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        "93d62078cef4428a9b01f88538cb25d0": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
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            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
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        "ed6a400e23e44e00abf45b08cbf3c0ef": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
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            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
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    },
    "accelerator": "TPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jm3TpnUPu4q3"
      },
      "source": [
        "**Web Apps**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "veVbRa4f1mkf"
      },
      "source": [
        "# Reading Checkpoint"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5KSWQwDxQMFy",
        "outputId": "9c5d4af5-dd34-45fa-bdfc-e5930babc371"
      },
      "source": [
        "!pip install tensorflow_hub\n",
        "!pip install keras tf-models-official pydot graphviz"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/96/08/81bbc275e8e9c6d1e03dd26daec3a67f45e6322804cbce3d51f93eae1961/tf_models_official-2.5.0-py2.py3-none-any.whl (1.6MB)\n",
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            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/7e/57/0c7ca4e31a126189dab99c19951910bd081dea5bbd25f24b77107750eae7/sacrebleu-1.5.1-py3-none-any.whl (54kB)\n",
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            "Collecting portalocker==2.0.0\n",
            "  Downloading https://files.pythonhosted.org/packages/89/a6/3814b7107e0788040870e8825eebf214d72166adf656ba7d4bf14759a06a/portalocker-2.0.0-py2.py3-none-any.whl\n",
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            "  Building wheel for py-cpuinfo (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "  Stored in directory: /root/.cache/pip/wheels/52/df/1b/45d75646c37428f7e626214704a0e35bd3cfc32eda37e59e5f\n",
            "Successfully built py-cpuinfo seqeval\n",
            "\u001b[31mERROR: tf-models-official 2.5.0 has requirement pyyaml>=5.1, but you'll have pyyaml 3.13 which is incompatible.\u001b[0m\n",
            "Installing collected packages: portalocker, sacrebleu, tensorflow-model-optimization, tensorflow-addons, py-cpuinfo, seqeval, tf-slim, sentencepiece, opencv-python-headless, tf-models-official\n",
            "Successfully installed opencv-python-headless-4.5.3.56 portalocker-2.0.0 py-cpuinfo-8.0.0 sacrebleu-1.5.1 sentencepiece-0.1.96 seqeval-1.2.2 tensorflow-addons-0.13.0 tensorflow-model-optimization-0.6.0 tf-models-official-2.5.0 tf-slim-1.1.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ayVgV9NqQVBd"
      },
      "source": [
        "import os\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "import tensorflow as tf\n",
        "import tensorflow_hub as hub\n",
        "\n",
        "from keras.utils import np_utils\n",
        "\n",
        "import official.nlp.bert.bert_models\n",
        "import official.nlp.bert.configs\n",
        "import official.nlp.bert.run_classifier\n",
        "import official.nlp.bert.tokenization as tokenization\n",
        "\n",
        "from official.modeling import tf_utils\n",
        "from official import nlp\n",
        "from official.nlp import bert\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "import matplotlib.pyplot as plt\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qdPLZ9HXQjRo",
        "outputId": "b84e12e4-d41c-4a8c-a9ff-bd7f2eb8201f"
      },
      "source": [
        "\n",
        "\n",
        "from google.colab import drive\n",
        "\n",
        "drive.mount('/content/gdrive')\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b7S1Aqh7LR25"
      },
      "source": [
        "from IPython.display import display, Javascript\n",
        "from google.colab.output import eval_js\n",
        "from base64 import b64decode\n",
        "\n",
        "def take_photo(filename='photo.jpg', quality=0.8):\n",
        "  js = Javascript('''\n",
        "    async function takePhoto(quality) {`s`\n",
        "      const div = document.createElement('div');\n",
        "      const capture = document.createElement('button');\n",
        "      capture.textContent = 'Capture';\n",
        "      div.appendChild(capture);\n",
        "\n",
        "      const video = document.createElement('video');\n",
        "      video.style.display = 'block';\n",
        "      const stream = await navigator.mediaDevices.getUserMedia({video: true});\n",
        "\n",
        "      document.body.appendChild(div);\n",
        "      div.appendChild(video);\n",
        "      video.srcObject = stream;\n",
        "      await video.play();\n",
        "\n",
        "      // Resize the output to fit the video element.\n",
        "      google.colab.output.setIframeHeight(document.documentElement.scrollHeight, true);\n",
        "\n",
        "      // Wait for Capture to be clicked.\n",
        "      await new Promise((resolve) => capture.onclick = resolve);\n",
        "\n",
        "      const canvas = document.createElement('canvas');\n",
        "      canvas.width = video.videoWidth;\n",
        "      canvas.height = video.videoHeight;\n",
        "      canvas.getContext('2d').drawImage(video, 0, 0);\n",
        "      stream.getVideoTracks()[0].stop();\n",
        "      div.remove();\n",
        "      return canvas.toDataURL('image/jpeg', quality);\n",
        "    }\n",
        "    ''')\n",
        "  display(js)\n",
        "  data = eval_js('takePhoto({})'.format(quality))\n",
        "  binary = b64decode(data.split(',')[1])\n",
        "  with open(filename, 'wb') as f:\n",
        "    f.write(binary)\n",
        "  return filename"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FbfKEty1nTDa"
      },
      "source": [
        "from tensorflow.keras.models import load_model\n",
        "from tensorflow import keras"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zasWB7oVQZRt",
        "outputId": "68d7757d-888e-4e7c-c3c2-56addafc5daf"
      },
      "source": [
        "path = \"/content/gdrive/My Drive/checkpoint\" \n",
        "optimizer = nlp.optimization.create_optimizer(\n",
        "    2e-5, num_train_steps=417\n",
        ", num_warmup_steps=41)\n",
        "model = keras.models.load_model(path, custom_objects={'AdamWeightDecay': optimizer})"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:SavedModel saved prior to TF 2.5 detected when loading Keras model. Please ensure that you are saving the model with model.save() or tf.keras.models.save_model(), *NOT* tf.saved_model.save(). To confirm, there should be a file named \"keras_metadata.pb\" in the SavedModel directory.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:SavedModel saved prior to TF 2.5 detected when loading Keras model. Please ensure that you are saving the model with model.save() or tf.keras.models.save_model(), *NOT* tf.saved_model.save(). To confirm, there should be a file named \"keras_metadata.pb\" in the SavedModel directory.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K2m5z7liTSnW"
      },
      "source": [
        "bert_layer = hub.KerasLayer(\"https://tfhub.dev/tensorflow/bert_multi_cased_L-12_H-768_A-12/2\",\n",
        "                            trainable=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r5RGsQLeTSe2"
      },
      "source": [
        "\n",
        "vocab_file = bert_layer.resolved_object.vocab_file.asset_path.numpy()\n",
        "do_lower_case = bert_layer.resolved_object.do_lower_case.numpy()\n",
        "tokenizer = tokenization.FullTokenizer(vocab_file, do_lower_case)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qZe7ccl8Vbn3"
      },
      "source": [
        "def encode_names(n, tokenizer):\n",
        "   tokens = list(tokenizer.tokenize(n))\n",
        "   tokens.append('[SEP]')\n",
        "   return tokenizer.convert_tokens_to_ids(tokens)\n",
        "\n",
        "def bert_encode(string_list, tokenizer, max_seq_length):\n",
        "  num_examples = len(string_list)\n",
        "  \n",
        "  string_tokens = tf.ragged.constant([\n",
        "      encode_names(n, tokenizer) for n in np.array(string_list)])\n",
        "\n",
        "  cls = [tokenizer.convert_tokens_to_ids(['[CLS]'])]*string_tokens.shape[0]\n",
        "  input_word_ids = tf.concat([cls, string_tokens], axis=-1)\n",
        "\n",
        "  input_mask = tf.ones_like(input_word_ids).to_tensor(shape=(None, max_seq_length))\n",
        "\n",
        "  type_cls = tf.zeros_like(cls)\n",
        "  type_tokens = tf.ones_like(string_tokens)\n",
        "  input_type_ids = tf.concat(\n",
        "      [type_cls, type_tokens], axis=-1).to_tensor(shape=(None, max_seq_length))\n",
        "\n",
        "  inputs = {\n",
        "      'input_word_ids': input_word_ids.to_tensor(shape=(None, max_seq_length)),\n",
        "      'input_mask': input_mask,\n",
        "      'input_type_ids': input_type_ids}\n",
        "\n",
        "  return inputs"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uPyM__IOU8sf",
        "outputId": "b40f6a76-3bcf-41bf-8461-081b1282a1b7"
      },
      "source": [
        "tokenizer"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<official.nlp.bert.tokenization.FullTokenizer at 0x7f7c203aacd0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CGrY18xwT9k6",
        "outputId": "14724bec-25b0-4e38-e562-4e54e9275bcb"
      },
      "source": [
        "pip install transformers==3\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting transformers==3\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/9c/35/1c3f6e62d81f5f0daff1384e6d5e6c5758682a8357ebc765ece2b9def62b/transformers-3.0.0-py3-none-any.whl (754kB)\n",
            "\u001b[K     |████████████████████████████████| 757kB 4.2MB/s \n",
            "\u001b[?25hRequirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from transformers==3) (20.9)\n",
            "Collecting sacremoses\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/75/ee/67241dc87f266093c533a2d4d3d69438e57d7a90abb216fa076e7d475d4a/sacremoses-0.0.45-py3-none-any.whl (895kB)\n",
            "\u001b[K     |████████████████████████████████| 901kB 51.3MB/s \n",
            "\u001b[?25hCollecting tokenizers==0.8.0-rc4\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/f7/82/0e82a95bd9db2b32569500cc1bb47aa7c4e0f57aa5e35cceba414096917b/tokenizers-0.8.0rc4-cp37-cp37m-manylinux1_x86_64.whl (3.0MB)\n",
            "\u001b[K     |████████████████████████████████| 3.0MB 48.7MB/s \n",
            "\u001b[?25hRequirement already satisfied: sentencepiece in /usr/local/lib/python3.7/dist-packages (from transformers==3) (0.1.96)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.7/dist-packages (from transformers==3) (4.41.1)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from transformers==3) (1.19.5)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers==3) (2019.12.20)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from transformers==3) (3.0.12)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from transformers==3) (2.23.0)\n",
            "Requirement already satisfied: pyparsing>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->transformers==3) (2.4.7)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers==3) (7.1.2)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers==3) (1.0.1)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers==3) (1.15.0)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->transformers==3) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->transformers==3) (2021.5.30)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->transformers==3) (3.0.4)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->transformers==3) (1.24.3)\n",
            "Installing collected packages: sacremoses, tokenizers, transformers\n",
            "Successfully installed sacremoses-0.0.45 tokenizers-0.8.0rc4 transformers-3.0.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7beDENunDyGx"
      },
      "source": [
