{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1ca7b759",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install -q git+https://github.com/srush/MiniChain\n",
    "!git clone https://github.com/srush/MiniChain; cp -fr MiniChain/examples/* . "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cc3f2a69",
   "metadata": {
    "lines_to_next_cell": 0,
    "tags": [
     "hide_inp"
    ]
   },
   "outputs": [],
   "source": [
    "desc = \"\"\"\n",
    "### Chat\n",
    "\n",
    "A chat-like example for multi-turn chat with state. [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/srush/MiniChain/blob/master/examples/chat.ipynb)\n",
    "\n",
    "(Adapted from [LangChain](https://langchain.readthedocs.io/en/latest/modules/memory/examples/chatgpt_clone.html)'s version of this [blog post](https://www.engraved.blog/building-a-virtual-machine-inside/).)\n",
    "\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1ca0a1eb",
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f4ac1417",
   "metadata": {},
   "outputs": [],
   "source": [
    "from dataclasses import dataclass, replace\n",
    "from typing import List, Tuple\n",
    "from minichain import OpenAI, prompt, show"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56c8ab3d",
   "metadata": {},
   "source": [
    "Generic stateful Memory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7c3ffeaa",
   "metadata": {
    "lines_to_next_cell": 1
   },
   "outputs": [],
   "source": [
    "MEMORY = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c309a94f",
   "metadata": {
    "lines_to_next_cell": 1
   },
   "outputs": [],
   "source": [
    "@dataclass\n",
    "class State:\n",
    "    memory: List[Tuple[str, str]]\n",
    "    human_input: str = \"\"\n",
    "\n",
    "    def push(self, response: str) -> \"State\":\n",
    "        memory = self.memory if len(self.memory) < MEMORY else self.memory[1:]\n",
    "        return State(memory + [(self.human_input, response)])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c9a82f1",
   "metadata": {},
   "source": [
    "Chat prompt with memory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "279179dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "@prompt(OpenAI(), template_file=\"chat.pmpt.tpl\")\n",
    "def chat_prompt(model, state: State) -> State:\n",
    "    out = model(state)\n",
    "    result = out.split(\"Assistant:\")[-1]\n",
    "    return state.push(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2d94b22d",
   "metadata": {},
   "outputs": [],
   "source": [
    "examples = [\n",
    "    \"ls ~\",\n",
    "    \"cd ~\",\n",
    "    \"{Please make a file jokes.txt inside and put some jokes inside}\",\n",
    "    \"\"\"echo -e \"x=lambda y:y*5+3;print('Result:' + str(x(6)))\" > run.py && python3 run.py\"\"\",\n",
    "    \"\"\"echo -e \"print(list(filter(lambda x: all(x%d for d in range(2,x)),range(2,3**10)))[:10])\" > run.py && python3 run.py\"\"\",\n",
    "    \"\"\"echo -e \"echo 'Hello from Docker\" > entrypoint.sh && echo -e \"FROM ubuntu:20.04\\nCOPY entrypoint.sh entrypoint.sh\\nENTRYPOINT [\\\"/bin/sh\\\",\\\"entrypoint.sh\\\"]\">Dockerfile && docker build . -t my_docker_image && docker run -t my_docker_image\"\"\",\n",
    "    \"nvidia-smi\"\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d77406cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "gradio = show(lambda command, state: chat_prompt(replace(state, human_input=command)),\n",
    "              initial_state=State([]),\n",
    "              subprompts=[chat_prompt],\n",
    "              examples=examples,\n",
    "              out_type=\"json\",\n",
    "              description=desc,\n",
    ")\n",
    "if __name__ == \"__main__\":\n",
    "    gradio.launch()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bd255c7b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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