n        \"Prakriti is a Sanskrit word that means the original or natural form or condition of anything, original or primary substance. It is a key concept in Hinduism, formulated by its Sankhya school. There are three types of doshas Vata, Pitta & Kapha\",\\n        \"Prakriti is an inherent nature of an individual determined at the time of your birth, which can not be changed during your lifetime. Ayurveda broadly classifies human nature in two types - Mental and Physical Nature.\"\\n      ]\\n    },\\n    {\\n      \"tag\": \"info_dosha\",\\n      \"patterns\": [\\n        \"What is doshas\",\\n        \"tell me about the doshas\",\\n        \"guide about the doshas\"\\n      ],\\n      \"responses\": [\\n        \"According to Ayurveda, doshas are energy patterns that flow around our bodies, governing our thinking and behavior. The three primary doshas are vata, pitta, and kapha. Each dosha is made up of a different combination of the five elements: air, space, fire, earth, and water\",\\n        \"According to Ayurveda, the doshas control the creation, maintenance, and breakdown of bodily tissue and elimination of wastes, as well as psychological aspects, such as emotions, understanding, and love. When balanced, the doshas maintain the systems of the body; when one of the doshas increases due to certain diet and lifestyle habits, imbalance is created. Ayurveda can help restore harmony to areas that have lost balance.\"\\n      ]\\n    },\\n    {\\n      \"tag\": \"info_kapha\",\\n      \"patterns\": [\\n        \"What is kapha\",\\n        \"explain about the kapha\",\\n        \"kapha\",\\n        \"what do you mean by kapha\"\\n      ],\\n      \"responses\": [\\n        \"Kapha is based on earth and water. It can be described as steady, stable, heavy, slow, cold, and soft. Spring is known as kapha season, as many parts of the world slowly exit hibernation. People with this dosha are described as strong, thick-boned, and caring.\"\\n      ]\\n    },\\n    {\\n      \"tag\": \"info_vata\",\\n      \"patterns\": [\\n        \"what is vata\",\\n        \"vata\",\\n        \"what do you mean about the vata\",\\n        \"Explain about the kapha\"\\n      ],\\n      \"responses\": [\\n        \"The word Vata means to blow or to move like the wind. Containing the elements like air and space, Vata manages all the movements in the mind and body. It controls blood flow, evacuation of wastes, breathing and the movement of thoughts across the brain.\"\\n      ]\\n    },\\n    {\\n      \"tag\": \"info_pitta\",\\n      \"patterns\": [\\n        \"What is Pitta\",\\n        \"pitta\",\\n        \"Explain aobut the pitta\",\\n        \"Guide about the pitta\",\\n        \"what does mean pitta\"\\n      ],\\n      \"responses\": [\\n        \"Pitta translates roughly as fire. However, the term does not mean fire in the literal sense, in the way you might experience or sense it as candlelight or in an open fire. Pitta dosha, the heat energy in the body, is invisible. It manifests itself in your metabolism.\"\\n      ]\\n    }\\n  ]\\n}\\n'"
       ]
      },
-     "execution_count": 60,
+     "execution_count": 54,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -129,12 +129,43 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Converting the intents file from bytes to json "
+    "Reading the Json file from Jsilo online Storage of JSON"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import requests"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "url = 'https://api.jsonsilo.com/5ff310a1-b136-45a7-86c3-0b7378ebfd6c'\n",
+    "headers = {\n",
+    "        'X-SILO-KEY': 'pVyv9PBnSNfIhrqS6piqF2P2npNqMydLp0kNxp8igK',\n",
+    "        'Content-Type': 'application/json'\n",
+    "      }"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "new_data=requests.get(url,headers=headers)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
    "metadata": {},
    "outputs": [
     {
@@ -148,16 +179,30 @@
        "    'Hello',\n",
        "    'Good day',\n",
        "    'Hiii',\n",
-       "    'Hii'],\n",
+       "    'Hii',\n",
+       "    'AyurBot'],\n",
        "   'responses': ['Hi there, what can I do for you?',\n",
        "    'Hey there, how can I help?']},\n",
+       "  {'tag': 'src_code',\n",
+       "   'patterns': ['What is your Source Code',\n",
+       "    'Source Code',\n",
+       "    'Where is your Source Code'],\n",
+       "   'responses': ['My Source Code is Available in Github Repository https://github.com/PrathameshDhande22/Prakriti-Determine.']},\n",
        "  {'tag': 'goodbye',\n",
        "   'patterns': ['Bye', 'See you there', 'Goodbye'],\n",
        "   'responses': ['See you later, thanks for visiting ',\n",
        "    'Have a nice day',\n",
        "    'Bye! Come back again soon.']},\n",
        "  {'tag': 'thanks',\n",
-       "   'patterns': ['Thanks', 'Thank you', \"That's helpful\", 'Nice', 'Good'],\n",
+       "   'patterns': ['Thanks',\n",
+       "    'Thank you',\n",
+       "    \"That's helpful\",\n",
+       "    'Nice',\n",
+       "    'Good',\n",
+       "    'Thank you Bot',\n",
+       "    'Thank you soo much',\n",
+       "    'You are Beautiful Bot',\n",
+       "    'You are Great'],\n",
        "   'responses': ['Happy to help!', 'Any time!', 'My pleasure']},\n",
        "  {'tag': 'about',\n",
        "   'patterns': ['about you',\n",
@@ -205,8 +250,8 @@
        "    'identify my prakriti',\n",
        "    'Determine my Prakriti',\n",
        "    'Determine'],\n",
-       "   'responses': ['Absolutely, just need you to answer a few questions. Answer yes to get started.',\n",
-       "    'I can definitely do that with your help in answering some questions. Answer yes to get started.']},\n",
+       "   'responses': ['Absolutely, just need you to answer a few questions.',\n",
+       "    'I can definitely do that with your help in answering some questions.']},\n",
        "  {'tag': 'creators',\n",
        "   'patterns': ['Who are the creators',\n",
        "    'who made these project',\n",
@@ -225,7 +270,6 @@
        "   'patterns': ['fuck you',\n",
        "    'Shut Up',\n",
        "    'i hate you',\n",
-       "    'hate',\n",
        "    \"You're so annoying\",\n",
        "    \"I can't stand you\",\n",
        "    'Go away',\n",
@@ -244,49 +288,53 @@
        "    'Respectful communication makes the conversation more productive.']},\n",
        "  {'tag': 'acknowledge',\n",
        "   'patterns': ['ok', 'yeah', 'This should be best', 'Now I Understand'],\n",
-       "   'responses': ['Great!',\n",
-       "    'Got it!',\n",
-       "    'Perfect!',\n",
-       "    'Awesome!',\n",
-       "    'Excellent!',\n",
-       "    'Fantastic!',\n",
-       "    'Terrific!']},\n",
+       "   'responses': ['Great!', 'Got it!', 'Perfect!', 'Awesome!']},\n",
        "  {'tag': 'age_inquiry',\n",
        "   'patterns': ['How old are you?', \"What's your age?\"],\n",
-       "   'responses': [\"I'm a virtual assistant, so I don't have an age!\"]},\n",
+       "   'responses': [\"I'm a Ayurbot, so I don't have an age!\"]},\n",
        "  {'tag': 'positive_feedback',\n",
        "   'patterns': ['I love this chatbot',\n",
        "    'This is great!',\n",
        "    \"You're doing a good job\",\n",
-       "    \"I'm impressed!\"],\n",
+       "    \"I'm impressed!\",\n",
+       "    'You are Great'],\n",
        "   'responses': [\"Thank you! I'm glad you're enjoying it.\",\n",
        "    'Appreciate the positive feedback!']},\n",
        "  {'tag': 'negative_feedback',\n",
-       "   'patterns': [\"I'm not satisfied\", \"This isn't helpful\", \"I'm disappointed\"],\n",
+       "   'patterns': [\"I'm not satisfied\",\n",
+       "    \"This isn't helpful\",\n",
+       "    \"I'm disappointed\",\n",
+       "    'Very bad bot.',\n",
+       "    'i will not believe you'],\n",
        "   'responses': [\"I'm sorry to hear that. How can I improve?\",\n",
        "    'Apologies if I fell short. Let me know how I can assist you better.']},\n",
        "  {'tag': 'information_request',\n",
        "   'patterns': ['Can you provide more information?',\n",
        "    'Tell me more',\n",
-       "    'I need more details'],\n",
+       "    'I need more details',\n",
+       "    'give me more information',\n",
+       "    'guide me'],\n",
        "   'responses': ['Sure, what specific information are you looking for?',\n",
        "    'Of course, what do you want to know more about?']},\n",
        "  {'tag': 'contact_support',\n",
        "   'patterns': ['I need to contact support',\n",
        "    'How can I reach customer service?',\n",
-       "    'Help me get in touch with support'],\n",
-       "   'responses': ['You can reach our support team at support@example.com',\n",
-       "    'For assistance, please contact our customer support team.']},\n",
+       "    'Help me get in touch with support',\n",
+       "    'who to contact'],\n",
+       "   'responses': ['You can reach our support team at developerprathamesh.coder@gmail.com',\n",
+       "    'For assistance, please contact our customer Support Team Prathamesh Dhande.']},\n",
        "  {'tag': 'diet_recommendation',\n",
        "   'patterns': ['Can you recommend a diet for me?',\n",
        "    'What foods should I eat?',\n",
-       "    'I need a diet plan'],\n",
+       "    'I need a diet plan',\n",
+       "    'Recommend me Diet'],\n",
        "   'responses': ['Sure, I can provide personalized diet recommendations based on your Prakriti. Please answer a few questions to get started.',\n",
        "    \"I'd be happy to recommend a diet plan tailored to your Ayurvedic constitution. Let's begin by understanding your Prakriti.\"]},\n",
        "  {'tag': 'exercise_suggestions',\n",
        "   'patterns': ['What exercises should I do?',\n",
        "    'Can you suggest some workouts?',\n",
-       "    'I need exercise tips'],\n",
+       "    'I need exercise tips',\n",
+       "    'Recommend Some Exercise.'],\n",
        "   'responses': [\"Certainly! I can recommend exercises based on your Prakriti and fitness goals. Let's get started!\",\n",
        "    'I can provide personalized exercise suggestions to help you stay healthy and balanced.']},\n",
        "  {'tag': 'stress_management',\n",
@@ -309,14 +357,63 @@
        "    'I can offer insights into nutrition and dietary principles to help you make informed choices for optimal health and wellness.']},\n",
        "  {'tag': 'exit',\n",
        "   'patterns': ['exit', 'quit', 'over'],\n",
-       "   'responses': ['Thank you for using me. Come back Later']}]}"
+       "   'responses': ['Thank you for using me. Come back Later',\n",
+       "    'Hope you found the bot Helpful.']},\n",
+       "  {'tag': 'info_prakriti',\n",
+       "   'patterns': ['what is prakriti',\n",
+       "    'tell me more about the prakriti',\n",
+       "    'why Should i Believe you'],\n",
+       "   'responses': ['Prakriti is a Sanskrit word that means the original or natural form or condition of anything, original or primary substance. It is a key concept in Hinduism, formulated by its Sankhya school. There are three types of doshas Vata, Pitta & Kapha',\n",
+       "    'Prakriti is an inherent nature of an individual determined at the time of your birth, which can not be changed during your lifetime. Ayurveda broadly classifies human nature in two types - Mental and Physical Nature.']},\n",
+       "  {'tag': 'info_dosha',\n",
+       "   'patterns': ['What is doshas',\n",
+       "    'tell me about the doshas',\n",
+       "    'guide about the doshas'],\n",
+       "   'responses': ['According to Ayurveda, doshas are energy patterns that flow around our bodies, governing our thinking and behavior. The three primary doshas are vata, pitta, and kapha. Each dosha is made up of a different combination of the five elements: air, space, fire, earth, and water',\n",
+       "    'According to Ayurveda, the doshas control the creation, maintenance, and breakdown of bodily tissue and elimination of wastes, as well as psychological aspects, such as emotions, understanding, and love. When balanced, the doshas maintain the systems of the body; when one of the doshas increases due to certain diet and lifestyle habits, imbalance is created. Ayurveda can help restore harmony to areas that have lost balance.']},\n",
+       "  {'tag': 'info_kapha',\n",
+       "   'patterns': ['What is kapha',\n",
+       "    'explain about the kapha',\n",
+       "    'kapha',\n",
+       "    'what do you mean by kapha'],\n",
+       "   'responses': ['Kapha is based on earth and water. It can be described as steady, stable, heavy, slow, cold, and soft. Spring is known as kapha season, as many parts of the world slowly exit hibernation. People with this dosha are described as strong, thick-boned, and caring.']},\n",
+       "  {'tag': 'info_vata',\n",
+       "   'patterns': ['what is vata',\n",
+       "    'vata',\n",
+       "    'what do you mean about the vata',\n",
+       "    'Explain about the kapha'],\n",
+       "   'responses': ['The word Vata means to blow or to move like the wind. Containing the elements like air and space, Vata manages all the movements in the mind and body. It controls blood flow, evacuation of wastes, breathing and the movement of thoughts across the brain.']},\n",
+       "  {'tag': 'info_pitta',\n",
+       "   'patterns': ['What is Pitta',\n",
+       "    'pitta',\n",
+       "    'Explain aobut the pitta',\n",
+       "    'Guide about the pitta',\n",
+       "    'what does mean pitta'],\n",
+       "   'responses': ['Pitta translates roughly as fire. However, the term does not mean fire in the literal sense, in the way you might experience or sense it as candlelight or in an open fire. Pitta dosha, the heat energy in the body, is invisible. It manifests itself in your metabolism.']}]}"
       ]
      },
-     "execution_count": 61,
+     "execution_count": 58,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
+   "source": [
+    "intents=new_data.json()\n",
+    "intents"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Converting the intents file from bytes to json "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "intents=json.loads(data_file)\n",
     "intents"
@@ -324,7 +421,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 59,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -345,7 +442,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 60,
    "metadata": {},
    "outputs": [
     {
@@ -373,6 +470,19 @@
        " 'day',\n",
        " 'Hiii',\n",
        " 'Hii',\n",
+       " 'AyurBot',\n",
+       " 'What',\n",
+       " 'is',\n",
+       " 'your',\n",
+       " 'Source',\n",
+       " 'Code',\n",
+       " 'Source',\n",
+       " 'Code',\n",
+       " 'Where',\n",
+       " 'is',\n",
+       " 'your',\n",
+       " 'Source',\n",
+       " 'Code',\n",
        " 'Bye',\n",
        " 'See',\n",
        " 'you',\n",
@@ -386,6 +496,20 @@
        " 'helpful',\n",
        " 'Nice',\n",
        " 'Good',\n",
+       " 'Thank',\n",
+       " 'you',\n",
+       " 'Bot',\n",
+       " 'Thank',\n",
+       " 'you',\n",
+       " 'soo',\n",
+       " 'much',\n",
+       " 'You',\n",
+       " 'are',\n",
+       " 'Beautiful',\n",
+       " 'Bot',\n",
+       " 'You',\n",
+       " 'are',\n",
+       " 'Great',\n",
        " 'about',\n",
        " 'you',\n",
        " 'Who',\n",
@@ -528,7 +652,6 @@
        " 'i',\n",
        " 'hate',\n",
        " 'you',\n",
-       " 'hate',\n",
        " 'You',\n",
        " \"'re\",\n",
        " 'so',\n",
@@ -587,6 +710,9 @@
        " \"'m\",\n",
        " 'impressed',\n",
        " '!',\n",
+       " 'You',\n",
+       " 'are',\n",
+       " 'Great',\n",
        " 'I',\n",
        " \"'m\",\n",
        " 'not',\n",
@@ -598,6 +724,15 @@
        " 'I',\n",
        " \"'m\",\n",
        " 'disappointed',\n",
+       " 'Very',\n",
+       " 'bad',\n",
+       " 'bot',\n",
+       " '.',\n",
+       " 'i',\n",
+       " 'will',\n",
+       " 'not',\n",
+       " 'believe',\n",
+       " 'you',\n",
        " 'Can',\n",
        " 'you',\n",
        " 'provide',\n",
@@ -611,6 +746,12 @@
        " 'need',\n",
        " 'more',\n",
        " 'details',\n",
+       " 'give',\n",
+       " 'me',\n",
+       " 'more',\n",
+       " 'information',\n",
+       " 'guide',\n",
+       " 'me',\n",
        " 'I',\n",