        "# Voice emotion extraction"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DBKriSiVD4g_"
      },
      "source": [
        "import soundfile\n",
        "import numpy as np\n",
        "import librosa\n",
        "import glob\n",
        "import os\n",
        "from sklearn.model_selection import train_test_split\n",
        "!python --version\n",
        "#!pip install pipwin\n",
        "#!pipwin install pyaudio\n",
        "!apt-get install libasound-dev portaudio19-dev libportaudio2 libportaudiocpp0\n",
        "!apt-get install ffmpeg libav-tools\n",
        "!pip install pyaudio"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YZQIsmOZTLN1"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S-qKA0PCQfaZ",
        "outputId": "0f44e8d7-7fba-4346-f808-00e4b273fe4e"
      },
      "source": [
        "import pyaudio\n",
        "import os\n",
        "import wave\n",
        "import pickle\n",
        "from sys import byteorder\n",
        "from array import array\n",
        "from struct import pack\n",
        "from sklearn.neural_network import MLPClassifier \n",
        "!pip install SpeechRecognition\n",
        "import speech_recognition as sr\n",
        "\n",
        "    # load the saved model (after training)\n",
        "def get_audio_sentiments(file_wv):\n",
        "    loading_model=open(\"/content/gdrive/My Drive/voice_model/mlp_classifier.model\", \"rb\")\n",
        "    model = pickle.load(loading_model)\n",
        "    file_wav=open(\"/content/gdrive/My Drive/roboart/03-01-02-01-02-02-01.wav\",\"rb\")\n",
        "\n",
        "    features = extract_feature(file_wav, mfcc=True, chroma=True, mel=True).reshape(1, -1)\n",
        "    result = model.predict(features)[0]\n",
        "   \n",
        "   \n",
        "\n",
        "\n",
        "    print(\"sentiment:\", result)\n",
        "    return result\n",
        "\n",
        "\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting SpeechRecognition\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/26/e1/7f5678cd94ec1234269d23756dbdaa4c8cfaed973412f88ae8adf7893a50/SpeechRecognition-3.8.1-py2.py3-none-any.whl (32.8MB)\n",
            "\u001b[K     |████████████████████████████████| 32.8MB 1.9MB/s \n",
            "\u001b[?25hInstalling collected packages: SpeechRecognition\n",
            "Successfully installed SpeechRecognition-3.8.1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rc7OKIGQHEG8"
      },
      "source": [
        "# all emotions on RAVDESS dataset\n",
        "int2emotion = {\n",
        "    \"01\": \"neutral\",\n",
        "    \"02\": \"calm\",\n",
        "    \"03\": \"happy\",\n",
        "    \"04\": \"sad\",\n",
        "    \"05\": \"angry\",\n",
        "    \"06\": \"fearful\",\n",
        "    \"07\": \"disgust\",\n",
        "    \"08\": \"surprised\"\n",
        "}\n",
        "\n",
        "# we allow only these emotions\n",
        "AVAILABLE_EMOTIONS = {\n",
        "    \"angry\",\n",
        "    \"sad\",\n",
        "    \"happy\",\n",
        "}\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O0RdLr7zHKGa"
      },
      "source": [
        "\n",
        "def extract_feature(file_name, **kwargs):\n",
        "    \"\"\"\n",
        "    Extract feature from audio file `file_name`\n",
        "        Features supported:\n",
        "            - MFCC (mfcc)\n",
        "            - Chroma (chroma)\n",
        "            - MEL Spectrogram Frequency (mel)\n",
        "            - Contrast (contrast)\n",
        "            - Tonnetz (tonnetz)\n",
        "        e.g:\n",
        "        `features = extract_feature(path, mel=True, mfcc=True)`\n",
        "    \"\"\"\n",
        "    mfcc = kwargs.get(\"mfcc\")\n",
        "    chroma = kwargs.get(\"chroma\")\n",
        "    mel = kwargs.get(\"mel\")\n",
        "    contrast = kwargs.get(\"contrast\")\n",
        "    tonnetz = kwargs.get(\"tonnetz\")\n",
        "    with soundfile.SoundFile(file_name) as sound_file:\n",
        "        X = sound_file.read(dtype=\"float32\")\n",
        "        sample_rate = sound_file.samplerate\n",
        "        if chroma or contrast:\n",
        "            stft = np.abs(librosa.stft(X))\n",
        "        result = np.array([])\n",
        "        if mfcc:\n",
        "            mfccs = np.mean(librosa.feature.mfcc(y=X, sr=sample_rate, n_mfcc=40).T, axis=0)\n",
        "            result = np.hstack((result, mfccs))\n",
        "        if chroma:\n",
        "            chroma = np.mean(librosa.feature.chroma_stft(S=stft, sr=sample_rate).T,axis=0)\n",
        "            result = np.hstack((result, chroma))\n",
        "        if mel:\n",
        "            mel = np.mean(librosa.feature.melspectrogram(X, sr=sample_rate).T,axis=0)\n",
        "            result = np.hstack((result, mel))\n",
        "        if contrast:\n",
        "            contrast = np.mean(librosa.feature.spectral_contrast(S=stft, sr=sample_rate).T,axis=0)\n",
        "            result = np.hstack((result, contrast))\n",
        "        if tonnetz:\n",
        "            tonnetz = np.mean(librosa.feature.tonnetz(y=librosa.effects.harmonic(X), sr=sample_rate).T,axis=0)\n",
        "            result = np.hstack((result, tonnetz))\n",
        "    return result\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "25Sz4EntHPJf"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mi0RJaJu041j"
      },
      "source": [
        "# Text"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XbPZtyz50_3J"
      },
      "source": [
        "from textblob import TextBlob\n",
        "def question_polarity(question):\n",
        "  blob = TextBlob(question)\n",
        "  if blob.sentiment.polarity<0:\n",
        "\n",
        "    return \".But It Appear you are not feeling good wanna talk with a real human?\"\n",
        "  elif blob.sentiment.polarity>0:\n",
        "    return \"\"\n",
        "  else:\n",
        "    return \" \""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fi2YSQZ9BFfD"
      },
      "source": [
        "#Face Emotion detection\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "otPSCa9VEJqo"
      },
      "source": [
        "from keras.models import load_model\n",
        "from IPython.display import display, Javascript\n",
        "from google.colab.output import eval_js\n",
        "from time import sleep\n",
        "from keras.preprocessing.image import img_to_array\n",
        "from keras.preprocessing import image\n",
        "import cv2\n",
        "import numpy as np\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B1KomyRlEKQw"
      },
      "source": [
        "face_classifier = cv2.CascadeClassifier('/content/gdrive/My Drive/haarcascade_frontalface_default.xml')\n",
        "classifier = load_model('/content/gdrive/My Drive/Emotion_Detection.h5')\n",
        "\n",
        "class_labels = ['Angry','Happy','Neutral','Sad','Surprise']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YPSd9MXwegM_"
      },
      "source": [
        "def video_stream():\n",
        "  js = Javascript('''\n",
        "    var video;\n",
        "    var div = null;\n",
        "    var stream;\n",
        "    var captureCanvas;\n",
        "    var imgElement;\n",
        "    var labelElement;\n",
        "    \n",
        "    var pendingResolve = null;\n",
        "    var shutdown = false;\n",
        "    \n",
        "    function removeDom() {\n",
        "       stream.getVideoTracks()[0].stop();\n",
        "       video.remove();\n",
        "       div.remove();\n",
        "       video = null;\n",
        "       div = null;\n",
        "       stream = null;\n",
        "       imgElement = null;\n",
        "       captureCanvas = null;\n",
        "       labelElement = null;\n",
        "    }\n",
        "    \n",
        "    function onAnimationFrame() {\n",
        "      if (!shutdown) {\n",
        "        window.requestAnimationFrame(onAnimationFrame);\n",
        "      }\n",
        "      if (pendingResolve) {\n",
        "        var result = \"\";\n",
        "        if (!shutdown) {\n",
        "          captureCanvas.getContext('2d').drawImage(video, 0, 0, 640, 480);\n",
        "          result = captureCanvas.toDataURL('image/jpeg', 0.8)\n",
        "        }\n",
        "        var lp = pendingResolve;\n",
        "        pendingResolve = null;\n",
        "        lp(result);\n",
        "      }\n",
        "    }\n",
        "    \n",
        "    async function createDom() {\n",
        "      if (div !== null) {\n",
        "        return stream;\n",
        "      }\n",
        "\n",
        "      div = document.createElement('div');\n",
        "      div.style.border = '2px solid black';\n",
        "      div.style.padding = '3px';\n",
        "      div.style.width = '100%';\n",
        "      div.style.maxWidth = '600px';\n",
        "      document.body.appendChild(div);\n",
        "      \n",
        "      const modelOut = document.createElement('div');\n",
        "      modelOut.innerHTML = \"<span>Status:</span>\";\n",
        "      labelElement = document.createElement('span');\n",
        "      labelElement.innerText = 'No data';\n",
        "      labelElement.style.fontWeight = 'bold';\n",
        "      modelOut.appendChild(labelElement);\n",
        "      div.appendChild(modelOut);\n",
        "           \n",
        "      video = document.createElement('video');\n",
        "      video.style.display = 'block';\n",
        "      video.width = div.clientWidth - 6;\n",
        "      video.setAttribute('playsinline', '');\n",
        "      video.onclick = () => { shutdown = true; };\n",
        "      stream = await navigator.mediaDevices.getUserMedia(\n",
        "          {video: { facingMode: \"environment\"}});\n",
        "      div.appendChild(video);\n",
        "\n",
        "      imgElement = document.createElement('img');\n",
        "      imgElement.style.position = 'absolute';\n",
        "      imgElement.style.zIndex = 1;\n",
        "      imgElement.onclick = () => { shutdown = true; };\n",
        "      div.appendChild(imgElement);\n",
        "      \n",
        "      const instruction = document.createElement('div');\n",
        "      instruction.innerHTML = \n",
        "          '<span style=\"color: red; font-weight: bold;\">' +\n",
        "          'When finished, click here or on the video to stop this demo</span>';\n",
        "      div.appendChild(instruction);\n",
        "      instruction.onclick = () => { shutdown = true; };\n",
        "      \n",
        "      video.srcObject = stream;\n",
        "      await video.play();\n",
        "\n",
        "      captureCanvas = document.createElement('canvas');\n",
        "      captureCanvas.width = 640; //video.videoWidth;\n",
        "      captureCanvas.height = 480; //video.videoHeight;\n",
        "      window.requestAnimationFrame(onAnimationFrame);\n",
        "      \n",
        "      return stream;\n",
        "    }\n",
        "    async function stream_frame(label, imgData) {\n",
        "      if (shutdown) {\n",
        "        removeDom();\n",
        "        shutdown = false;\n",
        "        return '';\n",
        "      }\n",
        "\n",
        "      var preCreate = Date.now();\n",
        "      stream = await createDom();\n",
        "      \n",
        "      var preShow = Date.now();\n",
        "      if (label != \"\") {\n",
        "        labelElement.innerHTML = label;\n",
        "      }\n",
        "            \n",
        "      if (imgData != \"\") {\n",
        "        var videoRect = video.getClientRects()[0];\n",
        "        imgElement.style.top = videoRect.top + \"px\";\n",
        "        imgElement.style.left = videoRect.left + \"px\";\n",
        "        imgElement.style.width = videoRect.width + \"px\";\n",
        "        imgElement.style.height = videoRect.height + \"px\";\n",
        "        imgElement.src = imgData;\n",
        "      }\n",
        "      \n",
        "      var preCapture = Date.now();\n",
        "      var result = await new Promise(function(resolve, reject) {\n",
