{
 "cells": [
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "---\n",
    "description: Gradio app.py\n",
    "output-file: app.html\n",
    "title: app\n",
    "\n",
    "---\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! -->"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "language": "python"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#| eval: false\n",
    "load_dotenv()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[GPT4 streaming output example on hugging face 🤗](https://huggingface.co/spaces/ysharma/ChatGPT4/blob/main/app.pyhttps://huggingface.co/spaces/ysharma/ChatGPT4/blob/main/app.py)  \n",
    "[Gradio lite let's you insert Gradio app in browser JS](https://www.gradio.app/guides/gradio-litehttps://www.gradio.app/guides/gradio-lite)  \n",
    "[Streaming output](https://www.gradio.app/main/guides/streaming-outputshttps://www.gradio.app/main/guides/streaming-outputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "language": "python"
   },
   "outputs": [],
   "source": [
    "#| eval: false\n",
    "client = OpenAI()\n",
    "assistant = client.beta.assistants.create(\n",
    "    name=\"Vegan Recipe Finder\",\n",
    "    instructions=SYSTEM_PROMPT,\n",
    "    # + \"\\nChoose the best single matching recipe to the user's query out of the vegan recipe search returned recipes\",\n",
    "    model=\"gpt-4o\",\n",
    "    tools=[VEGAN_RECIPE_SEARCH_TOOL_SCHEMA],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "language": "python"
   },
   "outputs": [],
   "source": [
    "class EventHandler(AssistantEventHandler):\n",
    "    @override\n",
    "    def on_event(self, event):\n",
    "        # Retrieve events that are denoted with 'requires_action'\n",
    "        # since these will have our tool_calls\n",
    "        if event.event == \"thread.run.requires_action\":\n",
    "            run_id = event.data.id  # Retrieve the run ID from the event data\n",
    "            self.handle_requires_action(event.data, run_id)\n",
    "\n",
    "    def handle_requires_action(self, data, run_id):\n",
    "        tool_outputs = []\n",
    "        for tool_call in data.required_action.submit_tool_outputs.tool_calls:\n",
    "            if tool_call.function.name == \"vegan_recipe_edamam_search\":\n",
    "                fn_args = json.loads(tool_call.function.arguments)\n",
    "                data = vegan_recipe_edamam_search(\n",
    "                    query=fn_args.get(\"query\"),\n",
    "                )\n",
    "                tool_outputs.append({\"tool_call_id\": tool_call.id, \"output\": data})\n",
    "\n",
    "        self.submit_tool_outputs(tool_outputs, run_id)\n",
    "\n",
    "    def submit_tool_outputs(self, tool_outputs, run_id):\n",
    "        client.beta.threads.runs.submit_tool_outputs_stream(\n",
    "            thread_id=self.current_run.thread_id,\n",
    "            run_id=self.current_run.id,\n",
    "            tool_outputs=tool_outputs,\n",
    "            event_handler=EventHandler(),\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "---\n",
       "\n",
       "[source](https://gitlab.com/animalequality/lv-recipe-chatbot/blob/main/lv_recipe_chatbot/app.py#L22){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
       "\n",
       "### handle_requires_action\n",
       "\n",
       ">      handle_requires_action (data)"
      ],
      "text/plain": [
       "---\n",
       "\n",
       "[source](https://gitlab.com/animalequality/lv-recipe-chatbot/blob/main/lv_recipe_chatbot/app.py#L22){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
       "\n",
       "### handle_requires_action\n",
       "\n",
       ">      handle_requires_action (data)"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#| echo: false\n",
    "#| output: asis\n",
    "show_doc(handle_requires_action)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "language": "python"
   },
   "outputs": [],
   "source": [
    "def run_conversation() -> str:\n",
    "    run = client.beta.threads.runs.create_and_poll(\n",
    "        thread_id=thread.id,\n",
    "        assistant_id=assistant.id,\n",
    "    )\n",
    "    while True:\n",
    "        tool_outputs = []\n",
    "        tool_calls = (\n",
    "            []\n",
    "            if not run.required_action\n",
    "            else run.required_action.submit_tool_outputs.tool_calls\n",
    "        )\n",
    "\n",
    "        for tool_call in tool_calls:\n",
    "            if tool_call.function.name == \"vegan_recipe_edamam_search\":\n",
    "                fn_args = json.loads(tool_call.function.arguments)\n",