        " 'need',\n",
        " 'to',\n",
@@ -630,6 +771,9 @@
        " 'touch',\n",
        " 'with',\n",
        " 'support',\n",
+       " 'who',\n",
+       " 'to',\n",
+       " 'contact',\n",
        " 'Can',\n",
        " 'you',\n",
        " 'recommend',\n",
@@ -649,6 +793,9 @@
        " 'a',\n",
        " 'diet',\n",
        " 'plan',\n",
+       " 'Recommend',\n",
+       " 'me',\n",
+       " 'Diet',\n",
        " 'What',\n",
        " 'exercises',\n",
        " 'should',\n",
@@ -665,6 +812,10 @@
        " 'need',\n",
        " 'exercise',\n",
        " 'tips',\n",
+       " 'Recommend',\n",
+       " 'Some',\n",
+       " 'Exercise',\n",
+       " '.',\n",
        " 'How',\n",
        " 'can',\n",
        " 'I',\n",
@@ -711,10 +862,81 @@
        " 'eating',\n",
        " 'exit',\n",
        " 'quit',\n",
-       " 'over']"
+       " 'over',\n",
+       " 'what',\n",
+       " 'is',\n",
+       " 'prakriti',\n",
+       " 'tell',\n",
+       " 'me',\n",
+       " 'more',\n",
+       " 'about',\n",
+       " 'the',\n",
+       " 'prakriti',\n",
+       " 'why',\n",
+       " 'Should',\n",
+       " 'i',\n",
+       " 'Believe',\n",
+       " 'you',\n",
+       " 'What',\n",
+       " 'is',\n",
+       " 'doshas',\n",
+       " 'tell',\n",
+       " 'me',\n",
+       " 'about',\n",
+       " 'the',\n",
+       " 'doshas',\n",
+       " 'guide',\n",
+       " 'about',\n",
+       " 'the',\n",
+       " 'doshas',\n",
+       " 'What',\n",
+       " 'is',\n",
+       " 'kapha',\n",
+       " 'explain',\n",
+       " 'about',\n",
+       " 'the',\n",
+       " 'kapha',\n",
+       " 'kapha',\n",
+       " 'what',\n",
+       " 'do',\n",
+       " 'you',\n",
+       " 'mean',\n",
+       " 'by',\n",
+       " 'kapha',\n",
+       " 'what',\n",
+       " 'is',\n",
+       " 'vata',\n",
+       " 'vata',\n",
+       " 'what',\n",
+       " 'do',\n",
+       " 'you',\n",
+       " 'mean',\n",
+       " 'about',\n",
+       " 'the',\n",
+       " 'vata',\n",
+       " 'Explain',\n",
+       " 'about',\n",
+       " 'the',\n",
+       " 'kapha',\n",
+       " 'What',\n",
+       " 'is',\n",
+       " 'Pitta',\n",
+       " 'pitta',\n",
+       " 'Explain',\n",
+       " 'aobut',\n",
+       " 'the',\n",
+       " 'pitta',\n",
+       " 'Guide',\n",
+       " 'about',\n",
+       " 'the',\n",
+       " 'pitta',\n",
+       " 'what',\n",
+       " 'does',\n",
+       " 'mean',\n",
+       " 'pitta']"
       ]
      },
-     "execution_count": 63,
+     "execution_count": 60,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -725,7 +947,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 61,
    "metadata": {},
    "outputs": [
     {
@@ -740,6 +962,10 @@
       "(['Good', 'day'], 'greeting')\n",
       "(['Hiii'], 'greeting')\n",
       "(['Hii'], 'greeting')\n",
+      "(['AyurBot'], 'greeting')\n",
+      "(['What', 'is', 'your', 'Source', 'Code'], 'src_code')\n",
+      "(['Source', 'Code'], 'src_code')\n",
+      "(['Where', 'is', 'your', 'Source', 'Code'], 'src_code')\n",
       "(['Bye'], 'goodbye')\n",
       "(['See', 'you', 'there'], 'goodbye')\n",
       "(['Goodbye'], 'goodbye')\n",
@@ -748,6 +974,10 @@
       "(['That', \"'s\", 'helpful'], 'thanks')\n",
       "(['Nice'], 'thanks')\n",
       "(['Good'], 'thanks')\n",
+      "(['Thank', 'you', 'Bot'], 'thanks')\n",
+      "(['Thank', 'you', 'soo', 'much'], 'thanks')\n",
+      "(['You', 'are', 'Beautiful', 'Bot'], 'thanks')\n",
+      "(['You', 'are', 'Great'], 'thanks')\n",
       "(['about', 'you'], 'about')\n",
       "(['Who', 'are', 'you', '?'], 'about')\n",
       "(['What', 'are', 'you', '?'], 'about')\n",
@@ -789,7 +1019,6 @@
       "(['fuck', 'you'], 'badwords')\n",
       "(['Shut', 'Up'], 'badwords')\n",
       "(['i', 'hate', 'you'], 'badwords')\n",
-      "(['hate'], 'badwords')\n",
       "(['You', \"'re\", 'so', 'annoying'], 'badwords')\n",
       "(['I', 'ca', \"n't\", 'stand', 'you'], 'badwords')\n",
       "(['Go', 'away'], 'badwords')\n",
@@ -806,21 +1035,29 @@
       "(['This', 'is', 'great', '!'], 'positive_feedback')\n",
       "(['You', \"'re\", 'doing', 'a', 'good', 'job'], 'positive_feedback')\n",
       "(['I', \"'m\", 'impressed', '!'], 'positive_feedback')\n",
+      "(['You', 'are', 'Great'], 'positive_feedback')\n",
       "(['I', \"'m\", 'not', 'satisfied'], 'negative_feedback')\n",
       "(['This', 'is', \"n't\", 'helpful'], 'negative_feedback')\n",
       "(['I', \"'m\", 'disappointed'], 'negative_feedback')\n",
+      "(['Very', 'bad', 'bot', '.'], 'negative_feedback')\n",
+      "(['i', 'will', 'not', 'believe', 'you'], 'negative_feedback')\n",
       "(['Can', 'you', 'provide', 'more', 'information', '?'], 'information_request')\n",
       "(['Tell', 'me', 'more'], 'information_request')\n",
       "(['I', 'need', 'more', 'details'], 'information_request')\n",
+      "(['give', 'me', 'more', 'information'], 'information_request')\n",
+      "(['guide', 'me'], 'information_request')\n",
       "(['I', 'need', 'to', 'contact', 'support'], 'contact_support')\n",
       "(['How', 'can', 'I', 'reach', 'customer', 'service', '?'], 'contact_support')\n",
       "(['Help', 'me', 'get', 'in', 'touch', 'with', 'support'], 'contact_support')\n",
+      "(['who', 'to', 'contact'], 'contact_support')\n",
       "(['Can', 'you', 'recommend', 'a', 'diet', 'for', 'me', '?'], 'diet_recommendation')\n",
       "(['What', 'foods', 'should', 'I', 'eat', '?'], 'diet_recommendation')\n",
       "(['I', 'need', 'a', 'diet', 'plan'], 'diet_recommendation')\n",
+      "(['Recommend', 'me', 'Diet'], 'diet_recommendation')\n",
       "(['What', 'exercises', 'should', 'I', 'do', '?'], 'exercise_suggestions')\n",
       "(['Can', 'you', 'suggest', 'some', 'workouts', '?'], 'exercise_suggestions')\n",
       "(['I', 'need', 'exercise', 'tips'], 'exercise_suggestions')\n",
+      "(['Recommend', 'Some', 'Exercise', '.'], 'exercise_suggestions')\n",
       "(['How', 'can', 'I', 'manage', 'stress', '?'], 'stress_management')\n",
       "(['Give', 'me', 'stress-relief', 'tips'], 'stress_management')\n",
       "(['I', \"'m\", 'feeling', 'stressed'], 'stress_management')\n",
@@ -832,7 +1069,26 @@
       "(['I', 'want', 'to', 'learn', 'about', 'healthy', 'eating'], 'nutrition_information')\n",
       "(['exit'], 'exit')\n",
       "(['quit'], 'exit')\n",
-      "(['over'], 'exit')\n"
+      "(['over'], 'exit')\n",
+      "(['what', 'is', 'prakriti'], 'info_prakriti')\n",
+      "(['tell', 'me', 'more', 'about', 'the', 'prakriti'], 'info_prakriti')\n",
+      "(['why', 'Should', 'i', 'Believe', 'you'], 'info_prakriti')\n",
+      "(['What', 'is', 'doshas'], 'info_dosha')\n",
+      "(['tell', 'me', 'about', 'the', 'doshas'], 'info_dosha')\n",
+      "(['guide', 'about', 'the', 'doshas'], 'info_dosha')\n",
+      "(['What', 'is', 'kapha'], 'info_kapha')\n",
+      "(['explain', 'about', 'the', 'kapha'], 'info_kapha')\n",
+      "(['kapha'], 'info_kapha')\n",
+      "(['what', 'do', 'you', 'mean', 'by', 'kapha'], 'info_kapha')\n",
+      "(['what', 'is', 'vata'], 'info_vata')\n",
+      "(['vata'], 'info_vata')\n",
+      "(['what', 'do', 'you', 'mean', 'about', 'the', 'vata'], 'info_vata')\n",
+      "(['Explain', 'about', 'the', 'kapha'], 'info_vata')\n",
+      "(['What', 'is', 'Pitta'], 'info_pitta')\n",
+      "(['pitta'], 'info_pitta')\n",
+      "(['Explain', 'aobut', 'the', 'pitta'], 'info_pitta')\n",
+      "(['Guide', 'about', 'the', 'pitta'], 'info_pitta')\n",
+      "(['what', 'does', 'mean', 'pitta'], 'info_pitta')\n"
      ]
     }
    ],
@@ -843,13 +1099,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 62,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "['greeting',\n",
+       " 'src_code',\n",
        " 'goodbye',\n",
        " 'thanks',\n",
        " 'about',\n",
@@ -870,10 +1127,15 @@
        " 'stress_management',\n",
        " 'sleep_tips',\n",
        " 'nutrition_information',\n",
-       " 'exit']"
+       " 'exit',\n",
+       " 'info_prakriti',\n",
+       " 'info_dosha',\n",
+       " 'info_kapha',\n",
+       " 'info_vata',\n",
+       " 'info_pitta']"
       ]
      },
-     "execution_count": 65,
+     "execution_count": 62,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -891,7 +1153,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 63,
    "metadata": {},
    "outputs": [
     {
@@ -915,6 +1177,19 @@
        " 'day',\n",
        " 'hiii',\n",
        " 'hii',\n",
+       " 'ayurbot',\n",
+       " 'what',\n",
+       " 'is',\n",
+       " 'your',\n",
+       " 'source',\n",
+       " 'code',\n",
+       " 'source',\n",
+       " 'code',\n",
+       " 'where',\n",
+       " 'is',\n",
+       " 'your',\n",
+       " 'source',\n",
+       " 'code',\n",
        " 'bye',\n",
        " 'see',\n",
        " 'you',\n",
@@ -928,6 +1203,20 @@
        " 'helpful',\n",
        " 'nice',\n",
        " 'good',\n",
+       " 'thank',\n",
+       " 'you',\n",
+       " 'bot',\n",
+       " 'thank',\n",
+       " 'you',\n",
+       " 'soo',\n",
+       " 'much',\n",
+       " 'you',\n",
+       " 'are',\n",
+       " 'beautiful',\n",
+       " 'bot',\n",
+       " 'you',\n",
+       " 'are',\n",
+       " 'great',\n",
        " 'about',\n",
        " 'you',\n",
        " 'who',\n",
@@ -1062,7 +1351,6 @@
        " 'i',\n",
        " 'hate',\n",
        " 'you',\n",
-       " 'hate',\n",
        " 'you',\n",
        " \"'re\",\n",
        " 'so',\n",
@@ -1117,6 +1405,9 @@
        " 'i',\n",
        " \"'m\",\n",
        " 'impressed',\n",
+       " 'you',\n",
+       " 'are',\n",
+       " 'great',\n",
        " 'i',\n",
        " \"'m\",\n",
        " 'not',\n",
@@ -1128,6 +1419,15 @@
        " 'i',\n",
        " \"'m\",\n",
        " 'disappointed',\n",
+       " 'very',\n",
+       " 'bad',\n",
+       " 'bot',\n",
+       " '.',\n",
+       " 'i',\n",
+       " 'will',\n",
+       " 'not',\n",
+       " 'believe',\n",
+       " 'you',\n",
        " 'can',\n",
        " 'you',\n",
        " 'provide',\n",
@@ -1140,6 +1440,12 @@
        " 'need',\n",
        " 'more',\n",
        " 'detail',\n",
+       " 'give',\n",
+       " 'me',\n",
+       " 'more',\n",
+       " 'information',\n",
+       " 'guide',\n",
+       " 'me',\n",
        " 'i',\n",
        " 'need',\n",
        " 'to',\n",
@@ -1158,6 +1464,9 @@
        " 'touch',\n",
        " 'with',\n",
        " 'support',\n",
+       " 'who',\n",
+       " 'to',\n",
+       " 'contact',\n",
        " 'can',\n",
        " 'you',\n",
        " 'recommend',\n",
@@ -1175,6 +1484,9 @@
        " 'a',\n",
        " 'diet',\n",
        " 'plan',\n",
+       " 'recommend',\n",
+       " 'me',\n",
+       " 'diet',\n",
        " 'what',\n",
        " 'exercise',\n",
        " 'should',\n",
@@ -1189,6 +1501,10 @@
        " 'need',\n",
        " 'exercise',\n",
        " 'tip',\n",
+       " 'recommend',\n",
+       " 'some',\n",
+       " 'exercise',\n",
+       " '.',\n",
        " 'how',\n",
        " 'can',\n",
        " 'i',\n",
@@ -1233,10 +1549,81 @@
        " 'eating',\n",
        " 'exit',\n",
        " 'quit',\n",
-       " 'over']"
+       " 'over',\n",
+       " 'what',\n",
+       " 'is',\n",
+       " 'prakriti',\n",
+       " 'tell',\n",
+       " 'me',\n",
+       " 'more',\n",
+       " 'about',\n",
+       " 'the',\n",
+       " 'prakriti',\n",
+       " 'why',\n",
+       " 'should',\n",
+       " 'i',\n",
+       " 'believe',\n",
+       " 'you',\n",
+       " 'what',\n",
+       " 'is',\n",
+       " 'doshas',\n",
+       " 'tell',\n",
+       " 'me',\n",
+       " 'about',\n",
+       " 'the',\n",
+       " 'doshas',\n",
+       " 'guide',\n",
+       " 'about',\n",
+       " 'the',\n",
+       " 'doshas',\n",
+       " 'what',\n",
+       " 'is',\n",
+       " 'kapha',\n",
+       " 'explain',\n",
+       " 'about',\n",
+       " 'the',\n",
+       " 'kapha',\n",
+       " 'kapha',\n",
+       " 'what',\n",
+       " 'do',\n",
+       " 'you',\n",
+       " 'mean',\n",
+       " 'by',\n",
+       " 'kapha',\n",
+       " 'what',\n",
+       " 'is',\n",
+       " 'vata',\n",
+       " 'vata',\n",
+       " 'what',\n",
+       " 'do',\n",
+       " 'you',\n",
+       " 'mean',\n",
+       " 'about',\n",
+       " 'the',\n",
+       " 'vata',\n",
+       " 'explain',\n",
+       " 'about',\n",
+       " 'the',\n",
+       " 'kapha',\n",
+       " 'what',\n",
+       " 'is',\n",
+       " 'pitta',\n",
+       " 'pitta',\n",
+       " 'explain',\n",
+       " 'aobut',\n",
+       " 'the',\n",
+       " 'pitta',\n",
+       " 'guide',\n",
+       " 'about',\n",
+       " 'the',\n",
+       " 'pitta',\n",
+       " 'what',\n",
+       " 'doe',\n",
+       " 'mean',\n",
+       " 'pitta']"
       ]
      },
-     "execution_count": 66,
+     "execution_count": 63,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1255,7 +1642,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 64,
    "metadata": {},
    "outputs": [
     {
@@ -1264,17 +1651,23 @@
        "[\"'m\",\n",
        " \"'re\",\n",
        " \"'s\",\n",
+       " '.',\n",
        " 'a',\n",
        " 'about',\n",
        " 'age',\n",
        " 'annoying',\n",
        " 'anyone',\n",
+       " 'aobut',\n",
        " 'are',\n",
        " 'away',\n",
+       " 'ayurbot',\n",
        " 'bad',\n",
        " 'be',\n",
+       " 'beautiful',\n",
        " 'been',\n",
+       " 'believe',\n",
        " 'best',\n",
+       " 'bot',\n",
        " 'by',\n",
        " 'bye',\n",
        " 'ca',\n",
@@ -1282,6 +1675,7 @@
        " 'can',\n",
        " 'chatbot',\n",
        " 'chatgpt',\n",
+       " 'code',\n",
        " 'complete',\n",
        " 'contact',\n",
        " 'could',\n",
@@ -1295,11 +1689,14 @@
        " 'diet',\n",
        " 'disappointed',\n",
        " 'do',\n",
+       " 'doe',\n",
        " 'doing',\n",
+       " 'doshas',\n",
        " 'eat',\n",
        " 'eating',\n",
        " 'exercise',\n",
        " 'exit',\n",
+       " 'explain',\n",
        " 'fact',\n",
        " 'feeling',\n",
        " 'figure',\n",
@@ -1313,6 +1710,7 @@
        " 'good',\n",
        " 'goodbye',\n",
        " 'great',\n",
+       " 'guide',\n",
        " 'hand',\n",
        " 'hate',\n",
        " 'have',\n",
@@ -1334,13 +1732,16 @@
        " 'irrelevant',\n",
        " 'is',\n",
        " 'job',\n",
+       " 'kapha',\n",
        " 'learn',\n",
        " 'love',\n",
        " 'made',\n",
        " 'make',\n",
        " 'manage',\n",
        " 'me',\n",
+       " 'mean',\n",
        " 'more',\n",
+       " 'much',\n",
        " 'my',\n",
        " \"n't\",\n",
        " 'name',\n",
@@ -1356,6 +1757,7 @@
        " 'old',\n",
        " 'out',\n",
        " 'over',\n",
+       " 'pitta',\n",
        " 'plan',\n",
        " 'please',\n",
        " 'prakriti',\n",
@@ -1375,6 +1777,8 @@
        " 'sleeping',\n",
        " 'so',\n",
        " 'some',\n",
+       " 'soo',\n",
+       " 'source',\n",
        " 'stand',\n",
        " 'stress',\n",
        " 'stress-relief',\n",
@@ -1396,11 +1800,16 @@
        " 'understand',\n",
        " 'up',\n",
        " 'user',\n",
+       " 'vata',\n",
+       " 'very',\n",
        " 'want',\n",
        " 'what',\n",
        " 'whats',\n",
+       " 'where',\n",
        " 'which',\n",
        " 'who',\n",
+       " 'why',\n",
+       " 'will',\n",
        " 'with',\n",
        " 'work',\n",
        " 'workout',\n",
@@ -1411,7 +1820,7 @@
        " 'your']"
       ]
      },
-     "execution_count": 67,
+     "execution_count": 64,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1430,7 +1839,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 65,
    "metadata": {},
    "outputs": [
     {
@@ -1448,6 +1857,11 @@
        " 'goodbye',\n",
        " 'greeting',\n",
        " 'help',\n",
+       " 'info_dosha',\n",
+       " 'info_kapha',\n",
+       " 'info_pitta',\n",
+       " 'info_prakriti',\n",
+       " 'info_vata',\n",
        " 'information_request',\n",
        " 'name',\n",
        " 'negative_feedback',\n",
@@ -1456,11 +1870,12 @@
        " 'positive_feedback',\n",
        " 'prakriti',\n",
        " 'sleep_tips',\n",
+       " 'src_code',\n",
        " 'stress_management',\n",
        " 'thanks']"
       ]
      },
-     "execution_count": 68,
+     "execution_count": 65,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1479,16 +1894,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 66,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "101 documents\n",