        "        pendingResolve = resolve;\n",
        "      });\n",
        "      shutdown = false;\n",
        "      \n",
        "      return {'create': preShow - preCreate, \n",
        "              'show': preCapture - preShow, \n",
        "              'capture': Date.now() - preCapture,\n",
        "              'img': result};\n",
        "    }\n",
        "    ''')\n",
        "\n",
        "  display(js)\n",
        "  \n",
        "def video_frame(label, bbox):\n",
        "  data = eval_js('stream_frame(\"{}\", \"{}\")'.format(label, bbox))\n",
        "  return data"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s6YR3odC-RpF"
      },
      "source": [
        "face_cascade = cv2.CascadeClassifier(cv2.samples.findFile(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml'))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EX7SCkSnuAcP"
      },
      "source": [
        "emotions=[]\n",
        "emotions"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m_6xOPUAsojD",
        "outputId": "7a964875-4d89-48d9-ca8b-b7a7e402fce9"
      },
      "source": [
        "emotions"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0nEu0YH5vwXT"
      },
      "source": [
        "def most_frequent(List):\n",
        "    occurence_count = Counter(List)\n",
        "    return occurence_count.most_common(1)[0][0]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vL09Ss5NN7tQ"
      },
      "source": [
        "##################################### mohema tala3ha foo2\n",
        "def js_to_image(js_reply):\n",
        "  \"\"\"\n",
        "  Params:\n",
        "          js_reply: JavaScript object containing image from webcam\n",
        "  Returns:\n",
        "          img: OpenCV BGR image\n",
        "  \"\"\"\n",
        "  # decode base64 image\n",
        "  image_bytes = b64decode(js_reply.split(',')[1])\n",
        "  # convert bytes to numpy array\n",
        "  jpg_as_np = np.frombuffer(image_bytes, dtype=np.uint8)\n",
        "  # decode numpy array into OpenCV BGR image\n",
        "  img = cv2.imdecode(jpg_as_np, flags=1)\n",
        "\n",
        "  return img"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "opleEdBbpu6c"
      },
      "source": [
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7NJXGX7qrZGl"
      },
      "source": [
        "##################################################################################      Working         ##############################################################################\n",
        "from collections import Counter\n",
        "label_html = 'Capturing...'\n",
        "\n",
        "# initialze bounding box to empty\n",
        "bbox = ''\n",
        "count = 0 \n",
        "def read_face_emotion():\n",
        "  video_stream()\n",
        "  emotions=[]\n",
        "  \n",
        "  while True:\n",
        "\n",
        "      js_reply = video_frame(label_html, bbox)\n",
        "      labels=[]\n",
        "      if not js_reply:\n",
        "          break\n",
        "\n",
        "      # convert JS response to OpenCV Image\n",
        "      img = js_to_image(js_reply[\"img\"])\n",
        "\n",
        "      # create transparent overlay for bounding box\n",
        "      bbox_array = np.zeros([480,640,4], dtype=np.uint8)\n",
        "\n",
        "      # grayscale image for face detection\n",
        "      gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)\n",
        "\n",
        "      # get face region coordinates\n",
        "      faces = face_cascade.detectMultiScale(gray)\n",
        "      # get face bounding box for overlay\n",
        "      for (x,y,w,h) in faces:\n",
        "        bbox_array = cv2.rectangle(bbox_array,(x,y),(x+w,y+h),(255,0,0),2)\n",
        "        roi_gray = gray[y:y+h,x:x+w]\n",
        "        roi_gray = cv2.resize(roi_gray,(48,48),interpolation=cv2.INTER_AREA)\n",
        "        \n",
        "\n",
        "        if np.sum([roi_gray])!=0:\n",
        "            roi = roi_gray.astype('float')/255.0\n",
        "            roi = img_to_array(roi)\n",
        "            roi = np.expand_dims(roi,axis=0)\n",
        "            preds = classifier.predict(roi)[0]\n",
        "           # print(\"\\nprediction = \",preds)\n",
        "            label=class_labels[preds.argmax()]\n",
        "          #  print(\"\\nprediction max = \",preds.argmax())\n",
        "          #  print(\"\\nlabel = \",label)\n",
        "\n",
        "            emotions.append(label)\n",
        "            try:\n",
        "              if(len(emotions)%7==0):\n",
        "                \n",
        "                return most_frequent(emotions)\n",
        "\n",
        "              \n",
        "            except Exception as e:\n",
        "              continue\n",
        "               \n",
        "\n",
        "\n",
        "            \n",
        "\n",
        "            label_position = (x,y)\n",
        "            cv2.putText(img,label,label_position,cv2.FONT_HERSHEY_SIMPLEX,2,(0,255,0),3)\n",
        "\n",
        "  # return(mode(emotions))\n",
        "\n",
        "    \n",
        "      # convert overlay of bbox into bytes\n",
        "      \n",
        "      # update bbox so next frame gets new overlay\n",
        "    "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VbX30ImXFyed",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "outputId": "868541c5-b778-4db2-f94d-119416377c1b"
      },
      "source": [
        "read_face_emotion()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "    var video;\n",
              "    var div = null;\n",
              "    var stream;\n",
              "    var captureCanvas;\n",
              "    var imgElement;\n",
              "    var labelElement;\n",
              "    \n",
              "    var pendingResolve = null;\n",
              "    var shutdown = false;\n",
              "    \n",
              "    function removeDom() {\n",
              "       stream.getVideoTracks()[0].stop();\n",
              "       video.remove();\n",
              "       div.remove();\n",
              "       video = null;\n",
              "       div = null;\n",
              "       stream = null;\n",
              "       imgElement = null;\n",
              "       captureCanvas = null;\n",
              "       labelElement = null;\n",
              "    }\n",
              "    \n",
              "    function onAnimationFrame() {\n",
              "      if (!shutdown) {\n",
              "        window.requestAnimationFrame(onAnimationFrame);\n",
              "      }\n",
              "      if (pendingResolve) {\n",
              "        var result = \"\";\n",
              "        if (!shutdown) {\n",
              "          captureCanvas.getContext('2d').drawImage(video, 0, 0, 640, 480);\n",
              "          result = captureCanvas.toDataURL('image/jpeg', 0.8)\n",
              "        }\n",
              "        var lp = pendingResolve;\n",
              "        pendingResolve = null;\n",
              "        lp(result);\n",
              "      }\n",
              "    }\n",
              "    \n",
              "    async function createDom() {\n",
              "      if (div !== null) {\n",
              "        return stream;\n",
              "      }\n",
              "\n",
              "      div = document.createElement('div');\n",
              "      div.style.border = '2px solid black';\n",
              "      div.style.padding = '3px';\n",
              "      div.style.width = '100%';\n",
              "      div.style.maxWidth = '600px';\n",
              "      document.body.appendChild(div);\n",
              "      \n",
              "      const modelOut = document.createElement('div');\n",
              "      modelOut.innerHTML = \"<span>Status:</span>\";\n",
              "      labelElement = document.createElement('span');\n",
              "      labelElement.innerText = 'No data';\n",
              "      labelElement.style.fontWeight = 'bold';\n",
              "      modelOut.appendChild(labelElement);\n",
              "      div.appendChild(modelOut);\n",
              "           \n",
              "      video = document.createElement('video');\n",
              "      video.style.display = 'block';\n",
              "      video.width = div.clientWidth - 6;\n",
              "      video.setAttribute('playsinline', '');\n",
              "      video.onclick = () => { shutdown = true; };\n",
              "      stream = await navigator.mediaDevices.getUserMedia(\n",
              "          {video: { facingMode: \"environment\"}});\n",
              "      div.appendChild(video);\n",
              "\n",
              "      imgElement = document.createElement('img');\n",
              "      imgElement.style.position = 'absolute';\n",
              "      imgElement.style.zIndex = 1;\n",
              "      imgElement.onclick = () => { shutdown = true; };\n",
              "      div.appendChild(imgElement);\n",
              "      \n",
              "      const instruction = document.createElement('div');\n",
              "      instruction.innerHTML = \n",
              "          '<span style=\"color: red; font-weight: bold;\">' +\n",
              "          'When finished, click here or on the video to stop this demo</span>';\n",
              "      div.appendChild(instruction);\n",
              "      instruction.onclick = () => { shutdown = true; };\n",
              "      \n",
              "      video.srcObject = stream;\n",
              "      await video.play();\n",
              "\n",
              "      captureCanvas = document.createElement('canvas');\n",
              "      captureCanvas.width = 640; //video.videoWidth;\n",
              "      captureCanvas.height = 480; //video.videoHeight;\n",
              "      window.requestAnimationFrame(onAnimationFrame);\n",
              "      \n",
              "      return stream;\n",
              "    }\n",
              "    async function stream_frame(label, imgData) {\n",
              "      if (shutdown) {\n",
              "        removeDom();\n",
              "        shutdown = false;\n",
              "        return '';\n",
              "      }\n",
              "\n",
              "      var preCreate = Date.now();\n",
              "      stream = await createDom();\n",
              "      \n",
              "      var preShow = Date.now();\n",
              "      if (label != \"\") {\n",
              "        labelElement.innerHTML = label;\n",
              "      }\n",
              "            \n",
              "      if (imgData != \"\") {\n",
              "        var videoRect = video.getClientRects()[0];\n",
              "        imgElement.style.top = videoRect.top + \"px\";\n",
              "        imgElement.style.left = videoRect.left + \"px\";\n",
              "        imgElement.style.width = videoRect.width + \"px\";\n",
              "        imgElement.style.height = videoRect.height + \"px\";\n",
              "        imgElement.src = imgData;\n",
              "      }\n",
              "      \n",
              "      var preCapture = Date.now();\n",
              "      var result = await new Promise(function(resolve, reject) {\n",
              "        pendingResolve = resolve;\n",
              "      });\n",
              "      shutdown = false;\n",
              "      \n",
              "      return {'create': preShow - preCreate, \n",
              "              'show': preCapture - preShow, \n",
              "              'capture': Date.now() - preCapture,\n",
              "              'img': result};\n",
              "    }\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a7B3vamnS6Cp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "08a22a06-553d-41fd-8088-41f290d272e1"
      },
      "source": [
        "emotions"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LEF0NRwEtz9c"
      },
      "source": [
        "# def most_common(List):\n",
        "#     return(mode(List))\n",
        "#read_face_emotion()\n",
        "# def threaded_face_emotion():\n",
