{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "KNRGKEn0EUl5",
    "outputId": "4d2eb404-b2b4-4004-9fff-b9c12c4c5444"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
     ]
    }
   ],
   "source": [
    "from google.colab import drive\n",
    "drive.mount('/content/drive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "tENqMaj5EZhe",
    "outputId": "cb4923d3-167f-453f-d67a-ce2c0829a584"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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      "Requirement already satisfied: pillow in /usr/local/lib/python3.8/dist-packages (from gradio) (7.1.2)\n",
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      "Requirement already satisfied: cffi>=1.12 in /usr/local/lib/python3.8/dist-packages (from cryptography>=2.5->paramiko->gradio) (1.15.1)\n",
      "Requirement already satisfied: pycparser in /usr/local/lib/python3.8/dist-packages (from cffi>=1.12->cryptography>=2.5->paramiko->gradio) (2.21)\n",
      "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.8/dist-packages (from requests->gradio) (3.0.4)\n",
      "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.8/dist-packages (from requests->gradio) (1.24.3)\n",
      "Requirement already satisfied: click>=7.0 in /usr/local/lib/python3.8/dist-packages (from uvicorn->gradio) (7.1.2)\n",
      "Reading package lists... Done\n",
      "Building dependency tree       \n",
      "Reading state information... Done\n",
      "timidity is already the newest version (2.13.2-41).\n",
      "The following package was automatically installed and is no longer required:\n",
      "  libnvidia-common-460\n",
      "Use 'apt autoremove' to remove it.\n",
      "0 upgraded, 0 newly installed, 0 to remove and 7 not upgraded.\n",
      "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
      "Collecting transformers\n",
      "  Downloading transformers-4.25.1-py3-none-any.whl (5.8 MB)\n",
      "\u001b[K     |████████████████████████████████| 5.8 MB 28.4 MB/s \n",
      "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.8/dist-packages (from transformers) (3.8.0)\n",
      "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.8/dist-packages (from transformers) (4.64.1)\n",
      "Collecting tokenizers!=0.11.3,<0.14,>=0.11.1\n",
      "  Downloading tokenizers-0.13.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.6 MB)\n",
      "\u001b[K     |████████████████████████████████| 7.6 MB 46.0 MB/s \n",
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      "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.8/dist-packages (from transformers) (2022.6.2)\n",
      "Collecting huggingface-hub<1.0,>=0.10.0\n",
      "  Downloading huggingface_hub-0.11.1-py3-none-any.whl (182 kB)\n",
      "\u001b[K     |████████████████████████████████| 182 kB 62.3 MB/s \n",
      "\u001b[?25hRequirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.8/dist-packages (from huggingface-hub<1.0,>=0.10.0->transformers) (4.1.1)\n",
      "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.8/dist-packages (from packaging>=20.0->transformers) (3.0.9)\n",
      "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.8/dist-packages (from requests->transformers) (2.10)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.8/dist-packages (from requests->transformers) (2022.9.24)\n",
      "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.8/dist-packages (from requests->transformers) (3.0.4)\n",
      "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.8/dist-packages (from requests->transformers) (1.24.3)\n",
      "Installing collected packages: tokenizers, huggingface-hub, transformers\n",
      "Successfully installed huggingface-hub-0.11.1 tokenizers-0.13.2 transformers-4.25.1\n"
     ]
    }
   ],
   "source": [
    "!pip install gradio\n",
    "!apt-get install timidity\n",
    "!pip install transformers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "wN-kzFUKEgbe"
   },
   "outputs": [],
   "source": [
    "import gradio as gr\n",
    "from pathlib import Path\n",
    "import subprocess\n",
    "from transformers import AutoTokenizer\n",
    "from transformers import TFAutoModelForCausalLM\n",
    "from transformers import TFAutoModelForSequenceClassification, AutoTokenizer\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 238,
     "referenced_widgets": [