-      "22 classes ['about', 'acknowledge', 'age_inquiry', 'badwords', 'contact_support', 'creators', 'diet_recommendation', 'exercise_suggestions', 'exit', 'goodbye', 'greeting', 'help', 'information_request', 'name', 'negative_feedback', 'noanswer', 'nutrition_information', 'positive_feedback', 'prakriti', 'sleep_tips', 'stress_management', 'thanks']\n",
-      "148 unique lemmatized words [\"'m\", \"'re\", \"'s\", 'a', 'about', 'age', 'annoying', 'anyone', 'are', 'away', 'bad', 'be', 'been', 'best', 'by', 'bye', 'ca', 'call', 'can', 'chatbot', 'chatgpt', 'complete', 'contact', 'could', 'created', 'creator', 'customer', 'day', 'despise', 'detail', 'determine', 'diet', 'disappointed', 'do', 'doing', 'eat', 'eating', 'exercise', 'exit', 'fact', 'feeling', 'figure', 'find', 'food', 'for', 'fuck', 'get', 'give', 'go', 'good', 'goodbye', 'great', 'hand', 'hate', 'have', 'healthy', 'hello', 'help', 'helpful', 'hey', 'hi', 'hii', 'hiii', 'how', 'i', 'identify', 'impressed', 'improve', 'in', 'information', 'irrelevant', 'is', 'job', 'learn', 'love', 'made', 'make', 'manage', 'me', 'more', 'my', \"n't\", 'name', 'need', 'nice', 'not', 'nothing', 'noting', 'now', 'nutrition', 'of', 'ok', 'old', 'out', 'over', 'plan', 'please', 'prakriti', 'project', 'provide', 'purpose', 'quit', 'reach', 'recommend', 'satisfied', 'see', 'service', 'should', 'shut', 'sick', 'sleep', 'sleeping', 'so', 'some', 'stand', 'stress', 'stress-relief', 'stressed', 'suggest', 'support', 'tell', 'thank', 'thanks', 'that', 'the', 'there', 'these', 'this', 'tip', 'to', 'touch', 'trouble', 'understand', 'up', 'user', 'want', 'what', 'whats', 'which', 'who', 'with', 'work', 'workout', 'worry', 'worthless', 'yeah', 'you', 'your']\n"
+      "135 documents\n",
+      "28 classes ['about', 'acknowledge', 'age_inquiry', 'badwords', 'contact_support', 'creators', 'diet_recommendation', 'exercise_suggestions', 'exit', 'goodbye', 'greeting', 'help', 'info_dosha', 'info_kapha', 'info_pitta', 'info_prakriti', 'info_vata', 'information_request', 'name', 'negative_feedback', 'noanswer', 'nutrition_information', 'positive_feedback', 'prakriti', 'sleep_tips', 'src_code', 'stress_management', 'thanks']\n",
+      "170 unique lemmatized words [\"'m\", \"'re\", \"'s\", '.', 'a', 'about', 'age', 'annoying', 'anyone', 'aobut', 'are', 'away', 'ayurbot', 'bad', 'be', 'beautiful', 'been', 'believe', 'best', 'bot', 'by', 'bye', 'ca', 'call', 'can', 'chatbot', 'chatgpt', 'code', 'complete', 'contact', 'could', 'created', 'creator', 'customer', 'day', 'despise', 'detail', 'determine', 'diet', 'disappointed', 'do', 'doe', 'doing', 'doshas', 'eat', 'eating', 'exercise', 'exit', 'explain', 'fact', 'feeling', 'figure', 'find', 'food', 'for', 'fuck', 'get', 'give', 'go', 'good', 'goodbye', 'great', 'guide', 'hand', 'hate', 'have', 'healthy', 'hello', 'help', 'helpful', 'hey', 'hi', 'hii', 'hiii', 'how', 'i', 'identify', 'impressed', 'improve', 'in', 'information', 'irrelevant', 'is', 'job', 'kapha', 'learn', 'love', 'made', 'make', 'manage', 'me', 'mean', 'more', 'much', 'my', \"n't\", 'name', 'need', 'nice', 'not', 'nothing', 'noting', 'now', 'nutrition', 'of', 'ok', 'old', 'out', 'over', 'pitta', 'plan', 'please', 'prakriti', 'project', 'provide', 'purpose', 'quit', 'reach', 'recommend', 'satisfied', 'see', 'service', 'should', 'shut', 'sick', 'sleep', 'sleeping', 'so', 'some', 'soo', 'source', 'stand', 'stress', 'stress-relief', 'stressed', 'suggest', 'support', 'tell', 'thank', 'thanks', 'that', 'the', 'there', 'these', 'this', 'tip', 'to', 'touch', 'trouble', 'understand', 'up', 'user', 'vata', 'very', 'want', 'what', 'whats', 'where', 'which', 'who', 'why', 'will', 'with', 'work', 'workout', 'worry', 'worthless', 'yeah', 'you', 'your']\n"
      ]
     }
    ],
@@ -1502,7 +1917,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 67,
    "metadata": {},
    "outputs": [
     {
@@ -1516,6 +1931,10 @@
        " (['Good', 'day'], 'greeting'),\n",
        " (['Hiii'], 'greeting'),\n",
        " (['Hii'], 'greeting'),\n",
+       " (['AyurBot'], 'greeting'),\n",
+       " (['What', 'is', 'your', 'Source', 'Code'], 'src_code'),\n",
+       " (['Source', 'Code'], 'src_code'),\n",
+       " (['Where', 'is', 'your', 'Source', 'Code'], 'src_code'),\n",
        " (['Bye'], 'goodbye'),\n",
        " (['See', 'you', 'there'], 'goodbye'),\n",
        " (['Goodbye'], 'goodbye'),\n",
@@ -1524,6 +1943,10 @@
        " (['That', \"'s\", 'helpful'], 'thanks'),\n",
        " (['Nice'], 'thanks'),\n",
        " (['Good'], 'thanks'),\n",
+       " (['Thank', 'you', 'Bot'], 'thanks'),\n",
+       " (['Thank', 'you', 'soo', 'much'], 'thanks'),\n",
+       " (['You', 'are', 'Beautiful', 'Bot'], 'thanks'),\n",
+       " (['You', 'are', 'Great'], 'thanks'),\n",
        " (['about', 'you'], 'about'),\n",
        " (['Who', 'are', 'you', '?'], 'about'),\n",
        " (['What', 'are', 'you', '?'], 'about'),\n",
@@ -1565,7 +1988,6 @@
        " (['fuck', 'you'], 'badwords'),\n",
        " (['Shut', 'Up'], 'badwords'),\n",
        " (['i', 'hate', 'you'], 'badwords'),\n",
-       " (['hate'], 'badwords'),\n",
        " (['You', \"'re\", 'so', 'annoying'], 'badwords'),\n",
        " (['I', 'ca', \"n't\", 'stand', 'you'], 'badwords'),\n",
        " (['Go', 'away'], 'badwords'),\n",
@@ -1582,23 +2004,31 @@
        " (['This', 'is', 'great', '!'], 'positive_feedback'),\n",
        " (['You', \"'re\", 'doing', 'a', 'good', 'job'], 'positive_feedback'),\n",
        " (['I', \"'m\", 'impressed', '!'], 'positive_feedback'),\n",
+       " (['You', 'are', 'Great'], 'positive_feedback'),\n",
        " (['I', \"'m\", 'not', 'satisfied'], 'negative_feedback'),\n",
        " (['This', 'is', \"n't\", 'helpful'], 'negative_feedback'),\n",
        " (['I', \"'m\", 'disappointed'], 'negative_feedback'),\n",
+       " (['Very', 'bad', 'bot', '.'], 'negative_feedback'),\n",
+       " (['i', 'will', 'not', 'believe', 'you'], 'negative_feedback'),\n",
        " (['Can', 'you', 'provide', 'more', 'information', '?'],\n",
        "  'information_request'),\n",
        " (['Tell', 'me', 'more'], 'information_request'),\n",
        " (['I', 'need', 'more', 'details'], 'information_request'),\n",
+       " (['give', 'me', 'more', 'information'], 'information_request'),\n",
+       " (['guide', 'me'], 'information_request'),\n",
        " (['I', 'need', 'to', 'contact', 'support'], 'contact_support'),\n",
        " (['How', 'can', 'I', 'reach', 'customer', 'service', '?'], 'contact_support'),\n",
        " (['Help', 'me', 'get', 'in', 'touch', 'with', 'support'], 'contact_support'),\n",
+       " (['who', 'to', 'contact'], 'contact_support'),\n",
        " (['Can', 'you', 'recommend', 'a', 'diet', 'for', 'me', '?'],\n",
        "  'diet_recommendation'),\n",
        " (['What', 'foods', 'should', 'I', 'eat', '?'], 'diet_recommendation'),\n",
        " (['I', 'need', 'a', 'diet', 'plan'], 'diet_recommendation'),\n",
+       " (['Recommend', 'me', 'Diet'], 'diet_recommendation'),\n",
        " (['What', 'exercises', 'should', 'I', 'do', '?'], 'exercise_suggestions'),\n",
        " (['Can', 'you', 'suggest', 'some', 'workouts', '?'], 'exercise_suggestions'),\n",
        " (['I', 'need', 'exercise', 'tips'], 'exercise_suggestions'),\n",
+       " (['Recommend', 'Some', 'Exercise', '.'], 'exercise_suggestions'),\n",
        " (['How', 'can', 'I', 'manage', 'stress', '?'], 'stress_management'),\n",
        " (['Give', 'me', 'stress-relief', 'tips'], 'stress_management'),\n",
        " (['I', \"'m\", 'feeling', 'stressed'], 'stress_management'),\n",
@@ -1611,10 +2041,29 @@
        "  'nutrition_information'),\n",
        " (['exit'], 'exit'),\n",
        " (['quit'], 'exit'),\n",
-       " (['over'], 'exit')]"
+       " (['over'], 'exit'),\n",
+       " (['what', 'is', 'prakriti'], 'info_prakriti'),\n",
+       " (['tell', 'me', 'more', 'about', 'the', 'prakriti'], 'info_prakriti'),\n",
+       " (['why', 'Should', 'i', 'Believe', 'you'], 'info_prakriti'),\n",
+       " (['What', 'is', 'doshas'], 'info_dosha'),\n",
+       " (['tell', 'me', 'about', 'the', 'doshas'], 'info_dosha'),\n",
+       " (['guide', 'about', 'the', 'doshas'], 'info_dosha'),\n",
+       " (['What', 'is', 'kapha'], 'info_kapha'),\n",
+       " (['explain', 'about', 'the', 'kapha'], 'info_kapha'),\n",
+       " (['kapha'], 'info_kapha'),\n",
+       " (['what', 'do', 'you', 'mean', 'by', 'kapha'], 'info_kapha'),\n",
+       " (['what', 'is', 'vata'], 'info_vata'),\n",
+       " (['vata'], 'info_vata'),\n",
+       " (['what', 'do', 'you', 'mean', 'about', 'the', 'vata'], 'info_vata'),\n",
+       " (['Explain', 'about', 'the', 'kapha'], 'info_vata'),\n",
+       " (['What', 'is', 'Pitta'], 'info_pitta'),\n",
+       " (['pitta'], 'info_pitta'),\n",
+       " (['Explain', 'aobut', 'the', 'pitta'], 'info_pitta'),\n",
+       " (['Guide', 'about', 'the', 'pitta'], 'info_pitta'),\n",
+       " (['what', 'does', 'mean', 'pitta'], 'info_pitta')]"
       ]
      },
-     "execution_count": 70,
+     "execution_count": 67,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1632,7 +2081,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 68,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1649,7 +2098,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 69,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1658,16 +2107,43 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 70,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]"
+       "[0,\n",
+       " 0,\n",
+       " 0,\n",
+       " 0,\n",
+       " 0,\n",
+       " 0,\n",
+       " 0,\n",
+       " 0,\n",
+       " 0,\n",
+       " 0,\n",
+       " 0,\n",
+       " 0,\n",
+       " 0,\n",
+       " 0,\n",
+       " 0,\n",
+       " 0,\n",
+       " 0,\n",
+       " 0,\n",
+       " 0,\n",
+       " 0,\n",
+       " 0,\n",
+       " 0,\n",
+       " 0,\n",
+       " 0,\n",
+       " 0,\n",
+       " 0,\n",
+       " 0,\n",
+       " 0]"
       ]
      },
-     "execution_count": 73,
+     "execution_count": 70,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1679,14 +2155,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 71,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
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@@ -1721,14 +2197,14 @@
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   {
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+   "execution_count": 72,
    "metadata": {},
    "outputs": [
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    ],
@@ -1874,218 +2384,286 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 75,
    "metadata": {},
    "outputs": [
     {
      "data": {
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        "      dtype=object)"
       ]
      },
-     "execution_count": 78,
+     "execution_count": 75,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2097,16 +2675,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 76,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(101, 2)"
+       "(135, 2)"
       ]
      },
-     "execution_count": 79,
+     "execution_count": 76,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2124,7 +2702,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 77,
    "metadata": {},
    "outputs": [
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@@ -2572,16 +3143,12 @@
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      },
-     "execution_count": 81,
+     "execution_count": 78,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -17220,7 +29479,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 79,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -17241,7 +29500,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": 80,
    "metadata": {},
    "outputs": [
     {
@@ -17252,7 +29511,7 @@
       "_________________________________________________________________\n",
       " Layer (type)                Output Shape              Param #   \n",
       "=================================================================\n",
-      " dense_3 (Dense)             (None, 128)               19072     \n",
+      " dense_3 (Dense)             (None, 128)               21888     \n",
       "                                                                 \n",
       " dropout_2 (Dropout)         (None, 128)               0         \n",
       "                                                                 \n",
@@ -17260,11 +29519,11 @@
       "                                                                 \n",
       " dropout_3 (Dropout)         (None, 64)                0         \n",
       "                                                                 \n",
-      " dense_5 (Dense)             (None, 22)                1430      \n",
+      " dense_5 (Dense)             (None, 28)                1820      \n",
       "                                                                 \n",
       "=================================================================\n",
-      "Total params: 28758 (112.34 KB)\n",
-      "Trainable params: 28758 (112.34 KB)\n",
+      "Total params: 31964 (124.86 KB)\n",
+      "Trainable params: 31964 (124.86 KB)\n",
       "Non-trainable params: 0 (0.00 Byte)\n",
       "_________________________________________________________________\n"
      ]
@@ -17283,7 +29542,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": 81,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -17293,428 +29552,422 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": 82,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 1/200\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "21/21 [==============================] - 1s 2ms/step - loss: 3.0796 - accuracy: 0.0594 \n",
+      "Epoch 1/200\n",
+      "27/27 [==============================] - 1s 2ms/step - loss: 3.3506 - accuracy: 0.0222\n",
       "Epoch 2/200\n",
-      "21/21 [==============================] - 0s 5ms/step - loss: 3.0873 - accuracy: 0.0594\n",
+      "27/27 [==============================] - 0s 5ms/step - loss: 3.3187 - accuracy: 0.0444\n",
       "Epoch 3/200\n",
-      "21/21 [==============================] - 0s 2ms/step - loss: 2.9841 - accuracy: 0.0990\n",
+      "27/27 [==============================] - 0s 2ms/step - loss: 3.2382 - accuracy: 0.0741\n",
       "Epoch 4/200\n",
-      "21/21 [==============================] - 0s 2ms/step - loss: 2.9469 - accuracy: 0.1782\n",
+      "27/27 [==============================] - 0s 3ms/step - loss: 3.1865 - accuracy: 0.1259\n",
       "Epoch 5/200\n",
-      "21/21 [==============================] - 0s 2ms/step - loss: 2.8477 - accuracy: 0.1485\n",
+      "27/27 [==============================] - 0s 5ms/step - loss: 3.1454 - accuracy: 0.1481\n",
       "Epoch 6/200\n",
-      "21/21 [==============================] - 0s 2ms/step - loss: 2.8246 - accuracy: 0.1287\n",
+      "27/27 [==============================] - 0s 2ms/step - loss: 3.0533 - accuracy: 0.1259\n",
       "Epoch 7/200\n",
-      "21/21 [==============================] - 0s 2ms/step - loss: 2.6575 - accuracy: 0.2871\n",
+      "27/27 [==============================] - 0s 2ms/step - loss: 2.9498 - accuracy: 0.1704\n",
       "Epoch 8/200\n",
-      "21/21 [==============================] - 0s 2ms/step - loss: 2.6479 - accuracy: 0.1980\n",
+      "27/27 [==============================] - 0s 5ms/step - loss: 2.9075 - accuracy: 0.2074\n",
       "Epoch 9/200\n",
-      "21/21 [==============================] - 0s 2ms/step - loss: 2.5570 - accuracy: 0.2178\n",
+      "27/27 [==============================] - 0s 2ms/step - loss: 2.8456 - accuracy: 0.1926\n",
       "Epoch 10/200\n",
-      "21/21 [==============================] - 0s 3ms/step - loss: 2.4903 - accuracy: 0.2277\n",
+      "27/27 [==============================] - 0s 4ms/step - loss: 2.7454 - accuracy: 0.2370\n",
       "Epoch 11/200\n",
-      "21/21 [==============================] - 0s 3ms/step - loss: 2.3121 - accuracy: 0.3267\n",
+      "27/27 [==============================] - 0s 2ms/step - loss: 2.5574 - accuracy: 0.3481\n",
       "Epoch 12/200\n",
-      "21/21 [==============================] - 0s 3ms/step - loss: 2.2545 - accuracy: 0.3564\n",
+      "27/27 [==============================] - 0s 2ms/step - loss: 2.4774 - accuracy: 0.3333\n",
       "Epoch 13/200\n",
-      "21/21 [==============================] - 0s 3ms/step - loss: 2.0692 - accuracy: 0.4059\n",
+      "27/27 [==============================] - 0s 5ms/step - loss: 2.2470 - accuracy: 0.4444\n",
       "Epoch 14/200\n",