        "#   read_face_emotion()\n",
        "#   most_common(emotions)\n",
        "  ################################### faksana"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-YQYbbphvVIS"
      },
      "source": [
        "# import threading\n",
        "\n",
        "# t1=threading.Thread(target=read_face_emotion)\n",
        "# t1.start()\n",
        "# print(threading.active_count() )\n",
        "# print('fierst')\n",
        "\n",
        "# print('hello')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aDRtarPIf3iz"
      },
      "source": [
        "# video_stream()\n",
        "  ################################### faksana  ################################### faksana  ################################### faksana  ################################### faksana\n",
        "# # label for video\n",
        "# label_html = 'Front Camera'\n",
        "# # initialze bounding box to empty\n",
        "# bbox = ''\n",
        "# count = 0 \n",
        "\n",
        "# emotions=[]\n",
        "# ###################################\n",
        "# def face_emotion():\n",
        "#   video_stream()\n",
        "#   print('out')\n",
        "#   while True:\n",
        "#     print('in')\n",
        "#     js_reply = video_frame(label_html, bbox)\n",
        "#     print('2222222222')\n",
        "#     if not js_reply:\n",
        "#         print('not')\n",
        "#         return\n",
        "#     labels = []\n",
        "#     print('1')\n",
        "#     # convert JS response to OpenCV Image\n",
        "#     img = js_to_image(js_reply[\"img\"])\n",
        "#     print('2')\n",
        "#     # create transparent overlay for bounding box\n",
        "#     bbox_array = np.zeros([480,640,4], dtype=np.uint8)\n",
        "#     print('3')\n",
        "#     # grayscale image for face detection\n",
        "#     gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)\n",
        "#     print('4')\n",
        "#     # get face region coordinates\n",
        "#     faces = face_cascade.detectMultiScale(gray)\n",
        "#     #faces = face_classifier.detectMultiScale(gray,1.3,5)\n",
        "\n",
        "\n",
        "#     print('before loop')\n",
        "\n",
        "#     # get face bounding box for overlay\n",
        "#     for (x,y,w,h) in faces:\n",
        "#         print('in for loop')\n",
        "#        # cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)\n",
        "#         roi_gray = gray[y:y+h,x:x+w]\n",
        "#         roi_gray = cv2.resize(roi_gray,(48,48),interpolation=cv2.INTER_AREA)\n",
        "\n",
        "        \n",
        "#         if np.sum([roi_gray])!=0:\n",
        "#             roi = roi_gray.astype('float')/255.0\n",
        "#             roi = img_to_array(roi)\n",
        "#             roi = np.expand_dims(roi,axis=0)\n",
        "\n",
        "#         # make a prediction on the ROI, then lookup the class\n",
        "\n",
        "#             preds = classifier.predict(roi)[0]\n",
        "#             print(\"\\nprediction = \",preds)\n",
        "#             label=class_labels[preds.argmax()]\n",
        "#             emotions.append(label)\n",
        "#             fs=True\n",
        "#             print(fs)\n",
        "#             print(len(emotions))\n",
        "#             try:\n",
        "#               print('in try')\n",
        "#               print(len(emotions))\n",
        "#               if len(emotions)%5==0:\n",
        "#                 print('most common emotion: '+most_common(emotions))\n",
        "#                 fs=False\n",
        "#                 emotions.clear()\n",
        "#             except Exception as e:\n",
        "#               print('unexpected error in face detection')\n",
        "#               print(\"\\nprediction max = \",preds.argmax())\n",
        "#             #print(\"\\nlabel = \",label)\n",
        "#             label_position = (x,y)\n",
        "#             cv2.putText(img,label,label_position,cv2.FONT_HERSHEY_SIMPLEX,2,(0,255,0),3)\n",
        "#         else:\n",
        "#             cv2.putText(img,'No Face Found',(20,60),cv2.FONT_HERSHEY_SIMPLEX,2,(0,255,0),3)\n",
        "#             print(\"else\")\n",
        "#     # update bbox so next frame gets new overlay\n",
        "# face_emotion()\n",
        "  ################################### faksana  ################################### faksana  ################################### faksana  ################################### faksana"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FUnsBV_oV_ly"
      },
      "source": [
        "# Q&A model now\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VuX5LS5WWCqe"
      },
      "source": [
        "from transformers import BertTokenizer\n",
        "from transformers import BertForQuestionAnswering\n",
        "import torch\n",
        "import textwrap\n",
        "from textblob import TextBlob\n",
        "import nltk"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3fWXvHxkGiVE"
      },
      "source": [
        "Load the Pretrained Model for Question Answering Bert"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 168,
          "referenced_widgets": [
            "3e85a7c77fea4b4393a7a2c360fa1b28",
            "e7cdc223299c41fb804f2c5cf1fe7bf5",
            "1efe074d03fb45e9a9de50b0992769e1",
            "7c018861330c4e0089407f5ca105c66d",
            "12e6ea3209b14d4dad604e5bbe26a305",
            "f1baee99143e4638ac300f4a78382a86",
            "bb14e63654af45e28e100edff33a92fe",
            "9496d104b4354493a19fa68ec417bec7",
            "75f417c9bd4642b4a5ab23f90fe336b5",
            "059d1644b39e4f45afca25ebdd589bd3",
            "3ffb98326d70422baa92cc31b0053e87",
            "4f638fa44699475dafa3edcb07f8e75e",
            "0d39b45d6ebe40268290958a8230ee75",
            "ce94adb808f44b548f442fd794413944",
            "d86199a222f34637bc1ba0ec8fce90af",
            "46f310757357464e94b57dddff32ab4e",
            "c74f0c4afc2c4041b3ae5f303920e730",
            "8acf67f0dc3a4311b65cdef498792bf5",
            "4aeef026048743f1a0b397c04937c5d2",
            "c4396ea0c1424347b2d737aca8fac088",
            "bd7b3b924db74f8facdd93ad733830db",
            "af5967f28896467aa69405dfb53e4090",
            "93d62078cef4428a9b01f88538cb25d0",
            "ed6a400e23e44e00abf45b08cbf3c0ef"
          ]
        },
        "id": "IGRSs5F2WFXW",
        "outputId": "ae176cd1-126f-45d8-be88-77fc03408804"
      },
      "source": [
        "from transformers import AutoTokenizer, AutoModelForQuestionAnswering\n",
        "tokenizer1 = AutoTokenizer.from_pretrained(\"bert-large-uncased-whole-word-masking-finetuned-squad\")\n",
        "\n",
        "model1 = AutoModelForQuestionAnswering.from_pretrained(\"bert-large-uncased-whole-word-masking-finetuned-squad\")\n",
        "\n",
        "#AutoTokenizer.from_pretrained(\"deepset/bert-large-uncased-whole-word-masking-squad2\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "3e85a7c77fea4b4393a7a2c360fa1b28",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=443.0, style=ProgressStyle(description_…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "75f417c9bd4642b4a5ab23f90fe336b5",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=231508.0, style=ProgressStyle(descripti…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c74f0c4afc2c4041b3ae5f303920e730",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=1340675298.0, style=ProgressStyle(descr…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NIahnOYgdlX7"
      },
      "source": [
        "wrapper = textwrap.TextWrapper(width=80)\n",
        "import concurrent.futures\n",
        "\n",
        "\n",
        "def answer_question(question, answer_text):\n",
        "    '''\n",
        "    Takes a `question` string and an `answer_text` string (which contains the\n",
        "    answer), and identifies the words within the `answer_text` that are the\n",
        "    answer. Prints them out.\n",
        "    '''\n",
        " \n",
        "\n",
        "    \n",
        "\n",
        "\n",
        "    input_ids = tokenizer1.encode(question, answer_text)\n",
        "\n",
        "\n",
        "    sep_index = input_ids.index(tokenizer1.sep_token_id)\n",
        "\n",
        "\n",
        "    num_seg_a = sep_index + 1\n",
        "\n",
        "    num_seg_b = len(input_ids) - num_seg_a\n",
        "\n",
        "    segment_ids = [0] * num_seg_a + [1] * num_seg_b\n",
        "\n",
        "    assert len(segment_ids) == len(input_ids)\n",
        "\n",
        "    start_scores, end_scores = model1(torch.tensor([input_ids]), \n",
        "                                     token_type_ids=torch.tensor(\n",
        "                                         [segment_ids]))  \n",
        "\n",
        "\n",
        "    answer_start = torch.argmax(start_scores)\n",
        "    answer_end = torch.argmax(end_scores)\n",
        "\n",
        "\n",
        "    tokens = tokenizer1.convert_ids_to_tokens(input_ids)\n",
        "\n",
        "\n",
        "    answer = tokens[answer_start]\n",
        "\n",
        "\n",
        "    for i in range(answer_start + 1, answer_end + 1):\n",
        "\n",
        "\n",
        "        if tokens[i][0:2] == '##':\n",
        "            answer += tokens[i][2:]\n",
        "\n",
        "\n",
        "        else:\n",
        "            answer += ' ' + tokens[i]\n",
        "    if \"CLS\" in answer:\n",
        "      print('You: '+ question )\n",
        "      print('Sorry i dont know')\n",
        "      return 'Sorry i cant handle that'\n",
        "    elif \"[SEP]\" in answer:\n",
        "      print('You: '+ question )\n",
        "      print('Sorry i dont know')\n",
        "      return 'Sorry i dont understand that yet'\n",
        "    else:\n",
        "      \n",
        "      print('You: '+ question )\n",
        "      print('Answer : '+ answer )\n",
        "      return answer\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yImsxDsKrSl6"
      },
      "source": [
        "def answer_this(question,y=1000):\n",
        "  \n",
        "  if(y<200):\n",
        "    return\n",
        " # question = ['which types of vaccines available to cure covid']\n",
        "  global Selected_Category\n",
        "  global inputs\n",
        "  global model\n",
        "  global prediction\n",
        "  question1=[question]\n",
        "  inputs = bert_encode(string_list=list(question1), \n",
        "                     tokenizer=tokenizer, \n",
        "                     max_seq_length=63)\n",
        "  prediction = model.predict(inputs)\n",
        "  possible_categories=['Comparison','Economic_Effects','Having_COVID','Individual_Response','Nomenclature','Origin','Prevention','Reporting','Societal Effects','Societal Response','Speculation','Symptoms','Testing','Transmission','Treatment']\n",
        "  for i in range (len(possible_categories)):\n",
        "    if np.argmax(prediction)== i:\n",
        "\n",
        "      Selected_Category=possible_categories[i]\n",
        "\n",
        "\n",
        "\n",
        "  # if np.argmax(prediction)== 0:\n",
        "  #   Selected_Category='Comparison'\n",
        "    \n",
        "  # if np.argmax(prediction)== 1:\n",
        "  #   Selected_Category='Economic_Effects'\n",
        "    \n",
        "  # if np.argmax(prediction)== 2:\n",
        "  #   Selected_Category='Having_COVID'\n",
        "    \n",
        "  # if np.argmax(prediction)== 3:\n",
        "  #   Selected_Category='Individual_Response'\n",
        "    \n",
        "  # if np.argmax(prediction)== 4:\n",
        "  #   Selected_Category='Nomenclature'\n",
        "    \n",
        "  # if np.argmax(prediction)== 5:\n",
        "  #   Selected_Category='Origin'\n",
        "    \n",
        "  # if np.argmax(prediction)== 6:\n",
        "  #   Selected_Category='Prevention'\n",
        "    \n",
        "  # if np.argmax(prediction)== 7:\n",
        "  #   Selected_Category='Reporting'\n",
        "    \n",
        "  # if np.argmax(prediction)== 8:\n",
        "  #   Selected_Category='Societal Effects'\n",
