Sentiment_Analysis_App_Deploy.ipynb

{
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  "metadata": {
    "colab": {
      "name": "Sentiment Analysis App Deploy.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "mount_file_id": "1M_Dqq_EzXeWIIpfNqIbYLwZ2m0ko0Hck",
      "authorship_tag": "ABX9TyPcfAP271SOeaeLr7DmJRrV",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/poornaditya1/Sentiment-Analysis-Machine-Learning-Model/blob/master/Sentiment_Analysis_App_Deploy.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dC0GBLdrWcbT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "aad08903-0c09-4222-bf2e-097f4414c375"
      },
      "source": [
        "pip install streamlit"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting streamlit\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/7a/95/c1f097bfd0ea06f97d02e09e6e0af9bfa4da2c1e761112d5916bfd3bf846/streamlit-0.65.2-py2.py3-none-any.whl (7.2MB)\n",
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            "Collecting enum-compat\n",
            "  Downloading https://files.pythonhosted.org/packages/55/ae/467bc4509246283bb59746e21a1a2f5a8aecbef56b1fa6eaca78cd438c8b/enum_compat-0.0.3-py3-none-any.whl\n",
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            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/1b/51/e2a9f3b757eb802f61dc1f2b09c8c99f6eb01cf06416c0671253536517b6/blinker-1.4.tar.gz (111kB)\n",
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            "Collecting watchdog\n",
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            "Collecting pathtools>=0.1.1\n",
            "  Downloading https://files.pythonhosted.org/packages/e7/7f/470d6fcdf23f9f3518f6b0b76be9df16dcc8630ad409947f8be2eb0ed13a/pathtools-0.1.2.tar.gz\n",
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            "Collecting ipykernel>=5.1.2; python_version >= \"3.4\"\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/52/19/c2812690d8b340987eecd2cbc18549b1d130b94c5d97fcbe49f5f8710edf/ipykernel-5.3.4-py3-none-any.whl (120kB)\n",
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            "Requirement already satisfied: nbconvert in /usr/local/lib/python3.6/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->pydeck>=0.1.dev5->streamlit) (5.6.1)\n",
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            "Requirement already satisfied: testpath in /usr/local/lib/python3.6/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->pydeck>=0.1.dev5->streamlit) (0.4.4)\n",
            "Requirement already satisfied: webencodings in /usr/local/lib/python3.6/dist-packages (from bleach->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.5.0->ipywidgets>=7.0.0->pydeck>=0.1.dev5->streamlit) (0.5.1)\n",
            "Building wheels for collected packages: blinker, watchdog, pathtools\n",
            "  Building wheel for blinker (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for blinker: filename=blinker-1.4-cp36-none-any.whl size=13450 sha256=5da04824d3d93496f6c6accd64b8e110caa5148eed0bace792a2692e4fe0bbb8\n",
            "  Stored in directory: /root/.cache/pip/wheels/92/a0/00/8690a57883956a301d91cf4ec999cc0b258b01e3f548f86e89\n",
            "  Building wheel for watchdog (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for watchdog: filename=watchdog-0.10.3-cp36-none-any.whl size=73873 sha256=30ee35a189f739da4a1e54b06a03ea175954f0bd176a07fbf00895b4c01d7bb5\n",
            "  Stored in directory: /root/.cache/pip/wheels/a8/1d/38/2c19bb311f67cc7b4d07a2ec5ea36ab1a0a0ea50db994a5bc7\n",
            "  Building wheel for pathtools (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pathtools: filename=pathtools-0.1.2-cp36-none-any.whl size=8785 sha256=c7b4fbccfddd0c2e89e7a7af71119ff3c6f46b5744fee431474c177f7ec0ebbd\n",
            "  Stored in directory: /root/.cache/pip/wheels/0b/04/79/c3b0c3a0266a3cb4376da31e5bfe8bba0c489246968a68e843\n",
            "Successfully built blinker watchdog pathtools\n",
            "\u001b[31mERROR: google-colab 1.0.0 has requirement ipykernel~=4.10, but you'll have ipykernel 5.3.4 which is incompatible.\u001b[0m\n",
            "Installing collected packages: base58, validators, enum-compat, blinker, pathtools, watchdog, ipykernel, pydeck, streamlit\n",
            "  Found existing installation: ipykernel 4.10.1\n",
            "    Uninstalling ipykernel-4.10.1:\n",
            "      Successfully uninstalled ipykernel-4.10.1\n",
            "Successfully installed base58-2.0.1 blinker-1.4 enum-compat-0.0.3 ipykernel-5.3.4 pathtools-0.1.2 pydeck-0.5.0b1 streamlit-0.65.2 validators-0.18.0 watchdog-0.10.3\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "ipykernel"
                ]
              }
            }
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aCFkpGRaaotc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "outputId": "67a31210-e199-4548-8b13-979f83cc21ca"
      },
      "source": [
        "!pip install pyngrok"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting pyngrok\n",