-      "21/21 [==============================] - 0s 2ms/step - loss: 2.0136 - accuracy: 0.3861\n",
+      "27/27 [==============================] - 0s 3ms/step - loss: 2.1133 - accuracy: 0.4222\n",
       "Epoch 15/200\n",
-      "21/21 [==============================] - 0s 2ms/step - loss: 1.9364 - accuracy: 0.4752\n",
+      "27/27 [==============================] - 0s 5ms/step - loss: 1.9202 - accuracy: 0.5111\n",
       "Epoch 16/200\n",
-      "21/21 [==============================] - 0s 2ms/step - loss: 1.9055 - accuracy: 0.4653\n",
+      "27/27 [==============================] - 0s 2ms/step - loss: 1.9957 - accuracy: 0.4148\n",
       "Epoch 17/200\n",
-      "21/21 [==============================] - 0s 3ms/step - loss: 1.6360 - accuracy: 0.5149\n",
+      "27/27 [==============================] - 0s 3ms/step - loss: 1.9250 - accuracy: 0.4519\n",
       "Epoch 18/200\n",
-      "21/21 [==============================] - 0s 3ms/step - loss: 1.5984 - accuracy: 0.4158\n",
+      "27/27 [==============================] - 0s 2ms/step - loss: 1.8084 - accuracy: 0.4519\n",
       "Epoch 19/200\n",
-      "21/21 [==============================] - 0s 2ms/step - loss: 1.4699 - accuracy: 0.5347\n",
+      "27/27 [==============================] - 0s 2ms/step - loss: 1.6794 - accuracy: 0.5259\n",
       "Epoch 20/200\n",
-      "21/21 [==============================] - 0s 2ms/step - loss: 1.4947 - accuracy: 0.5743\n",
+      "27/27 [==============================] - 0s 4ms/step - loss: 1.4983 - accuracy: 0.5481\n",
       "Epoch 21/200\n",
-      "21/21 [==============================] - 0s 2ms/step - loss: 1.3565 - accuracy: 0.5743\n",
+      "27/27 [==============================] - 0s 5ms/step - loss: 1.4518 - accuracy: 0.5926\n",
       "Epoch 22/200\n",
-      "21/21 [==============================] - 0s 2ms/step - loss: 1.1361 - accuracy: 0.6337\n",
+      "27/27 [==============================] - 0s 4ms/step - loss: 1.3945 - accuracy: 0.5926\n",
       "Epoch 23/200\n",
-      "21/21 [==============================] - 0s 3ms/step - loss: 1.2921 - accuracy: 0.5842\n",
+      "27/27 [==============================] - 0s 2ms/step - loss: 1.3763 - accuracy: 0.5704\n",
       "Epoch 24/200\n",
-      "21/21 [==============================] - 0s 3ms/step - loss: 1.0150 - accuracy: 0.6931\n",
+      "27/27 [==============================] - 0s 2ms/step - loss: 1.2073 - accuracy: 0.6148\n",
       "Epoch 25/200\n",
-      "21/21 [==============================] - 0s 3ms/step - loss: 1.1849 - accuracy: 0.6139\n",
+      "27/27 [==============================] - 0s 3ms/step - loss: 1.1997 - accuracy: 0.6519\n",
       "Epoch 26/200\n",
-      "21/21 [==============================] - 0s 2ms/step - loss: 1.1288 - accuracy: 0.7030\n",
+      "27/27 [==============================] - 0s 2ms/step - loss: 1.0275 - accuracy: 0.6963\n",
       "Epoch 27/200\n",
-      "21/21 [==============================] - 0s 2ms/step - loss: 1.1520 - accuracy: 0.6634\n",
+      "27/27 [==============================] - 0s 4ms/step - loss: 1.0334 - accuracy: 0.6593\n",
       "Epoch 28/200\n",
-      "21/21 [==============================] - 0s 2ms/step - loss: 0.8347 - accuracy: 0.7822\n",
+      "27/27 [==============================] - 0s 2ms/step - loss: 0.9008 - accuracy: 0.7630\n",
       "Epoch 29/200\n",
-      "21/21 [==============================] - 0s 3ms/step - loss: 0.8946 - accuracy: 0.7129\n",
+      "27/27 [==============================] - 0s 5ms/step - loss: 0.9426 - accuracy: 0.7778\n",
       "Epoch 30/200\n",
-      "21/21 [==============================] - 0s 3ms/step - loss: 0.9903 - accuracy: 0.6931\n",
+      "27/27 [==============================] - 0s 6ms/step - loss: 0.8903 - accuracy: 0.7630\n",
       "Epoch 31/200\n",
-      "21/21 [==============================] - 0s 5ms/step - loss: 0.8642 - accuracy: 0.7822\n",
+      "27/27 [==============================] - 0s 5ms/step - loss: 0.8738 - accuracy: 0.7333\n",
       "Epoch 32/200\n",
-      "21/21 [==============================] - 0s 3ms/step - loss: 0.7667 - accuracy: 0.7624\n",
+      "27/27 [==============================] - 0s 3ms/step - loss: 0.7491 - accuracy: 0.8148\n",
       "Epoch 33/200\n",
-      "21/21 [==============================] - 0s 3ms/step - loss: 0.6216 - accuracy: 0.8218\n",
+      "27/27 [==============================] - 0s 3ms/step - loss: 0.7326 - accuracy: 0.7556\n",
       "Epoch 34/200\n",
-      "21/21 [==============================] - 0s 4ms/step - loss: 0.8609 - accuracy: 0.7129\n",
+      "27/27 [==============================] - 0s 4ms/step - loss: 0.6996 - accuracy: 0.8444\n",
       "Epoch 35/200\n",
-      "21/21 [==============================] - 0s 3ms/step - loss: 0.7709 - accuracy: 0.7030\n",
+      "27/27 [==============================] - 0s 3ms/step - loss: 0.5823 - accuracy: 0.7926\n",
       "Epoch 36/200\n",
-      "21/21 [==============================] - 0s 2ms/step - loss: 0.7482 - accuracy: 0.7723\n",
+      "27/27 [==============================] - 0s 3ms/step - loss: 0.6125 - accuracy: 0.8148\n",
       "Epoch 37/200\n",
-      "21/21 [==============================] - 0s 2ms/step - loss: 0.7098 - accuracy: 0.7822\n",
+      "27/27 [==============================] - 0s 4ms/step - loss: 0.7255 - accuracy: 0.7926\n",
       "Epoch 38/200\n",
-      "21/21 [==============================] - 0s 3ms/step - loss: 0.5770 - accuracy: 0.8713\n",
+      "27/27 [==============================] - 0s 2ms/step - loss: 0.7091 - accuracy: 0.7778\n",
       "Epoch 39/200\n",
-      "21/21 [==============================] - 0s 2ms/step - loss: 0.5455 - accuracy: 0.8218\n",
+      "27/27 [==============================] - 0s 4ms/step - loss: 0.6636 - accuracy: 0.8074\n",
       "Epoch 40/200\n",
-      "21/21 [==============================] - 0s 3ms/step - loss: 0.5842 - accuracy: 0.8119\n",
+      "27/27 [==============================] - 0s 3ms/step - loss: 0.5241 - accuracy: 0.8593\n",
       "Epoch 41/200\n",
-      "21/21 [==============================] - 0s 5ms/step - loss: 0.5544 - accuracy: 0.8218\n",
+      "27/27 [==============================] - 0s 2ms/step - loss: 0.7512 - accuracy: 0.7704\n",
       "Epoch 42/200\n",
-      "21/21 [==============================] - 0s 3ms/step - loss: 0.6344 - accuracy: 0.8317\n",
+      "27/27 [==============================] - 0s 2ms/step - loss: 0.6254 - accuracy: 0.7852\n",
       "Epoch 43/200\n",
-      "21/21 [==============================] - 0s 3ms/step - loss: 0.5202 - accuracy: 0.8416\n",
+      "27/27 [==============================] - 0s 4ms/step - loss: 0.5980 - accuracy: 0.8296\n",
       "Epoch 44/200\n",
-      "21/21 [==============================] - 0s 3ms/step - loss: 0.4690 - accuracy: 0.8812\n",
+      "27/27 [==============================] - 0s 2ms/step - loss: 0.5192 - accuracy: 0.8444\n",
       "Epoch 45/200\n",
-      "21/21 [==============================] - 0s 5ms/step - loss: 0.4679 - accuracy: 0.8416\n",
+      "27/27 [==============================] - 0s 4ms/step - loss: 0.5458 - accuracy: 0.8148\n",
       "Epoch 46/200\n",
-      "21/21 [==============================] - 0s 3ms/step - loss: 0.3369 - accuracy: 0.9208\n",
+      "27/27 [==============================] - 0s 2ms/step - loss: 0.4503 - accuracy: 0.8667\n",
       "Epoch 47/200\n",
-      "21/21 [==============================] - 0s 3ms/step - loss: 0.3467 - accuracy: 0.9010\n",
+      "27/27 [==============================] - 0s 4ms/step - loss: 0.4899 - accuracy: 0.8370\n",
       "Epoch 48/200\n",
-      "21/21 [==============================] - 0s 2ms/step - loss: 0.4851 - accuracy: 0.8416\n",
+      "27/27 [==============================] - 0s 2ms/step - loss: 0.5428 - accuracy: 0.8296\n",
       "Epoch 49/200\n",
-      "21/21 [==============================] - 0s 2ms/step - loss: 0.4126 - accuracy: 0.8614\n",
+      "27/27 [==============================] - 0s 4ms/step - loss: 0.5189 - accuracy: 0.8667\n",
       "Epoch 50/200\n",
-      "21/21 [==============================] - 0s 2ms/step - loss: 0.3685 - accuracy: 0.9307\n",
+      "27/27 [==============================] - 0s 4ms/step - loss: 0.3985 - accuracy: 0.8963\n",
       "Epoch 51/200\n",
-      "21/21 [==============================] - 0s 2ms/step - loss: 0.4156 - accuracy: 0.8416\n",
+      "27/27 [==============================] - 0s 2ms/step - loss: 0.3924 - accuracy: 0.8815\n",
       "Epoch 52/200\n",
-      "21/21 [==============================] - 0s 2ms/step - loss: 0.3152 - accuracy: 0.8911\n",
+      "27/27 [==============================] - 0s 4ms/step - loss: 0.4403 - accuracy: 0.8593\n",
       "Epoch 53/200\n",
-      "21/21 [==============================] - 0s 3ms/step - loss: 0.2847 - accuracy: 0.9208\n",
+      "27/27 [==============================] - 0s 3ms/step - loss: 0.4757 - accuracy: 0.8815\n",
       "Epoch 54/200\n",
-      "21/21 [==============================] - 0s 5ms/step - loss: 0.3861 - accuracy: 0.8515\n",
+      "27/27 [==============================] - 0s 3ms/step - loss: 0.3748 - accuracy: 0.8889\n",
       "Epoch 55/200\n",
-      "21/21 [==============================] - 0s 3ms/step - loss: 0.2892 - accuracy: 0.9406\n",
+      "27/27 [==============================] - 0s 4ms/step - loss: 0.2789 - accuracy: 0.9259\n",
       "Epoch 56/200\n",
-      "21/21 [==============================] - 0s 2ms/step - loss: 0.3265 - accuracy: 0.8812\n",
+      "27/27 [==============================] - 0s 2ms/step - loss: 0.4144 - accuracy: 0.8741\n",
       "Epoch 57/200\n",
-      "21/21 [==============================] - 0s 3ms/step - loss: 0.2522 - accuracy: 0.9604\n",
+      "27/27 [==============================] - 0s 4ms/step - loss: 0.4196 - accuracy: 0.8815\n",
       "Epoch 58/200\n",
-      "21/21 [==============================] - 0s 3ms/step - loss: 0.2983 - accuracy: 0.9208\n",
+      "27/27 [==============================] - 0s 3ms/step - loss: 0.3765 - accuracy: 0.8963\n",
       "Epoch 59/200\n",
-      "21/21 [==============================] - 0s 2ms/step - loss: 0.3014 - accuracy: 0.9208\n",
+      "27/27 [==============================] - 0s 2ms/step - loss: 0.3425 - accuracy: 0.8741\n",
       "Epoch 60/200\n",
-      "21/21 [==============================] - 0s 2ms/step - loss: 0.2608 - accuracy: 0.9010\n",
+      "27/27 [==============================] - 0s 4ms/step - loss: 0.3023 - accuracy: 0.8963\n",
       "Epoch 61/200\n",
-      "21/21 [==============================] - 0s 2ms/step - loss: 0.3390 - accuracy: 0.9010\n",
+      "27/27 [==============================] - 0s 4ms/step - loss: 0.3580 - accuracy: 0.8889\n",
       "Epoch 62/200\n",
-      "21/21 [==============================] - 0s 2ms/step - loss: 0.3109 - accuracy: 0.8812\n",
+      "27/27 [==============================] - 0s 2ms/step - loss: 0.4284 - accuracy: 0.8963\n",
       "Epoch 63/200\n",
-      "21/21 [==============================] - 0s 6ms/step - loss: 0.3881 - accuracy: 0.9010\n",
+      "27/27 [==============================] - 0s 4ms/step - loss: 0.3619 - accuracy: 0.8815\n",
       "Epoch 64/200\n",
-      "21/21 [==============================] - 0s 3ms/step - loss: 0.2117 - accuracy: 0.9703\n",
+      "27/27 [==============================] - 0s 2ms/step - loss: 0.3391 - accuracy: 0.8963\n",
       "Epoch 65/200\n",
-      "21/21 [==============================] - 0s 2ms/step - loss: 0.3163 - accuracy: 0.8911\n",
+      "27/27 [==============================] - 0s 3ms/step - loss: 0.3532 - accuracy: 0.8963\n",
       "Epoch 66/200\n",
-      "21/21 [==============================] - 0s 2ms/step - loss: 0.2665 - accuracy: 0.9109\n",
+      "27/27 [==============================] - 0s 2ms/step - loss: 0.4567 - accuracy: 0.8444\n",
       "Epoch 67/200\n",
-      "21/21 [==============================] - 0s 2ms/step - loss: 0.4290 - accuracy: 0.8911\n",
+      "27/27 [==============================] - 0s 4ms/step - loss: 0.3258 - accuracy: 0.9333\n",
       "Epoch 68/200\n",
-      "21/21 [==============================] - 0s 3ms/step - loss: 0.1695 - accuracy: 0.9604\n",
+      "27/27 [==============================] - 0s 2ms/step - loss: 0.3080 - accuracy: 0.9259\n",
       "Epoch 69/200\n",
-      "21/21 [==============================] - 0s 3ms/step - loss: 0.2544 - accuracy: 0.9109\n",
+      "27/27 [==============================] - 0s 4ms/step - loss: 0.3526 - accuracy: 0.8815\n",
       "Epoch 70/200\n",
-      "21/21 [==============================] - 0s 5ms/step - loss: 0.3027 - accuracy: 0.9307\n",
+      "27/27 [==============================] - 0s 3ms/step - loss: 0.2669 - accuracy: 0.9407\n",
       "Epoch 71/200\n",
-      "21/21 [==============================] - 0s 3ms/step - loss: 0.2598 - accuracy: 0.9109\n",
+      "27/27 [==============================] - 0s 4ms/step - loss: 0.2613 - accuracy: 0.9333\n",
       "Epoch 72/200\n",
-      "21/21 [==============================] - 0s 2ms/step - loss: 0.2356 - accuracy: 0.9406\n",
+      "27/27 [==============================] - 0s 4ms/step - loss: 0.3158 - accuracy: 0.9037\n",
       "Epoch 73/200\n",
-      "21/21 [==============================] - 0s 3ms/step - loss: 0.3004 - accuracy: 0.9307\n",
+      "27/27 [==============================] - 0s 2ms/step - loss: 0.1835 - accuracy: 0.9481\n",
       "Epoch 74/200\n",
-      "21/21 [==============================] - 0s 3ms/step - loss: 0.2116 - accuracy: 0.9406\n",
+      "27/27 [==============================] - 0s 2ms/step - loss: 0.2191 - accuracy: 0.9333\n",
       "Epoch 75/200\n",
-      "21/21 [==============================] - 0s 2ms/step - loss: 0.2566 - accuracy: 0.9010\n",
+      "27/27 [==============================] - 0s 4ms/step - loss: 0.3235 - accuracy: 0.9111\n",
       "Epoch 76/200\n",
-      "21/21 [==============================] - 0s 2ms/step - loss: 0.2390 - accuracy: 0.9505\n",
+      "27/27 [==============================] - 0s 2ms/step - loss: 0.2295 - accuracy: 0.9407\n",
       "Epoch 77/200\n",
-      "21/21 [==============================] - 0s 2ms/step - loss: 0.4060 - accuracy: 0.9109\n",
+      "27/27 [==============================] - 0s 4ms/step - loss: 0.2255 - accuracy: 0.9037\n",
       "Epoch 78/200\n",
-      "21/21 [==============================] - 0s 4ms/step - loss: 0.2035 - accuracy: 0.9505\n",
+      "27/27 [==============================] - 0s 2ms/step - loss: 0.3336 - accuracy: 0.9037\n",
       "Epoch 79/200\n",
-      "21/21 [==============================] - 0s 3ms/step - loss: 0.2020 - accuracy: 0.9208\n",
+      "27/27 [==============================] - 0s 2ms/step - loss: 0.2599 - accuracy: 0.9481\n",
       "Epoch 80/200\n",
-      "21/21 [==============================] - 0s 3ms/step - loss: 0.2726 - accuracy: 0.9208\n",
+      "27/27 [==============================] - 0s 3ms/step - loss: 0.2804 - accuracy: 0.9111\n",
       "Epoch 81/200\n",
-      "21/21 [==============================] - 0s 3ms/step - loss: 0.2304 - accuracy: 0.9406\n",
+      "27/27 [==============================] - 0s 2ms/step - loss: 0.3614 - accuracy: 0.8593\n",
       "Epoch 82/200\n",
-      "21/21 [==============================] - 0s 2ms/step - loss: 0.1787 - accuracy: 0.9406\n",
+      "27/27 [==============================] - 0s 2ms/step - loss: 0.2705 - accuracy: 0.9259\n",
       "Epoch 83/200\n",
-      "21/21 [==============================] - 0s 3ms/step - loss: 0.1960 - accuracy: 0.9703\n",
+      "27/27 [==============================] - 0s 4ms/step - loss: 0.2219 - accuracy: 0.9185\n",
       "Epoch 84/200\n",
-      "21/21 [==============================] - 0s 3ms/step - loss: 0.1546 - accuracy: 0.9505\n",
+      "27/27 [==============================] - 0s 2ms/step - loss: 0.2398 - accuracy: 0.9259\n",
       "Epoch 85/200\n",
-      "21/21 [==============================] - 0s 3ms/step - loss: 0.2412 - accuracy: 0.9406\n",
+      "27/27 [==============================] - 0s 4ms/step - loss: 0.3328 - accuracy: 0.9407\n",
       "Epoch 86/200\n",
-      "21/21 [==============================] - 0s 5ms/step - loss: 0.1590 - accuracy: 0.9406\n",
+      "27/27 [==============================] - 0s 2ms/step - loss: 0.3489 - accuracy: 0.9037\n",
       "Epoch 87/200\n",
-      "21/21 [==============================] - 0s 4ms/step - loss: 0.1886 - accuracy: 0.9406\n",
+      "27/27 [==============================] - 0s 4ms/step - loss: 0.3088 - accuracy: 0.9407\n",