        "    \n",
        "  # if np.argmax(prediction)== 9:\n",
        "  #   Selected_Category='Societal Response'\n",
        "    \n",
        "  # if np.argmax(prediction)== 10:\n",
        "  #   Selected_Category='Speculation'\n",
        "    \n",
        "  # if np.argmax(prediction)== 11:\n",
        "  #   Selected_Category='Symptoms'\n",
        "    \n",
        "  # if np.argmax(prediction)== 12:\n",
        "  #   Selected_Category='Testing'\n",
        "    \n",
        "  # if np.argmax(prediction)== 13:\n",
        "  #   Selected_Category='Transmission'\n",
        "    \n",
        "  # if np.argmax(prediction)== 14:\n",
        "  #   Selected_Category='Treatment'\n",
        "    \n",
        "\n",
        "  \n",
        "  flag_exception = False\n",
        "\n",
        "  try:\n",
        "    with open('/content/gdrive/My Drive/Categories/'+Selected_Category+'.txt') as f:\n",
        "      x=f.readlines()\n",
        "    Categorial_Abtract=x\n",
        "    bert_abstract=''.join(Categorial_Abtract)\n",
        "\n",
        "    bert_abstract\n",
        "  except Exception as e:\n",
        "    print('You: '+ question )\n",
        "    print(Selected_Category +' Category is still not available yet.')\n",
        "    return \"Category is still not available yet\"\n",
        "    flag_exception = True\n",
        "\n",
        "  if  flag_exception == True:\n",
        "    print(Selected_Category +' Category is still not available yet.')\n",
        "    return \"Category is still not available yet\"\n",
        "  else:\n",
        "    try:\n",
        "      \n",
        "      \n",
        "      #print(bert_abstract)\n",
        "      return answer_question(question,bert_abstract[:y])\n",
        "    except Exception as e:\n",
        "      print ('ops an error occured  '+ str(e))\n",
        "      answer_this(question,y-100)\n",
        "      \n",
        "\n",
        "  "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "id": "lcW-y9H5CB9Z",
        "outputId": "f20cbe42-1e0a-4462-99ad-1a6f5620f7df"
      },
      "source": [
        "# answer_this('what is covid cure')\n",
        "# answer_this('and where did covid start')\n",
        "# answer_this('what is the symptoms?')\n",
        "# answer_this('what is the incubation period?')\n",
        "# answer_this('when will corona end?')\n",
        "answer_this('what is sars')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "You: what is sars\n",
            "Answer : severe acute respiratory syndrome coronavirus 2\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'severe acute respiratory syndrome coronavirus 2'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DNXVhRvH-2gM"
      },
      "source": [
        "import concurrent.futures\n",
        "import time\n",
        "\n",
        "# message=input()  \n",
        "\n",
        "\n",
        "# with concurrent.futures.ThreadPoolExecutor() as executor:\n",
        "#       future = executor.submit(read_face_emotion)\n",
        "#       future2=executor.submit(answer_this,message)\n",
        "        \n",
        "#       r=future.result()\n",
        "#       print('your face is '+r)\n",
        "    \n",
        "\n",
        "\n",
        "  \n",
        "      "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TW4YSTmRPvfh"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h-9BXTfI8cSW"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MbpILxT0roah"
      },
      "source": [
        "\n",
        "  #answer_this('what is covid cure')\n",
        "# answer_this('and where did covid start')\n",
        "# answer_this('what is the symptoms?')\n",
        "# answer_this('what is the incubation period?')\n",
        "# answer_this('how does corona spread?')\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d_f-4RuMD8S6",
        "outputId": "da23ec54-ce3c-4105-f001-ce296fb84b93"
      },
      "source": [
        "\n",
        "\n",
        "\n",
        "from google.colab import drive\n",
        "\n",
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount(\"/content/gdrive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cv_nefniNIf1",
        "outputId": "852e5a98-aa34-4fe9-e62a-0e7154ff2fce"
      },
      "source": [
        "from flask import Flask\n",
        "!pip install flask-ngrok\n",
        "!pip install flask-wtf\n",
        "! pip install wtforms==2.2.1\n",
        "!pip install pyngrok\n",
        "!ngrok authtoken 1tqnKSRNTcXG8w4GrKSnjYvPVlv_3G54329xNgebzaRBUpGeq"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting flask-ngrok\n",
            "  Downloading https://files.pythonhosted.org/packages/af/6c/f54cb686ad1129e27d125d182f90f52b32f284e6c8df58c1bae54fa1adbc/flask_ngrok-0.0.25-py3-none-any.whl\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from flask-ngrok) (2.23.0)\n",
            "Requirement already satisfied: Flask>=0.8 in /usr/local/lib/python3.7/dist-packages (from flask-ngrok) (1.1.4)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->flask-ngrok) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->flask-ngrok) (2021.5.30)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->flask-ngrok) (3.0.4)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->flask-ngrok) (1.24.3)\n",
            "Requirement already satisfied: click<8.0,>=5.1 in /usr/local/lib/python3.7/dist-packages (from Flask>=0.8->flask-ngrok) (7.1.2)\n",
            "Requirement already satisfied: Jinja2<3.0,>=2.10.1 in /usr/local/lib/python3.7/dist-packages (from Flask>=0.8->flask-ngrok) (2.11.3)\n",
            "Requirement already satisfied: Werkzeug<2.0,>=0.15 in /usr/local/lib/python3.7/dist-packages (from Flask>=0.8->flask-ngrok) (1.0.1)\n",
            "Requirement already satisfied: itsdangerous<2.0,>=0.24 in /usr/local/lib/python3.7/dist-packages (from Flask>=0.8->flask-ngrok) (1.1.0)\n",
            "Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.7/dist-packages (from Jinja2<3.0,>=2.10.1->Flask>=0.8->flask-ngrok) (2.0.1)\n",
            "Installing collected packages: flask-ngrok\n",
            "Successfully installed flask-ngrok-0.0.25\n",
            "Collecting flask-wtf\n",
            "  Downloading https://files.pythonhosted.org/packages/16/df/ba01a1d9a8b9d933c0e746c7ca015bfeee2b1e4e32f0527de3bbe6ac65a7/Flask_WTF-0.15.1-py2.py3-none-any.whl\n",
            "Collecting WTForms\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/e0/31/614fc7dc7d76005b0acb8c0c8920d962b83d7422b4ba912886dfb63f86ff/WTForms-2.3.3-py2.py3-none-any.whl (169kB)\n",
            "\u001b[K     |████████████████████████████████| 174kB 5.4MB/s \n",
            "\u001b[?25hRequirement already satisfied: Flask in /usr/local/lib/python3.7/dist-packages (from flask-wtf) (1.1.4)\n",
            "Requirement already satisfied: itsdangerous in /usr/local/lib/python3.7/dist-packages (from flask-wtf) (1.1.0)\n",
            "Requirement already satisfied: MarkupSafe in /usr/local/lib/python3.7/dist-packages (from WTForms->flask-wtf) (2.0.1)\n",
            "Requirement already satisfied: Jinja2<3.0,>=2.10.1 in /usr/local/lib/python3.7/dist-packages (from Flask->flask-wtf) (2.11.3)\n",
            "Requirement already satisfied: Werkzeug<2.0,>=0.15 in /usr/local/lib/python3.7/dist-packages (from Flask->flask-wtf) (1.0.1)\n",
            "Requirement already satisfied: click<8.0,>=5.1 in /usr/local/lib/python3.7/dist-packages (from Flask->flask-wtf) (7.1.2)\n",
            "Installing collected packages: WTForms, flask-wtf\n",
            "Successfully installed WTForms-2.3.3 flask-wtf-0.15.1\n",
            "Collecting wtforms==2.2.1\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/9f/c8/dac5dce9908df1d9d48ec0e26e2a250839fa36ea2c602cc4f85ccfeb5c65/WTForms-2.2.1-py2.py3-none-any.whl (166kB)\n",
            "\u001b[K     |████████████████████████████████| 174kB 6.9MB/s \n",
            "\u001b[?25hInstalling collected packages: wtforms\n",
            "  Found existing installation: WTForms 2.3.3\n",
            "    Uninstalling WTForms-2.3.3:\n",
            "      Successfully uninstalled WTForms-2.3.3\n",
            "Successfully installed wtforms-2.2.1\n",
            "Collecting pyngrok\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/6b/4e/a2fe095bbe17cf26424c4abcd22a0490e22d01cc628f25af5e220ddbf6f0/pyngrok-5.0.5.tar.gz (745kB)\n",
            "\u001b[K     |████████████████████████████████| 747kB 5.0MB/s \n",
            "\u001b[?25hRequirement already satisfied: PyYAML in /usr/local/lib/python3.7/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-5.0.5-cp37-none-any.whl size=19262 sha256=f374795a5cc3a183780f76da59dee7ea320160afe245f8a636edd73f60f72d13\n",
            "  Stored in directory: /root/.cache/pip/wheels/0c/13/64/5ebbcc22eaf53fdf5766b397c1fb17c83f5775fdccf0ea1b88\n",
            "Successfully built pyngrok\n",
            "Installing collected packages: pyngrok\n",
            "Successfully installed pyngrok-5.0.5\n",
            "Authtoken saved to configuration file: /root/.ngrok2/ngrok.yml\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZaOd49m_uKmP"
      },
      "source": [
        "\n",
        "# Database"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "25tpP13otpgi",
        "outputId": "f571adb0-0b91-4308-c446-dad7c54234b4"
      },
      "source": [
        "!pip install Flask-SQLAlchemy"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting Flask-SQLAlchemy\n",
            "  Downloading https://files.pythonhosted.org/packages/26/2c/9088b6bd95bca539230bbe9ad446737ed391aab9a83aff403e18dded3e75/Flask_SQLAlchemy-2.5.1-py2.py3-none-any.whl\n",
            "Requirement already satisfied: Flask>=0.10 in /usr/local/lib/python3.7/dist-packages (from Flask-SQLAlchemy) (1.1.4)\n",
            "Requirement already satisfied: SQLAlchemy>=0.8.0 in /usr/local/lib/python3.7/dist-packages (from Flask-SQLAlchemy) (1.4.20)\n",
            "Requirement already satisfied: Jinja2<3.0,>=2.10.1 in /usr/local/lib/python3.7/dist-packages (from Flask>=0.10->Flask-SQLAlchemy) (2.11.3)\n",
            "Requirement already satisfied: click<8.0,>=5.1 in /usr/local/lib/python3.7/dist-packages (from Flask>=0.10->Flask-SQLAlchemy) (7.1.2)\n",
            "Requirement already satisfied: Werkzeug<2.0,>=0.15 in /usr/local/lib/python3.7/dist-packages (from Flask>=0.10->Flask-SQLAlchemy) (1.0.1)\n",
            "Requirement already satisfied: itsdangerous<2.0,>=0.24 in /usr/local/lib/python3.7/dist-packages (from Flask>=0.10->Flask-SQLAlchemy) (1.1.0)\n",
            "Requirement already satisfied: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from SQLAlchemy>=0.8.0->Flask-SQLAlchemy) (4.6.0)\n",
            "Requirement already satisfied: greenlet!=0.4.17; python_version >= \"3\" in /usr/local/lib/python3.7/dist-packages (from SQLAlchemy>=0.8.0->Flask-SQLAlchemy) (1.1.0)\n",
            "Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.7/dist-packages (from Jinja2<3.0,>=2.10.1->Flask>=0.10->Flask-SQLAlchemy) (2.0.1)\n",
            "Requirement already satisfied: typing-extensions>=3.6.4; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from importlib-metadata; python_version < \"3.8\"->SQLAlchemy>=0.8.0->Flask-SQLAlchemy) (3.7.4.3)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata; python_version < \"3.8\"->SQLAlchemy>=0.8.0->Flask-SQLAlchemy) (3.4.1)\n",
            "Installing collected packages: Flask-SQLAlchemy\n",
            "Successfully installed Flask-SQLAlchemy-2.5.1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UOePJe6DnYen"
      },
      "source": [
        "\n",
        "from flask import Flask,redirect,url_for,render_template,request,jsonify\n",
        "from flask_wtf import FlaskForm\n",
        "from flask_ngrok import run_with_ngrok\n",
        "#!ngrok authtoken 1tqnKSRNTcXG8w4GrKSnjYvPVlv_3G54329xNgebzaRBUpGeq\n",
        "from flask_sqlalchemy import SQLAlchemy\n",
        "from wtforms import StringField, PasswordField, SubmitField, BooleanField\n",
        "from wtforms.validators import DataRequired, Length, Email, EqualTo, ValidationError\n",
