Memorize_DB.ipynb

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    "language_info": {
      "name": "python"
    }
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  "cells": [
    {
      "cell_type": "code",
      "source": [
        "!pip install pandasql\n"
      ],
      "metadata": {
        "id": "3vpB5U5y8MOh",
        "outputId": "82c0bb60-115b-4f23-85bd-e23515244c78",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: pandasql in /usr/local/lib/python3.8/dist-packages (0.7.3)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.8/dist-packages (from pandasql) (1.3.5)\n",
            "Requirement already satisfied: sqlalchemy in /usr/local/lib/python3.8/dist-packages (from pandasql) (1.4.46)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.8/dist-packages (from pandasql) (1.21.6)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.8/dist-packages (from pandas->pandasql) (2022.7)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.8/dist-packages (from pandas->pandasql) (2.8.2)\n",
            "Requirement already satisfied: greenlet!=0.4.17 in /usr/local/lib/python3.8/dist-packages (from sqlalchemy->pandasql) (2.0.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.8/dist-packages (from python-dateutil>=2.7.3->pandas->pandasql) (1.15.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IrnNziij7hFe"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "from fuzzywuzzy import fuzz\n",
        "from fuzzywuzzy import process\n",
        "import os\n",
        "import openai\n",
        "\n",
        "df=pd.read_csv('/content/color_srgb.csv')\n",
        "df2 = pd.read_csv(\"/content/data-book.csv\", sep=\";\")\n",
        "df2=df2[df2.columns[:-1]]\n",
        "df3 = pd.read_csv(\"/content/data-company.csv\", sep=\",\")\n",
        "df4 = pd.read_csv(\"/content/data-person.csv\", sep=\";\")\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "def match_value(df,column1, match_string, return_column):\n",
        "    match = process.extractOne(match_string, df[column1], scorer=fuzz.token_set_ratio, score_cutoff=50)\n",
        "    if match:\n",
        "        return df.loc[df[column1] == match[0], return_column].iloc[0]\n",
        "    else:\n",
        "        return None\n"
      ],
      "metadata": {
        "id": "HztEpAlX8QpV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "api_key =\"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\"\n",
        "openai.api_key = api_key\n",
        "print(\"Question : \")\n",
        "a=input()\n",
        "response = openai.Completion.create(\n",
        "  model=\"text-davinci-003\",\n",
        "  prompt=\"give me only df#inforamtion column#real inforamtion#asking\\nBook Csv: Title;Author;Genre;Height;Publisher; -> df2\\nCompany: Serial Number, Company Name, Employee Markme, Description,Leave -> df3\\nColor: Name, HEX,RGB -> df\\nPerson: Index;User Id;First Name;Last Name;Sex;Email;Phone;Date of birth;Job Title -> df4\\n\\nQ: What is the name color with RGB(100,100,100)\\nA: df#RGB#RGB(100,100,100)#Name\\n\\nQ: What is the height of the book published by Wiley\\nA:  df2#Publisher#Wiley#height\\n\\nQ:\"+a+\" \\nA:\",\n",
        "  temperature=0.7,\n",
        "  max_tokens=256,\n",
        "  top_p=1,\n",
        "  frequency_penalty=0,\n",
        "  presence_penalty=0\n",
        ")\n",
        "if len(response.choices[0].text.split(\"#\"))==4:\n",
        "  try:\n",
        "    tempdef,infotb,info,requir=response.choices[0].text.split(\"#\")\n",
        "    tempdef=eval(tempdef)\n",
        "    print(match_value(tempdef, infotb, info, requir))\n",
        "  except:\n",
        "    print(\"No info Provided\")\n",
        "else:\n",
        "  print(\"Please Provide the Full Information\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uMcgZ8dr8aRK",
        "outputId": "08de2444-03ca-4723-dd6e-bc34e5667579"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Question : \n",
            "Who's the author of the book title superfreakonomics\n",
            "Dubner, Stephen\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(response.choices[0].text.split(\",\"))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IcfVe3IMc7GY",
        "outputId": "ad5f3674-8e29-40f4-e802-4e248c0fa6bf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6"
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "match_string=\"Guswami\"\n",
        "column1=\"Author\"\n",
        "match = process.extractOne(match_string, tempdef[column1], scorer=fuzz.token_set_ratio, score_cutoff=50)\n",
        "print(match)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uP8cLWqrTwe2",
        "outputId": "cccbca82-997c-43a3-f5b4-e752e985f8b0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "('Goswami, Jaideva', 55, 0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tempdef=eval(tempdef)\n"
      ],
      "metadata": {
        "id": "_xq6CkX5VOeO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tempdef"
      ],
      "metadata": {
        "id": "SN5biRcQYLTO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "FMOtXY3lYP00"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}