l6-langchain-chat.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO\n",
    "import os \n",
    "import openai \n",
    "from dotenv import load_dotenv \n",
    "\n",
    "load_dotenv() \n",
    "\n",
    "openai.api_key = os.getenv(\"OPENAI_API_KEY\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Constants \n",
    "llm_name = \"gpt-3.5-turbo\" \n",
    "index_name = \"deeplearningai-langchain\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain_pinecone import PineconeVectorStore \n",
    "from langchain_openai import OpenAIEmbeddings\n",
    "embedding = OpenAIEmbeddings() \n",
    "vectorstore = PineconeVectorStore(embedding=embedding, index_name=index_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Document(metadata={'page': 5.0, 'source': 'data/MachineLearning-Lecture01.pdf'}, page_content=\"So all right, online resources. The class has a home page, so it's in on the handouts. I \\nwon't write on the chalkboard — http:// cs229.stanford.edu. And so when there are \\nhomework assignments or things like that, we  usually won't sort of — in the mission of \\nsaving trees, we will usually not give out many handouts in class. So homework \\nassignments, homework solutions will be posted online at the course home page.  \\nAs far as this class, I've also written, a nd I guess I've also revised every year a set of \\nfairly detailed lecture notes that cover the te chnical content of this  class. And so if you \\nvisit the course homepage, you'll also find the detailed lecture notes that go over in detail \\nall the math and equations and so on  that I'll be doing in class.  \\nThere's also a newsgroup, su.class.cs229, also written on the handout. This is a \\nnewsgroup that's sort of a forum for people in  the class to get to  know each other and \\nhave whatever discussions you want to ha ve amongst yourselves. So the class newsgroup \\nwill not be monitored by the TAs and me. But this is a place for you to form study groups \\nor find project partners or discuss homework problems and so on, and it's not monitored \\nby the TAs and me. So feel free to ta lk trash about this class there.  \\nIf you want to contact the teaching staff, pl ease use the email address written down here, \\ncs229-qa@cs.stanford.edu. This goes to an acc ount that's read by all the TAs and me. So \\nrather than sending us email individually, if you send email to this account, it will \\nactually let us get back to you maximally quickly with answers to your questions.  \\nIf you're asking questions about homework probl ems, please say in the subject line which \\nassignment and which question the email refers to, since that will also help us to route \\nyour question to the appropriate TA or to me  appropriately and get the response back to \\nyou quickly.  \\nLet's see. Skipping ahead — let's see — for homework, one midterm, one open and term \\nproject. Notice on the honor code. So one thi ng that I think will help you to succeed and \\ndo well in this class and even help you to enjoy this cla ss more is if you form a study \\ngroup.  \\nSo start looking around where you' re sitting now or at the end of class today, mingle a \\nlittle bit and get to know your classmates. I strongly encourage you to form study groups \\nand sort of have a group of people to study with and have a group of your fellow students \\nto talk over these concepts with. You can also  post on the class news group if you want to \\nuse that to try to form a study group.  \\nBut some of the problems sets in this cla ss are reasonably difficult.  People that have \\ntaken the class before may tell you they were very difficult. And just I bet it would be \\nmore fun for you, and you'd probably have a be tter learning experience if you form a \\nstudy group of people to work with. So I definitely encourage you to do that.  \\nAnd just to say a word on the honor code, whic h is I definitely en courage you to form a \\nstudy group and work together, discuss homew ork problems together. But if you discuss \"),\n",
       " Document(metadata={'page': 5.0, 'source': 'data/MachineLearning-Lecture01.pdf'}, page_content=\"So all right, online resources. The class has a home page, so it's in on the handouts. I \\nwon't write on the chalkboard — http:// cs229.stanford.edu. And so when there are \\nhomework assignments or things like that, we  usually won't sort of — in the mission of \\nsaving trees, we will usually not give out many handouts in class. So homework \\nassignments, homework solutions will be posted online at the course home page.  \\nAs far as this class, I've also written, a nd I guess I've also revised every year a set of \\nfairly detailed lecture notes that cover the te chnical content of this  class. And so if you \\nvisit the course homepage, you'll also find the detailed lecture notes that go over in detail \\nall the math and equations and so on  that I'll be doing in class.  \\nThere's also a newsgroup, su.class.cs229, also written on the handout. This is a \\nnewsgroup that's sort of a forum for people in  the class to get to  know each other and \\nhave whatever discussions you want to ha ve amongst yourselves. So the class newsgroup \\nwill not be monitored by the TAs and me. But this is a place for you to form study groups \\nor find project partners or discuss homework problems and so on, and it's not monitored \\nby the TAs and me. So feel free to ta lk trash about this class there.  \\nIf you want to contact the teaching staff, pl ease use the email address written down here, \\ncs229-qa@cs.stanford.edu. This goes to an acc ount that's read by all the TAs and me. So \\nrather than sending us email individually, if you send email to this account, it will \\nactually let us get back to you maximally quickly with answers to your questions.  \\nIf you're asking questions about homework probl ems, please say in the subject line which \\nassignment and which question the email refers to, since that will also help us to route \\nyour question to the appropriate TA or to me  appropriately and get the response back to \\nyou quickly.  \\nLet's see. Skipping ahead — let's see — for homework, one midterm, one open and term \\nproject. Notice on the honor code. So one thi ng that I think will help you to succeed and \\ndo well in this class and even help you to enjoy this cla ss more is if you form a study \\ngroup.  \\nSo start looking around where you' re sitting now or at the end of class today, mingle a \\nlittle bit and get to know your classmates. I strongly encourage you to form study groups \\nand sort of have a group of people to study with and have a group of your fellow students \\nto talk over these concepts with. You can also  post on the class news group if you want to \\nuse that to try to form a study group.  \\nBut some of the problems sets in this cla ss are reasonably difficult.  People that have \\ntaken the class before may tell you they were very difficult. And just I bet it would be \\nmore fun for you, and you'd probably have a be tter learning experience if you form a \\nstudy group of people to work with. So I definitely encourage you to do that.  \\nAnd just to say a word on the honor code, whic h is I definitely en courage you to form a \\nstudy group and work together, discuss homew ork problems together. But if you discuss \"),\n",