      "df1428c1ca23400090c2e46fec51dccd",
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      "ae46b6d525544482b4c60210e89a7aa6",
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      "0944a45c260c40d38f38a1e7ac234142",
      "9b5f8784c47343efb6a73e63cbbf80db",
      "5ac86cc5b8c2493db6407e89d6630cb7",
      "8341fee4f0c1405f9ca60454e6ffed66",
      "73513b59dd8c4f41b2f6a2471b9e1701"
     ]
    },
    "id": "MZ5-7-jHShqU",
    "outputId": "60059f64-c9c1-4799-fce9-9c3888eab520"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "All model checkpoint layers were used when initializing TFGPT2LMHeadModel.\n",
      "\n",
      "All the layers of TFGPT2LMHeadModel were initialized from the model checkpoint at drive/MyDrive/FIRE_3rd Sem/peom_gn/.\n",
      "If your task is similar to the task the model of the checkpoint was trained on, you can already use TFGPT2LMHeadModel for predictions without further training.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "df1428c1ca23400090c2e46fec51dccd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading:   0%|          | 0.00/762 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5176b0b18a6a429fb22604a2d5cbb066",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2c20b24106cb44999e4761deaacc701b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1aba382e81914213b1fafdbd4863bf9a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#load poem generation model\n",
    "model = TFAutoModelForCausalLM.from_pretrained('drive/MyDrive/FIRE_3rd Sem/peom_gn/')\n",
    "base_model = \"distilgpt2\"\n",
    "tokenizer = AutoTokenizer.from_pretrained(base_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 257,
     "referenced_widgets": [
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    },
    "id": "LlW4Xnh3TcaL",
    "outputId": "2dabcc70-97e5-4af6-b73d-8f9ce8d24b0c"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some layers from the model checkpoint at drive/MyDrive/FIRE_3rd Sem/sen_analysis/bert were not used when initializing TFDistilBertForSequenceClassification: ['dropout_19']\n",
      "- This IS expected if you are initializing TFDistilBertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing TFDistilBertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
      "Some layers of TFDistilBertForSequenceClassification were not initialized from the model checkpoint at drive/MyDrive/FIRE_3rd Sem/sen_analysis/bert and are newly initialized: ['dropout_38']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
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   ],
   "source": [
    "#load sentiment analysis\n",
    "model_v1 = TFAutoModelForSequenceClassification.from_pretrained('drive/MyDrive/FIRE_3rd Sem/sen_analysis/bert')\n",
    "base_model = \"distilbert-base-uncased\"\n",
    "tokenizer_v1 = AutoTokenizer.from_pretrained(base_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "AvLP8kQkUbBB",
    "outputId": "636aec1b-21c6-48a9-f360-136708694e7a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#music generation\n",
    "base_path = \"/content/drive/MyDrive/FIRE_3rd Sem/music_gn/\"\n",
    "#path_mid_file -> Replace this with model generated file path\n",
    "path_mid_file = base_path + \"Comic_Relief.mid\"\n",
    "path_wav_file = base_path + \"output_comic.wav\"\n",
    "subprocess.call(['timidity', path_mid_file, \"-Ow\", \"-o\", path_wav_file])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "x3SogoL_Xv_C"
   },
   "source": [
    "## Music Generation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "IiiTtEdTJtzD"
   },
   "outputs": [],
   "source": [
    "def inference_music_gen():\n",
    "  return Path(path_wav_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "x9uLu5IOX3Ab"
   },
   "outputs": [],
   "source": [
    "music_gen_interface = gr.Interface(\n",
    "    inference_music_gen, \n",
    "    inputs = None, \n",
    "    outputs = gr.outputs.Audio(type=\"filepath\", label=\"Output\")\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XJoDisI3XYJM"
   },
   "source": [
    "## Sentiment Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "0asjbECxRUDw"
   },
   "outputs": [],
   "source": [
    "def inference_sentiment_analysis(sen):\n",
    "  tokenized_v1 = tokenizer_v1([sen], return_tensors=\"np\", padding=\"longest\")\n",
    "  outputs_v1 = model_v1(tokenized_v1).logits\n",
