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  1. S19_app.ipynb +277 -0
  2. app.py +56 -0
S19_app.ipynb ADDED
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+ {
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+ "nbformat": 4,
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+ "nbformat_minor": 0,
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+ "metadata": {
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+ "colab": {
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+ "provenance": []
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+ },
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+ "kernelspec": {
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+ "name": "python3",
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+ "display_name": "Python 3"
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+ },
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+ "language_info": {
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+ "name": "python"
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+ }
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+ },
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+ "cells": [
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+ {
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+ "cell_type": "code",
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+ "source": [
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+ "!pip install gradio"
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+ ],
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+ "metadata": {
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+ "colab": {
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+ "base_uri": "https://localhost:8080/"
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+ },
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+ "id": "M-8-kXX3AmPE",
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+ "outputId": "2b8cb780-2dd4-45ee-cc1f-864056678a9d"
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+ },
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+ "execution_count": 1,
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+ "outputs": [
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+ {
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+ "output_type": "stream",
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+ "name": "stdout",
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+ "text": [
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+ "Requirement already satisfied: gradio in /usr/local/lib/python3.10/dist-packages (4.36.1)\n",
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+ "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.10/dist-packages (from markdown-it-py>=2.2.0->rich>=10.11.0->typer<1.0,>=0.12->gradio) (0.1.2)\n"
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+ ]
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+ }
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 2,
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+ "metadata": {
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+ "id": "243RszHVACUE"
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+ },
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+ "outputs": [],
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+ "source": [
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+ "import gradio as gr\n",
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+ "import os\n",
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+ "\n",
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+ "import torch\n",
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+ "import torch.nn as nn\n",
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+ "import torch.nn.functional as F\n",
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+ "from model import BigramLanguageModel, ModelConfig"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "source": [
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+ "with open('input.txt', 'r', encoding='utf-8') as f:\n",
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+ " text = f.read()\n",
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+ "\n",
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+ "# unique characters from text\n",
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+ "chars = sorted(list(set(text)))\n",
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+ "vocab_size = len(chars)\n",
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+ "\n",
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+ "# create a mapping from chars to ints\n",
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+ "stoi = { ch:i for i,ch in enumerate(chars) }\n",
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+ "itos = { i:ch for i,ch in enumerate(chars) }\n",
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+ "encode = lambda s: [stoi[c] for c in s] # encoder: take a string, output a list of integers\n",
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+ "decode = lambda l: ''.join([itos[i] for i in l]) # decoder: take a list of integers, output a string"
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+ ],
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+ "metadata": {
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+ "id": "3UjZaD3NBJN0"
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+ },
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+ "execution_count": 3,
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+ "outputs": []
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+ },
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+ {
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+ "cell_type": "code",
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+ "source": [
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+ "# load model\n",
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+ "model_spec = torch.load(\"ckpt.pt\", map_location=torch.device('cpu'))\n",
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+ "model_args = model_spec['model_args']\n",
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+ "model_weights = model_spec['model']\n",
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+ "modelconf = ModelConfig(**model_args)\n",
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+ "trained_model = BigramLanguageModel(modelconf)\n",
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+ "trained_model.load_state_dict(model_weights)"
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+ ],
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+ "metadata": {
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+ "colab": {
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+ "base_uri": "https://localhost:8080/"
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+ },
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+ "id": "xL-YJTFSBfq9",
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+ "outputId": "5081861c-c025-4b06-f148-cde79bbcf4e1"
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+ },
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+ "execution_count": 4,
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+ "outputs": [
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+ {
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+ "output_type": "execute_result",
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+ "data": {
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+ "text/plain": [
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+ "<All keys matched successfully>"
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+ ]
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+ },
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+ "metadata": {},
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+ "execution_count": 4
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+ }
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "source": [
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+ "def generate_text(seed_text, max_new_tokens, temperature):\n",
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+ " text = seed_text if seed_text is not None else \" \"\n",
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+ " text = text if text.endswith(\" \") else seed_text + \" \"\n",
190
+ " context = torch.tensor(encode(text), dtype=torch.long).unsqueeze(0)\n",
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+ " temperature = temperature if temperature > 0 else 1e-5\n",
192
+ " return decode(trained_model.generate(context, temperature = temperature, max_new_tokens=max_new_tokens)[0].tolist())"
193
+ ],
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+ "metadata": {
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+ "id": "8srx4zfYC4yS"
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+ },
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+ "execution_count": 5,
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+ "outputs": []
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+ },
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+ {
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+ "cell_type": "code",