       " Document(metadata={'page': 5.0, 'source': 'data/MachineLearning-Lecture01.pdf'}, page_content=\"So all right, online resources. The class has a home page, so it's in on the handouts. I \\nwon't write on the chalkboard — http:// cs229.stanford.edu. And so when there are \\nhomework assignments or things like that, we  usually won't sort of — in the mission of \\nsaving trees, we will usually not give out many handouts in class. So homework \\nassignments, homework solutions will be posted online at the course home page.  \\nAs far as this class, I've also written, a nd I guess I've also revised every year a set of \\nfairly detailed lecture notes that cover the te chnical content of this  class. And so if you \\nvisit the course homepage, you'll also find the detailed lecture notes that go over in detail \\nall the math and equations and so on  that I'll be doing in class.  \\nThere's also a newsgroup, su.class.cs229, also written on the handout. This is a \\nnewsgroup that's sort of a forum for people in  the class to get to  know each other and \\nhave whatever discussions you want to ha ve amongst yourselves. So the class newsgroup \\nwill not be monitored by the TAs and me. But this is a place for you to form study groups \\nor find project partners or discuss homework problems and so on, and it's not monitored \\nby the TAs and me. So feel free to ta lk trash about this class there.  \\nIf you want to contact the teaching staff, pl ease use the email address written down here, \\ncs229-qa@cs.stanford.edu. This goes to an acc ount that's read by all the TAs and me. So \\nrather than sending us email individually, if you send email to this account, it will \\nactually let us get back to you maximally quickly with answers to your questions.  \\nIf you're asking questions about homework probl ems, please say in the subject line which \\nassignment and which question the email refers to, since that will also help us to route \\nyour question to the appropriate TA or to me  appropriately and get the response back to \\nyou quickly.  \\nLet's see. Skipping ahead — let's see — for homework, one midterm, one open and term \\nproject. Notice on the honor code. So one thi ng that I think will help you to succeed and \\ndo well in this class and even help you to enjoy this cla ss more is if you form a study \\ngroup.  \\nSo start looking around where you' re sitting now or at the end of class today, mingle a \\nlittle bit and get to know your classmates. I strongly encourage you to form study groups \\nand sort of have a group of people to study with and have a group of your fellow students \\nto talk over these concepts with. You can also  post on the class news group if you want to \\nuse that to try to form a study group.  \\nBut some of the problems sets in this cla ss are reasonably difficult.  People that have \\ntaken the class before may tell you they were very difficult. And just I bet it would be \\nmore fun for you, and you'd probably have a be tter learning experience if you form a \\nstudy group of people to work with. So I definitely encourage you to do that.  \\nAnd just to say a word on the honor code, whic h is I definitely en courage you to form a \\nstudy group and work together, discuss homew ork problems together. But if you discuss \")]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "question = \"What are major topics for this class?\" \n",
    "docs = vectorstore.similarity_search(question, k=3) \n",
    "docs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Hello! How can I assist you today?'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from langchain_openai import ChatOpenAI \n",
    "llm = ChatOpenAI(model_name=llm_name, temperature=0) \n",
    "llm.predict(\"Hello world!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Yes, probability is a class topic. Thanks for asking!'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Build prompt\n",
    "from langchain.prompts import PromptTemplate\n",
    "template = \"\"\"Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer. Use three sentences maximum. Keep the answer as concise as possible. Always say \"thanks for asking!\" at the end of the answer. \n",
    "{context}\n",
    "Question: {question}\n",
    "Helpful Answer:\"\"\"\n",
    "QA_CHAIN_PROMPT = PromptTemplate(input_variables=[\"context\", \"question\"],template=template,)\n",
    "\n",
    "# Run chain\n",
    "from langchain.chains import RetrievalQA\n",
    "question = \"Is probability a class topic?\"\n",
    "qa_chain = RetrievalQA.from_chain_type(llm,\n",
    "                                       retriever=vectorstore.as_retriever(),\n",
    "                                       return_source_documents=True,\n",
    "                                       chain_type_kwargs={\"prompt\": QA_CHAIN_PROMPT})\n",
    "\n",
    "\n",
    "result = qa_chain({\"query\": question})\n",
    "result[\"result\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Memory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Implement Memory \n",
    "from langchain.memory import ConversationBufferMemory\n",
    "memory = ConversationBufferMemory(\n",
    "    memory_key=\"chat_history\",\n",
    "    return_messages=True\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ConversationalRetrievalChain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.chains import ConversationalRetrievalChain \n",
    "retriever = vectorstore.as_retriever()\n",
    "qa = ConversationalRetrievalChain.from_llm(\n",
