Welcome_To_Colaboratory.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/souragc/Multiuserchat/blob/master/Welcome_To_Colaboratory.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wf5KrEb6vrkR"
      },
      "source": [
        "<div class=\"markdown-google-sans\">\n",
        "  <h1>Welcome to Colab!</h1>\n",
        "</div>\n",
        "\n",
        "<!-- TODO(b/319266067) remove temporary advert after a few weeks. -->\n",
        "<div class=\"markdown-google-sans\">\n",
        "  <h2>(New) Try the Gemini API</h2>\n",
        "  <ul>\n",
        "  <li><a href=\"https://makersuite.google.com/app/apikey\">Generate a Gemini API key</a></li>\n",
        "  <li><a href=\"https://colab.research.google.com/github/googlecolab/colabtools/blob/main/notebooks/Talk_to_Gemini_with_Google%27s_Speech_to_Text_API.ipynb?utm_medium=link&utm_campaign=gemini\">Talk to Gemini with the Speech-to-Text API</a></li>\n",
        "  <li><a href=\"https://colab.research.google.com/github/google/generative-ai-docs/blob/main/site/en/tutorials/python_quickstart.ipynb?utm_medium=link&utm_campaign=gemini\">Gemini API: Quickstart with Python</a></li>\n",
        "  <li><a href=\"https://colab.research.google.com/notebooks/snippets/gemini.ipynb?utm_medium=link&utm_campaign=gemini\">Gemini API code sample</a></li>\n",
        "  <li><a href=\"https://colab.research.google.com/github/googlecolab/colabtools/blob/main/notebooks/Learning_with_Gemini_and_ChatGPT.ipynb?utm_medium=link&utm_campaign=gemini\">Compare Gemini with ChatGPT</a></li>  \n",
        "  <li><a href=\"https://colab.google/notebooks/?utm_medium=link&utm_campaign=gemini\">More notebooks</a></li>\n",
        "  </ul>\n",
        "</div>\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Nma_JWh-W-IF"
      },
      "source": [
        "If you're already familiar with Colab, check out this video to learn about interactive tables, the executed code history view, and the command palette.\n",
        "\n",
        "<center>\n",
        "  <a href=\"https://www.youtube.com/watch?v=rNgswRZ2C1Y\" target=\"_blank\">\n",
        "  <img alt='Thumbnail for a video showing 3 cool Google Colab features' src=\"data:image/png;base64,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\" height=\"188\" width=\"336\">\n",
        "  </a>\n",
        "</center>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zwFnJsE6vjf8"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5fCEDCU_qrC0"
      },
      "source": [
        "<div class=\"markdown-google-sans\">\n",
        "  <h2>What is Colab?</h2>\n",
        "</div>\n",
        "\n",
        "Colab, or \"Colaboratory\", allows you to write and execute Python in your browser, with\n",
        "- Zero configuration required\n",
        "- Access to GPUs free of charge\n",
        "- Easy sharing\n",
        "\n",
        "Whether you're a **student**, a **data scientist** or an **AI researcher**, Colab can make your work easier. Watch [Introduction to Colab](https://www.youtube.com/watch?v=inN8seMm7UI) to learn more, or just get started below!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GJBs_flRovLc"
      },
      "source": [
        "<div class=\"markdown-google-sans\">\n",
        "\n",
        "## **Getting started**\n",
        "</div>\n",
        "\n",
        "The document you are reading is not a static web page, but an interactive environment called a **Colab notebook** that lets you write and execute code.\n",
        "\n",
        "For example, here is a **code cell** with a short Python script that computes a value, stores it in a variable, and prints the result:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "id": "gJr_9dXGpJ05",
        "outputId": "9f556d03-ec67-4950-a485-cfdba9ddd14d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "86400"
            ]
          },
          "execution_count": 0,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "seconds_in_a_day = 24 * 60 * 60\n",
        "seconds_in_a_day"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2fhs6GZ4qFMx"
      },
      "source": [
        "To execute the code in the above cell, select it with a click and then either press the play button to the left of the code, or use the keyboard shortcut \"Command/Ctrl+Enter\". To edit the code, just click the cell and start editing.\n",
        "\n",
        "Variables that you define in one cell can later be used in other cells:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "id": "-gE-Ez1qtyIA",
        "outputId": "94cb2224-0edf-457b-90b5-0ac3488d8a97"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "604800"
            ]
          },