    "  classifications_v1 = np.argmax(outputs_v1, axis=1)\n",
    "  if classifications_v1[0] == 1:\n",
    "      res = \"Positive :)\"\n",
    "  else:\n",
    "      res = \"Negative :(\"\n",
    "  return res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "tsGob-9sUK1s"
   },
   "outputs": [],
   "source": [
    "sentiment_analysis_interface = gr.Interface(\n",
    "    fn=inference_sentiment_analysis,\n",
    "    inputs=gr.Textbox(lines=2, placeholder=\"Enter a Sentence\"),\n",
    "    outputs=\"text\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BVvYvw5KXcxM"
   },
   "source": [
    "## Peom Generation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "YQAd6I9eRj1t"
   },
   "outputs": [],
   "source": [
    "def inference_poem_gen(start):\n",
    "  tokenized = tokenizer(start, return_tensors=\"np\")\n",
    "  outputs = model.generate(**tokenized, max_new_tokens=20)\n",
    "  res = tokenizer.decode(outputs[0])\n",
    "  return res.replace(\"<LINE>\", \"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "aYoJ1FsfSXS2"
   },
   "outputs": [],
   "source": [
    "poem_gen_interface = gr.Interface(\n",
    "    fn=inference_poem_gen,\n",
    "    inputs=gr.Textbox(lines=2, placeholder=\"Start Here...\"),\n",
    "    outputs=\"text\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "feEFQ8zoXqvw"
   },
   "source": [
    "## Combine All"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 944
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    "id": "ZSGf7iKuj3Qc",
    "outputId": "d11d9eb1-b3d3-4462-b4ce-935c8ecb6b0d"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.8/dist-packages/gradio/blocks.py:717: UserWarning: api_name predict already exists, using predict_1\n",
      "  warnings.warn(\n",
      "/usr/local/lib/python3.8/dist-packages/gradio/blocks.py:717: UserWarning: api_name predict already exists, using predict_2\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Colab notebook detected. This cell will run indefinitely so that you can see errors and logs. To turn off, set debug=False in launch().\n",
      "Running on public URL: https://632e922be0d60ff3.gradio.app\n",
      "\n",
      "This share link expires in 72 hours. For free permanent hosting and GPU upgrades (NEW!), check out Spaces: https://huggingface.co/spaces\n"
     ]
    },
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       "<div><iframe src=\"https://632e922be0d60ff3.gradio.app\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
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      ]
     },
     "metadata": {},
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Exception in thread Thread-22:\n",
      "Traceback (most recent call last):\n",
      "  File \"/usr/lib/python3.8/threading.py\", line 932, in _bootstrap_inner\n",
      "    self.run()\n",
      "  File \"/usr/lib/python3.8/threading.py\", line 870, in run\n",
      "    self._target(*self._args, **self._kwargs)\n",
      "  File \"/usr/local/lib/python3.8/dist-packages/gradio/tunneling.py\", line 39, in handler\n",
      "    chan.send(data)\n",
      "  File \"/usr/local/lib/python3.8/dist-packages/paramiko/channel.py\", line 801, in send\n",
      "    return self._send(s, m)\n",
      "  File \"/usr/local/lib/python3.8/dist-packages/paramiko/channel.py\", line 1198, in _send\n",
      "    raise socket.error(\"Socket is closed\")\n",
      "OSError: Socket is closed\n",
      "Setting `pad_token_id` to 50256 (first `eos_token_id`) to generate sequence\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Keyboard interruption in main thread... closing server.\n"
     ]
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "title = \"Music Generation\"\n",
    "description = \"Add Project description\"\n",
    "article = \"<p style='text-align: center'><a href='https://github.com/' target='_blank'>Github Repo</a></p>\"\n",
    "#we can add other project related stuff as well\n",
    "\n",
    "demo = gr.TabbedInterface([music_gen_interface, poem_gen_interface, sentiment_analysis_interface], \n",
    "                          [\"Music Generation\", \"Poem Generation\", \"Sentiment Analysis\"])\n",
    "demo.launch(debug=True, share=True)"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "eYAysXfiJt8s"
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   "source": []
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