(0.1)_Update_Chat_with_Nishat_AI.ipynb

{
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  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyMwIRjZcwIrc+YTkTm1N6xJ",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/NishatVasker/Machine_learning_projects/blob/main/(0.1)_Update_Chat_with_Nishat_AI.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "M1A6rpgUc_VF",
        "outputId": "459440b1-60ea-4bee-fc96-33ac14e29b38"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting openai\n",
            "  Downloading openai-0.26.3.tar.gz (55 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m55.5/55.5 KB\u001b[0m \u001b[31m2.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
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            "Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /usr/local/lib/python3.8/dist-packages (from aiohttp->openai) (4.0.2)\n",
            "Building wheels for collected packages: openai\n",
            "  Building wheel for openai (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for openai: filename=openai-0.26.3-py3-none-any.whl size=67489 sha256=ce02247e69789fcb771b7c74d442490e0cf525218fe6972fe58b978cbaa1e3d6\n",
            "  Stored in directory: /root/.cache/pip/wheels/fa/6b/ac/76fa8f79ea603d4bd896c0d185e970a7dbfab23df6bb14c351\n",
            "Successfully built openai\n",
            "Installing collected packages: openai\n",
            "Successfully installed openai-0.26.3\n"
          ]
        }
      ],
      "source": [
        "pip install openai"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import openai\n",
        "\n",
        "openai.api_key = (\"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\") # replace you API key\n",
        "\n",
        "response = openai.Completion.create(\n",
        "  model=\"text-davinci-003\",\n",
        "  prompt=input(\"enter your question: \"),\n",
        "  temperature=0.7,\n",
        "  max_tokens=4000,\n",
        "  top_p=1,\n",
        "  frequency_penalty=0,\n",
        "  presence_penalty=0\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "0PGIHelGc_9a",
        "outputId": "e31b69b0-67ba-4fc1-dc32-c5109aee04b8"
      },
      "execution_count": 5,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "enter your question: how are you?\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(response['choices'][0]['text'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "WDP-7q9BdAAu",
        "outputId": "29c2e89f-9df2-40a5-dbd9-aa5f80f42228"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "I'm doing well, thank you! How about you?\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(response)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "fqovX0--dGGl",
        "outputId": "bb1e3176-bfd5-43ec-d097-d3a153aa709e"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{\n",
            "  \"choices\": [\n",
            "    {\n",
            "      \"finish_reason\": \"stop\",\n",
            "      \"index\": 0,\n",
            "      \"logprobs\": null,\n",
            "      \"text\": \"\\n\\nI'm doing well, thank you! How about you?\"\n",
            "    }\n",
            "  ],\n",
            "  \"created\": 1674677230,\n",
            "  \"id\": \"cmpl-6cgL0EM4bqkVrcMyFGgmngWrZ4orr\",\n",
            "  \"model\": \"text-davinci-003\",\n",
            "  \"object\": \"text_completion\",\n",
            "  \"usage\": {\n",
            "    \"completion_tokens\": 14,\n",
            "    \"prompt_tokens\": 4,\n",
            "    \"total_tokens\": 18\n",
            "  }\n",
            "}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "     ########## 1ST VERSION (1st priority)\n",
        "\n",
        "\n",
        "!pip install gradio\n",
        "import gradio as gr\n",
        "\n",
        "def openai_response(prompt):\n",
        "    openai.api_key = (\"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\")\n",
        "    response = openai.Completion.create(\n",
        "        model=\"text-davinci-003\",\n",
        "        prompt=prompt,\n",
        "        temperature=0.7,\n",
        "        max_tokens=4000,\n",
        "        top_p=1,\n",
        "        frequency_penalty=0,\n",
        "        presence_penalty=0\n",
        "    )\n",
        "    return response['choices'][0]['text']\n",
        "\n",
        "input_text = gr.inputs.Textbox(label=\"Enter your question:\")\n",
        "output_text = gr.outputs.Textbox(label=\"Output:\")\n",
        "\n",
        "\n",
        "gr.Interface(openai_response, input_text, output_text).launch()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1797
        },
        "id": "7eKdnfvFi0XU",
        "outputId": "de0af19a-c70e-46ff-ec9d-d04779b6ac97"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
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          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/gradio/inputs.py:27: UserWarning: Usage of gradio.inputs is deprecated, and will not be supported in the future, please import your component from gradio.components\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.8/dist-packages/gradio/deprecation.py:40: UserWarning: `optional` parameter is deprecated, and it has no effect\n",
            "  warnings.warn(value)\n",
            "/usr/local/lib/python3.8/dist-packages/gradio/deprecation.py:40: UserWarning: `numeric` parameter is deprecated, and it has no effect\n",
            "  warnings.warn(value)\n",