    "    llm, \n",
    "    retriever=retriever,\n",
    "    memory=memory\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'question': 'Is probability a class topic?',\n",
       " 'chat_history': [HumanMessage(content='Is probability a class topic?'),\n",
       "  AIMessage(content='Yes, probability is a class topic in the course being discussed. The instructor assumes familiarity with basic probability and statistics, so it will likely be covered in the class.')],\n",
       " 'answer': 'Yes, probability is a class topic in the course being discussed. The instructor assumes familiarity with basic probability and statistics, so it will likely be covered in the class.'}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "question = \"Is probability a class topic?\"\n",
    "result = qa({\"question\": question})\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Yes, probability is a class topic in the course being discussed. The instructor assumes familiarity with basic probability and statistics, so it will likely be covered in the class.'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result['answer']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'question': 'Why are those prerequisites needed?',\n",
       " 'chat_history': [HumanMessage(content='Is probability a class topic?'),\n",
       "  AIMessage(content='Yes, probability is a class topic in the course being discussed. The instructor assumes familiarity with basic probability and statistics, so it will likely be covered in the class.'),\n",
       "  HumanMessage(content='Why are those prerequisites needed?'),\n",
       "  AIMessage(content='The prerequisites for the course being discussed include familiarity with basic probability and statistics, as well as basic linear algebra. For probability and statistics, knowledge of random variables, expectation, variance, and basic concepts is assumed. For linear algebra, understanding matrices, vectors, matrix multiplication, matrix inverse, and eigenvectors is expected. Undergraduate courses like Stat 116 for statistics and Math 51, 103, Math 113, or CS205 for linear algebra are considered sufficient preparation.')],\n",
       " 'answer': 'The prerequisites for the course being discussed include familiarity with basic probability and statistics, as well as basic linear algebra. For probability and statistics, knowledge of random variables, expectation, variance, and basic concepts is assumed. For linear algebra, understanding matrices, vectors, matrix multiplication, matrix inverse, and eigenvectors is expected. Undergraduate courses like Stat 116 for statistics and Math 51, 103, Math 113, or CS205 for linear algebra are considered sufficient preparation.'}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "question = \"Why are those prerequisites needed?\"\n",
    "\n",
    "result = qa({\"question\": question})\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'The prerequisites for the course being discussed include familiarity with basic probability and statistics, as well as basic linear algebra. For probability and statistics, knowledge of random variables, expectation, variance, and basic concepts is assumed. For linear algebra, understanding matrices, vectors, matrix multiplication, matrix inverse, and eigenvectors is expected. Undergraduate courses like Stat 116 for statistics and Math 51, 103, Math 113, or CS205 for linear algebra are considered sufficient preparation.'"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result['answer']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a chatbot that works on your documents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pip install docarray jupyter-bokeh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain_openai import OpenAIEmbeddings, ChatOpenAI\n",
    "from langchain.text_splitter import CharacterTextSplitter, RecursiveCharacterTextSplitter\n",
    "from langchain.vectorstores import DocArrayInMemorySearch \n",
    "from langchain.document_loaders import TextLoader, PyPDFLoader\n",
    "from langchain.chains import RetrievalQA, ConversationalRetrievalChain \n",
    "from langchain.memory import ConversationBufferMemory "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_db(file: str, chain_type, k): \n",
    "    # Load documents\n",
    "    loader = PyPDFLoader(file) \n",
    "    documents = loader.load() \n",
    "\n",
    "    # Split documents\n",
    "    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=150)\n",
    "    docs = text_splitter.split_documents(documents)\n",
    "\n",
    "    # Define embeddings \n",
    "    embeddings = OpenAIEmbeddings()\n",
    "\n",
    "    # Create vector database from data \n",
    "    db = DocArrayInMemorySearch.from_documents(docs, embedding=embeddings) \n",
    "\n",
    "    # Define retriever \n",
    "    retriever = db.as_retriever(search_type=\"similarity\", search_kwargs={\"k\": k}) \n",
    "\n",
    "    # Create a chatbot chain. Memory is managed separately \n",
    "    qa = ConversationalRetrievalChain.from_llm(\n",
    "        llm=ChatOpenAI(model_name=llm_name, temperature=0), \n",
    "        chain_type=chain_type, \n",
    "        retriever=retriever, \n",
    "        return_source_documents=True, \n",
    "        return_generated_question=True\n",
    "    )\n",
    "    return qa\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "import panel as pn\n",
    "import param\n",
    "\n",
    "class cbfs(param.Parameterized):\n",
    "    chat_history = param.List([])\n",
    "    answer = param.String(\"\")\n",
    "    db_query  = param.String(\"\")\n",
    "    db_response = param.List([])\n",
    "    \n",
    "    def __init__(self,  **params):\n",
    "        super(cbfs, self).__init__( **params)\n",
    "        self.panels = []\n",
    "        self.loaded_file = \"data/MachineLearning-Lecture01.pdf\"\n",
    "        self.qa = load_db(self.loaded_file,\"stuff\", 4)\n",
    "    \n",
    "    def call_load_db(self, count):\n",
    "        if count == 0 or file_input.value is None:  # init or no file specified :\n",
    "            return pn.pane.Markdown(f\"Loaded File: {self.loaded_file}\")\n",
    "        else:\n",
    "            file_input.save(\"temp.pdf\")  # local copy\n",
    "            self.loaded_file = file_input.filename\n",
    "            button_load.button_style=\"outline\"\n",
    "            self.qa = load_db(\"temp.pdf\", \"stuff\", 4)\n",
