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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m92.0/92.0 kB\u001b[0m \u001b[31m6.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m305.1/305.1 kB\u001b[0m \u001b[31m24.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m75.9/75.9 kB\u001b[0m \u001b[31m6.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m138.7/138.7 kB\u001b[0m \u001b[31m2.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m381.9/381.9 kB\u001b[0m \u001b[31m15.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m45.7/45.7 kB\u001b[0m \u001b[31m4.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m60.5/60.5 kB\u001b[0m \u001b[31m4.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m129.9/129.9 kB\u001b[0m \u001b[31m10.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.1/2.1 MB\u001b[0m \u001b[31m34.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.3/58.3 kB\u001b[0m \u001b[31m7.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m71.1/71.1 kB\u001b[0m \u001b[31m7.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m76.9/76.9 kB\u001b[0m \u001b[31m6.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for ffmpy (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "lida 0.0.10 requires kaleido, which is not installed.\n",
            "llmx 0.0.15a0 requires cohere, which is not installed.\n",
            "llmx 0.0.15a0 requires openai, which is not installed.\n",
            "llmx 0.0.15a0 requires tiktoken, which is not installed.\n",
            "tensorflow-probability 0.22.0 requires typing-extensions<4.6.0, but you have typing-extensions 4.9.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0m"
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    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 626
        },
        "id": "QfdKBjwI8CSM",
        "outputId": "e6724272-eee8-427f-a921-1c1831a52e36"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "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",
            "\n",
            "Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n",
            "Running on public URL: https://24597b8d8dd76735a2.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://24597b8d8dd76735a2.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "import gradio as gr\n",
        "\n",
        "def answer(text):\n",
        "    result_1 = \"I want to eat raw salad with lots of onion\"\n",
        "    result_2 = \"I'm in the mood for a spicy curry with rice\"\n",
        "    result_3 = \"How about a refreshing fruit salad for dessert?\"\n",
        "    return result_1, result_2, result_3\n",
        "\n",
        "input_textbox = gr.Textbox(\"Hello Vishal, what would you like to eat today for lunch\")\n",
        "\n",
        "gr.Interface(\n",
        "    fn=answer,\n",
        "    inputs=input_textbox,\n",
        "    outputs=['textbox', 'textbox', 'textbox']\n",
        ").launch();"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import gradio as gr\n",
        "\n",
        "demo = gr.Blocks()\n",
        "\n",
        "\n",
        "def answer1(x):\n",
        "    return \"I want to eat raw salad with lots of onion\"\n",
        "\n",
        "\n",
        "def anser2(x):\n",
        "    return \"I'm in the mood for a spicy curry with rice and chicken\"\n",
        "\n",
        "\n",
        "with demo:\n",
        "    gr.Markdown(\"Flip text or image files using this demo.\")\n",
        "    with gr.Tabs():\n",
        "        with gr.TabItem(\"Vegetarian\"):\n",
        "            with gr.Row():\n",
        "                text_input = gr.Textbox(\"Hello Vishal, what would you like to eat today for lunch\")\n",
        "                text_output = gr.Textbox()\n",
        "            text_button = gr.Button(\"Suggest\")\n",
        "        with gr.TabItem(\"Non Vegetarian\"):\n",
        "            with gr.Row():\n",
        "                text_input2 = gr.Textbox(\"Hello Vishal, what would you like to eat today for lunch\")\n",
        "                text_output2 = gr.Textbox()\n",
        "            text_button2 = gr.Button(\"Suggest\")\n",
        "\n",
        "    text_button.click(answer1, inputs=text_input, outputs=text_output)\n",
        "    text_button2.click(anser2, inputs=text_input2, outputs=text_output2)\n",
        "\n",
        "demo.launch()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 646
        },
        "id": "-TJ879rCBYWT",
        "outputId": "b950e817-2ba7-461c-92be-3a51a20b9f4c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "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",
            "\n",
            "Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n",