        "app = Flask(__name__,template_folder='/content/gdrive/MyDrive/roboart',static_folder='/content/gdrive/MyDrive/roboart/static',)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VdS7nOk9oRT7",
        "outputId": "13b3d19a-4baf-4486-fe78-86bad8eca733"
      },
      "source": [
        "app.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///gdrive/MyDrive/roboart/chatbot.db'\n",
        "db = SQLAlchemy(app)\n",
        "from datetime import datetime\n",
        "from flask_login import UserMixin\n",
        "\n",
        "#                                                                     Creating Database\n",
        "class User(db.Model,UserMixin):\n",
        "\n",
        "  id = db.Column(db.Integer, primary_key=True)\n",
        "  name = db.Column(db.String(20), nullable=False)\n",
        "  email = db.Column(db.String(120), unique=True, nullable=False)\n",
        "  phone_number=db.Column(db.String(11),nullable= False)\n",
        "  password = db.Column(db.String(60), nullable=False)\n",
        "  is_active=db.Column(db.Boolean,default=True)\n",
        "  admin=db.Column(db.Boolean,default=False)\n",
        "\n",
        "  def __repr__(self):\n",
        "    return f\"User('{self.name}', '{self.email}', ' {self.phone_number} ','{self.password} ,', {self.is_active} ,', {self.admin} )'\"\n",
        "\n",
        "  \n",
        "\n",
        "class FeedBack(db.Model):\n",
        "  id = db.Column(db.Integer, primary_key=True)\n",
        "  rate=db.Column(db.Integer, default=3)\n",
        "  text=db.Column(db.Text, nullable=False)\n",
        "  user_id = db.Column(db.Integer, db.ForeignKey('user.id'), nullable=False)\n",
        "  def __repr__(self):\n",
        "    return f\"FeedBack('{self.rate}', '{self.text}', '{self.user_id}')\"\n",
        "\n",
        "class Message(db.Model):\n",
        "  id=db.Column(db.Integer, primary_key=True)\n",
        "  text=db.Column(db.Text, nullable=False)\n",
        "  date_posted = db.Column(db.DateTime, nullable=False, default=datetime.utcnow)\n",
        "  sender= db.Column(db.Boolean,default=False)\n",
        "  user_id = db.Column(db.Integer, db.ForeignKey('user.id'), nullable=False)\n",
        "  def __repr__(self):\n",
        "    return f\"Message('{self.text}', '{self.date_posted}', '{self.sender}',' {self.user_id}   ')\"\n",
        "# db.create_all()\n",
        "\n",
        "#                                                                  End of creating Database\n",
        "# user1=User(name='albert2', email='b2@gmail.com',phone_number=\"01220651816\",password=\"asdasd\")\n",
        "# db.session.add(user1)\n",
        "# db.session.commit()\n",
        "User.query.all()\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/flask_sqlalchemy/__init__.py:873: FSADeprecationWarning: SQLALCHEMY_TRACK_MODIFICATIONS adds significant overhead and will be disabled by default in the future.  Set it to True or False to suppress this warning.\n",
            "  'SQLALCHEMY_TRACK_MODIFICATIONS adds significant overhead and '\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[User('albert2', 'b@gmail.com', ' 01220651816 ','asdasd ,', True ,', False )',\n",
              " User('albert2', 'b2@gmail.com', ' 01220651816 ','asdasd ,', True ,', False )']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u2n11L8oRlmc"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q3TFa-bwuBZw",
        "outputId": "55869ae9-72f8-4460-c8ac-3c2e39e43514"
      },
      "source": [
        "pip install flask_login\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting flask_login\n",
            "  Downloading https://files.pythonhosted.org/packages/2b/83/ac5bf3279f969704fc1e63f050c50e10985e50fd340e6069ec7e09df5442/Flask_Login-0.5.0-py2.py3-none-any.whl\n",
            "Requirement already satisfied: Flask in /usr/local/lib/python3.7/dist-packages (from flask_login) (1.1.4)\n",
            "Requirement already satisfied: Jinja2<3.0,>=2.10.1 in /usr/local/lib/python3.7/dist-packages (from Flask->flask_login) (2.11.3)\n",
            "Requirement already satisfied: click<8.0,>=5.1 in /usr/local/lib/python3.7/dist-packages (from Flask->flask_login) (7.1.2)\n",
            "Requirement already satisfied: Werkzeug<2.0,>=0.15 in /usr/local/lib/python3.7/dist-packages (from Flask->flask_login) (1.0.1)\n",
            "Requirement already satisfied: itsdangerous<2.0,>=0.24 in /usr/local/lib/python3.7/dist-packages (from Flask->flask_login) (1.1.0)\n",
            "Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.7/dist-packages (from Jinja2<3.0,>=2.10.1->Flask->flask_login) (2.0.1)\n",
            "Installing collected packages: flask-login\n",
            "Successfully installed flask-login-0.5.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TtmxvrbIQKeU"
      },
      "source": [
        "import cv2\n",
        "\n",
        "\n",
        "camera=cv2.VideoCapture(0)\n",
        "\n",
        "def generate_frames():\n",
        "    while True:\n",
        "            \n",
        "        ## read the camera frame\n",
        "        success,frame=camera.read()\n",
        "        if not success:\n",
        "            break\n",
        "        else:\n",
        "            ret,buffer=cv2.imencode('.jpg',frame)\n",
        "            frame=buffer.tobytes()\n",
        "\n",
        "        yield(b'--frame\\r\\n'\n",
        "                   b'Content-Type: image/jpeg\\r\\n\\r\\n' + frame + b'\\r\\n')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "woSaMyvkQM1j",
        "outputId": "81f27e83-1e3c-4ac3-d3ab-79ca4843b437"
      },
      "source": [
        "generate_frames()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<generator object generate_frames at 0x7f5e6cd31750>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DBzpiIuqil-f",
        "outputId": "ceb27d38-d538-4562-f29c-c7c6bdd80a64"
      },
      "source": [
        "pip install camera\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: camera in /usr/local/lib/python3.7/dist-packages (1.3.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N-5xr_wkkY3e",
        "outputId": "86a7a6a7-9b56-4d6c-b006-85681da4dfd0"
      },
      "source": [
        "pip install numpy\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (1.19.5)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jx3gLf2JhqvX"
      },
      "source": [
        "from camera import Camera\n",
        "import cv2\n",
        "import numpy as np\n",
        "\n",
        "def gen(camera):\n",
        "    camera=cv2.VideoCapture(0)\n",
        "    frame=camera.read()\n",
        "    while True:\n",
        "        ret,buffer=cv2.imencode('.jpg',frame)\n",
        "        # frame = cv2.UMat(frame).get()\n",
        "        frame = np.array(frame)\n",
        "        #frame = buffer.tobytes()\n",
        "\n",
        "        yield (b'--frame\\r\\n'\n",
        "               b'Content-Type: image/jpeg\\r\\n\\r\\n' + frame + b'\\r\\n')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zxzbKLnXkX8f"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QLvIcqT0h1dZ"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QrJKrQ-1CbZw"
      },
      "source": [
        "def download_file(url):\n",
        "    local_filename = url.split('/')[-1]\n",
        "    # NOTE the stream=True parameter below\n",
        "    with requests.get(url, stream=True) as r:\n",
        "        r.raise_for_status()\n",
        "        with open(local_filename, 'wb') as f:\n",
        "            for chunk in r.iter_content(chunk_size=8192): \n",
        "                # If you have chunk encoded response uncomment if\n",
        "                # and set chunk_size parameter to None.\n",
        "                #if chunk: \n",
        "                f.write(chunk)\n",
        "    return local_filename"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rzeYeYdhNCPt"
      },
      "source": [
        "\n",
        "# Web App\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xKEMM3LvNI8x",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "32f040d7-88be-429c-fcb3-56e07b93bd25"
      },
      "source": [
        "app = Flask(__name__,template_folder='/content/gdrive/MyDrive/roboart',static_folder='/content/gdrive/MyDrive/roboart/static',)\n",
        "\n",
        "app._static_folder = '/content/gdrive/MyDrive/roboart/static'\n",
        "run_with_ngrok(app)   \n",
        "app.config['SECRET_KEY'] = '5791628bb0b13ce0c676dfde585ba245'\n",
        "from flask_login import login_user\n",
        "from flask import request\n",
        "from flask_login import LoginManager\n",
        "from flask import Response\n",
        "import webbrowser\n",
        "import requests\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "@app.route(\"/options\")\n",
        "def pricing():\n",
        "    return render_template('pricing.html')    \n",
        "@app.route(\"/team\")\n",
        "def team():\n",
        "    return render_template('team.html')  \n",
        "\n",
        "@app.route(\"/faq\")\n",
        "def faq():\n",
        "    return render_template('faq.html')\n",
        "@app.route(\"/\")\n",
        "@app.route(\"/contact\")\n",
        "def contact():\n",
        "    return render_template('contact.html')\n",
        "\n",
        "@app.route(\"/index\", methods=['GET','POST'])\n",
        "def home():\n",
        "    return render_template('index-2.html')\n",
        "@app.route(\"/about\")\n",
        "def about():\n",
        "    return render_template('about.html')\n",
        "\n",
        "# @app.route(\"/\")\n",
        "# def videoindex():\n",
        "#     return render_template('videoindex.html')\n",
        "# @app.route(\"/videostream\")\n",
        "# def videostream():\n",
        "#     return Response(generate_frames(),mimetype='multipart/x-mixed-replace; boundary=frame')\n",
        "@app.route('/answer/video',methods=['GET','POST'])\n",
        "def answer_video():\n",
        "  url=request.args.get('video')\n",
        "  print(url)\n",
        "  webbrowser.open(url)\n",
        "  r = requests.get(url, allow_redirects=True)\n",
        "  open('video.mp4', 'wb').write(r.content)\n",
        "  print('this is the request')\n",
        "  print(r)\n",
        "  # print('awel el function')\n",
        "  # video_mp4=request.files[\"video_data\"]\n",
        "  # print(type(video_mp4))\n",
        "  # print(\"wslt hena le b3d el function\")\n",
        "  # with open('Albert.webm', 'wb') as video:\n",
        "  #   video_mp4.save(video)\n",
        "\n",
        "  return jsonify({'emotion':'deen om'  })\n",
        "\n",
        "\n",
        " \n",
        "\n",
        "  \n",
        "@app.route('/answer/voice')\n",
        "def answer_voice():\n",
        " \n",
        "  audio_wav=request.args.get(\"audio\")\n",
        "  # r = sr.Recognizer()\n",
        "  # with sr.AudioFile(audio_wav) as source:\n",
        "\n",
        "  #   audio_data = r.record(source)\n",
        "\n",
        "  # text = r.recognize_google(audio_data)\n",
        "  # print(text)\n",
        "  result=get_audio_sentiments(audio_wav)\n",
        "  return jsonify({'emotion':result  })\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "@app.route(\"/chatbotmic\")\n",
        "def chatbotmic():\n",
        "    return render_template('chatbotmic.html')    \n",
        "@app.route(\"/chatbotvideo\")\n",
        "def chatbotvideo():\n",
        "    return render_template('chatbotvid.html')    \n",
        "\n",
        "\n",
        "@app.route(\"/login\", methods=['POST','GET'])\n",
        "def login():\n",
        "    print('after def login')\n",
        "    form = LoginForm()\n",
        "    if form.validate_on_submit():\n",
        "        user = User.query.filter_by(email=form.email.data,password=form.password.data).first()\n",
        "        if user:\n",
        "            return '<h1>Login success<h1>'\n",
        "            login_user(user, remember=form.remember.data)\n",
        "            next_page = request.args.get('\"/team\"')\n",
        "            return redirect(next_page) if next_page else redirect(url_for('/'))\n",
        "        else:\n",