    "            button_load.button_style=\"solid\"\n",
    "        self.clr_history()\n",
    "        return pn.pane.Markdown(f\"Loaded File: {self.loaded_file}\")\n",
    "\n",
    "    def convchain(self, query):\n",
    "        if not query:\n",
    "            return pn.WidgetBox(pn.Row('User:', pn.pane.Markdown(\"\", width=600)), scroll=True)\n",
    "        result = self.qa({\"question\": query, \"chat_history\": self.chat_history})\n",
    "        self.chat_history.extend([(query, result[\"answer\"])])\n",
    "        self.db_query = result[\"generated_question\"]\n",
    "        self.db_response = result[\"source_documents\"]\n",
    "        self.answer = result['answer'] \n",
    "        self.panels.extend([\n",
    "            pn.Row('User:', pn.pane.Markdown(query, width=600)),\n",
    "            pn.Row('ChatBot:', pn.pane.Markdown(self.answer, width=600, style={'background-color': '#F6F6F6'}))\n",
    "        ])\n",
    "        inp.value = ''  #clears loading indicator when cleared\n",
    "        return pn.WidgetBox(*self.panels,scroll=True)\n",
    "\n",
    "    @param.depends('db_query ', )\n",
    "    def get_lquest(self):\n",
    "        if not self.db_query :\n",
    "            return pn.Column(\n",
    "                pn.Row(pn.pane.Markdown(f\"Last question to DB:\", styles={'background-color': '#F6F6F6'})),\n",
    "                pn.Row(pn.pane.Str(\"no DB accesses so far\"))\n",
    "            )\n",
    "        return pn.Column(\n",
    "            pn.Row(pn.pane.Markdown(f\"DB query:\", styles={'background-color': '#F6F6F6'})),\n",
    "            pn.pane.Str(self.db_query )\n",
    "        )\n",
    "\n",
    "    @param.depends('db_response', )\n",
    "    def get_sources(self):\n",
    "        if not self.db_response:\n",
    "            return \n",
    "        rlist=[pn.Row(pn.pane.Markdown(f\"Result of DB lookup:\", styles={'background-color': '#F6F6F6'}))]\n",
    "        for doc in self.db_response:\n",
    "            rlist.append(pn.Row(pn.pane.Str(doc)))\n",
    "        return pn.WidgetBox(*rlist, width=600, scroll=True)\n",
    "\n",
    "    @param.depends('convchain', 'clr_history') \n",
    "    def get_chats(self):\n",
    "        if not self.chat_history:\n",
    "            return pn.WidgetBox(pn.Row(pn.pane.Str(\"No History Yet\")), width=600, scroll=True)\n",
    "        rlist=[pn.Row(pn.pane.Markdown(f\"Current Chat History variable\", styles={'background-color': '#F6F6F6'}))]\n",
    "        for exchange in self.chat_history:\n",
    "            rlist.append(pn.Row(pn.pane.Str(exchange)))\n",
    "        return pn.WidgetBox(*rlist, width=600, scroll=True)\n",
    "\n",
    "    def clr_history(self,count=0):\n",
    "        self.chat_history = []\n",
    "        return "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": "(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n  var py_version = '3.1.1'.replace('rc', '-rc.');\n  var is_dev = py_version.indexOf(\"+\") !== -1 || py_version.indexOf(\"-\") !== -1;\n  var reloading = false;\n  var Bokeh = root.Bokeh;\n  var bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n\n  if (typeof (root._bokeh_timeout) === \"undefined\" || force) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks;\n    }\n    console.debug(\"Bokeh: all callbacks have finished\");\n  }\n\n  function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n    if (js_modules == null) js_modules = [];\n    if (js_exports == null) js_exports = {};\n\n    root._bokeh_onload_callbacks.push(callback);\n\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n      return null;\n    }\n    if (js_urls.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n      run_callbacks();\n      return null;\n    }\n    if (!reloading) {\n      console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n    }\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n        run_callbacks()\n      }\n    }\n    window._bokeh_on_load = on_load\n\n    function on_error() {\n      console.error(\"failed to load \" + url);\n    }\n\n    var skip = [];\n    if (window.requirejs) {\n      window.requirejs.config({'packages': {}, 'paths': {'jspanel': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/jspanel', 'jspanel-modal': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal', 'jspanel-tooltip': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip', 'jspanel-hint': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint', 'jspanel-layout': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout', 'jspanel-contextmenu': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu', 'jspanel-dock': 'https://cdn.jsdelivr.net/npm/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock', 'gridstack': 'https://cdn.jsdelivr.net/npm/gridstack@7.2.3/dist/gridstack-all', 'notyf': 'https://cdn.jsdelivr.net/npm/notyf@3/notyf.min'}, 'shim': {'jspanel': {'exports': 'jsPanel'}, 'gridstack': {'exports': 'GridStack'}}});\n      require([\"jspanel\"], function(jsPanel) {\n\twindow.jsPanel = jsPanel\n\ton_load()\n      })\n      require([\"jspanel-modal\"], function() {\n\ton_load()\n      })\n      require([\"jspanel-tooltip\"], function() {\n\ton_load()\n      })\n      require([\"jspanel-hint\"], function() {\n\ton_load()\n      })\n      require([\"jspanel-layout\"], function() {\n\ton_load()\n      })\n      require([\"jspanel-contextmenu\"], function() {\n\ton_load()\n      })\n      require([\"jspanel-dock\"], function() {\n\ton_load()\n      })\n      require([\"gridstack\"], function(GridStack) {\n\twindow.GridStack = GridStack\n\ton_load()\n      })\n      require([\"notyf\"], function() {\n\ton_load()\n      })\n      root._bokeh_is_loading = css_urls.length + 9;\n    } else {\n      root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n    }\n\n    var existing_stylesheets = []\n    var links = document.getElementsByTagName('link')\n    for (var i = 0; i < links.length; i++) {\n      var link = links[i]\n      if (link.href != null) {\n\texisting_stylesheets.push(link.href)\n      }\n    }\n    for (var i = 0; i < css_urls.length; i++) {\n      var url = css_urls[i];\n      if (existing_stylesheets.indexOf(url) !== -1) {\n\ton_load()\n\tcontinue;\n      }\n      const element = document.createElement(\"link\");\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.rel = \"stylesheet\";\n      element.type = \"text/css\";\n      element.href = url;\n      console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n      document.body.appendChild(element);\n    }    if (((window['jsPanel'] !