       "Epoch 88/200\n",
-      "21/21 [==============================] - 0s 3ms/step - loss: 0.1168 - accuracy: 0.9802\n",
+      "27/27 [==============================] - 0s 2ms/step - loss: 0.3000 - accuracy: 0.9111\n",
       "Epoch 89/200\n",
-      "21/21 [==============================] - 0s 3ms/step - loss: 0.2944 - accuracy: 0.9208\n",
+      "27/27 [==============================] - 0s 2ms/step - loss: 0.2851 - accuracy: 0.9333\n",
       "Epoch 90/200\n",
-      "21/21 [==============================] - 0s 3ms/step - loss: 0.1865 - accuracy: 0.9307\n",
+      "27/27 [==============================] - 0s 4ms/step - loss: 0.2172 - accuracy: 0.9185\n",
       "Epoch 91/200\n",
-      "21/21 [==============================] - 0s 3ms/step - loss: 0.1348 - accuracy: 0.9703\n",
+      "27/27 [==============================] - 0s 2ms/step - loss: 0.1631 - accuracy: 0.9630\n",
       "Epoch 92/200\n",
-      "21/21 [==============================] - 0s 3ms/step - loss: 0.1192 - accuracy: 0.9703\n",
+      "27/27 [==============================] - 0s 4ms/step - loss: 0.1365 - accuracy: 0.9778\n",
       "Epoch 93/200\n",
-      "21/21 [==============================] - 0s 3ms/step - loss: 0.1790 - accuracy: 0.9406\n",
+      "27/27 [==============================] - 0s 2ms/step - loss: 0.1967 - accuracy: 0.9481\n",
       "Epoch 94/200\n",
-      "21/21 [==============================] - 0s 3ms/step - loss: 0.1349 - accuracy: 0.9604\n",
+      "27/27 [==============================] - 0s 2ms/step - loss: 0.2804 - accuracy: 0.9037\n",
       "Epoch 95/200\n",
-      "21/21 [==============================] - 0s 3ms/step - loss: 0.1679 - accuracy: 0.9604\n",
+      "27/27 [==============================] - 0s 4ms/step - loss: 0.2878 - accuracy: 0.8963\n",
       "Epoch 96/200\n",
-      "21/21 [==============================] - 0s 3ms/step - loss: 0.2359 - accuracy: 0.9307\n",
+      "27/27 [==============================] - 0s 3ms/step - loss: 0.2302 - accuracy: 0.9259\n",
       "Epoch 97/200\n",
-      "21/21 [==============================] - 0s 2ms/step - loss: 0.1731 - accuracy: 0.9604\n",
+      "27/27 [==============================] - 0s 3ms/step - loss: 0.1778 - accuracy: 0.9481\n",
       "Epoch 98/200\n",
-      "21/21 [==============================] - 0s 3ms/step - loss: 0.0975 - accuracy: 0.9901\n",
+      "27/27 [==============================] - 0s 4ms/step - loss: 0.1932 - accuracy: 0.9481\n",
       "Epoch 99/200\n",
-      "21/21 [==============================] - 0s 3ms/step - loss: 0.1918 - accuracy: 0.9307\n",
+      "27/27 [==============================] - 0s 2ms/step - loss: 0.1535 - accuracy: 0.9556\n",
       "Epoch 100/200\n",
-      "21/21 [==============================] - 0s 3ms/step - loss: 0.1673 - accuracy: 0.9406\n",
+      "27/27 [==============================] - 0s 4ms/step - loss: 0.2078 - accuracy: 0.9407\n",
       "Epoch 101/200\n",
-      "21/21 [==============================] - 0s 5ms/step - loss: 0.2585 - accuracy: 0.9406\n",
+      "27/27 [==============================] - 0s 4ms/step - loss: 0.2714 - accuracy: 0.9333\n",
       "Epoch 102/200\n",
-      "21/21 [==============================] - 0s 3ms/step - loss: 0.2007 - accuracy: 0.9406\n",
+      "27/27 [==============================] - 0s 2ms/step - loss: 0.2089 - accuracy: 0.9111\n",
       "Epoch 103/200\n",
-      "21/21 [==============================] - 0s 2ms/step - loss: 0.1483 - accuracy: 0.9802\n",
+      "27/27 [==============================] - 0s 3ms/step - loss: 0.2506 - accuracy: 0.9037\n",
       "Epoch 104/200\n",
-      "21/21 [==============================] - 0s 2ms/step - loss: 0.2900 - accuracy: 0.9109\n",
+      "27/27 [==============================] - 0s 3ms/step - loss: 0.2797 - accuracy: 0.9185\n",
       "Epoch 105/200\n",
-      "21/21 [==============================] - 0s 2ms/step - loss: 0.2592 - accuracy: 0.9208\n",
+      "27/27 [==============================] - 0s 2ms/step - loss: 0.1352 - accuracy: 0.9704\n",
       "Epoch 106/200\n",
-      "21/21 [==============================] - 0s 2ms/step - loss: 0.1293 - accuracy: 0.9703\n",
+      "27/27 [==============================] - 0s 4ms/step - loss: 0.1123 - accuracy: 0.9704\n",
       "Epoch 107/200\n",
-      "21/21 [==============================] - 0s 2ms/step - loss: 0.1252 - accuracy: 0.9505\n",
+      "27/27 [==============================] - 0s 3ms/step - loss: 0.1956 - accuracy: 0.9556\n",
       "Epoch 108/200\n",
-      "21/21 [==============================] - 0s 3ms/step - loss: 0.1443 - accuracy: 0.9604\n",
+      "27/27 [==============================] - 0s 2ms/step - loss: 0.1626 - accuracy: 0.9481\n",
       "Epoch 109/200\n",
-      "21/21 [==============================] - 0s 2ms/step - loss: 0.1178 - accuracy: 0.9703\n",
+      "27/27 [==============================] - 0s 2ms/step - loss: 0.2488 - accuracy: 0.9407\n",
       "Epoch 110/200\n",
-      "21/21 [==============================] - 0s 2ms/step - loss: 0.2023 - accuracy: 0.9406\n",
+      "27/27 [==============================] - 0s 4ms/step - loss: 0.1852 - accuracy: 0.9333\n",
       "Epoch 111/200\n",
-      "21/21 [==============================] - 0s 2ms/step - loss: 0.1769 - accuracy: 0.9406\n",
+      "27/27 [==============================] - 0s 2ms/step - loss: 0.2016 - accuracy: 0.9111\n",
       "Epoch 112/200\n",
-      "21/21 [==============================] - 0s 2ms/step - loss: 0.0995 - accuracy: 0.9604\n",
+      "27/27 [==============================] - 0s 2ms/step - loss: 0.1030 - accuracy: 0.9556\n",
       "Epoch 113/200\n",
-      "21/21 [==============================] - 0s 3ms/step - loss: 0.3169 - accuracy: 0.9208\n",
+      "27/27 [==============================] - 0s 4ms/step - loss: 0.1780 - accuracy: 0.9481\n",
       "Epoch 114/200\n",
-      "21/21 [==============================] - 0s 5ms/step - loss: 0.2240 - accuracy: 0.9406\n",
+      "27/27 [==============================] - 0s 4ms/step - loss: 0.1703 - accuracy: 0.9407\n",
       "Epoch 115/200\n",
-      "21/21 [==============================] - 0s 3ms/step - loss: 0.2204 - accuracy: 0.9010\n",
+      "27/27 [==============================] - 0s 3ms/step - loss: 0.2027 - accuracy: 0.9333\n",
       "Epoch 116/200\n",
-      "21/21 [==============================] - 0s 2ms/step - loss: 0.1413 - accuracy: 0.9703\n",
+      "27/27 [==============================] - 0s 2ms/step - loss: 0.1962 - accuracy: 0.9556\n",
       "Epoch 117/200\n",
-      "21/21 [==============================] - 0s 2ms/step - loss: 0.1540 - accuracy: 0.9703\n",
+      "27/27 [==============================] - 0s 4ms/step - loss: 0.2133 - accuracy: 0.9185\n",
       "Epoch 118/200\n",
-      "21/21 [==============================] - 0s 2ms/step - loss: 0.1282 - accuracy: 0.9703\n",
+      "27/27 [==============================] - 0s 2ms/step - loss: 0.1864 - accuracy: 0.9556\n",
       "Epoch 119/200\n",
-      "21/21 [==============================] - 0s 2ms/step - loss: 0.1773 - accuracy: 0.9406\n",
+      "27/27 [==============================] - 0s 2ms/step - loss: 0.1747 - accuracy: 0.9481\n",
       "Epoch 120/200\n",
-      "21/21 [==============================] - 0s 2ms/step - loss: 0.2519 - accuracy: 0.9406\n",
+      "27/27 [==============================] - 0s 4ms/step - loss: 0.2204 - accuracy: 0.9111\n",
       "Epoch 121/200\n",
-      "21/21 [==============================] - 0s 2ms/step - loss: 0.1401 - accuracy: 0.9406\n",
+      "27/27 [==============================] - 0s 2ms/step - loss: 0.2416 - accuracy: 0.9333\n",
       "Epoch 122/200\n",
-      "21/21 [==============================] - 0s 2ms/step - loss: 0.1958 - accuracy: 0.9604\n",
+      "27/27 [==============================] - 0s 3ms/step - loss: 0.1312 - accuracy: 0.9556\n",
       "Epoch 123/200\n",
-      "21/21 [==============================] - 0s 2ms/step - loss: 0.1246 - accuracy: 0.9604\n",
+      "27/27 [==============================] - 0s 4ms/step - loss: 0.1500 - accuracy: 0.9556\n",
       "Epoch 124/200\n",
-      "21/21 [==============================] - 0s 2ms/step - loss: 0.0959 - accuracy: 0.9703\n",
+      "27/27 [==============================] - 0s 2ms/step - loss: 0.1963 - accuracy: 0.9407\n",
       "Epoch 125/200\n",
-      "21/21 [==============================] - 0s 5ms/step - loss: 0.1359 - accuracy: 0.9802\n",
+      "27/27 [==============================] - 0s 3ms/step - loss: 0.1450 - accuracy: 0.9556\n",
       "Epoch 126/200\n",
-      "21/21 [==============================] - 0s 3ms/step - loss: 0.1924 - accuracy: 0.9406\n",
+      "27/27 [==============================] - 0s 4ms/step - loss: 0.1045 - accuracy: 0.9778\n",
       "Epoch 127/200\n",
-      "21/21 [==============================] - 0s 3ms/step - loss: 0.1163 - accuracy: 0.9802\n",
+      "27/27 [==============================] - 0s 2ms/step - loss: 0.2514 - accuracy: 0.9185\n",
       "Epoch 128/200\n",
-      "21/21 [==============================] - 0s 2ms/step - loss: 0.1078 - accuracy: 0.9802\n",
+      "27/27 [==============================] - 0s 3ms/step - loss: 0.1283 - accuracy: 0.9704\n",
       "Epoch 129/200\n",
-      "21/21 [==============================] - 0s 2ms/step - loss: 0.1414 - accuracy: 0.9703\n",
+      "27/27 [==============================] - 0s 2ms/step - loss: 0.1439 - accuracy: 0.9704\n",
       "Epoch 130/200\n",
-      "21/21 [==============================] - 0s 2ms/step - loss: 0.1301 - accuracy: 0.9505\n",
+      "27/27 [==============================] - 0s 4ms/step - loss: 0.1613 - accuracy: 0.9481\n",
       "Epoch 131/200\n",
-      "21/21 [==============================] - 0s 2ms/step - loss: 0.1192 - accuracy: 0.9505\n",
+      "27/27 [==============================] - 0s 2ms/step - loss: 0.2340 - accuracy: 0.9185\n",
       "Epoch 132/200\n",
-      "21/21 [==============================] - 0s 2ms/step - loss: 0.1839 - accuracy: 0.9406\n",
+      "27/27 [==============================] - 0s 4ms/step - loss: 0.1982 - accuracy: 0.9481\n",
       "Epoch 133/200\n",
-      "21/21 [==============================] - 0s 2ms/step - loss: 0.1954 - accuracy: 0.9604\n",
+      "27/27 [==============================] - 0s 2ms/step - loss: 0.2042 - accuracy: 0.9259\n",
       "Epoch 134/200\n",
-      "21/21 [==============================] - 0s 5ms/step - loss: 0.0691 - accuracy: 1.0000\n",
+      "27/27 [==============================] - 0s 4ms/step - loss: 0.1741 - accuracy: 0.9481\n",
       "Epoch 135/200\n",
-      "21/21 [==============================] - 0s 3ms/step - loss: 0.1313 - accuracy: 0.9505\n",
+      "27/27 [==============================] - 0s 2ms/step - loss: 0.1283 - accuracy: 0.9556\n",
       "Epoch 136/200\n",
-      "21/21 [==============================] - 0s 2ms/step - loss: 0.1251 - accuracy: 0.9604\n",
+      "27/27 [==============================] - 0s 4ms/step - loss: 0.1570 - accuracy: 0.9407\n",
       "Epoch 137/200\n",
-      "21/21 [==============================] - 0s 2ms/step - loss: 0.1412 - accuracy: 0.9505\n",
+      "27/27 [==============================] - 0s 2ms/step - loss: 0.1687 - accuracy: 0.9630\n",
       "Epoch 138/200\n",
-      "21/21 [==============================] - 0s 5ms/step - loss: 0.0696 - accuracy: 0.9703\n",
+      "27/27 [==============================] - 0s 2ms/step - loss: 0.2300 - accuracy: 0.9185\n",
       "Epoch 139/200\n",
-      "21/21 [==============================] - 0s 3ms/step - loss: 0.0832 - accuracy: 0.9703\n",
+      "27/27 [==============================] - 0s 4ms/step - loss: 0.0860 - accuracy: 0.9704\n",
       "Epoch 140/200\n",
-      "21/21 [==============================] - 0s 3ms/step - loss: 0.0765 - accuracy: 0.9703\n",
+      "27/27 [==============================] - 0s 2ms/step - loss: 0.1589 - accuracy: 0.9407\n",
       "Epoch 141/200\n",
-      "21/21 [==============================] - 0s 4ms/step - loss: 0.1490 - accuracy: 0.9505\n",
+      "27/27 [==============================] - 0s 5ms/step - loss: 0.1737 - accuracy: 0.9259\n",
       "Epoch 142/200\n",
-      "21/21 [==============================] - 0s 3ms/step - loss: 0.1101 - accuracy: 0.9703\n",
+      "27/27 [==============================] - 0s 3ms/step - loss: 0.1942 - accuracy: 0.9333\n",
       "Epoch 143/200\n",
-      "21/21 [==============================] - 0s 2ms/step - loss: 0.1984 - accuracy: 0.9604\n",
+      "27/27 [==============================] - 0s 5ms/step - loss: 0.1538 - accuracy: 0.9481\n",
       "Epoch 144/200\n",
-      "21/21 [==============================] - 0s 5ms/step - loss: 0.1561 - accuracy: 0.9505\n",
+      "27/27 [==============================] - 0s 2ms/step - loss: 0.1238 - accuracy: 0.9630\n",
       "Epoch 145/200\n",
-      "21/21 [==============================] - 0s 3ms/step - loss: 0.0621 - accuracy: 1.0000\n",
+      "27/27 [==============================] - 0s 2ms/step - loss: 0.1281 - accuracy: 0.9556\n",
       "Epoch 146/200\n",
-      "21/21 [==============================] - 0s 3ms/step - loss: 0.1040 - accuracy: 0.9802\n",
+      "27/27 [==============================] - 0s 2ms/step - loss: 0.2017 - accuracy: 0.9481\n",
       "Epoch 147/200\n",
-      "21/21 [==============================] - 0s 3ms/step - loss: 0.1010 - accuracy: 0.9703\n",
+      "27/27 [==============================] - 0s 4ms/step - loss: 0.1058 - accuracy: 0.9630\n",
       "Epoch 148/200\n",
-      "21/21 [==============================] - 0s 5ms/step - loss: 0.0714 - accuracy: 0.9901\n",
+      "27/27 [==============================] - 0s 3ms/step - loss: 0.1313 - accuracy: 0.9556\n",
       "Epoch 149/200\n",
-      "21/21 [==============================] - 0s 3ms/step - loss: 0.0740 - accuracy: 0.9802\n",
+      "27/27 [==============================] - 0s 3ms/step - loss: 0.1416 - accuracy: 0.9333\n",
       "Epoch 150/200\n",
-      "21/21 [==============================] - 0s 2ms/step - loss: 0.0724 - accuracy: 0.9802\n",
+      "27/27 [==============================] - 0s 3ms/step - loss: 0.1670 - accuracy: 0.9407\n",
       "Epoch 151/200\n",
-      "21/21 [==============================] - 0s 2ms/step - loss: 0.2013 - accuracy: 0.9802\n",
+      "27/27 [==============================] - 0s 6ms/step - loss: 0.1802 - accuracy: 0.9481\n",
       "Epoch 152/200\n",
-      "21/21 [==============================] - 0s 5ms/step - loss: 0.1636 - accuracy: 0.9505\n",
+      "27/27 [==============================] - 0s 3ms/step - loss: 0.1432 - accuracy: 0.9630\n",
       "Epoch 153/200\n",
-      "21/21 [==============================] - 0s 3ms/step - loss: 0.1004 - accuracy: 0.9703\n",
+      "27/27 [==============================] - 0s 4ms/step - loss: 0.2071 - accuracy: 0.9407\n",
       "Epoch 154/200\n",
-      "21/21 [==============================] - 0s 3ms/step - loss: 0.0874 - accuracy: 0.9802\n",
+      "27/27 [==============================] - 0s 3ms/step - loss: 0.1564 - accuracy: 0.9481\n",
       "Epoch 155/200\n",
-      "21/21 [==============================] - 0s 3ms/step - loss: 0.0620 - accuracy: 0.9703\n",
+      "27/27 [==============================] - 0s 2ms/step - loss: 0.2045 - accuracy: 0.9333\n",
       "Epoch 156/200\n",
-      "21/21 [==============================] - 0s 6ms/step - loss: 0.0624 - accuracy: 0.9901\n",
+      "27/27 [==============================] - 0s 4ms/step - loss: 0.0563 - accuracy: 0.9852\n",
       "Epoch 157/200\n",
-      "21/21 [==============================] - 0s 3ms/step - loss: 0.1166 - accuracy: 0.9703\n",
+      "27/27 [==============================] - 0s 2ms/step - loss: 0.1545 - accuracy: 0.9407\n",
       "Epoch 158/200\n",
-      "21/21 [==============================] - 0s 2ms/step - loss: 0.1438 - accuracy: 0.9505\n",
+      "27/27 [==============================] - 0s 3ms/step - loss: 0.1548 - accuracy: 0.9407\n",
       "Epoch 159/200\n",
-      "21/21 [==============================] - 0s 2ms/step - loss: 0.1088 - accuracy: 0.9703\n",
+      "27/27 [==============================] - 0s 2ms/step - loss: 0.1531 - accuracy: 0.9481\n",
       "Epoch 160/200\n",
-      "21/21 [==============================] - 0s 5ms/step - loss: 0.0824 - accuracy: 0.9802\n",
+      "27/27 [==============================] - 0s 4ms/step - loss: 0.1024 - accuracy: 0.9852\n",
       "Epoch 161/200\n",
-      "21/21 [==============================] - 0s 2ms/step - loss: 0.0883 - accuracy: 0.9703\n",