    "                data = vegan_recipe_edamam_search(\n",
    "                    query=fn_args.get(\"query\"),\n",
    "                )\n",
    "                tool_outputs.append({\"tool_call_id\": tool_call.id, \"output\": data})\n",
    "\n",
    "        if tool_outputs:\n",
    "            try:\n",
    "                run = client.beta.threads.runs.submit_tool_outputs_and_poll(\n",
    "                    thread_id=thread.id,\n",
    "                    run_id=run.id,\n",
    "                    tool_outputs=tool_outputs,\n",
    "                )\n",
    "                print(\"Tool outputs submitted successfully.\")\n",
    "\n",
    "            except Exception as e:\n",
    "                print(\"Failed to submit tool outputs:\", e)\n",
    "                return \"Sorry failed to run tools. Try again with a different query.\"\n",
    "\n",
    "        if run.status == \"completed\":\n",
    "            messages = client.beta.threads.messages.list(thread_id=thread.id)\n",
    "            data = messages.data\n",
    "            content = data[0].content\n",
    "            return content[0].text.value\n",
    "        time.sleep(0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "---\n",
       "\n",
       "[source](https://gitlab.com/animalequality/lv-recipe-chatbot/blob/main/lv_recipe_chatbot/app.py#L34){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
       "\n",
       "### run_convo_stream\n",
       "\n",
       ">      run_convo_stream (thread, content:str, client:openai.OpenAI, assistant)"
      ],
      "text/plain": [
       "---\n",
       "\n",
       "[source](https://gitlab.com/animalequality/lv-recipe-chatbot/blob/main/lv_recipe_chatbot/app.py#L34){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
       "\n",
       "### run_convo_stream\n",
       "\n",
       ">      run_convo_stream (thread, content:str, client:openai.OpenAI, assistant)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#| echo: false\n",
    "#| output: asis\n",
    "show_doc(run_convo_stream)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "language": "python"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "skip\n"
     ]
    }
   ],
   "source": [
    "thread = client.beta.threads.create()\n",
    "\n",
    "test_msgs = [\n",
    "    \"Hello\",\n",
    "    \"What can I make with tempeh, whole wheat bread, and lettuce?\",\n",
    "]\n",
    "for m in test_msgs:\n",
    "    for txt in run_convo_stream(thread, m, client, assistant):\n",
    "        print(txt, end=\"\")\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "---\n",
       "\n",
       "[source](https://gitlab.com/animalequality/lv-recipe-chatbot/blob/main/lv_recipe_chatbot/app.py#L62){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
       "\n",
       "### predict\n",
       "\n",
       ">      predict (message, history, client:openai.OpenAI, assistant, thread)"
      ],
      "text/plain": [
       "---\n",
       "\n",
       "[source](https://gitlab.com/animalequality/lv-recipe-chatbot/blob/main/lv_recipe_chatbot/app.py#L62){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
       "\n",
       "### predict\n",
       "\n",
       ">      predict (message, history, client:openai.OpenAI, assistant, thread)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#| echo: false\n",
    "#| output: asis\n",
    "show_doc(predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "---\n",
       "\n",
       "[source](https://gitlab.com/animalequality/lv-recipe-chatbot/blob/main/lv_recipe_chatbot/app.py#L102){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
       "\n",
       "### create_demo\n",
       "\n",
       ">      create_demo (client:openai.OpenAI, assistant)"
      ],
      "text/plain": [
       "---\n",
       "\n",
       "[source](https://gitlab.com/animalequality/lv-recipe-chatbot/blob/main/lv_recipe_chatbot/app.py#L102){target=\"_blank\" style=\"float:right; font-size:smaller\"}\n",
       "\n",
       "### create_demo\n",
       "\n",
       ">      create_demo (client:openai.OpenAI, assistant)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#| echo: false\n",
    "#| output: asis\n",
    "show_doc(create_demo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "language": "python"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "skip\n"
     ]
    }
   ],
   "source": [
    "if \"demo\" in globals():\n",
    "    demo.close()\n",
    "\n",
    "demo = create_demo(client, assistant)\n",
    "demo.launch()"
   ]
  }
 ],
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