          "execution_count": 0,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "seconds_in_a_week = 7 * seconds_in_a_day\n",
        "seconds_in_a_week"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lSrWNr3MuFUS"
      },
      "source": [
        "Colab notebooks allow you to combine **executable code** and **rich text** in a single document, along with **images**, **HTML**, **LaTeX** and more. When you create your own Colab notebooks, they are stored in your Google Drive account. You can easily share your Colab notebooks with co-workers or friends, allowing them to comment on your notebooks or even edit them. To learn more, see [Overview of Colab](/notebooks/basic_features_overview.ipynb). To create a new Colab notebook you can use the File menu above, or use the following link: [create a new Colab notebook](http://colab.research.google.com#create=true).\n",
        "\n",
        "Colab notebooks are Jupyter notebooks that are hosted by Colab. To learn more about the Jupyter project, see [jupyter.org](https://www.jupyter.org)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UdRyKR44dcNI"
      },
      "source": [
        "<div class=\"markdown-google-sans\">\n",
        "\n",
        "## Data science\n",
        "</div>\n",
        "\n",
        "With Colab you can harness the full power of popular Python libraries to analyze and visualize data. The code cell below uses **numpy** to generate some random data, and uses **matplotlib** to visualize it. To edit the code, just click the cell and start editing."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 333
        },
        "id": "C4HZx7Gndbrh",
        "outputId": "b113e0f6-8852-4d53-e054-84689232a454"
      },
      "outputs": [
        {
          "data": {
            "text/markdown": [
              "![Sample Visualization](data:image/png;base64,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)"
            ],
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "import numpy as np\n",
        "import IPython.display as display\n",
        "from matplotlib import pyplot as plt\n",
        "import io\n",
        "import base64\n",
        "\n",
        "ys = 200 + np.random.randn(100)\n",
        "x = [x for x in range(len(ys))]\n",
        "\n",
        "fig = plt.figure(figsize=(4, 3), facecolor='w')\n",
        "plt.plot(x, ys, '-')\n",
        "plt.fill_between(x, ys, 195, where=(ys > 195), facecolor='g', alpha=0.6)\n",
        "plt.title(\"Sample Visualization\", fontsize=10)\n",
        "\n",
        "data = io.BytesIO()\n",
        "plt.savefig(data)\n",
        "image = F\"data:image/png;base64,{base64.b64encode(data.getvalue()).decode()}\"\n",
        "alt = \"Sample Visualization\"\n",
        "display.display(display.Markdown(F\"\"\"![{alt}]({image})\"\"\"))\n",
        "plt.close(fig)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4_kCnsPUqS6o"
      },
      "source": [
        "You can import your own data into Colab notebooks from your Google Drive account, including from spreadsheets, as well as from Github and many other sources. To learn more about importing data, and how Colab can be used for data science, see the links below under [Working with Data](#working-with-data)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OwuxHmxllTwN"
      },
      "source": [
        "<div class=\"markdown-google-sans\">\n",
        "\n",
        "## Machine learning\n",
        "</div>\n",
        "\n",
        "With Colab you can import an image dataset, train an image classifier on it, and evaluate the model, all in just [a few lines of code](https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/tutorials/quickstart/beginner.ipynb). Colab notebooks execute code on Google's cloud servers, meaning you can leverage the power of Google hardware, including [GPUs and TPUs](#using-accelerated-hardware), regardless of the power of your machine. All you need is a browser."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ufxBm1yRnruN"
      },
      "source": [
        "Colab is used extensively in the machine learning community with applications including:\n",
        "- Getting started with TensorFlow\n",
        "- Developing and training neural networks\n",
        "- Experimenting with TPUs\n",
        "- Disseminating AI research\n",
        "- Creating tutorials\n",
        "\n",
        "To see sample Colab notebooks that demonstrate machine learning applications, see the [machine learning examples](#machine-learning-examples) below."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-Rh3-Vt9Nev9"
      },
      "source": [
        "<div class=\"markdown-google-sans\">\n",
        "\n",