+      "27/27 [==============================] - 0s 2ms/step - loss: 0.1736 - accuracy: 0.9630\n",
       "Epoch 162/200\n",
-      "21/21 [==============================] - 0s 2ms/step - loss: 0.0711 - accuracy: 0.9703\n",
+      "27/27 [==============================] - 0s 4ms/step - loss: 0.1224 - accuracy: 0.9630\n",
       "Epoch 163/200\n",
-      "21/21 [==============================] - 0s 2ms/step - loss: 0.1514 - accuracy: 0.9505\n",
+      "27/27 [==============================] - 0s 3ms/step - loss: 0.1623 - accuracy: 0.9407\n",
       "Epoch 164/200\n",
-      "21/21 [==============================] - 0s 2ms/step - loss: 0.1767 - accuracy: 0.9208\n",
+      "27/27 [==============================] - 0s 5ms/step - loss: 0.0824 - accuracy: 0.9704\n",
       "Epoch 165/200\n",
-      "21/21 [==============================] - 0s 2ms/step - loss: 0.1761 - accuracy: 0.9406\n",
+      "27/27 [==============================] - 0s 3ms/step - loss: 0.1647 - accuracy: 0.9481\n",
       "Epoch 166/200\n",
-      "21/21 [==============================] - 0s 5ms/step - loss: 0.1666 - accuracy: 0.9505\n",
+      "27/27 [==============================] - 0s 4ms/step - loss: 0.1275 - accuracy: 0.9704\n",
       "Epoch 167/200\n",
-      "21/21 [==============================] - 0s 3ms/step - loss: 0.0756 - accuracy: 0.9703\n",
+      "27/27 [==============================] - 0s 3ms/step - loss: 0.1029 - accuracy: 0.9704\n",
       "Epoch 168/200\n",
-      "21/21 [==============================] - 0s 3ms/step - loss: 0.0835 - accuracy: 0.9802\n",
+      "27/27 [==============================] - 0s 4ms/step - loss: 0.1341 - accuracy: 0.9556\n",
       "Epoch 169/200\n",
-      "21/21 [==============================] - 0s 3ms/step - loss: 0.0782 - accuracy: 0.9802\n",
+      "27/27 [==============================] - 0s 4ms/step - loss: 0.0952 - accuracy: 0.9704\n",
       "Epoch 170/200\n",
-      "21/21 [==============================] - 0s 6ms/step - loss: 0.1037 - accuracy: 0.9703\n",
+      "27/27 [==============================] - 0s 2ms/step - loss: 0.1072 - accuracy: 0.9630\n",
       "Epoch 171/200\n",
-      "21/21 [==============================] - 0s 3ms/step - loss: 0.0772 - accuracy: 0.9703\n",
+      "27/27 [==============================] - 0s 5ms/step - loss: 0.1492 - accuracy: 0.9704\n",
       "Epoch 172/200\n",
-      "21/21 [==============================] - 0s 2ms/step - loss: 0.1397 - accuracy: 0.9505\n",
+      "27/27 [==============================] - 0s 2ms/step - loss: 0.1109 - accuracy: 0.9704\n",
       "Epoch 173/200\n",
-      "21/21 [==============================] - 0s 5ms/step - loss: 0.0808 - accuracy: 0.9703\n",
+      "27/27 [==============================] - 0s 2ms/step - loss: 0.2332 - accuracy: 0.9333\n",
       "Epoch 174/200\n",
-      "21/21 [==============================] - 0s 3ms/step - loss: 0.1203 - accuracy: 0.9604\n",
+      "27/27 [==============================] - 0s 5ms/step - loss: 0.1081 - accuracy: 0.9778\n",
       "Epoch 175/200\n",
-      "21/21 [==============================] - 0s 3ms/step - loss: 0.0886 - accuracy: 0.9703\n",
+      "27/27 [==============================] - 0s 3ms/step - loss: 0.1087 - accuracy: 0.9630\n",
       "Epoch 176/200\n",
-      "21/21 [==============================] - 0s 2ms/step - loss: 0.0628 - accuracy: 0.9802\n",
+      "27/27 [==============================] - 0s 3ms/step - loss: 0.2525 - accuracy: 0.9333\n",
       "Epoch 177/200\n",
-      "21/21 [==============================] - 0s 6ms/step - loss: 0.0379 - accuracy: 0.9901\n",
+      "27/27 [==============================] - 0s 2ms/step - loss: 0.1183 - accuracy: 0.9481\n",
       "Epoch 178/200\n",
-      "21/21 [==============================] - 0s 3ms/step - loss: 0.0681 - accuracy: 0.9802\n",
+      "27/27 [==============================] - 0s 4ms/step - loss: 0.1208 - accuracy: 0.9481\n",
       "Epoch 179/200\n",
-      "21/21 [==============================] - 0s 2ms/step - loss: 0.0800 - accuracy: 0.9703\n",
+      "27/27 [==============================] - 0s 4ms/step - loss: 0.1440 - accuracy: 0.9407\n",
       "Epoch 180/200\n",
-      "21/21 [==============================] - 0s 5ms/step - loss: 0.1187 - accuracy: 0.9505\n",
+      "27/27 [==============================] - 0s 3ms/step - loss: 0.2581 - accuracy: 0.8963\n",
       "Epoch 181/200\n",
-      "21/21 [==============================] - 0s 3ms/step - loss: 0.1268 - accuracy: 0.9703\n",
+      "27/27 [==============================] - 0s 4ms/step - loss: 0.1167 - accuracy: 0.9778\n",
       "Epoch 182/200\n",
-      "21/21 [==============================] - 0s 2ms/step - loss: 0.1379 - accuracy: 0.9505\n",
+      "27/27 [==============================] - 0s 4ms/step - loss: 0.1107 - accuracy: 0.9556\n",
       "Epoch 183/200\n",
-      "21/21 [==============================] - 0s 2ms/step - loss: 0.0726 - accuracy: 0.9802\n",
+      "27/27 [==============================] - 0s 2ms/step - loss: 0.0738 - accuracy: 0.9704\n",
       "Epoch 184/200\n",
-      "21/21 [==============================] - 0s 5ms/step - loss: 0.0996 - accuracy: 0.9703\n",
+      "27/27 [==============================] - 0s 4ms/step - loss: 0.0687 - accuracy: 0.9778\n",
       "Epoch 185/200\n",
-      "21/21 [==============================] - 0s 3ms/step - loss: 0.0698 - accuracy: 0.9802\n",
+      "27/27 [==============================] - 0s 2ms/step - loss: 0.1426 - accuracy: 0.9778\n",
       "Epoch 186/200\n",
-      "21/21 [==============================] - 0s 3ms/step - loss: 0.1092 - accuracy: 0.9604\n",
+      "27/27 [==============================] - 0s 5ms/step - loss: 0.1569 - accuracy: 0.9407\n",
       "Epoch 187/200\n",
-      "21/21 [==============================] - 0s 3ms/step - loss: 0.1824 - accuracy: 0.9406\n",
+      "27/27 [==============================] - 0s 2ms/step - loss: 0.1107 - accuracy: 0.9704\n",
       "Epoch 188/200\n",
-      "21/21 [==============================] - 0s 3ms/step - loss: 0.0633 - accuracy: 0.9802\n",
+      "27/27 [==============================] - 0s 4ms/step - loss: 0.1262 - accuracy: 0.9630\n",
       "Epoch 189/200\n",
-      "21/21 [==============================] - 0s 2ms/step - loss: 0.0497 - accuracy: 0.9802\n",
+      "27/27 [==============================] - 0s 2ms/step - loss: 0.1374 - accuracy: 0.9556\n",
       "Epoch 190/200\n",
-      "21/21 [==============================] - 0s 4ms/step - loss: 0.0642 - accuracy: 0.9802\n",
+      "27/27 [==============================] - 0s 5ms/step - loss: 0.1515 - accuracy: 0.9481\n",
       "Epoch 191/200\n",
-      "21/21 [==============================] - 0s 4ms/step - loss: 0.2325 - accuracy: 0.9208\n",
+      "27/27 [==============================] - 0s 2ms/step - loss: 0.1510 - accuracy: 0.9407\n",
       "Epoch 192/200\n",
-      "21/21 [==============================] - 0s 3ms/step - loss: 0.1312 - accuracy: 0.9604\n",
+      "27/27 [==============================] - 0s 5ms/step - loss: 0.0891 - accuracy: 0.9778\n",
       "Epoch 193/200\n",
-      "21/21 [==============================] - 0s 3ms/step - loss: 0.1458 - accuracy: 0.9604\n",
+      "27/27 [==============================] - 0s 4ms/step - loss: 0.1206 - accuracy: 0.9630\n",
       "Epoch 194/200\n",
-      "21/21 [==============================] - 0s 5ms/step - loss: 0.0649 - accuracy: 0.9703\n",
+      "27/27 [==============================] - 0s 4ms/step - loss: 0.1216 - accuracy: 0.9556\n",
       "Epoch 195/200\n",
-      "21/21 [==============================] - 0s 3ms/step - loss: 0.0364 - accuracy: 1.0000\n",
+      "27/27 [==============================] - 0s 3ms/step - loss: 0.2060 - accuracy: 0.9333\n",
       "Epoch 196/200\n",
-      "21/21 [==============================] - 0s 3ms/step - loss: 0.0971 - accuracy: 0.9703\n",
+      "27/27 [==============================] - 0s 5ms/step - loss: 0.1128 - accuracy: 0.9630\n",
       "Epoch 197/200\n",
-      "21/21 [==============================] - 0s 6ms/step - loss: 0.0657 - accuracy: 0.9802\n",
+      "27/27 [==============================] - 0s 2ms/step - loss: 0.1075 - accuracy: 0.9556\n",
       "Epoch 198/200\n",
-      "21/21 [==============================] - 0s 2ms/step - loss: 0.0496 - accuracy: 0.9901\n",
+      "27/27 [==============================] - 0s 2ms/step - loss: 0.1474 - accuracy: 0.9704\n",
       "Epoch 199/200\n",
-      "21/21 [==============================] - 0s 2ms/step - loss: 0.0302 - accuracy: 1.0000\n",
+      "27/27 [==============================] - 0s 4ms/step - loss: 0.1314 - accuracy: 0.9630\n",
       "Epoch 200/200\n",
-      "21/21 [==============================] - 0s 2ms/step - loss: 0.0836 - accuracy: 0.9703\n"
+      "27/27 [==============================] - 0s 4ms/step - loss: 0.1122 - accuracy: 0.9704\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<keras.src.callbacks.History at 0x1dea2245e70>"
+       "<keras.src.callbacks.History at 0x230ad546170>"
       ]
      },
-     "execution_count": 85,
+     "execution_count": 82,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -17725,7 +29978,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": 83,
    "metadata": {},
    "outputs": [
     {
@@ -17740,7 +29993,7 @@
        "       [0, 0, 0, ..., 0, 0, 0]])"
       ]
      },
-     "execution_count": 86,
+     "execution_count": 83,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -17760,7 +30013,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": 84,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -17772,7 +30025,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
+   "execution_count": 85,
    "metadata": {},
    "outputs": [
     {
@@ -17781,7 +30034,7 @@
        "['hello', 'who', 'are', 'you']"
       ]
      },
-     "execution_count": 88,
+     "execution_count": 85,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -17792,7 +30045,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": 86,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -17814,7 +30067,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
+   "execution_count": 87,
    "metadata": {},
    "outputs": [
     {
@@ -17830,16 +30083,17 @@
     {
      "data": {
       "text/plain": [
-       "array([0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
        "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
-       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
        "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "       0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
        "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
        "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
-       "       0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0])"
+       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
+       "       0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0])"
       ]
      },
-     "execution_count": 90,
+     "execution_count": 87,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -17850,7 +30104,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 91,
+   "execution_count": 88,
    "metadata": {},
    "outputs": [
     {
@@ -17859,18 +30113,18 @@
        "'are'"
       ]
      },
-     "execution_count": 91,
+     "execution_count": 88,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "words[8]"
+    "words[10]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 92,
+   "execution_count": 89,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -17887,40 +30141,47 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
+   "execution_count": 91,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "1/1 [==============================] - 0s 77ms/step\n",
-      "[1.6769778e-03 6.9250003e-02 5.6960792e-03 1.4154404e-02 2.0683878e-03\n",
-      " 4.5999959e-03 1.3237487e-03 2.1827954e-04 8.0603957e-02 4.7268443e-02\n",
-      " 6.2666863e-01 1.2104318e-04 1.6269991e-04 6.8012514e-04 9.8488666e-04\n",
-      " 3.5844527e-02 3.3920960e-04 4.3283179e-03 1.6242097e-03 4.9097469e-04\n",
-      " 3.8144349e-03 9.8080620e-02]\n",
-      "maximum index = 10 probabiliy =  0.62666863\n"
+      "1/1 [==============================] - 0s 61ms/step\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1.1574853e-14 9.5326369e-09 4.3513040e-08 7.0933792e-14 2.1599963e-06\n",
+      " 3.8922358e-11 4.9235338e-14 2.0948534e-09 3.5325613e-08 1.2096518e-10\n",
+      " 6.6025324e-10 7.1856685e-09 2.5194644e-09 2.3273753e-08 7.3510509e-09\n",
+      " 1.0438244e-09 7.4113096e-08 3.2386416e-10 2.3401985e-09 5.3935346e-12\n",
+      " 2.4149907e-13 9.7792198e-11 3.9399047e-11 3.0961744e-10 1.1912894e-09\n",
+      " 9.9999762e-01 3.9610359e-09 2.6771334e-15]\n",
+      "maximum index = 25 probabiliy =  0.9999976\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "{'intent': 'greeting', 'probability': '0.62666863'}"
+       "{'intent': 'src_code', 'probability': '0.9999976'}"
       ]
      },
-     "execution_count": 93,
+     "execution_count": 91,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "predict_class(\"stupid bot\",model)"
+    "predict_class(\"source code\",model)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 94,
+   "execution_count": 92,
    "metadata": {},
    "outputs": [
     {
@@ -17938,6 +30199,11 @@
        " 'goodbye',\n",
        " 'greeting',\n",
        " 'help',\n",
+       " 'info_dosha',\n",
+       " 'info_kapha',\n",
+       " 'info_pitta',\n",
+       " 'info_prakriti',\n",
+       " 'info_vata',\n",
        " 'information_request',\n",
        " 'name',\n",
        " 'negative_feedback',\n",
@@ -17946,11 +30212,12 @@
        " 'positive_feedback',\n",
        " 'prakriti',\n",
        " 'sleep_tips',\n",
+       " 'src_code',\n",
        " 'stress_management',\n",
        " 'thanks']"
       ]
      },
-     "execution_count": 94,
+     "execution_count": 92,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -17961,7 +30228,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 95,
+   "execution_count": 93,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -17977,35 +30244,43 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 96,
+   "execution_count": 94,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "1/1 [==============================] - 0s 28ms/step\n",
-      "[4.5445495e-07 1.5017363e-09 2.2133837e-07 8.5058707e-13 5.6947789e-09\n",
-      " 1.2275012e-10 4.0441570e-11 2.7722299e-08 6.2139573e-12 9.5216912e-13\n",
-      " 9.0846436e-12 9.6519370e-09 3.8461040e-10 9.9999893e-01 1.7863953e-10\n",
-      " 3.1761289e-08 4.5803215e-08 2.7517121e-11 2.3078539e-11 2.9247582e-07\n",
-      " 1.3008218e-10 1.1704132e-12]\n",
-      "maximum index = 13 probabiliy =  0.9999989\n"
+      "1/1 [==============================] - 0s 41ms/step\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[5.86497679e-20 5.60154874e-12 1.56286498e-10 9.38451811e-19\n",
+      " 1.05999227e-08 5.17447557e-15 2.13829394e-18 1.48113759e-12\n",
+      " 1.84389379e-11 8.87155373e-15 1.10396325e-13 6.36104494e-12\n",
+      " 1.92624866e-12 5.38796438e-12 3.52130174e-12 1.93711019e-12\n",
+      " 5.99858219e-11 2.32186801e-13 8.28273734e-12 1.31109509e-15\n",
+      " 5.79618897e-18 1.75468753e-14 4.77606939e-15 4.01133628e-14\n",
+      " 2.83500816e-13 1.00000000e+00 4.82485102e-12 4.20374157e-21]\n",
+      "maximum index = 25 probabiliy =  1.0\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "\"I'm AyurBot!\""
+       "'My Source Code is Available in Github Repository https://github.com/PrathameshDhande22/Prakriti-Determine.'"
       ]
      },
-     "execution_count": 96,
+     "execution_count": 94,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "getResponse(predict_class(\"what is your name\",model),intents)"
+    "getResponse(predict_class(\"where is your source code\",model),intents)"
    ]
   },
   {
diff --git a/bot/Training/botmodel.py b/bot/Training/botmodel.py
index ba9518b..945ff1c 100644
--- a/bot/Training/botmodel.py
+++ b/bot/Training/botmodel.py
@@ -1,5 +1,20 @@
+import pandas as pd
+from nltk.stem import WordNetLemmatizer
+import nltk
+import requests
+import json
+import numpy as np
+from keras.optimizers import SGD
+from keras.models import Sequential
+from keras.layers import Dense, Dropout
+import random
+from joblib import dump
 import logging
 import os
+from venv import logger
+from dotenv import load_dotenv
+
+load_dotenv(os.path.join(".env"))
 
