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MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25h" ] } ], "source": [ "!pip install -q requests torch bitsandbytes transformers sentencepiece accelerate openai gradio" ] }, { "cell_type": "code", "source": [ "#imports\n", "\n", "import time\n", "from io import StringIO\n", "import torch\n", "import numpy as np\n", "import pandas as pd\n", "import random\n", "from openai import OpenAI\n", "from sqlalchemy import create_engine\n", "from google.colab import drive, userdata\n", "import gradio as gr\n", "from huggingface_hub import login\n", "from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig" ], "metadata": { "id": "JXpWOzKve7kr" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# Model Constants\n", "LLAMA = \"meta-llama/Meta-Llama-3.1-8B-Instruct\"" ], "metadata": { "id": "rcv0lCS5GRPX" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# Authentication\n", "\n", "hf_token = userdata.get(\"HF_TOKEN\")\n", "openai_api_key = userdata.get(\"OPENAI_API_KEY\")\n", "if not hf_token or not openai_api_key:\n", " raise ValueError(\"Missing HF_TOKEN or OPENAI_API_KEY. Set them as environment variables.\")\n", "\n", "login(hf_token, add_to_git_credential=True)\n", "openai = OpenAI(api_key=openai_api_key)" ], "metadata": { "id": "3XS-s_CwFSQU" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# Tokenizer Setup\n", "\n", "tokenizer = AutoTokenizer.from_pretrained(LLAMA)\n", "tokenizer.pad_token = tokenizer.eos_token" ], "metadata": { "id": "oRdmdzXoF_f9", "colab": { "base_uri": "https://localhost:8080/", "height": 113, "referenced_widgets": [ "4e96acd5f9b844828892423e01e985af", "de1eda1f836f4524aa3f9946992b3518", "11c7606385d9493a9f8e6844f91bf984", "ce253fcd9ac744dc817132f40c5a4449", "30bd0ada61fe44aaa355d8f8ba8608c9", "f3a7735eff734c8fa6619cf55f9250fe", "06f884acd7b84247a2b11f466986425b", "b88f70ba045142a6b2f7432d0b198823", "b4259e7cd7a9486b8e5241ac194773c8", "8671c9fe95774ce393938e81e4ac0fea", "c6c24948c71b4dae9b22f82d681f6d14", "168db3ccd3584d8787372a430e81afc1", "5dbe37818214460a883f8854a7a22ddd", "10fbda2b3d854ef5b42f36a1e6886df0", "5685b60bff9648ea9fbf426b6c6c34b7", "1ee68b5cb4914705962a8efdd6f3089f", "49cb0c97bcfb4be78e934120938e37f3", "f8dbf020aa9f4d8fa29ce7657d980259", "3e96b2e9fb6443dfaf289ecdc3ef6e86", "f08e6a34891d4873a0fb7fcf8ef016c6", "9ac1e95865934a7d8a71bc49c4c6d78e", "83701fdbe9174a4f83ab0fa90da5c20b", "aab110b0331242d0a2c103f15a3ac9bc", "c8cb81d97dd14b8a84b7caf93cc94667", "24c00069c1f4415fa6bd9b2eee09d53a", "ce64d54649b84b71b420867452873172", "4e2799deeb954c42ac88d2f52a021929", "66e6db7334b241ad9519e28527c17ef0", "ab9b46c7f454447cb7b4b97e48ad2dbd", "2d74528d3e4947fc9588b635aa1a375e", "4ca4229ff72a470a8984d91b3dd774aa", "f7a40eeb26da47e38b9da249b97cb484", "dc27afa005844dafafaf05431ec4d70f" ] }, "outputId": "ad3d1ca1-b6c1-4456-c563-41c7eb1d283c" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "tokenizer_config.json: 0%| | 0.00/55.4k [00:00⚠️ Address type is required. Please select one.\",\n", " gr.update(interactive=False, elem_classes=\"yellow_btn\")\n", " )\n", " # Return success message and set button to blue and enabled\n", " return (\n", " \"Ready to generate dataset.\",\n", " gr.update(interactive=True, elem_classes=\"blue_btn\")\n", " )\n" ], "metadata": { "id": "z5pFDbnTz-fP" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# Gradio UI\n", "with gr.Blocks() as app:\n", " gr.Markdown(\"## Dynamic CSV Dataset Viewer\")\n", "\n", " num_records_slider = gr.Slider(minimum=5, maximum=50, step=5, value=20, label=\"Number of Records\")\n", "\n", " with gr.Row(equal_height=True):\n", " address_type_radio = gr.Radio(\n", " [\"us_only\", \"international\", \"us_international\", \"americas\", \"europe\"],\n", " value=\"\",\n", " label=\"Address and Phone Type\",\n", " info=\"Select the type of addresses and phone numbers\"\n", " )\n", " status_text = gr.Markdown(\n", " \"⚠️ Please select an address type above to proceed.\",\n", " elem_id=\"status_text\"\n", " )\n", "\n", " generate_btn = gr.Button(\"Generate Data\", interactive=True, elem_id=\"generate_btn\")\n", "\n", " response_text = gr.Textbox(value=\"\", label=\"Generated Driver List CSV\", interactive=False)\n", " dataframe_output = gr.Dataframe(value=pd.DataFrame(), label=\"Generated Driver List Dataset\")\n", "\n", " # Update status text and button style dynamically\n", " address_type_radio.change(fn=check_address_selection, inputs=[address_type_radio], outputs=[status_text, generate_btn])\n", "\n", " generate_btn.click(update_dataset, inputs=[num_records_slider, address_type_radio], outputs=[dataframe_output, response_text])\n", "\n", " # Custom CSS for button colors\n", " app.css = \"\"\"\n", " .blue_btn {\n", " background-color: green;\n", " color: white;\n", " }\n", " \"\"\"\n", "\n", "app.launch(share=True) # Ensure sharing is enabled in Colab" ], "metadata": { "id": "z3K6PfAiL2ZA", "colab": { "base_uri": "https://localhost:8080/", "height": 591 }, "outputId": "50cd6ee9-8229-4f58-ad1f-da67990dfc28" }, "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", "* Running on public URL: https://2d1cc8e9fab628c1ca.gradio.live\n", "\n", "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from the terminal in the working directory to deploy to Hugging Face Spaces (https://huggingface.co/spaces)\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "
" ] }, "metadata": {} }, { "output_type": "execute_result", "data": { "text/plain": [] }, "metadata": {}, "execution_count": 16 } ] } ] }