        "## More Resources\n",
        "\n",
        "### Working with Notebooks in Colab\n",
        "\n",
        "</div>\n",
        "\n",
        "- [Overview of Colaboratory](/notebooks/basic_features_overview.ipynb)\n",
        "- [Guide to Markdown](/notebooks/markdown_guide.ipynb)\n",
        "- [Importing libraries and installing dependencies](/notebooks/snippets/importing_libraries.ipynb)\n",
        "- [Saving and loading notebooks in GitHub](https://colab.research.google.com/github/googlecolab/colabtools/blob/main/notebooks/colab-github-demo.ipynb)\n",
        "- [Interactive forms](/notebooks/forms.ipynb)\n",
        "- [Interactive widgets](/notebooks/widgets.ipynb)\n",
        "\n",
        "<div class=\"markdown-google-sans\">\n",
        "\n",
        "<a name=\"working-with-data\"></a>\n",
        "### Working with Data\n",
        "</div>\n",
        "\n",
        "- [Loading data: Drive, Sheets, and Google Cloud Storage](/notebooks/io.ipynb)\n",
        "- [Charts: visualizing data](/notebooks/charts.ipynb)\n",
        "- [Getting started with BigQuery](/notebooks/bigquery.ipynb)\n",
        "\n",
        "<div class=\"markdown-google-sans\">\n",
        "\n",
        "### Machine Learning Crash Course\n",
        "\n",
        "<div>\n",
        "\n",
        "These are a few of the notebooks from Google's online Machine Learning course. See the [full course website](https://developers.google.com/machine-learning/crash-course/) for more.\n",
        "- [Intro to Pandas DataFrame](https://colab.research.google.com/github/google/eng-edu/blob/main/ml/cc/exercises/pandas_dataframe_ultraquick_tutorial.ipynb)\n",
        "- [Linear regression with tf.keras using synthetic data](https://colab.research.google.com/github/google/eng-edu/blob/main/ml/cc/exercises/linear_regression_with_synthetic_data.ipynb)\n",
        "\n",
        "<div class=\"markdown-google-sans\">\n",
        "\n",
        "<a name=\"using-accelerated-hardware\"></a>\n",
        "### Using Accelerated Hardware\n",
        "</div>\n",
        "\n",
        "- [TensorFlow with GPUs](/notebooks/gpu.ipynb)\n",
        "- [TensorFlow with TPUs](/notebooks/tpu.ipynb)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P-H6Lw1vyNNd"
      },
      "source": [
        "<div class=\"markdown-google-sans\">\n",
        "\n",
        "<a name=\"machine-learning-examples\"></a>\n",
        "\n",
        "### Featured examples\n",
        "\n",
        "</div>\n",
        "\n",
        "- [NeMo Voice Swap](https://colab.research.google.com/github/NVIDIA/NeMo/blob/stable/tutorials/VoiceSwapSample.ipynb): Use Nvidia's NeMo conversational AI Toolkit to swap a voice in an audio fragment with a computer generated one.\n",
        "\n",
        "- [Retraining an Image Classifier](https://tensorflow.org/hub/tutorials/tf2_image_retraining): Build a Keras model on top of a pre-trained image classifier to distinguish flowers.\n",
        "- [Text Classification](https://tensorflow.org/hub/tutorials/tf2_text_classification): Classify IMDB movie reviews as either *positive* or *negative*.\n",
        "- [Style Transfer](https://tensorflow.org/hub/tutorials/tf2_arbitrary_image_stylization): Use deep learning to transfer style between images.\n",
        "- [Multilingual Universal Sentence Encoder Q&A](https://tensorflow.org/hub/tutorials/retrieval_with_tf_hub_universal_encoder_qa): Use a machine learning model to answer questions from the SQuAD dataset.\n",
        "- [Video Interpolation](https://tensorflow.org/hub/tutorials/tweening_conv3d): Predict what happened in a video between the first and the last frame.\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import os\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import tensorflow as tf\n",
        "import keras\n",
        "from PIL import Image\n",
        "from keras.preprocessing import image\n",
        "from keras.models import Sequential, Model\n",
        "from keras.layers import Conv2D, MaxPool2D, Flatten, Dense, Dropout, BatchNormalization\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "import cv2\n",
        "from keras.layers import Input, Lambda, Dense, Flatten\n",
        "from tensorflow.keras.applications import VGG16, InceptionResNetV2\n",
        "from keras import regularizers\n",
        "from tensorflow.keras.optimizers import Adam, RMSprop, SGD, Adamax\n",
        "from google.colab import drive"
      ],
      "metadata": {
        "id": "hQdmL4_0gSbv"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        },
        "id": "m1wLCF3AgfiW",
        "outputId": "34d9e718-7a1d-4743-879f-62c180fa8b84"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "error",