 logging.basicConfig(
     format="%(asctime)s - %(filename)s - %(levelname)s - Line No : %(lineno)d - %(message)s",
@@ -7,16 +22,6 @@
 )
 
 # Importing the required Modules
-from joblib import dump
-import random
-from keras.layers import Dense, Dropout
-from keras.models import Sequential
-from keras.optimizers import SGD
-import numpy as np
-import json
-import nltk
-from nltk.stem import WordNetLemmatizer
-import pandas as pd
 
 logging.info("Starting the Chatbot Model Training")
 
@@ -33,9 +38,19 @@
 documents = []
 ignore_words = ["?", "!", ","]
 
-logging.info("Reading the Intents.json File")
-data_file = open(os.path.join("intents.json")).read()
-intents: dict[str, list] = json.loads(data_file)
+# logging.info("Reading the Intents.json File")
+# data_file = open(os.path.join("intents.json")).read()
+# intents: dict[str, list] = json.loads(data_file)
+
+# If you are running locally in your system then comment this line and uncomment the above lines.
+logger.info("Reading the json file from JSILO online Storage")
+url = 'https://api.jsonsilo.com/5ff310a1-b136-45a7-86c3-0b7378ebfd6c'
+headers = {
+    'X-SILO-KEY': os.environ.get("JSILO_API_KEY"),
+    'Content-Type': 'application/json'
+}
+new_data = requests.get(url, headers=headers)
+intents = new_data.json()
 