        "            flash('Login Unsuccessful. Please check email and password', 'danger')\n",
        "    return render_template('login.html', title='home', form=form)\n",
        "\n",
        "    # #form = LoginForm()\n",
        "    # #print (form.errors)\n",
        "    # if request.method=='POST':\n",
        "    #     return '<h1>'+request.form['Email']+' ',request.form['Password']+'<h1>' \n",
        "    #     print('submit button is pressed')\n",
        "    #     user = User.query.filter_by(email=request.form['Email'].data,password=request.form['Password']).first()\n",
        "    #     print(user)\n",
        "    #     if user:\n",
        "    #         #login_user(user, remember=form.remember.data)\n",
        "    #         #return redirect(next_page) if next_page else redirect(url_for('/'))\n",
        "    #             return render_template('index-2.html')\n",
        "    #     else:\n",
        "    #         flash('Login Unsuccessful. Please check email and password', 'danger')\n",
        "    # print('end of function')\n",
        "    # return render_template('login1.html', title='home')\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "class LoginForm(FlaskForm):\n",
        "    email = StringField('Email',\n",
        "                        validators=[DataRequired(), Email()])\n",
        "    password = PasswordField('Password', validators=[DataRequired()])\n",
        "    remember = BooleanField('Remember Me')\n",
        "    submit = SubmitField('Login')\n",
        "\n",
        "\n",
        "@app.route(\"/chatbot-text\",methods=[\"GET\"])\n",
        "\n",
        "def chatbot():\n",
        "    return render_template('chatbottext.html')\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "@app.route(\"/chatbot/answer\", methods=['GET'])\n",
        "def get_bot_response():\n",
        "  \n",
        "  \n",
        "    \n",
        "  question = request.args.get(\"question\",0,type=str)\n",
        "  print(\"testing 3 message = \"+question )\n",
        "  answer=answer_this(question)\n",
        "  print(\"testing\" +answer)\n",
        "  sent= question_polarity(question)\n",
        "  print(sent)\n",
        "  final=answer+\" \"+sent  \n",
        "  return jsonify({'response':final})\n",
        "\n",
        "#app.debug = True\n",
        "\n",
        "login_manager = LoginManager()\n",
        "login_manager.init_app(app)\n",
        "login_manager.login_view = 'login'\n",
        "@login_manager.user_loader\n",
        "def load_user(user_id):\n",
        "    try:\n",
        "        return User.query.get(user_id)\n",
        "    except:\n",
        "        return None\n",
        "app.run()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            " * Serving Flask app \"__main__\" (lazy loading)\n",
            " * Environment: production\n",
            "\u001b[31m   WARNING: This is a development server. Do not use it in a production deployment.\u001b[0m\n",
            "\u001b[2m   Use a production WSGI server instead.\u001b[0m\n",
            " * Debug mode: off\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            " * Running on http://127.0.0.1:5000/ (Press CTRL+C to quit)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            " * Running on http://e52b3beba18f.ngrok.io\n",
            " * Traffic stats available on http://127.0.0.1:4040\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "127.0.0.1 - - [13/Jul/2021 18:12:28] \"\u001b[37mGET / HTTP/1.1\u001b[0m\" 200 -\n",
            "127.0.0.1 - - [13/Jul/2021 18:12:28] \"\u001b[37mGET /static/css/responsive.css HTTP/1.1\u001b[0m\" 200 -\n",
            "127.0.0.1 - - [13/Jul/2021 18:12:29] \"\u001b[37mGET /static/css/style.css HTTP/1.1\u001b[0m\" 200 -\n",
            "127.0.0.1 - - [13/Jul/2021 18:12:29] \"\u001b[37mGET /static/css/bootstrap.min.css HTTP/1.1\u001b[0m\" 200 -\n",
            "127.0.0.1 - - [13/Jul/2021 18:12:29] \"\u001b[37mGET /static/css/owl.carousel.css HTTP/1.1\u001b[0m\" 200 -\n",
            "127.0.0.1 - - [13/Jul/2021 18:12:29] \"\u001b[37mGET /static/css/owl.transitions.css HTTP/1.1\u001b[0m\" 200 -\n",
            "127.0.0.1 - - [13/Jul/2021 18:12:29] \"\u001b[37mGET /static/css/animate.css HTTP/1.1\u001b[0m\" 200 -\n",
            "127.0.0.1 - - [13/Jul/2021 18:12:29] \"\u001b[37mGET /static/css/meanmenu.min.css HTTP/1.1\u001b[0m\" 200 -\n",
            "127.0.0.1 - - [13/Jul/2021 18:12:29] \"\u001b[37mGET /static/css/font-awesome.min.css HTTP/1.1\u001b[0m\" 200 -\n",
            "127.0.0.1 - - [13/Jul/2021 18:12:29] \"\u001b[37mGET /static/css/flaticon.css HTTP/1.1\u001b[0m\" 200 -\n",
            "127.0.0.1 - - [13/Jul/2021 18:12:29] \"\u001b[37mGET /static/css/icon.css HTTP/1.1\u001b[0m\" 200 -\n",
            "127.0.0.1 - - [13/Jul/2021 18:12:29] \"\u001b[37mGET /static/css/magnific.min.css HTTP/1.1\u001b[0m\" 200 -\n",
            "127.0.0.1 - - [13/Jul/2021 18:12:29] \"\u001b[37mGET /static/js/vendor/jquery-1.12.4.min.js HTTP/1.1\u001b[0m\" 200 -\n",
            "127.0.0.1 - - [13/Jul/2021 18:12:29] \"\u001b[37mGET /static/js/bootstrap.min.js HTTP/1.1\u001b[0m\" 200 -\n",
            "127.0.0.1 - - [13/Jul/2021 18:12:30] \"\u001b[37mGET /static/js/owl.carousel.min.js HTTP/1.1\u001b[0m\" 200 -\n",
            "127.0.0.1 - - [13/Jul/2021 18:12:30] \"\u001b[37mGET /static/js/jquery.counterup.min.js HTTP/1.1\u001b[0m\" 200 -\n",
            "127.0.0.1 - - [13/Jul/2021 18:12:30] \"\u001b[37mGET /static/js/waypoints.js HTTP/1.1\u001b[0m\" 200 -\n",
            "127.0.0.1 - - [13/Jul/2021 18:12:30] \"\u001b[37mGET /static/js/jquery.stellar.min.js HTTP/1.1\u001b[0m\" 200 -\n",
            "127.0.0.1 - - [13/Jul/2021 18:12:30] \"\u001b[37mGET /static/js/magnific.min.js HTTP/1.1\u001b[0m\" 200 -\n",
            "127.0.0.1 - - [13/Jul/2021 18:12:30] \"\u001b[37mGET /static/css/img/logo/preloader.gif HTTP/1.1\u001b[0m\" 200 -\n",
            "127.0.0.1 - - [13/Jul/2021 18:12:30] \"\u001b[37mGET /static/css/img/background/bread-bg.jpg HTTP/1.1\u001b[0m\" 200 -\n",
            "127.0.0.1 - - [13/Jul/2021 18:12:30] \"\u001b[33mGET /static/fonts/fontawesome-webfont.woff2?v=4.5.0 HTTP/1.1\u001b[0m\" 404 -\n",
            "127.0.0.1 - - [13/Jul/2021 18:12:30] \"\u001b[37mGET /static/img/logo/logo3.png HTTP/1.1\u001b[0m\" 200 -\n",
            "127.0.0.1 - - [13/Jul/2021 18:12:30] \"\u001b[37mGET /static/js/wow.min.js HTTP/1.1\u001b[0m\" 200 -\n",
            "127.0.0.1 - - [13/Jul/2021 18:12:30] \"\u001b[37mGET /static/js/jquery.meanmenu.js HTTP/1.1\u001b[0m\" 200 -\n",
            "127.0.0.1 - - [13/Jul/2021 18:12:30] \"\u001b[37mGET /static/js/form-validator.min.js HTTP/1.1\u001b[0m\" 200 -\n",
            "127.0.0.1 - - [13/Jul/2021 18:12:30] \"\u001b[33mGET /static/fonts/fontawesome-webfont.woff?v=4.5.0 HTTP/1.1\u001b[0m\" 404 -\n",
            "127.0.0.1 - - [13/Jul/2021 18:12:30] \"\u001b[37mGET /static/js/plugins.js HTTP/1.1\u001b[0m\" 200 -\n",
            "127.0.0.1 - - [13/Jul/2021 18:12:30] \"\u001b[37mGET /static/js/main.js HTTP/1.1\u001b[0m\" 200 -\n",
            "127.0.0.1 - - [13/Jul/2021 18:12:30] \"\u001b[37mGET /static/img/logo/logo2.png HTTP/1.1\u001b[0m\" 200 -\n",
            "127.0.0.1 - - [13/Jul/2021 18:12:30] \"\u001b[37mGET /static/img/logo/logo.png HTTP/1.1\u001b[0m\" 200 -\n",
            "127.0.0.1 - - [13/Jul/2021 18:12:31] \"\u001b[33mGET /static/fonts/fontawesome-webfont.ttf?v=4.5.0 HTTP/1.1\u001b[0m\" 404 -\n",
            "127.0.0.1 - - [13/Jul/2021 18:12:31] \"\u001b[37mGET /static/css/img/background/fg.jpg HTTP/1.1\u001b[0m\" 200 -\n",
            "127.0.0.1 - - [13/Jul/2021 18:12:31] \"\u001b[37mGET /static/css/img/background/shape-footer.png HTTP/1.1\u001b[0m\" 200 -\n",
            "127.0.0.1 - - [13/Jul/2021 18:12:31] \"\u001b[37mGET /static/img/logo/favicon.ico HTTP/1.1\u001b[0m\" 200 -\n",
            "127.0.0.1 - - [13/Jul/2021 18:12:37] \"\u001b[37mGET /chatbotvideo HTTP/1.1\u001b[0m\" 200 -\n",
            "127.0.0.1 - - [13/Jul/2021 18:12:37] \"\u001b[37mGET /static/css/owl.transitions.css HTTP/1.1\u001b[0m\" 200 -\n",
            "127.0.0.1 - - [13/Jul/2021 18:12:37] \"\u001b[37mGET /static/css/chat.css HTTP/1.1\u001b[0m\" 200 -\n",
            "127.0.0.1 - - [13/Jul/2021 18:12:37] \"\u001b[37mGET /static/css/home.css HTTP/1.1\u001b[0m\" 200 -\n",
            "127.0.0.1 - - [13/Jul/2021 18:12:37] \"\u001b[37mGET /static/css/owl.carousel.css HTTP/1.1\u001b[0m\" 200 -\n",
            "127.0.0.1 - - [13/Jul/2021 18:12:37] \"\u001b[37mGET /static/css/magnific.min.css HTTP/1.1\u001b[0m\" 200 -\n",
            "127.0.0.1 - - [13/Jul/2021 18:12:37] \"\u001b[37mGET /static/js/responses.js HTTP/1.1\u001b[0m\" 200 -\n",
            "127.0.0.1 - - [13/Jul/2021 18:12:37] \"\u001b[37mGET /static/js/chat.js HTTP/1.1\u001b[0m\" 200 -\n",
            "127.0.0.1 - - [13/Jul/2021 18:12:37] \"\u001b[37mGET /static/img/team/t2.jpg HTTP/1.1\u001b[0m\" 200 -\n",
            "127.0.0.1 - - [13/Jul/2021 18:12:38] \"\u001b[37mGET /static/img/logo/favicon.ico HTTP/1.1\u001b[0m\" 200 -\n",
            "[2021-07-13 18:12:44,414] ERROR in app: Exception on /answer/video [POST]\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/flask/app.py\", line 2447, in wsgi_app\n",
            "    response = self.full_dispatch_request()\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/flask/app.py\", line 1952, in full_dispatch_request\n",
            "    rv = self.handle_user_exception(e)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/flask/app.py\", line 1821, in handle_user_exception\n",
            "    reraise(exc_type, exc_value, tb)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/flask/_compat.py\", line 39, in reraise\n",
            "    raise value\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/flask/app.py\", line 1950, in full_dispatch_request\n",
            "    rv = self.dispatch_request()\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/flask/app.py\", line 1936, in dispatch_request\n",
            "    return self.view_functions[rule.endpoint](**req.view_args)\n",
            "  File \"<ipython-input-118-3f553c6aaab9>\", line 51, in answer_video\n",
            "    r = requests.get(url, allow_redirects=True)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/requests/api.py\", line 76, in get\n",
            "    return request('get', url, params=params, **kwargs)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/requests/api.py\", line 61, in request\n",
            "    return session.request(method=method, url=url, **kwargs)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/requests/sessions.py\", line 516, in request\n",
            "    prep = self.prepare_request(req)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/requests/sessions.py\", line 459, in prepare_request\n",
            "    hooks=merge_hooks(request.hooks, self.hooks),\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/requests/models.py\", line 314, in prepare\n",
            "    self.prepare_url(url, params)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/requests/models.py\", line 388, in prepare_url\n",
            "    raise MissingSchema(error)\n",
            "requests.exceptions.MissingSchema: Invalid URL 'None': No schema supplied. Perhaps you meant http://None?\n",
            "127.0.0.1 - - [13/Jul/2021 18:12:44] \"\u001b[35m\u001b[1mPOST /answer/video HTTP/1.1\u001b[0m\" 500 -\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "None\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[2021-07-13 18:12:46,531] ERROR in app: Exception on /answer/video [GET]\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/flask/app.py\", line 2447, in wsgi_app\n",
            "    response = self.full_dispatch_request()\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/flask/app.py\", line 1952, in full_dispatch_request\n",
            "    rv = self.handle_user_exception(e)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/flask/app.py\", line 1821, in handle_user_exception\n",
            "    reraise(exc_type, exc_value, tb)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/flask/_compat.py\", line 39, in reraise\n",