== undefined) && (!(window['jsPanel'] instanceof HTMLElement))) || window.requirejs) {\n      var urls = ['https://cdn.holoviz.org/panel/1.1.0/dist/bundled/floatpanel/jspanel4@4.12.0/dist/jspanel.js', 'https://cdn.holoviz.org/panel/1.1.0/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal.js', 'https://cdn.holoviz.org/panel/1.1.0/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip.js', 'https://cdn.holoviz.org/panel/1.1.0/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint.js', 'https://cdn.holoviz.org/panel/1.1.0/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout.js', 'https://cdn.holoviz.org/panel/1.1.0/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu.js', 'https://cdn.holoviz.org/panel/1.1.0/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock.js'];\n      for (var i = 0; i < urls.length; i++) {\n        skip.push(urls[i])\n      }\n    }    if (((window['GridStack'] !== undefined) && (!(window['GridStack'] instanceof HTMLElement))) || window.requirejs) {\n      var urls = ['https://cdn.holoviz.org/panel/1.1.0/dist/bundled/gridstack/gridstack@7.2.3/dist/gridstack-all.js'];\n      for (var i = 0; i < urls.length; i++) {\n        skip.push(urls[i])\n      }\n    }    if (((window['Notyf'] !== undefined) && (!(window['Notyf'] instanceof HTMLElement))) || window.requirejs) {\n      var urls = ['https://cdn.holoviz.org/panel/1.1.0/dist/bundled/notificationarea/notyf@3/notyf.min.js'];\n      for (var i = 0; i < urls.length; i++) {\n        skip.push(urls[i])\n      }\n    }    var existing_scripts = []\n    var scripts = document.getElementsByTagName('script')\n    for (var i = 0; i < scripts.length; i++) {\n      var script = scripts[i]\n      if (script.src != null) {\n\texisting_scripts.push(script.src)\n      }\n    }\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n\tif (!window.requirejs) {\n\t  on_load();\n\t}\n\tcontinue;\n      }\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n    for (var i = 0; i < js_modules.length; i++) {\n      var url = js_modules[i];\n      if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n\tif (!window.requirejs) {\n\t  on_load();\n\t}\n\tcontinue;\n      }\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      element.type = \"module\";\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      document.head.appendChild(element);\n    }\n    for (const name in js_exports) {\n      var url = js_exports[name];\n      if (skip.indexOf(url) >= 0 || root[name] != null) {\n\tif (!window.requirejs) {\n\t  on_load();\n\t}\n\tcontinue;\n      }\n      var element = document.createElement('script');\n      element.onerror = on_error;\n      element.async = false;\n      element.type = \"module\";\n      console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n      element.textContent = `\n      import ${name} from \"${url}\"\n      window.${name} = ${name}\n      window._bokeh_on_load()\n      `\n      document.head.appendChild(element);\n    }\n    if (!js_urls.length && !js_modules.length) {\n      on_load()\n    }\n  };\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\"style\");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.1.1.min.js\", \"https://cdn.holoviz.org/panel/1.1.0/dist/panel.min.js\"];\n  var js_modules = [];\n  var js_exports = {};\n  var css_urls = [];\n  var inline_js = [    function(Bokeh) {\n      Bokeh.set_log_level(\"info\");\n    },\nfunction(Bokeh) {} // ensure no trailing comma for IE\n  ];\n\n  function run_inline_js() {\n    if ((root.Bokeh !== undefined) || (force === true)) {\n      for (var i = 0; i < inline_js.length; i++) {\n        inline_js[i].call(root, root.Bokeh);\n      }\n      // Cache old bokeh versions\n      if (Bokeh != undefined && !reloading) {\n\tvar NewBokeh = root.Bokeh;\n\tif (Bokeh.versions === undefined) {\n\t  Bokeh.versions = new Map();\n\t}\n\tif (NewBokeh.version !== Bokeh.version) {\n\t  Bokeh.versions.set(NewBokeh.version, NewBokeh)\n\t}\n\troot.Bokeh = Bokeh;\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    }\n    root._bokeh_is_initializing = false\n  }\n\n  function load_or_wait() {\n    // Implement a backoff loop that tries to ensure we do not load multiple\n    // versions of Bokeh and its dependencies at the same time.\n    // In recent versions we use the root._bokeh_is_initializing flag\n    // to determine whether there is an ongoing attempt to initialize\n    // bokeh, however for backward compatibility we also try to ensure\n    // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n    // before older versions are fully initialized.\n    if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n      root._bokeh_is_initializing = false;\n      root._bokeh_onload_callbacks = undefined;\n      console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n      load_or_wait();\n    } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n      setTimeout(load_or_wait, 100);\n    } else {\n      Bokeh = root.Bokeh;\n      bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n      root._bokeh_is_initializing = true\n      root._bokeh_onload_callbacks = []\n      if (!reloading && (!bokeh_loaded || is_dev)) {\n\troot.Bokeh = undefined;\n      }\n      load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n\tconsole.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n\trun_inline_js();\n      });\n    }\n  }\n  // Give older versions of the autoload script a head-start to ensure\n  // they initialize before we start loading newer version.\n  setTimeout(load_or_wait, 100)\n}(window));",
      "application/vnd.holoviews_load.v0+json": ""
