{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Kd2GokldHjAI",
        "outputId": "f453ddd3-4787-4c60-974d-9ce885e91229"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        }
      ],
      "source": [
        "!pip install gradio"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XWhpCGp8HrGD"
      },
      "outputs": [],
      "source": [
        "from transformers import pipeline, set_seed, GenerationConfig, AutoModelForCausalLM\n",
        "import gradio as gr\n",
        "import torch"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 209,
          "referenced_widgets": [
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        },
        "id": "T6J2zJx2UdHw",
        "outputId": "b0455d65-4486-49c7-d473-412471c25547"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/4.40G [00:00<?, ?B/s]"
            ],
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "generation_config.json:   0%|          | 0.00/124 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2c30c458967d4ae38b3eaa3df2ab54c4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/1.29k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.model:   0%|          | 0.00/500k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7f46e51d18f34efd98139fb3b7f8726b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/1.84M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "05f76d1f2b244da59bdd8d6cfca09ed2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/551 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "bff71877ad8f48c0bde2af77b4b25f05"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "gpt2_generator = pipeline('text-generation', model='gpt2')\n",
        "tinyllama_generator = pipeline('text-generation', model='as-cle-bert/tinyllama-essay-scorer')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 594
        },
        "id": "HDl2z-MxICTT",
        "outputId": "327527bd-7568-464e-9774-86a7dae09444"
      },
      "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://28792579f31a66e85d.gradio.live\n",
            "\n",
            "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"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<div><iframe src=\"https://28792579f31a66e85d.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": []
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "def load_model(model_name):\n",
        "    global generator\n",
        "    generator = pipeline('text-generation', model=model_name, trust_remote_code=True)\n",
        "\n",
        "def generate_text(model, prompt, temperature, max_length, top_p):\n",
        "    if temperature == 0:\n",
        "        do_sample = False\n",
        "    else:\n",
        "        do_sample = True\n",
        "\n",
        "    load_model(model)\n",
        "\n",
        "    response = generator(prompt, max_length=max_length, do_sample=do_sample, temperature=temperature, top_p=top_p)[0][\"generated_text\"]\n",
        "    return response\n",
        "\n",
        "interface = gr.Interface(\n",
        "    fn=generate_text,\n",
        "    inputs=[\n",
        "        gr.components.Dropdown(label=\"Choose a Model\", choices=['gpt2', 'as-cle-bert/tinyllama-essay-scorer'], value='gpt2', info=\"Select the model for generating text.\"),\n",
        "        gr.components.Dropdown(label=\"Prompt\", choices=['Write a tagline for an ice cream shop', 'Write a poem about spring', 'Write an introduction to the University of Zurich'], value='Write a tagline for an ice cream shop'),\n",
        "        gr.components.Slider(minimum=0, maximum=2, step=0.01, value = 1, label=\"Temperature\", info = \"(For τ = 1, the distribution is unchanged;For τ > 1, the distribution becomes more uniform; For τ < 1, the distribution becomes more peaked.)\"),\n",
        "        gr.components.Slider(minimum=1, maximum=256, step=1, value = 16, label=\"Max Length\", info =\"(Maximum length is the maximum limit of the generated text.)\"),\n",
        "        gr.components.Slider(minimum=0, maximum=1, step=0.01, value=1, label=\"Top-p\", info=\"(Top-p sampling is to keep the top p percent of the probability mass.)\")\n",
        "    ],\n",
        "    outputs=[gr.Textbox(label=\"Output\", lines=3, placeholder = \"Hello, World!\")],\n",
        "    title=\"Text Generation Control Panel\",\n",
        "    description=\"Adjust the settings to control the text generation parameters.\"\n",
        ")\n",
        "\n",
        "\n",
        "interface.launch(share=True)"
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