            "  Downloading https://files.pythonhosted.org/packages/05/4a/d16a3337672b5ca3ce5d67be9d26a62aecd4479785099b0a8ad40167e115/pyngrok-4.1.10.tar.gz\n",
            "Requirement already satisfied: future in /usr/local/lib/python3.6/dist-packages (from pyngrok) (0.16.0)\n",
            "Requirement already satisfied: PyYAML in /usr/local/lib/python3.6/dist-packages (from pyngrok) (3.13)\n",
            "Building wheels for collected packages: pyngrok\n",
            "  Building wheel for pyngrok (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pyngrok: filename=pyngrok-4.1.10-cp36-none-any.whl size=16378 sha256=c5e66258bf890ddd26d380ef48f588c59fca4e6f9a6d6525cdd6baf7c535584e\n",
            "  Stored in directory: /root/.cache/pip/wheels/0d/7f/06/e8079dd6f80eb6683fac47c4e1cb720b61cd82d6e4c13d87a5\n",
            "Successfully built pyngrok\n",
            "Installing collected packages: pyngrok\n",
            "Successfully installed pyngrok-4.1.10\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MiKYiaOBawxv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "c1928c51-af79-451b-c2d8-e0785c9b4089"
      },
      "source": [
        "%%writefile ML-app.py\n",
        "import streamlit as st\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.naive_bayes import MultinomialNB\n",
        "import pandas as pd\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix,classification_report\n",
        "\n",
        "\n",
        "df = pd.read_csv('/content/drive/My Drive/ML/Project/Amazon Review.csv')\n",
        "df1 = df.dropna(subset=['reviews.text'])\n",
        "df1 = df1.dropna(subset=['reviews.rating'])\n",
        "X = df1.iloc[:,16].values\n",
        "y = df1.iloc[:,14].values\n",
        "import numpy as np\n",
        "Y=[0] * (len(y))\n",
        "for i in range(0,(len(y))):\n",
        "  if y[i]==1 or y[i]==2 or y[i]==3:\n",
        "    Y[i] = 0              # Negative sentiments\n",
        "  else:\n",
        "    Y[i] = 1              # Positive sentiments\n",
        "text_model = Pipeline([('tfidf',TfidfVectorizer(binary = True,max_df=0.611111111111111,norm = 'l2')),('model',MultinomialNB(alpha = 0.058,fit_prior=True))])\n",
        "\n",
        "text_model.fit(X,Y)\n",
        "\n",
        "def u_in():\n",
        "  text = st.text_input(\"Enter your review: \")\n",
        "  a = []\n",
        "  a.append(text)\n",
        "  return a\n",
        "\n",
        "st.title(\"ML Project\")\n",
        "df = u_in()\n",
        "pred = text_model.predict(df)\n",
        "if pred == 0:\n",
        "  st.write(\"Negative Sentiment\")\n",
        "else:\n",
        "  st.write(\"Positive Sentiment\")\n",
        "\n"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Overwriting ML-app.py\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SwI4wgb-d201",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "18c24730-9a7d-4e47-9adf-44ccbe755875"
      },
      "source": [
        "from pyngrok import ngrok\n",
        "ngrok.connect(port='8501')"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'http://73d13cfeba57.ngrok.io'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0SbiA6Kvd5uW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 346
        },
        "outputId": "ef5b7d6d-f975-4a0c-f1d7-3ab8d9efc7e1"
      },
      "source": [
        "!streamlit run ML-app.py"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[0m\n",
            "\u001b[34m\u001b[1m  You can now view your Streamlit app in your browser.\u001b[0m\n",
            "\u001b[0m\n",
            "\u001b[34m  Network URL: \u001b[0m\u001b[1mhttp://172.28.0.2:8501\u001b[0m\n",
            "\u001b[34m  External URL: \u001b[0m\u001b[1mhttp://104.199.190.42:8501\u001b[0m\n",
            "\u001b[0m\n",
            "/usr/lib/python3.6/threading.py:864: DtypeWarning: Columns (1,10) have mixed types.Specify dtype option on import or set low_memory=False.\n",
            "  self._target(*self._args, **self._kwargs)\n",
            "/usr/lib/python3.6/threading.py:864: DtypeWarning: Columns (1,10) have mixed types.Specify dtype option on import or set low_memory=False.\n",
            "  self._target(*self._args, **self._kwargs)\n",
            "/usr/lib/python3.6/threading.py:864: DtypeWarning: Columns (1,10) have mixed types.Specify dtype option on import or set low_memory=False.\n",
            "  self._target(*self._args, **self._kwargs)\n",
            "/usr/lib/python3.6/threading.py:864: DtypeWarning: Columns (1,10) have mixed types.Specify dtype option on import or set low_memory=False.\n",
            "  self._target(*self._args, **self._kwargs)\n",
            "/usr/lib/python3.6/threading.py:864: DtypeWarning: Columns (1,10) have mixed types.Specify dtype option on import or set low_memory=False.\n",
            "  self._target(*self._args, **self._kwargs)\n",
            "\u001b[34m  Stopping...\u001b[0m\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v_9AJ1qMd9Bv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}