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": "\nif ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n  window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n}\n\n\n    function JupyterCommManager() {\n    }\n\n    JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n      if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n        var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n        comm_manager.register_target(comm_id, function(comm) {\n          comm.on_msg(msg_handler);\n        });\n      } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n        window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n          comm.onMsg = msg_handler;\n        });\n      } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n        google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n          var messages = comm.messages[Symbol.asyncIterator]();\n          function processIteratorResult(result) {\n            var message = result.value;\n            console.log(message)\n            var content = {data: message.data, comm_id};\n            var buffers = []\n            for (var buffer of message.buffers || []) {\n              buffers.push(new DataView(buffer))\n            }\n            var metadata = message.metadata || {};\n            var msg = {content, buffers, metadata}\n            msg_handler(msg);\n            return messages.next().then(processIteratorResult);\n          }\n          return messages.next().then(processIteratorResult);\n        })\n      }\n    }\n\n    JupyterCommManager.prototype.get_client_comm = function(plot_id, comm_id, msg_handler) {\n      if (comm_id in window.PyViz.comms) {\n        return window.PyViz.comms[comm_id];\n      } else if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n        var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n        var comm = comm_manager.new_comm(comm_id, {}, {}, {}, comm_id);\n        if (msg_handler) {\n          comm.on_msg(msg_handler);\n        }\n      } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n        var comm = window.PyViz.kernels[plot_id].connectToComm(comm_id);\n        comm.open();\n        if (msg_handler) {\n          comm.onMsg = msg_handler;\n        }\n      } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n        var comm_promise = google.colab.kernel.comms.open(comm_id)\n        comm_promise.then((comm) => {\n          window.PyViz.comms[comm_id] = comm;\n          if (msg_handler) {\n            var messages = comm.messages[Symbol.asyncIterator]();\n            function processIteratorResult(result) {\n              var message = result.value;\n              var content = {data: message.data};\n              var metadata = message.metadata || {comm_id};\n              var msg = {content, metadata}\n              msg_handler(msg);\n              return messages.next().then(processIteratorResult);\n            }\n            return messages.next().then(processIteratorResult);\n          }\n        }) \n        var sendClosure = (data, metadata, buffers, disposeOnDone) => {\n          return comm_promise.then((comm) => {\n            comm.send(data, metadata, buffers, disposeOnDone);\n          });\n        };\n        var comm = {\n          send: sendClosure\n        };\n      }\n      window.PyViz.comms[comm_id] = comm;\n      return comm;\n    }\n    window.PyViz.comm_manager = new JupyterCommManager();\n    \n\n\nvar JS_MIME_TYPE = 'application/javascript';\nvar HTML_MIME_TYPE = 'text/html';\nvar EXEC_MIME_TYPE = 'application/vnd.holoviews_exec.v0+json';\nvar CLASS_NAME = 'output';\n\n/**\n * Render data to the DOM node\n */\nfunction render(props, node) {\n  var div = document.createElement(\"div\");\n  var script = document.createElement(\"script\");\n  node.appendChild(div);\n  node.appendChild(script);\n}\n\n/**\n * Handle when a new output is added\n */\nfunction handle_add_output(event, handle) {\n  var output_area = handle.output_area;\n  var output = handle.output;\n  if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n    return\n  }\n  var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n  var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n  if (id !== undefined) {\n    var nchildren = toinsert.length;\n    var html_node = toinsert[nchildren-1].children[0];\n    html_node.innerHTML = output.data[HTML_MIME_TYPE];\n    var scripts = [];\n    var nodelist = html_node.querySelectorAll(\"script\");\n    for (var i in nodelist) {\n      if (nodelist.hasOwnProperty(i)) {\n        scripts.push(nodelist[i])\n      }\n    }\n\n    scripts.forEach( function (oldScript) {\n      var newScript = document.createElement(\"script\");\n      var attrs = [];\n      var nodemap = oldScript.attributes;\n      for (var j in nodemap) {\n        if (nodemap.hasOwnProperty(j)) {\n          attrs.push(nodemap[j])\n        }\n      }\n      attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n      newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n      oldScript.parentNode.replaceChild(newScript, oldScript);\n    });\n    if (JS_MIME_TYPE in output.data) {\n      toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n    }\n    output_area._hv_plot_id = id;\n    if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n      window.PyViz.plot_index[id] = Bokeh.index[id];\n    } else {\n      window.PyViz.plot_index[id] = null;\n    }\n  } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n    var bk_div = document.createElement(\"div\");\n    bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n    var script_attrs = bk_div.children[0].attributes;\n    for (var i = 0; i < script_attrs.length; i++) {\n      toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n    }\n    // store reference to server id on output_area\n    output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n  }\n}\n\n/**\n * Handle when an output is cleared or removed\n */\nfunction handle_clear_output(event, handle) {\n  var id = handle.cell.output_area._hv_plot_id;\n  var server_id = handle.cell.output_area._bokeh_server_id;\n  if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n  var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n  if (server_id !== null) {\n    comm.send({event_type: 'server_delete', 'id': server_id});\n    return;\n  } else if (comm !== null) {\n    comm.send({event_type: 'delete', 'id': id});\n  }\n  delete PyViz.plot_index[id];\n  if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n    var doc = window.Bokeh.index[id].model.document\n    doc.clear();\n    const i = window.Bokeh.documents.indexOf(doc);\n    if (i > -1) {\n      window.Bokeh.documents.splice(i, 1);\n    }\n  }\n}\n\n/**\n * Handle kernel restart event\n */\nfunction handle_kernel_cleanup(event, handle) {\n  delete PyViz.comms[\"hv-extension-comm\"];\n  window.PyViz.plot_index = {}\n}\n\n/**\n * Handle update_display_data messages\n */\nfunction handle_update_output(event, handle) {\n  handle_clear_output(event, {cell: {output_area: handle.output_area}})\n  handle_add_output(event, handle)\n}\n\nfunction register_renderer(events, OutputArea) {\n  function append_mime(data, metadata, element) {\n    // create a DOM node to render to\n    var toinsert = this.create_output_subarea(\n    metadata,\n    CLASS_NAME,\n    EXEC_MIME_TYPE\n    );\n    this.keyboard_manager.register_events(toinsert);\n    // Render to node\n    var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n    render(props, toinsert[0]);\n    element.append(toinsert);\n    return toinsert\n  }\n\n  events.on('output_added.OutputArea', handle_add_output);\n  events.on('output_updated.OutputArea', handle_update_output);\n  events.on('clear_output.CodeCell', handle_clear_output);\n  events.on('delete.Cell', handle_clear_output);\n  events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n\n  OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n    safe: true,\n    index: 0\n  });\n}\n\nif (window.Jupyter !