examples/chart_conversation_chain.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Install and Import dependencies\n",
    "\n",
    "Get started with installing the necessary libraries ```pip install -U picachain openai transformers```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys \n",
    "sys.path.append(\"/home/home/dev/picachain/\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/home/dev/picachain/.venv/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from picachain.chains.unstructured.charts import ChartConversationChain\n",
    "from dotenv import load_dotenv\n",
    "load_dotenv()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Use ChartConversationChain to build a Q&A chain "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's view the chart first"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=514x230>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# importing modules \n",
    "import urllib \n",
    "from PIL import Image \n",
    "from io import BytesIO\n",
    "img_url = \"https://docs.tibco.com/pub/spotfire/7.0.1/doc/html/images/bar_chart_example2.png\"\n",
    "\n",
    "# Read the image from the URL using the io module\n",
    "with urllib.request.urlopen(img_url) as url_res:\n",
    "   img_data = url_res.read()\n",
    "\n",
    "img = Image.open(BytesIO(img_data))\n",
    "img\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Initiate the chain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'The total fruit sales in 2011 are 756.'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ccc = ChartConversationChain(\n",
    "    chart=img\n",
    ")\n",
    "ccc.run(query=\"What is the total fruit sales in 2011?\")"
   ]
  }
 ],
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