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+ "source": [
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+ "with gr.Blocks() as demo:\n",
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+ " gr.HTML(\"<h1 align = 'center'> Generate Text Based on simple GPT model <br> (Dataset = William Shakespeare) </h1>\")\n",
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+ "\n",
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+ " content = gr.Textbox(label = \"Enter initial text to generate content\")\n",
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+ " with gr.Row(equal_height=True):\n",
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+ " with gr.Column():\n",
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+ " max_tokens = gr.Number(label = \"Maximum tokens to generate content\", value = 100)\n",
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+ " temp_val = gr.Slider(label = \"Temparature (slide to higher value for higher creativity)\", minimum = 0.0, maximum= 1.0,value = 0.7)\n",
211
+ " generate_btn = gr.Button(value = 'Generate Text')\n",
212
+ " with gr.Column():\n",
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+ " outputs = [gr.TextArea(label = \"Generated text (William Shakespeare)\", lines = 8)]\n",
214
+ " inputs = [\n",
215
+ " content,\n",
216
+ " max_tokens,\n",
217
+ " temp_val\n",
218
+ " ]\n",
219
+ " generate_btn.click(fn = generate_text, inputs= inputs, outputs = outputs)\n",
220
+ "\n",
221
+ "# for collab\n",
222
+ "demo.launch(debug=True)"
223
+ ],
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+ "metadata": {
225
+ "colab": {
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+ "base_uri": "https://localhost:8080/",
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+ "height": 680
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+ },
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+ "id": "RqcV9SR0C-XW",
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+ "outputId": "e24224de-7406-4071-83ab-7d54b4f429fd"
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+ },
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+ "execution_count": 23,
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+ "outputs": [
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+ {
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+ "output_type": "stream",
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+ "name": "stdout",
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+ "text": [
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+ "Setting queue=True in a Colab notebook requires sharing enabled. Setting `share=True` (you can turn this off by setting `share=False` in `launch()` explicitly).\n",
239
+ "\n",
240
+ "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",
241
+ "Running on public URL: https://eaf77dd6c0a8cb86e0.gradio.live\n",
242
+ "\n",
243
+ "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n"
244
+ ]
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+ },
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+ {
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+ "output_type": "display_data",
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+ "data": {
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+ "text/plain": [
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+ "<IPython.core.display.HTML object>"
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+ ],
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+ "text/html": [
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+ "<div><iframe src=\"https://eaf77dd6c0a8cb86e0.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
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+ ]
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+ },
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+ "metadata": {}
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+ },
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+ {
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+ "output_type": "stream",
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+ "name": "stdout",
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+ "text": [
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+ "Keyboard interruption in main thread... closing server.\n",
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+ "Killing tunnel 127.0.0.1:7860 <> https://eaf77dd6c0a8cb86e0.gradio.live\n"
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+ ]
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+ },
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+ {
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+ "output_type": "execute_result",
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+ "data": {
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+ "text/plain": []
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+ },
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+ "metadata": {},
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+ "execution_count": 23
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+ }
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+ ]
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+ }
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+ ]
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+ }
app.py ADDED
@@ -0,0 +1,56 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ import gradio as gr
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+ import os
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+
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+ import torch
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+ import torch.nn as nn
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+ import torch.nn.functional as F
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+ from model import BigramLanguageModel, ModelConfig
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+
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+ with open('input.txt', 'r', encoding='utf-8') as f:
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+ text = f.read()
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+
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+ # unique characters from text
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+ chars = sorted(list(set(text)))
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+ vocab_size = len(chars)
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+
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+ # create a mapping from chars to ints
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+ stoi = { ch:i for i,ch in enumerate(chars) }
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+ itos = { i:ch for i,ch in enumerate(chars) }
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+ encode = lambda s: [stoi[c] for c in s] # encoder: take a string, output a list of integers
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+ decode = lambda l: ''.join([itos[i] for i in l]) # decoder: take a list of integers, output a string
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+
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+ # load model
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+ model_spec = torch.load("ckpt.pt", map_location=torch.device('cpu'))
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+ model_args = model_spec['model_args']
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+ model_weights = model_spec['model']
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+ modelconf = ModelConfig(**model_args)
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+ trained_model = BigramLanguageModel(modelconf)
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+ trained_model.load_state_dict(model_weights)
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+
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+ def generate_text(seed_text, max_new_tokens, temperature):
31
+ text = seed_text if seed_text is not None else " "
32
+ text = text if text.endswith(" ") else seed_text + " "
33
+ context = torch.tensor(encode(text), dtype=torch.long).unsqueeze(0)
34
+ temperature = temperature if temperature > 0 else 1e-5
35
+ return decode(trained_model.generate(context, temperature = temperature, max_new_tokens=max_new_tokens)[0].tolist())
36
+
37
+ with gr.Blocks() as demo:
38
+ gr.HTML("<h1 align = 'center'> Generate Text Based on simple GPT model <br> (Dataset = William Shakespeare) </h1>")
39
+
40
+ content = gr.Textbox(label = "Enter initial text to generate content")
41
+ with gr.Row(equal_height=True):
42
+ with gr.Column():
43
+ max_tokens = gr.Number(label = "Maximum tokens to generate content", value = 100)
44
+ temp_val = gr.Slider(label = "Temparature (slide to higher value for higher creativity)", minimum = 0.0, maximum= 1.0,value = 0.7)
45
+ generate_btn = gr.Button(value = 'Generate Text')
46
+ with gr.Column():
47
+ outputs = [gr.TextArea(label = "Generated text (William Shakespeare)", lines = 7)]
48
+ inputs = [
49
+ content,
50
+ max_tokens,
51
+ temp_val
52
+ ]
53
+ generate_btn.click(fn = generate_text, inputs= inputs, outputs = outputs)
54
+
55
+ if __name__ == '__main__':
56
+ demo.launch()