          "ename": "MessageError",
          "evalue": "Error: credential propagation was unsuccessful",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mMessageError\u001b[0m                              Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-2-d5df0069828e>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mgoogle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolab\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdrive\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdrive\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmount\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/content/drive'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/google/colab/drive.py\u001b[0m in \u001b[0;36mmount\u001b[0;34m(mountpoint, force_remount, timeout_ms, readonly)\u001b[0m\n\u001b[1;32m     98\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mmount\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmountpoint\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mforce_remount\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout_ms\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m120000\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreadonly\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     99\u001b[0m   \u001b[0;34m\"\"\"Mount your Google Drive at the specified mountpoint path.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 100\u001b[0;31m   return _mount(\n\u001b[0m\u001b[1;32m    101\u001b[0m       \u001b[0mmountpoint\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    102\u001b[0m       \u001b[0mforce_remount\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mforce_remount\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/google/colab/drive.py\u001b[0m in \u001b[0;36m_mount\u001b[0;34m(mountpoint, force_remount, timeout_ms, ephemeral, readonly)\u001b[0m\n\u001b[1;32m    131\u001b[0m   )\n\u001b[1;32m    132\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0mephemeral\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 133\u001b[0;31m     _message.blocking_request(\n\u001b[0m\u001b[1;32m    134\u001b[0m         \u001b[0;34m'request_auth'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrequest\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m'authType'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'dfs_ephemeral'\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout_sec\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    135\u001b[0m     )\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/google/colab/_message.py\u001b[0m in \u001b[0;36mblocking_request\u001b[0;34m(request_type, request, timeout_sec, parent)\u001b[0m\n\u001b[1;32m    174\u001b[0m       \u001b[0mrequest_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrequest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparent\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparent\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexpect_reply\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    175\u001b[0m   )\n\u001b[0;32m--> 176\u001b[0;31m   \u001b[0;32mreturn\u001b[0m \u001b[0mread_reply_from_input\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout_sec\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/google/colab/_message.py\u001b[0m in \u001b[0;36mread_reply_from_input\u001b[0;34m(message_id, timeout_sec)\u001b[0m\n\u001b[1;32m    101\u001b[0m     ):\n\u001b[1;32m    102\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0;34m'error'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mreply\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 103\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mMessageError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreply\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'error'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    104\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0mreply\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'data'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    105\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mMessageError\u001b[0m: Error: credential propagation was unsuccessful"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import shutil\n",
        "\n",
        "source_dir = '/content/drive/MyDrive/Doctors prescriptions handwriting.v1i.multiclass'  # Path to the already unzipped directory\n",
        "output_dir = 'extracted_files'  # Destination directory\n",
        "\n",
        "# Check if the source directory exists\n",
        "if os.path.exists(source_dir):\n",
        "    # Create the destination directory if it doesn't exist\n",