            "Running on public URL: https://83ff707f34952e966d.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://83ff707f34952e966d.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": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install transformers\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WvUZtlZUG1SR",
        "outputId": "0c34b035-70d3-411f-bab9-3eb60a52ba6b"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.35.2)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.13.1)\n",
            "Requirement already satisfied: huggingface-hub<1.0,>=0.16.4 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.20.2)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.23.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (23.2)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (6.0.1)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2023.6.3)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.31.0)\n",
            "Requirement already satisfied: tokenizers<0.19,>=0.14 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.15.0)\n",
            "Requirement already satisfied: safetensors>=0.3.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.4.1)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.66.1)\n",
            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.16.4->transformers) (2023.6.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.16.4->transformers) (4.9.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.6)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2023.11.17)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from transformers import pipeline\n",
        "\n",
        "pipe = pipeline(\"text-generation\", model=\"VishalMysore/cookgptlama\")"
      ],
      "metadata": {
        "id": "-ZIQUvklH6TI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241,
          "referenced_widgets": [
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        },
        "outputId": "bd7fd09b-e9ff-4dc7-a61f-079f652a136f"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/698 [00:00<?, ?B/s]"
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        {
          "output_type": "display_data",
          "data": {
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              "model.safetensors:   0%|          | 0.00/2.20G [00:00<?, ?B/s]"
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        {
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        {
          "output_type": "display_data",
          "data": {
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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          "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": {
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            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import pipeline\n",
        "def askllmpipe(text):\n",
        "    messages = [{\"role\": \"system\",\"content\": \"You are my personal chef experienced in Indian spicy food\",}, {\"role\": \"user\", \"content\": text},]\n",
        "    prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)\n",
        "    outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)\n",
        "    return_text=outputs[0][\"generated_text\"]\n",
        "    return return_text"
      ],
      "metadata": {
        "id": "FS3EpsmBMYAY"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pipe_8_bit = pipeline(\"text-generation\", model=\"VishalMysore/cookgptlma-8bit\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 246,
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        },
        "id": "MgRVQELgMjS3",
        "outputId": "7432930e-da06-4be9-a1a3-f833dbe089ce"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/1.07k [00:00<?, ?B/s]"
            ],
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Detected the presence of a `quantization_config` attribute in the model's configuration but you don't have the correct `bitsandbytes` version to support int8 serialization. Please install the latest version of `bitsandbytes` with  `pip install --upgrade bitsandbytes`.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/1.23G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0a42fdcb8caf40f78394dc20dd21f2f7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9d75dca6f12543a7a6e37ccf7ba83d9f"
            }
          },
          "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,
              "version_minor": 0,
              "model_id": "d8356733f5a54f86938705a7f8580cc8"
            }
          },
          "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": "2e50f2a96ccc4727ab6f2054f021ccf5"
            }
          },
          "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": "ba34e53eef9740369e6632b41c67b6d4"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import pipeline\n",
        "def askllmpipe_8_bit(text):\n",
