{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# app\n",
    "\n",
    "> Gradio app.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#| default_exp app"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#| hide\n",
    "from nbdev.showdoc import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#| export\n",
    "import os\n",
    "\n",
    "import gradio as gr\n",
    "from fastcore.utils import in_jupyter\n",
    "from langchain.chains import ConversationChain\n",
    "from langchain.chat_models import ChatOpenAI\n",
    "from langchain.memory import ConversationBufferMemory\n",
    "from langchain.prompts.chat import (\n",
    "    ChatPromptTemplate,\n",
    "    HumanMessagePromptTemplate,\n",
    "    MessagesPlaceholder,\n",
    ")\n",
    "\n",
    "from lv_recipe_chatbot.engineer_prompt import init_prompt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#| export\n",
    "class ConversationBot:\n",
    "    def __init__(\n",
    "        self,\n",
    "    ):\n",
    "        self.chat = ChatOpenAI(temperature=1, verbose=True)\n",
    "        self.memory = ConversationBufferMemory(return_messages=True)\n",
    "        self.init_prompt_msgs = init_prompt.messages\n",
    "        self.ai_prompt_questions = {\n",
    "            \"ingredients\": self.init_prompt_msgs[1],\n",
    "            \"allergies\": self.init_prompt_msgs[3],\n",
    "            \"recipe_open_params\": self.init_prompt_msgs[5],\n",
    "        }\n",
    "\n",
    "    def respond(self, user_msg, chat_history):\n",
    "        response = self._get_bot_response(user_msg, chat_history)\n",
    "        chat_history.append((user_msg, response))\n",
    "        return \"\", chat_history\n",
    "\n",
    "    def init_conversation(self, formatted_chat_prompt):\n",
    "        self.conversation = ConversationChain(\n",
    "            llm=self.chat,\n",
    "            memory=self.memory,\n",
    "            prompt=formatted_chat_prompt,\n",
    "            verbose=True,\n",
    "        )\n",
    "\n",
    "    def reset(self):\n",
    "        self.memory.clear()\n",
    "\n",
    "    def _get_bot_response(self, user_msg: str, chat_history) -> str:\n",
    "        if len(chat_history) < 2:\n",
    "            return self.ai_prompt_questions[\"allergies\"].prompt.template\n",
    "\n",
    "        if len(chat_history) < 3:\n",
    "            return self.ai_prompt_questions[\"recipe_open_params\"].prompt.template\n",
    "\n",
    "        if len(chat_history) < 4:\n",
    "            user = 0\n",
    "            ai = 1\n",
    "            user_msgs = [msg_pair[user] for msg_pair in chat_history[1:]]\n",
    "            f_init_prompt = init_prompt.format_prompt(\n",
    "                ingredients=user_msgs[0],\n",
    "                allergies=user_msgs[1],\n",
    "                recipe_freeform_input=user_msg,\n",
    "            )\n",
    "            chat_msgs = f_init_prompt.to_messages()\n",
    "            results = self.chat.generate([chat_msgs])\n",
    "            chat_msgs.extend(\n",
    "                [\n",
    "                    results.generations[0][0].message,\n",
    "                    MessagesPlaceholder(variable_name=\"history\"),\n",
    "                    HumanMessagePromptTemplate.from_template(\"{input}\"),\n",
    "                ]\n",
    "            )\n",
    "            open_prompt = ChatPromptTemplate.from_messages(chat_msgs)\n",
    "            # prepare the open conversation chain from this point\n",
    "            self.init_conversation(open_prompt)\n",
    "            return results.generations[0][0].message.content\n",
    "\n",
    "        response = self.conversation.predict(input=user_msg)\n",
    "        return response"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#| export\n",
    "def launch_demo():\n",
    "    with gr.Blocks() as demo:\n",
    "        bot = ConversationBot()\n",
    "        chatbot = gr.Chatbot(\n",
    "            value=[(None, bot.ai_prompt_questions[\"ingredients\"].prompt.template)]\n",
    "        )\n",
    "\n",
    "        msg = gr.Textbox()\n",
    "        clear = gr.Button(\"Clear\")\n",
    "\n",
    "        msg.submit(\n",
    "            fn=bot.respond, inputs=[msg, chatbot], outputs=[msg, chatbot], queue=False\n",
    "        )\n",
    "        clear.click(lambda: None, None, chatbot, queue=False).then(bot.reset)\n",
    "        demo.launch(\n",
    "            auth=(\n",
    "                os.environ[\"GRADIO_DEMO_USERNAME\"],\n",
    "                os.environ[\"GRADIO_DEMO_PASSWORD\"],\n",
    "            )\n",
    "        )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load environment for the demo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dotenv import load_dotenv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running on local URL:  http://127.0.0.1:7862\n",
      "\n",
      "To create a public link, set `share=True` in `launch()`.\n"
     ]
    }
   ],
   "source": [
    "load_dotenv()\n",
    "launch_demo()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#| hide\n",
    "import nbdev\n",
    "\n",
    "nbdev.nbdev_export()"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "python3",
   "language": "python",
   "name": "python3"
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 "nbformat": 4,
 "nbformat_minor": 4
}