        "    if not os.path.exists(output_dir):\n",
        "        os.makedirs(output_dir)\n",
        "\n",
        "    # Move all files from the source directory to the destination directory\n",
        "    for file_name in os.listdir(source_dir):\n",
        "        source_file_path = os.path.join(source_dir, file_name)\n",
        "        destination_file_path = os.path.join(output_dir, file_name)\n",
        "        shutil.move(source_file_path, destination_file_path)\n",
        "\n",
        "    print(\"Files moved successfully to:\", output_dir)\n",
        "else:\n",
        "    print(\"Error: The specified source directory does not exist.\")\n"
      ],
      "metadata": {
        "id": "4Qhe43TSgkyh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_dir='/content/drive/MyDrive/Doctors prescriptions handwriting.v1i.multiclass (1)/train'\n",
        "\n",
        "test_dir='/content/drive/MyDrive/Doctors prescriptions handwriting.v1i.multiclass (1)/test'"
      ],
      "metadata": {
        "id": "cVBfk7bfgu8w"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import OneHotEncoder"
      ],
      "metadata": {
        "id": "FzDeG5HrgzXW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Verify directory structure and contents\n",
        "print(\"Number of images in train directory:\", sum(len(files) for _, _, files in os.walk(train_dir)))\n",
        "print(\"Number of images in test directory:\", sum(len(files) for _, _, files in os.walk(test_dir)))\n",
        "\n",
        "# Data Augmentation\n",
        "train_datagen = ImageDataGenerator(\n",
        "    rescale=1./255,\n",
        "    rotation_range=40,\n",
        "    width_shift_range=0.2,\n",
        "    height_shift_range=0.2,\n",
        "    shear_range=0.2,\n",
        "    zoom_range=0.2,\n",
        "    horizontal_flip=True,\n",
        "    fill_mode='nearest')\n",
        "\n",
        "test_datagen = ImageDataGenerator(rescale=1./255)\n",
        "\n",
        "# Handle empty directories\n",
        "def check_directory(directory):\n",
        "    if not os.listdir(directory):\n",
        "        print(\"Error: Directory\", directory, \"is empty.\")\n",
        "        return False\n",
        "    return True\n",
        "\n",
        "if not (check_directory(train_dir) and check_directory(test_dir)):\n",
        "    print(\"Error: Training or testing directory is empty.\")\n",
        "else:\n",
        "    train_generator = train_datagen.flow_from_directory(\n",
        "        train_dir,\n",
        "        target_size=(150, 150),\n",
        "        batch_size=32,\n",
        "        class_mode='categorical')\n",
        "\n",
        "    test_generator = test_datagen.flow_from_directory(\n",
        "        test_dir,\n",
        "        target_size=(150, 150),\n",
        "        batch_size=32,\n",
        "        class_mode='categorical')"
      ],
      "metadata": {
        "id": "wh8Jzxqogzo3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Load pre-trained CNN model\n",
        "base_model = VGG16(weights='imagenet', include_top=False, input_shape=(150, 150, 3))"
      ],
      "metadata": {
        "id": "bi1YLDSzgzsR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Freeze the pre-trained layers\n",
        "for layer in base_model.layers:\n",
        "    layer.trainable = False"
      ],
      "metadata": {
        "id": "vowUlg-hgzwc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Add custom layers for classification\n",
        "x = Flatten()(base_model.output)\n",
        "x = Dense(512, activation='relu')(x)\n",
        "x = Dropout(0.5)(x)\n",
        "output = Dense(3, activation='softmax')(x)\n",
        "\n",
        "model = Model(base_model.input, output)\n",
        "\n",
        "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n",
        "print(model)"
      ],
      "metadata": {
        "id": "ibIKjW14gzzA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "id": "NcizIwfsgz1o"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Path to the uploaded image\n",
        "uploaded_image_path = '/content/paraceta new.jpg'\n",
        "\n",
        "# Load the uploaded image\n",
        "uploaded_image = Image.open((uploaded_image_path))\n",
        "uploaded_image = uploaded_image.resize((150, 150))  # Resize the image to match input shape\n",
        "uploaded_image = image.img_to_array(uploaded_image)\n",
        "uploaded_image = uploaded_image / 255.0  # Normalize the pixel values\n",
        "\n",
        "# Make predictions on the uploaded image\n",
        "predictions = model.predict(np.expand_dims(uploaded_image, axis=0))\n",