 logging.info("Tokenizing the each pattern in the intents.json file")
 for intent in intents["intent"]:
@@ -47,7 +62,8 @@
             classes.append(intent["tag"])
 
 logging.info("removing the words which doesn't mean anything")
-words = [lemmatizer.lemmatize(w.lower()) for w in words if w not in ignore_words]
+words = [lemmatizer.lemmatize(w.lower())
+         for w in words if w not in ignore_words]
 
 logging.info("Sorting words, classes and documents")
 words = sorted(list(set(words)))
@@ -58,8 +74,8 @@
 print(len(words), "unique lemmatized words", words)
 
 logging.info("Dumping the words and classes as an binary object")
-dump(words,os.path.join("Models/words"))
-dump(classes,os.path.join("Models/classes"))
+dump(words, os.path.join("Models/words"))
+dump(classes, os.path.join("Models/classes"))
 
 logging.info("Initiating the Training data")
 training = []
@@ -67,7 +83,8 @@
 for doc in documents:
     bag = []
     pattern_words = doc[0]
-    pattern_words = [lemmatizer.lemmatize(word.lower()) for word in pattern_words]
+    pattern_words = [lemmatizer.lemmatize(
+        word.lower()) for word in pattern_words]
     for w in words:
         bag.append(1) if w in pattern_words else bag.append(0)
     output_row = list(output_empty)
@@ -108,11 +125,13 @@
 
 logging.info("Compiling the Model")
 sgd = SGD(learning_rate=0.01, momentum=0.9, nesterov=True)
-model.compile(loss="categorical_crossentropy", optimizer=sgd, metrics=["accuracy"])
+model.compile(loss="categorical_crossentropy",
+              optimizer=sgd, metrics=["accuracy"])
 
 logging.info("Training the Model with X and Y data")
-model.fit(np.array(train_x), np.array(train_y), epochs=200, batch_size=5, verbose=1)
+model.fit(np.array(train_x), np.array(train_y),
+          epochs=200, batch_size=5, verbose=1)
 
 logging.info("Model has been Trained")
 logging.info("dumping the Model")
-dump(model, os.path.join("Models/nlpbot"))
\ No newline at end of file
+dump(model, os.path.join("Models/nlpbot"))
diff --git a/bot/chatModule.py b/bot/chatModule.py
index b4d0723..b0d00bd 100644
--- a/bot/chatModule.py
+++ b/bot/chatModule.py
@@ -10,6 +10,7 @@
 
 from database import Chat, Prakriti
 from diet import recommend_Diet
+from intentsLoader import getIntents
 from logger import logger
 from models import ChatResponse, PrakritBotResponse, Reply, Response
 from question import questions
@@ -19,10 +20,10 @@
 words: list = load(os.path.join("Models/words"))
 classes: list = load(os.path.join("Models/classes"))
 prakriti_Model = load(os.path.join("Models/prakriti"))
-intents = json.loads(open(os.path.join("intents.json"), "r").read())
+# intents = json.loads(open(os.path.join("intents.json"), "r").read())
+intents = getIntents()
 lemmatizer = WordNetLemmatizer()
 
-
 def getResponseTag(msg: str) -> str:
     sentence_words = word_tokenize(msg)
     sentence_words = [lemmatizer.lemmatize(w) for w in sentence_words]
diff --git a/bot/models.py b/bot/models.py
index e6bd247..4a9179b 100644
--- a/bot/models.py
+++ b/bot/models.py
@@ -18,3 +18,13 @@ class Reply(TypedDict):
 class PrakritBotResponse(NamedTuple):
     index: int
     prakriti: str
+
+
+class Intent(TypedDict):
+    tag: str
+    patterns: list[str]
+    responses: list[str]
+
+
+class Intents(TypedDict):
+    intent: list[Intent]