            "    raise value\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/flask/app.py\", line 1950, in full_dispatch_request\n",
            "    rv = self.dispatch_request()\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/flask/app.py\", line 1936, in dispatch_request\n",
            "    return self.view_functions[rule.endpoint](**req.view_args)\n",
            "  File \"<ipython-input-118-3f553c6aaab9>\", line 51, in answer_video\n",
            "    r = requests.get(url, allow_redirects=True)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/requests/api.py\", line 76, in get\n",
            "    return request('get', url, params=params, **kwargs)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/requests/api.py\", line 61, in request\n",
            "    return session.request(method=method, url=url, **kwargs)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/requests/sessions.py\", line 530, in request\n",
            "    resp = self.send(prep, **send_kwargs)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/requests/sessions.py\", line 637, in send\n",
            "    adapter = self.get_adapter(url=request.url)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/requests/sessions.py\", line 728, in get_adapter\n",
            "    raise InvalidSchema(\"No connection adapters were found for {!r}\".format(url))\n",
            "requests.exceptions.InvalidSchema: No connection adapters were found for 'blob:https://e52b3beba18f.ngrok.io/14478212-09c5-49e1-86a5-412c268ed5e8'\n",
            "127.0.0.1 - - [13/Jul/2021 18:12:46] \"\u001b[35m\u001b[1mGET /answer/video?video=blob%3Ahttps%3A%2F%2Fe52b3beba18f.ngrok.io%2F14478212-09c5-49e1-86a5-412c268ed5e8 HTTP/1.1\u001b[0m\" 500 -\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "blob:https://e52b3beba18f.ngrok.io/14478212-09c5-49e1-86a5-412c268ed5e8\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 800
        },
        "id": "KLqrRvxfKJNW",
        "outputId": "acdbd781-c53e-48cd-a95d-6726f9dca766"
      },
      "source": [
        "read_face_emotion()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "\n",
              "    var video;\n",
              "    var div = null;\n",
              "    var stream;\n",
              "    var captureCanvas;\n",
              "    var imgElement;\n",
              "    var labelElement;\n",
              "    \n",
              "    var pendingResolve = null;\n",
              "    var shutdown = false;\n",
              "    \n",
              "    function removeDom() {\n",
              "       stream.getVideoTracks()[0].stop();\n",
              "       video.remove();\n",
              "       div.remove();\n",
              "       video = null;\n",
              "       div = null;\n",
              "       stream = null;\n",
              "       imgElement = null;\n",
              "       captureCanvas = null;\n",
              "       labelElement = null;\n",
              "    }\n",
              "    \n",
              "    function onAnimationFrame() {\n",
              "      if (!shutdown) {\n",
              "        window.requestAnimationFrame(onAnimationFrame);\n",
              "      }\n",
              "      if (pendingResolve) {\n",
              "        var result = \"\";\n",
              "        if (!shutdown) {\n",
              "          captureCanvas.getContext('2d').drawImage(video, 0, 0, 640, 480);\n",
              "          result = captureCanvas.toDataURL('image/jpeg', 0.8)\n",
              "        }\n",
              "        var lp = pendingResolve;\n",
              "        pendingResolve = null;\n",
              "        lp(result);\n",
              "      }\n",
              "    }\n",
              "    \n",
              "    async function createDom() {\n",
              "      if (div !== null) {\n",
              "        return stream;\n",
              "      }\n",
              "\n",
              "      div = document.createElement('div');\n",
              "      div.style.border = '2px solid black';\n",
              "      div.style.padding = '3px';\n",
              "      div.style.width = '100%';\n",
              "      div.style.maxWidth = '600px';\n",
              "      document.body.appendChild(div);\n",
              "      \n",
              "      const modelOut = document.createElement('div');\n",
              "      modelOut.innerHTML = \"<span>Status:</span>\";\n",
              "      labelElement = document.createElement('span');\n",
              "      labelElement.innerText = 'No data';\n",
              "      labelElement.style.fontWeight = 'bold';\n",
              "      modelOut.appendChild(labelElement);\n",
              "      div.appendChild(modelOut);\n",
              "           \n",
              "      video = document.createElement('video');\n",
              "      video.style.display = 'block';\n",
              "      video.width = div.clientWidth - 6;\n",
              "      video.setAttribute('playsinline', '');\n",
              "      video.onclick = () => { shutdown = true; };\n",
              "      stream = await navigator.mediaDevices.getUserMedia(\n",
              "          {video: { facingMode: \"environment\"}});\n",
              "      div.appendChild(video);\n",
              "\n",
              "      imgElement = document.createElement('img');\n",
              "      imgElement.style.position = 'absolute';\n",
              "      imgElement.style.zIndex = 1;\n",
              "      imgElement.onclick = () => { shutdown = true; };\n",
              "      div.appendChild(imgElement);\n",
              "      \n",
              "      const instruction = document.createElement('div');\n",
              "      instruction.innerHTML = \n",
              "          '<span style=\"color: red; font-weight: bold;\">' +\n",
              "          'When finished, click here or on the video to stop this demo</span>';\n",
              "      div.appendChild(instruction);\n",
              "      instruction.onclick = () => { shutdown = true; };\n",
              "      \n",
              "      video.srcObject = stream;\n",
              "      await video.play();\n",
              "\n",
              "      captureCanvas = document.createElement('canvas');\n",
              "      captureCanvas.width = 640; //video.videoWidth;\n",
              "      captureCanvas.height = 480; //video.videoHeight;\n",
              "      window.requestAnimationFrame(onAnimationFrame);\n",
              "      \n",
              "      return stream;\n",
              "    }\n",
              "    async function stream_frame(label, imgData) {\n",
              "      if (shutdown) {\n",
              "        removeDom();\n",
              "        shutdown = false;\n",
              "        return '';\n",
              "      }\n",
              "\n",
              "      var preCreate = Date.now();\n",
              "      stream = await createDom();\n",
              "      \n",
              "      var preShow = Date.now();\n",
              "      if (label != \"\") {\n",
              "        labelElement.innerHTML = label;\n",
              "      }\n",
              "            \n",
              "      if (imgData != \"\") {\n",
              "        var videoRect = video.getClientRects()[0];\n",
              "        imgElement.style.top = videoRect.top + \"px\";\n",
              "        imgElement.style.left = videoRect.left + \"px\";\n",
              "        imgElement.style.width = videoRect.width + \"px\";\n",
              "        imgElement.style.height = videoRect.height + \"px\";\n",
              "        imgElement.src = imgData;\n",
              "      }\n",
              "      \n",
              "      var preCapture = Date.now();\n",
              "      var result = await new Promise(function(resolve, reject) {\n",
              "        pendingResolve = resolve;\n",
              "      });\n",
              "      shutdown = false;\n",
              "      \n",
              "      return {'create': preShow - preCreate, \n",
              "              'show': preCapture - preShow, \n",
              "              'capture': Date.now() - preCapture,\n",
              "              'img': result};\n",
              "    }\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-26-3d92b198e318>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mread_face_emotion\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[0;32m<ipython-input-21-6c4ad2325764>\u001b[0m in \u001b[0;36mread_face_emotion\u001b[0;34m()\u001b[0m\n\u001b[1;32m     12\u001b[0m   \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m       \u001b[0mjs_reply\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvideo_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabel_html\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbbox\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     15\u001b[0m       \u001b[0mlabels\u001b[0m\u001b[0;34m=\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     16\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mjs_reply\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-11-991d36e826cd>\u001b[0m in \u001b[0;36mvideo_frame\u001b[0;34m(label, bbox)\u001b[0m\n\u001b[1;32m    131\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    132\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mvideo_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbbox\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[0;32m--> 133\u001b[0;31m   \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0meval_js\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'stream_frame(\"{}\", \"{}\")'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbbox\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    134\u001b[0m   \u001b[0;32mreturn\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/google/colab/output/_js.py\u001b[0m in \u001b[0;36meval_js\u001b[0;34m(script, ignore_result, timeout_sec)\u001b[0m\n\u001b[1;32m     38\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0mignore_result\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     39\u001b[0m     \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 40\u001b[0;31m   \u001b[0;32mreturn\u001b[0m \u001b[0m_message\u001b[0m\u001b[0;34m.\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[1;32m     41\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     42\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/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     99\u001b[0m     \u001b[0mreply\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_read_next_input_message\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    100\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mreply\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0m_NOT_READY\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreply\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\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[0;32m--> 101\u001b[0;31m       \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msleep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0.025\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    102\u001b[0m       \u001b[0;32mcontinue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    103\u001b[0m     if (reply.get('type') == 'colab_reply' and\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BoPHJT_qt4xS"
      },
      "source": [
        "# !ngrok authtoken 1tqnKSRNTcXG8w4GrKSnjYvPVlv_3G54329xNgebzaRBUpGeq"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5WdGJvqvzN32"
      },
      "source": [
        "\n",
        "\n",
        "# End of Code"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uDKzIEbgP5pv"
      },
      "source": [
        "# import concurrent.futures\n",
        "\n",
        "# def foo():\n",
        "#   return 2\n",
        "\n",
        "# with concurrent.futures.ThreadPoolExecutor() as executor:\n",
        "#     future = executor.submit(foo)\n",
        "#     return_value = future.result()\n",
        "#     print(return_value)"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}