== undefined) {\n  try {\n    var events = require('base/js/events');\n    var OutputArea = require('notebook/js/outputarea').OutputArea;\n    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n      register_renderer(events, OutputArea);\n    }\n  } catch(err) {\n  }\n}\n",
      "application/vnd.holoviews_load.v0+json": ""
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style>*[data-root-id],\n",
       "*[data-root-id] > * {\n",
       "  box-sizing: border-box;\n",
       "  font-family: var(--jp-ui-font-family);\n",
       "  font-size: var(--jp-ui-font-size1);\n",
       "  color: var(--vscode-editor-foreground, var(--jp-ui-font-color1));\n",
       "}\n",
       "\n",
       "/* Override VSCode background color */\n",
       ".cell-output-ipywidget-background:has(> .cell-output-ipywidget-background\n",
       "    > .lm-Widget\n",
       "    > *[data-root-id]),\n",
       ".cell-output-ipywidget-background:has(> .lm-Widget > *[data-root-id]) {\n",
       "  background-color: transparent !important;\n",
       "}\n",
       "</style>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.holoviews_exec.v0+json": "",
      "text/html": [
       "<div id='b22176b5-a89d-4bbc-9fa1-d326b927fd60'>\n",
       "  <div id=\"e9b6b3be-e707-417b-b913-6df516046f88\" data-root-id=\"b22176b5-a89d-4bbc-9fa1-d326b927fd60\" style=\"display: contents;\"></div>\n",
       "</div>\n",
       "<script type=\"application/javascript\">(function(root) {\n",
       "  var docs_json = {\"7b42bd67-2f5e-4402-940c-b34649bf2f27\":{\"version\":\"3.1.1\",\"title\":\"Bokeh Application\",\"defs\":[{\"type\":\"model\",\"name\":\"ReactiveHTML1\"},{\"type\":\"model\",\"name\":\"FlexBox1\",\"properties\":[{\"name\":\"align_content\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"align_items\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"flex_direction\",\"kind\":\"Any\",\"default\":\"row\"},{\"name\":\"flex_wrap\",\"kind\":\"Any\",\"default\":\"wrap\"},{\"name\":\"justify_content\",\"kind\":\"Any\",\"default\":\"flex-start\"}]},{\"type\":\"model\",\"name\":\"FloatPanel1\",\"properties\":[{\"name\":\"config\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"contained\",\"kind\":\"Any\",\"default\":true},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"right-top\"},{\"name\":\"offsetx\",\"kind\":\"Any\",\"default\":null},{\"name\":\"offsety\",\"kind\":\"Any\",\"default\":null},{\"name\":\"theme\",\"kind\":\"Any\",\"default\":\"primary\"},{\"name\":\"status\",\"kind\":\"Any\",\"default\":\"normalized\"}]},{\"type\":\"model\",\"name\":\"GridStack1\",\"properties\":[{\"name\":\"mode\",\"kind\":\"Any\",\"default\":\"warn\"},{\"name\":\"ncols\",\"kind\":\"Any\",\"default\":null},{\"name\":\"nrows\",\"kind\":\"Any\",\"default\":null},{\"name\":\"allow_resize\",\"kind\":\"Any\",\"default\":true},{\"name\":\"allow_drag\",\"kind\":\"Any\",\"default\":true},{\"name\":\"state\",\"kind\":\"Any\",\"default\":[]}]},{\"type\":\"model\",\"name\":\"drag1\",\"properties\":[{\"name\":\"slider_width\",\"kind\":\"Any\",\"default\":5},{\"name\":\"slider_color\",\"kind\":\"Any\",\"default\":\"black\"},{\"name\":\"value\",\"kind\":\"Any\",\"default\":50}]},{\"type\":\"model\",\"name\":\"click1\",\"properties\":[{\"name\":\"terminal_output\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"debug_name\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"clears\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"FastWrapper1\",\"properties\":[{\"name\":\"object\",\"kind\":\"Any\",\"default\":null},{\"name\":\"style\",\"kind\":\"Any\",\"default\":null}]},{\"type\":\"model\",\"name\":\"NotificationAreaBase1\",\"properties\":[{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"NotificationArea1\",\"properties\":[{\"name\":\"notifications\",\"kind\":\"Any\",\"default\":[]},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0},{\"name\":\"types\",\"kind\":\"Any\",\"default\":[{\"type\":\"map\",\"entries\":[[\"type\",\"warning\"],[\"background\",\"#ffc107\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-exclamation-triangle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]},{\"type\":\"map\",\"entries\":[[\"type\",\"info\"],[\"background\",\"#007bff\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-info-circle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]}]}]},{\"type\":\"model\",\"name\":\"Notification\",\"properties\":[{\"name\":\"background\",\"kind\":\"Any\",\"default\":null},{\"name\":\"duration\",\"kind\":\"Any\",\"default\":3000},{\"name\":\"icon\",\"kind\":\"Any\",\"default\":null},{\"name\":\"message\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"notification_type\",\"kind\":\"Any\",\"default\":null},{\"name\":\"_destroyed\",\"kind\":\"Any\",\"default\":false}]},{\"type\":\"model\",\"name\":\"TemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"BootstrapTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"MaterialTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]}],\"roots\":[{\"type\":\"object\",\"name\":\"panel.models.browser.BrowserInfo\",\"id\":\"b22176b5-a89d-4bbc-9fa1-d326b927fd60\"},{\"type\":\"object\",\"name\":\"panel.models.comm_manager.CommManager\",\"id\":\"be9ccca0-27a4-42b9-a78e-7cbf5ee183cc\",\"attributes\":{\"plot_id\":\"b22176b5-a89d-4bbc-9fa1-d326b927fd60\",\"comm_id\":\"76379df47a544d1e9f257fec9e8be56e\",\"client_comm_id\":\"8dc8c16a946d40b3be9c2cf1a2ba4632\"}}],\"callbacks\":{\"type\":\"map\"}}};\n",
       "  var render_items = [{\"docid\":\"7b42bd67-2f5e-4402-940c-b34649bf2f27\",\"roots\":{\"b22176b5-a89d-4bbc-9fa1-d326b927fd60\":\"e9b6b3be-e707-417b-b913-6df516046f88\"},\"root_ids\":[\"b22176b5-a89d-4bbc-9fa1-d326b927fd60\"]}];\n",