        "\n",
        "# Get class labels\n",
        "class_labels = train_generator.class_indices\n",
        "class_labels = {v: k for k, v in class_labels.items()}\n"
      ],
      "metadata": {
        "id": "JvL2lbEngz4O"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Load and preprocess the medical prescription image\n",
        "def load_and_preprocess_prescription(uploaded_image_path):\n",
        "    prescription_image = cv2.imread(uploaded_image_path)\n",
        "    prescription_text = pytesseract.image_to_string(Image.open(uploaded_image_path))\n",
        "    return prescription_text"
      ],
      "metadata": {
        "id": "acqYGmuMgz62"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pip install pytesseract #ocr technique"
      ],
      "metadata": {
        "id": "MWY_c6tNhRnz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pip install tesseract #ocr"
      ],
      "metadata": {
        "id": "lk1V13nJhRtx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!sudo apt install tesseract-ocr"
      ],
      "metadata": {
        "id": "J2m1OSJdhRwh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pytesseract"
      ],
      "metadata": {
        "id": "-f6ghe8uhRzc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Load and preprocess the prescription image\n",
        "prescription_text = load_and_preprocess_prescription(uploaded_image_path)\n",
        "print(\"Prescription Text:\")\n",
        "print(prescription_text)"
      ],
      "metadata": {
        "id": "oQ5psDFnhR1u"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -U spacy\n",
        "\n",
        "!pip install scispacy #processing biomedical clinical text\n",
        "\n",
        "!pip install https://s3-us-west-2.amazonaws.com/ai2-s2-scispacy/releases/v0.5.1/en_ner_bc5cdr_md-0.5.1.tar.gz\n",
        "\n",
        "!pip install https://s3-us-west-2.amazonaws.com/ai2-s2-scispacy/releases/v0.5.1/en_core_sci_sm-0.5.1.tar.gz\n",
        "\n",
        "!pip install https://s3-us-west-2.amazonaws.com/ai2-s2-scispacy/releases/v0.5.1/en_core_sci_md-0.5.1.tar.gz\n"
      ],
      "metadata": {
        "id": "56-OpNsmhR-Q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import en_core_sci_sm\n",
        "from spacy import displacy"
      ],
      "metadata": {
        "id": "kpzn7Wldgz9n"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "nlp_sm = en_core_sci_sm.load()  # Load the English language model from spaCy\n",
        "fi = prescription_text           # Assign the extracted text to the variable `fi`\n",
        "k=[]\n",
        "for token in fi:\n",
        "  k.append(token)          # Append each token (character or word) from `fi` to the list `k`\n",
        "\n",
        "  # Process the text using the loaded spaCy model (`nlp_sm`)\n",
        "doc = nlp_sm(fi)           # Create a spaCy Doc object by applying the model to the text `fi`\n",
        "\n",
        "visualization = displacy.render(doc, jupyter=True,style='ent')   #Generate a visualization"
      ],
      "metadata": {
        "id": "4Fae_qMchc98"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import en_ner_bc5cdr_md\n",
        "\n",
        "nlp_bc = en_ner_bc5cdr_md.load()"
      ],
      "metadata": {
        "id": "-gPnWyo1hdA1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "chemical_names = []\n",
        "doc = nlp_bc(fi)\n",
        "for k in doc.ents:\n",
        "    if (k.label_=='CHEMICAL'):\n",
        "      #print(k.text)\n",
        "      chemical_name = k.text  # Assign the chemical name to a variable\n",
        "      chemical_names.append(chemical_name)\n",
        "for name in chemical_names:\n",
        "    print(name)"
      ],
      "metadata": {
        "id": "6chpIyzWhdE0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import csv\n",
        "import difflib\n",
        "\n",
        "\n",
        "# Read the CSV file and store medication names with their side effects in a dictionary\n",
        "medications_data = {}\n",
        "with open('/content/side.csv', newline='') as csvfile:\n",
        "    reader = csv.DictReader(csvfile)\n",
        "    for row in reader:\n",
        "        medication_name = row['Drug_name']\n",
        "        side_effects = row['side_effects'].split(',')\n",
        "        medications_data[medication_name] = side_effects\n",
        "\n",
        "# Loop through each stored medication name and check if it exists in the dataset\n",
        "for name in chemical_names:\n",
        "    matched_medication = difflib.get_close_matches(name, medications_data.keys(), n=1, cutoff=0.8)\n",
        "    if matched_medication:\n",