            "/usr/local/lib/python3.8/dist-packages/gradio/outputs.py:22: UserWarning: Usage of gradio.outputs is deprecated, and will not be supported in the future, please import your components from gradio.components\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n",
            "Note: opening Chrome Inspector may crash demo inside Colab notebooks.\n",
            "\n",
            "To create a public link, set `share=True` in `launch()`.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "(async (port, path, width, height, cache, element) => {\n",
              "                        if (!google.colab.kernel.accessAllowed && !cache) {\n",
              "                            return;\n",
              "                        }\n",
              "                        element.appendChild(document.createTextNode(''));\n",
              "                        const url = await google.colab.kernel.proxyPort(port, {cache});\n",
              "\n",
              "                        const external_link = document.createElement('div');\n",
              "                        external_link.innerHTML = `\n",
              "                            <div style=\"font-family: monospace; margin-bottom: 0.5rem\">\n",
              "                                Running on <a href=${new URL(path, url).toString()} target=\"_blank\">\n",
              "                                    https://localhost:${port}${path}\n",
              "                                </a>\n",
              "                            </div>\n",
              "                        `;\n",
              "                        element.appendChild(external_link);\n",
              "\n",
              "                        const iframe = document.createElement('iframe');\n",
              "                        iframe.src = new URL(path, url).toString();\n",
              "                        iframe.height = height;\n",
              "                        iframe.allow = \"autoplay; camera; microphone; clipboard-read; clipboard-write;\"\n",
              "                        iframe.width = width;\n",
              "                        iframe.style.border = 0;\n",
              "                        element.appendChild(iframe);\n",
              "                    })(7864, \"/\", \"100%\", 500, false, window.element)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": []
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "     ########## 2ND VERSION\n",
        "\n",
        "\n",
        "\n",
        "import gradio as gr\n",
        "\n",
        "def my_model(prompt):\n",
        "    openai.api_key = (\"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\") # replace you API key\n",
        "    response = openai.Completion.create(\n",
        "    model=\"text-davinci-003\",\n",
        "    prompt=prompt,\n",
        "    temperature=0.7,\n",
        "    max_tokens=4000,\n",
        "    top_p=1,\n",
        "    frequency_penalty=0,\n",
        "    presence_penalty=0\n",
        "    )\n",
        "    return response['choices'][0]['text']\n",
        "\n",
        "gr.Interface(fn=my_model, inputs=\"text\", outputs=\"text\").launch()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 616
        },
        "id": "5OPKei2Bp8ie",
        "outputId": "3b779c0c-0b8e-4eaa-f640-a226922ca2c4"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n",
            "Note: opening Chrome Inspector may crash demo inside Colab notebooks.\n",
            "\n",
            "To create a public link, set `share=True` in `launch()`.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "(async (port, path, width, height, cache, element) => {\n",
              "                        if (!google.colab.kernel.accessAllowed && !cache) {\n",
              "                            return;\n",
              "                        }\n",
              "                        element.appendChild(document.createTextNode(''));\n",
              "                        const url = await google.colab.kernel.proxyPort(port, {cache});\n",
              "\n",
              "                        const external_link = document.createElement('div');\n",
              "                        external_link.innerHTML = `\n",
              "                            <div style=\"font-family: monospace; margin-bottom: 0.5rem\">\n",
              "                                Running on <a href=${new URL(path, url).toString()} target=\"_blank\">\n",
              "                                    https://localhost:${port}${path}\n",
              "                                </a>\n",
              "                            </div>\n",
              "                        `;\n",
              "                        element.appendChild(external_link);\n",
              "\n",
              "                        const iframe = document.createElement('iframe');\n",
              "                        iframe.src = new URL(path, url).toString();\n",
              "                        iframe.height = height;\n",
              "                        iframe.allow = \"autoplay; camera; microphone; clipboard-read; clipboard-write;\"\n",
              "                        iframe.width = width;\n",
              "                        iframe.style.border = 0;\n",
              "                        element.appendChild(iframe);\n",
              "                    })(7866, \"/\", \"100%\", 500, false, window.element)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": []
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "_QKa25RYr9jl"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}