       "  var docs = Object.values(docs_json)\n",
       "  if (!docs) {\n",
       "    return\n",
       "  }\n",
       "  const py_version = docs[0].version.replace('rc', '-rc.')\n",
       "  const is_dev = py_version.indexOf(\"+\") !== -1 || py_version.indexOf(\"-\") !== -1\n",
       "  function embed_document(root) {\n",
       "    var Bokeh = get_bokeh(root)\n",
       "    Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "    for (const render_item of render_items) {\n",
       "      for (const root_id of render_item.root_ids) {\n",
       "\tconst id_el = document.getElementById(root_id)\n",
       "\tif (id_el.children.length && (id_el.children[0].className === 'bk-root')) {\n",
       "\t  const root_el = id_el.children[0]\n",
       "\t  root_el.id = root_el.id + '-rendered'\n",
       "\t}\n",
       "      }\n",
       "    }\n",
       "  }\n",
       "  function get_bokeh(root) {\n",
       "    if (root.Bokeh === undefined) {\n",
       "      return null\n",
       "    } else if (root.Bokeh.version !== py_version && !is_dev) {\n",
       "      if (root.Bokeh.versions === undefined || !root.Bokeh.versions.has(py_version)) {\n",
       "\treturn null\n",
       "      }\n",
       "      return root.Bokeh.versions.get(py_version);\n",
       "    } else if (root.Bokeh.version === py_version) {\n",
       "      return root.Bokeh\n",
       "    }\n",
       "    return null\n",
       "  }\n",
       "  function is_loaded(root) {\n",
       "    var Bokeh = get_bokeh(root)\n",
       "    return (Bokeh != null && Bokeh.Panel !== undefined)\n",
       "  }\n",
       "  if (is_loaded(root)) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (is_loaded(root)) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else if (document.readyState == \"complete\") {\n",
       "        attempts++;\n",
       "        if (attempts > 200) {\n",
       "          clearInterval(timer);\n",
       "\t  var Bokeh = get_bokeh(root)\n",
       "\t  if (Bokeh == null || Bokeh.Panel == null) {\n",
       "            console.warn(\"Panel: ERROR: Unable to run Panel code because Bokeh or Panel library is missing\");\n",
       "\t  } else {\n",
       "\t    console.warn(\"Panel: WARNING: Attempting to render but not all required libraries could be resolved.\")\n",
       "\t    embed_document(root)\n",
       "\t  }\n",
       "        }\n",
       "      }\n",
       "    }, 25, root)\n",
       "  }\n",
       "})(window);</script>"
      ]
     },
     "metadata": {
      "application/vnd.holoviews_exec.v0+json": {
       "id": "b22176b5-a89d-4bbc-9fa1-d326b927fd60"
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f377d0df181d4fcd8f09e5a39dc435ba",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "BokehModel(combine_events=True, render_bundle={'docs_json': {'7242e05c-8776-43ef-9214-80aba4a61bfe': {'version…"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Windows\\AppData\\Local\\Temp\\ipykernel_16200\\870896171.py:38: PanelDeprecationWarning: 'style' is deprecated and will be removed in version 1.1, use 'styles' instead.\n",
      "  pn.Row('ChatBot:', pn.pane.Markdown(self.answer, width=600, style={'background-color': '#F6F6F6'}))\n",
      "C:\\Users\\Windows\\AppData\\Local\\Temp\\ipykernel_16200\\870896171.py:38: PanelDeprecationWarning: 'style' is deprecated and will be removed in version 1.1, use 'styles' instead.\n",
      "  pn.Row('ChatBot:', pn.pane.Markdown(self.answer, width=600, style={'background-color': '#F6F6F6'}))\n",
      "C:\\Users\\Windows\\AppData\\Local\\Temp\\ipykernel_16200\\870896171.py:38: PanelDeprecationWarning: 'style' is deprecated and will be removed in version 1.1, use 'styles' instead.\n",
      "  pn.Row('ChatBot:', pn.pane.Markdown(self.answer, width=600, style={'background-color': '#F6F6F6'}))\n",
      "C:\\Users\\Windows\\AppData\\Local\\Temp\\ipykernel_16200\\870896171.py:38: PanelDeprecationWarning: 'style' is deprecated and will be removed in version 1.1, use 'styles' instead.\n",
      "  pn.Row('ChatBot:', pn.pane.Markdown(self.answer, width=600, style={'background-color': '#F6F6F6'}))\n",
      "C:\\Users\\Windows\\AppData\\Local\\Temp\\ipykernel_16200\\870896171.py:38: PanelDeprecationWarning: 'style' is deprecated and will be removed in version 1.1, use 'styles' instead.\n",
      "  pn.Row('ChatBot:', pn.pane.Markdown(self.answer, width=600, style={'background-color': '#F6F6F6'}))\n",
      "C:\\Users\\Windows\\AppData\\Local\\Temp\\ipykernel_16200\\870896171.py:38: PanelDeprecationWarning: 'style' is deprecated and will be removed in version 1.1, use 'styles' instead.\n",
      "  pn.Row('ChatBot:', pn.pane.Markdown(self.answer, width=600, style={'background-color': '#F6F6F6'}))\n"
     ]
    }
   ],
   "source": [
    "cb = cbfs()\n",
    "\n",
    "file_input = pn.widgets.FileInput(accept='.pdf')\n",
    "button_load = pn.widgets.Button(name=\"Load DB\", button_type='primary')\n",
    "button_clearhistory = pn.widgets.Button(name=\"Clear History\", button_type='warning')\n",
    "button_clearhistory.on_click(cb.clr_history)\n",
    "inp = pn.widgets.TextInput( placeholder='Enter text here…')\n",
    "\n",
    "bound_button_load = pn.bind(cb.call_load_db, button_load.param.clicks)\n",
    "conversation = pn.bind(cb.convchain, inp) \n",
    "\n",
    "jpg_pane = pn.pane.Image( './img/convchain.jpg')\n",
    "\n",
    "tab1 = pn.Column(\n",
    "    pn.Row(inp),\n",
    "    pn.layout.Divider(),\n",
    "    pn.panel(conversation,  loading_indicator=True, height=300),\n",
    "    pn.layout.Divider(),\n",
    ")\n",
    "tab2= pn.Column(\n",
    "    pn.panel(cb.get_lquest),\n",
    "    pn.layout.Divider(),\n",
    "    pn.panel(cb.get_sources ),\n",
    ")\n",
    "tab3= pn.Column(\n",
    "    pn.panel(cb.get_chats),\n",
    "    pn.layout.Divider(),\n",
    ")\n",
    "tab4=pn.Column(\n",
    "    pn.Row( file_input, button_load, bound_button_load),\n",
    "    pn.Row( button_clearhistory, pn.pane.Markdown(\"Clears chat history. Can use to start a new topic\" )),\n",
    "    pn.layout.Divider(),\n",
    "    pn.Row(jpg_pane.clone(width=400))\n",
    ")\n",
    "dashboard = pn.Column(\n",
    "    pn.Row(pn.pane.Markdown('# ChatWithYourData_Bot')),\n",
    "    pn.Tabs(('Conversation', tab1), ('Database', tab2), ('Chat History', tab3),('Configure', tab4))\n",
    ")\n",
    "pn.extension()\n",
    "dashboard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}