        "    messages = [{\"role\": \"system\",\"content\": \"You are my personal chef experienced in Indian spicy food\",}, {\"role\": \"user\", \"content\": text},]\n",
        "    prompt = pipe_8_bit.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)\n",
        "    outputs = pipe_8_bit(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)\n",
        "    return_text=outputs[0][\"generated_text\"]\n",
        "    return return_text"
      ],
      "metadata": {
        "id": "9LPQ-y3JMwq4"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "askllmpipe(\"provide me receipe of paneer bhurji with cook time \")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 140
        },
        "id": "ds-GlK34MbWH",
        "outputId": "694dbb46-ca93-4c0d-ccc6-83beb7e71974"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\"<|system|>\\nYou are my personal chef experienced in Indian spicy food</s>\\n<|user|>\\nprovide me receipe of paneer bhurji with cook time </s>\\n<|assistant|>\\nSure, here's a recipe for paneer bhurji, a popular Indian dish that's made with spicy paneer (cottage cheese) and served with a tangy, spicy sauce:\\n\\nIngredients:\\n- 2 cups paneer (cottage cheese)\\n- 1 onion, chopped\\n- 2 garlic cloves, minced\\n- 2 tbsp ginger-garlic paste\\n- 1 tsp cumin powder\\n- 1 tsp coriander powder\\n- 1 tsp turmeric powder\\n- 1 tsp chili powder\\n- 1 tsp garam masala\\n- 1 tbsp water\\n- Salt to taste\\n- 1/2 cup chopped fresh cilantro\\n- 1/4 cup ghee\\n- 2 tbsp chopped fresh coriander leaves\\n- 2 tbsp chopped fresh mint leaves\\n- 1/4 cup raisins\\n\\nInstructions:\\n\\n1. In a pan, heat the ghee over medium heat.\""
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "askllmpipe_8_bit(\"provide me receipe of paneer bhurji with cook time \")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 157
        },
        "id": "i9dcwffvM5li",
        "outputId": "6ff0f85e-ebc8-44b1-ece0-60f51c2ae318"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'<|system|>\\nYou are my personal chef experienced in Indian spicy food</s>\\n<|user|>\\nprovide me receipe of paneer bhurji with cook time </s>\\n<|assistant|>\\nIngredients:\\n- 2 cups paneer, diced\\n- 2 onions, diced\\n- 2 cloves garlic, minced\\n- 1 green bell pepper, diced\\n- 1 red bell pepper, diced\\n- 1 tsp ginger, minced\\n- 1 tsp cumin seeds, toasted\\n- 2 tsp coriander powder, toasted\\n- 1 tbsp ghee\\n- 1 tsp turmeric powder\\n- 1 tsp garam masala powder\\n- 2 cups water\\n\\nInstructions:\\n1. In a large pot, heat the ghee over medium heat. 2. Add the onions, peppers, and garlic, and saut the vegetables for 10 minutes, or until they begin to soften. 3. Add the cumin powder, coriander powder, turmeric powder, and water. 4. Bring the vegetables to the pot. Cook for 50 minutes, stirring occasionally, until the vegetables are cooked through and the vegetables begin'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import gradio as gr\n",
        "\n",
        "demo = gr.Blocks()\n",
        "\n",
        "\n",
        "def answer1(x):\n",
        "    return askllmpipe(x)\n",
        "\n",
        "\n",
        "def anser2(x):\n",
        "    return  askllmpipe_8_bit(x)\n",
        "\n",
        "with demo:\n",
        "    gr.Markdown(\"Compared CookGPT 32 bit and 8 bit model from this demo.\")\n",
        "    with gr.Tabs():\n",
        "        with gr.TabItem(\"32 bit\"):\n",
        "            with gr.Row():\n",
        "                text_input = gr.Textbox(\"Give me recipe for Paneer Butter Masala\")\n",
        "                text_output = gr.Textbox()\n",
        "            text_button = gr.Button(\"Suggest\")\n",
        "        with gr.TabItem(\"8 bit\"):\n",
        "            with gr.Row():\n",
        "                text_input2 = gr.Textbox(\"Give me recipe for Paneer Butter Masala\")\n",
        "                text_output2 = gr.Textbox()\n",
        "            text_button2 = gr.Button(\"Suggest\")\n",
        "\n",
        "    text_button.click(answer1, inputs=text_input, outputs=text_output)\n",
        "    text_button2.click(anser2, inputs=text_input2, outputs=text_output2)\n",
        "\n",
        "demo.launch()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 626
        },
        "id": "FzWvvxydNDYv",
        "outputId": "a22e465b-7810-45d3-f250-c5f6341cd4b4"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "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",
            "\n",
            "Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n",
            "Running on public URL: https://68c00bd8ba60fa643b.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://68c00bd8ba60fa643b.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": 9
        }
      ]
    }
  ]
}