        "        matched_name = matched_medication[0]\n",
        "        output_text = f\"Medication Name: {matched_name}\\nSide Effects:\\n\"\n",
        "        for side_effect in medications_data[matched_name]:\n",
        "            output_text += side_effect + \"\\n\"\n",
        "        # Print the output text\n",
        "        print(output_text)\n"
      ],
      "metadata": {
        "id": "mrgEIPpMhdHb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pip install gtts"
      ],
      "metadata": {
        "id": "3rfav5tthdNI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from gtts import gTTS\n",
        "import IPython.display as ipd\n",
        "\n",
        "# Convert text to speech\n",
        "tts = gTTS(output_text)\n",
        "tts.save(\"output_audio.mp3\")\n",
        "\n",
        "# Display the speech\n",
        "ipd.Audio(\"output_audio.mp3\", autoplay=True)"
      ],
      "metadata": {
        "id": "3sohbYiMhdP1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pip install googletrans==4.0.0-rc1"
      ],
      "metadata": {
        "id": "uj5CScM9hdSG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from googletrans import Translator\n",
        "import difflib\n",
        "\n",
        "def translate_text(text, target_lang):\n",
        "    translator = Translator()\n",
        "    translated = translator.translate(text, dest=target_lang)\n",
        "    return translated.text\n",
        "\n",
        "def main():\n",
        "    global output_text  # Access the output_text from the global scope\n",
        "\n",
        "    print(\"Select the  language:\")\n",
        "    print(\"1. Assamese\")\n",
        "    print(\"2. Bengali\")\n",
        "    print(\"3. Bodo\")\n",
        "    print(\"4. Dogri\")\n",
        "    print(\"5. English\")\n",
        "    print(\"6. Gujarati\")\n",
        "    print(\"7. Hindi\")\n",
        "    print(\"8. Kannada\")\n",
        "    print(\"9. Kashmiri\")\n",
        "    print(\"10. Konkani\")\n",
        "    print(\"11. Maithili\")\n",
        "    print(\"12. Malayalam\")\n",
        "    print(\"13. Marathi\")\n",
        "    print(\"14. Meitei\")\n",
        "    print(\"15. Nepali\")\n",
        "    print(\"16. Odia\")\n",
        "    print(\"17. Punjabi\")\n",
        "    print(\"18. Sanskrit\")\n",
        "    print(\"19. Santali\")\n",
        "    print(\"20. Sindhi\")\n",
        "    print(\"21. Tamil\")\n",
        "    print(\"22. Telugu\")\n",
        "    print(\"23. Urdu\")\n",
        "    print(\"24. Arabic\")  # Added Arabic\n",
        "    # Add more languages as needed\n",
        "\n",
        "    target_choice = input(\"Enter your choice: \").lower()\n",
        "    target_lang = {\n",
        "        '1': 'as',  # Assamese\n",
        "        '2': 'bn',  # Bengali\n",
        "        '3': 'br',  # Bodo\n",
        "        '4': 'doi',  # Dogri\n",
        "        '5': 'en',  # English\n",
        "        '6': 'gu',  # Gujarati\n",
        "        '7': 'hi',  # Hindi\n",
        "        '8': 'kn',  # Kannada\n",
        "        '9': 'ks',  # Kashmiri\n",
        "        '10': 'kok',  # Konkani\n",
        "        '11': 'mai',  # Maithili\n",
        "        '12': 'ml',  # Malayalam\n",
        "        '13': 'mr',  # Marathi\n",
        "        '14': 'mni',  # Meitei\n",
        "        '15': 'ne',  # Nepali\n",
        "        '16': 'or',  # Odia\n",
        "        '17': 'pa',  # Punjabi\n",
        "        '18': 'sa',  # Sanskrit\n",
        "        '19': 'sat',  # Santali\n",
        "        '20': 'sd',  # Sindhi\n",
        "        '21': 'ta',  # Tamil\n",
        "        '22': 'te',  # Telugu\n",
        "        '23': 'ur',  # Urdu\n",
        "        '24': 'ar'   # Arabic\n",
        "        # Add more languages as needed\n",
        "    }.get(target_choice)\n",
        "\n",
        "    if not target_lang:\n",
        "        print(\"Invalid choice!\")\n",
        "        return\n",
        "\n",
        "    translated_text = translate_text(output_text, target_lang)  # Corrected function call\n",
        "\n",
        "    print(\"\\nTranslated text:\")\n",
        "    print(translated_text)\n",
        "if __name__ == \"__main__\":\n",
        "    main()"
      ],
      "metadata": {
        "id": "xqkCv6hvhdUo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "XQQD_nFfhdXT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "87ZvG_7ChdaI"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "name": "Welcome To Colaboratory",
      "toc_visible": true,
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}