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tokenizers-0.13.2-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.6 MB)\n", "\u001b[K |████████████████████████████████| 7.6 MB 43.6 MB/s \n", "\u001b[?25hRequirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (1.21.6)\n", "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.7/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/python3.7/dist-packages (from packaging>=20.0->transformers) (3.0.9)\n", "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->transformers) (3.10.0)\n", "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2.10)\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) (1.24.3)\n", "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2022.9.24)\n", "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (3.0.4)\n", "Installing collected packages: tokenizers, huggingface-hub, transformers\n", "Successfully installed huggingface-hub-0.10.1 tokenizers-0.13.2 transformers-4.24.0\n" ] } ], "source": [ "!pip install gradio\n", "!pip install transformers" ] }, { "cell_type": "code", "source": [ "import gradio as gr\n", "\n", "from transformers import AutoTokenizer\n", "from transformers import TFAutoModelForCausalLM" ], "metadata": { "id": "1yVHOnYwKZwJ" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "#load model\n", "model = TFAutoModelForCausalLM.from_pretrained('drive/MyDrive/FIRE_3rd Sem/peom_gn/')\n", "base_model = \"distilgpt2\"\n", "tokenizer = AutoTokenizer.from_pretrained(base_model)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 236, "referenced_widgets": [ "530e92427f96443e8db1a97abd6c6a52", "e02f05cf2a5d42b7bee0bbe719198b8d", "173b02af44c346e2a56283ee6a146c0e", "4b42f65e2e0945fd8f51d39200f29fa8", "246e208413d143949a101914a6216ea8", "0c75250aaa454c638fc8bed7365ce415", "8965848a3d594be188f056ee3644ecb5", "0395259b5c674039b9c51500e33c37e9", "1b65db5804f64ceaaac4cac9c526cd3f", "812aa6f521ce4b798c08bffab3329b67", "4794a81d2ef44fbeb1e88998085a5f43", "6b7df437d08d4c01be16b79e41019e3b", "23563f674f0f4f1693a0acde9623573c", "8c427e0fc2d34a24867fd5d711b080e2", "2a694b94ca2043b286ce17d3c36ed54b", "020e7d8888d0405b93d1a3127a16ba15", "b86ccf32ba324ee198481b3611617dbc", "621717f94333429ab4e47224550771fe", "c2e4edd790054604a7756a1d7b9f497a", "e58ef4fb5651473c9cf9b889624ccd55", "473c51fb7519486bbe3050c77ab7080c", "b4382f19bea946a9a6b2a87475604e46", "52b302d4ea1d4999a11652906f9395e6", "7e8fe68702cb47749524717a2f0f2678", "e35c5da585b74cda96cf944dca0f1522", "3d8d2200ba91491cb90473ab36f11b2a", "aa8f858b51374b5fb8f33e4bd82e2bdf", "875fd933bfbf4d2c869bfca5ee5e5b88", "e04f3eb8192c4da28816aabe24e7c126", "8d9ea460130947e1b30567c14d804a33", "9e3ce0f556c54eba95a1aa4db1e177f3", "153545ef1fc84847bf0d437fc37c0afd", "a74454be91b34441b1b05e1714b7d9b6", "926d77fde11f4f31920e3c6604ad2512", "c761c42270c14cca8818e9e8e5fefaf7", "15073b5721a941a88948bd1d4a4e30a2", "51e0e9a4829e45e0b2518380e8a7614a", "db76248f90524c94bdbf680ca185816e", "e2f414e22bfe438295eb504d191ca25a", "6f86e52c4844489b902c355a67f07c57", "0525588beb664916a5c499669dea218c", "b914433a72d9420fa451da42fb87b3a9", "0a32118ff47246d38c61ed31c4e27bc0", "6210e1a9cb474898acbde80d6fe1be58" ] }, "id": "cQ9woCBaKogJ", "outputId": "1d825c73-6532-449b-8234-d27f330ed228" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "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" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Downloading: 0%| | 0.00/762 [00:00 1:\n", " prev_sen = prev_sen.split(input)[1]\n", " else:\n", " break\n", "\n", " res = \"\".join(res).replace(\"\", \"\\n\")\n", " return res\"\"\"" ], "metadata": { "id": "0r5Jh3YOKbeo" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "def model_inference1(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(\"\", \"\\n\")" ], "metadata": { "id": "NscfxxiSZBSN" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "demo = gr.Interface(\n", " fn=model_inference1,\n", " inputs=gr.Textbox(lines=2, placeholder=\"Start Here...\"),\n", " outputs=\"text\",\n", ")\n", "demo.launch()\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 671 }, "id": "lFhwwyBUI-wj", "outputId": "f6f1f76f-705e-469f-d9a0-0c3a2c6ce427" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Colab notebook detected. To show errors in colab notebook, set `debug=True` in `launch()`\n", "\n", "Using Embedded Colab Mode (NEW). If you have issues, please use share=True and file an issue at https://github.com/gradio-app/gradio/\n", "Note: opening the browser inspector may crash Embedded Colab Mode.\n", "\n", "To create a public link, set `share=True` in `launch()`.\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "application/javascript": [ "(async (port, path, width, height, cache, element) => {\n", " if (!google.colab.kernel.accessAllowed && !cache) {\n", " return;\n", " }\n", " element.appendChild(document.createTextNode(''));\n", " const url = await google.colab.kernel.proxyPort(port, {cache});\n", "\n", " const external_link = document.createElement('div');\n", " external_link.innerHTML = `\n", "
\n", " Running on \n", " https://localhost:${port}${path}\n", " \n", "
\n", " `;\n", " element.appendChild(external_link);\n", "\n", " const iframe = document.createElement('iframe');\n", " iframe.src = new URL(path, url).toString();\n", " iframe.height = height;\n", " iframe.allow = \"autoplay; camera; microphone; clipboard-read; clipboard-write;\"\n", " iframe.width = width;\n", " iframe.style.border = 0;\n", " element.appendChild(iframe);\n", " })(7860, \"/\", \"100%\", 500, false, window.element)" ] }, "metadata": {} }, { "output_type": "execute_result", "data": { "text/plain": [ "(, 'http://127.0.0.1:7860/', None)" ] }, "metadata": {}, "execution_count": 7 } ] }, { "cell_type": "code", "source": [], "metadata": { "id": "6g5QwJB6JtP7" }, "execution_count": null, "outputs": [] } ] }