01_Workshop_Jan_29_2024_gradio.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ua-datalab/DataEngineering/blob/main/01_Workshop_Jan_29_2024_gradio.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8RD3RTHe1LGc",
        "outputId": "fdbbe237-92b4-4185-f674-aa9acc52db48"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Building wheels for collected packages: ffmpy\n",
            "  Building wheel for ffmpy (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for ffmpy: filename=ffmpy-0.3.1-py3-none-any.whl size=5579 sha256=ad005987427d7e1771b2e178f727b380d48e56b652c1da489be0b7791ce5ef7d\n",
            "  Stored in directory: /root/.cache/pip/wheels/01/a6/d1/1c0828c304a4283b2c1639a09ad86f83d7c487ef34c6b4a1bf\n",
            "Successfully built ffmpy\n",
            "Installing collected packages: pydub, ffmpy, websockets, typing-extensions, tomlkit, shellingham, semantic-version, ruff, python-multipart, orjson, h11, colorama, annotated-types, aiofiles, uvicorn, starlette, pydantic-core, httpcore, pydantic, httpx, gradio-client, fastapi, gradio\n",
            "  Attempting uninstall: typing-extensions\n",
            "    Found existing installation: typing_extensions 4.5.0\n",
            "    Uninstalling typing_extensions-4.5.0:\n",
            "      Successfully uninstalled typing_extensions-4.5.0\n",
            "  Attempting uninstall: pydantic\n",
            "    Found existing installation: pydantic 1.10.14\n",
            "    Uninstalling pydantic-1.10.14:\n",
            "      Successfully uninstalled pydantic-1.10.14\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[0mSuccessfully installed aiofiles-23.2.1 annotated-types-0.6.0 colorama-0.4.6 fastapi-0.109.0 ffmpy-0.3.1 gradio-4.16.0 gradio-client-0.8.1 h11-0.14.0 httpcore-1.0.2 httpx-0.26.0 orjson-3.9.12 pydantic-2.6.0 pydantic-core-2.16.1 pydub-0.25.1 python-multipart-0.0.6 ruff-0.1.14 semantic-version-2.10.0 shellingham-1.5.4 starlette-0.35.1 tomlkit-0.12.0 typing-extensions-4.9.0 uvicorn-0.27.0.post1 websockets-11.0.3\n"
          ]
        }
      ],
      "source": [
        "!pip install gradio"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Adding Title, User Input and Output**"
      ],
      "metadata": {
        "id": "VEaWLzpCOIM3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import gradio as gr\n",
        "\n",
        "def greet(name):\n",
        "    return \"Hello \" + name + \"!\"\n",
        "\n",
        "iface = gr.Interface(fn=greet, inputs=\"text\", outputs=\"text\", title=\"My first gradio demo\")\n"
      ],
      "metadata": {
        "id": "lNxxCn321pGX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "iface.launch(share=True)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 591
        },
        "id": "mQDRQhB81pI7",
        "outputId": "e09b0e11-88b9-46c7-fd07-1fa843353aba"
      },
      "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://1dea5419c382bb1d58.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://1dea5419c382bb1d58.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": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 715
        },
        "id": "iT1HFp-E1pLr",
        "outputId": "5b576d73-7a57-4a71-ff78-75a8dfe98e4e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/gradio/utils.py:840: UserWarning: Expected 3 arguments for function <function update_dropdown at 0x7c717d4f49d0>, received 1.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/gradio/utils.py:844: UserWarning: Expected at least 3 arguments for function <function update_dropdown at 0x7c717d4f49d0>, received 1.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "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://c4520e2abf3b81c0d2.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://c4520e2abf3b81c0d2.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": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **File upload and visualization of tabular data**"
      ],
      "metadata": {
        "id": "QZPWz7fYWC4n"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import gradio as gr\n",
        "import pandas as pd\n",
        "\n",
        "def visualize_csv(uploaded_file, show_raw_data):\n",
        "    if uploaded_file is None:\n",
        "        return \"Please upload a CSV file.\", None\n",
        "\n",
        "    # Read the CSV file\n",
        "    data = pd.read_csv(uploaded_file.name)\n",
        "    output_message = \"CSV file loaded successfully.\"\n",
        "\n",
        "    if show_raw_data:\n",
        "        return output_message, data\n",
        "    else:\n",
        "        return output_message, None\n",
        "\n",
        "# Define the Gradio interface\n",
        "interface = gr.Interface(\n",
        "    fn=visualize_csv,\n",
        "    inputs=[\n",
        "        gr.File(label=\"Choose a CSV file\"),\n",
        "        gr.Checkbox(label=\"Show raw data\")\n",
        "    ],\n",
        "    outputs=[\n",
        "        \"text\",\n",
        "        gr.Dataframe()\n",
        "    ],\n",
        "    title=\"CSV Visualizer\"\n",
        ")\n",
        "# Launch the interface\n",
        "interface.launch(share=True)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 591
        },
        "id": "wjZyUV7j1pQK",
        "outputId": "3b4c7992-13a9-43e5-b6ce-c9c7eeb4dad6"
      },
      "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://64103d25313a16849f.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://64103d25313a16849f.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": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Plotting data from tables**"
      ],
      "metadata": {
        "id": "nDKvGkrZXHbX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import gradio as gr\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def visualize_csv(uploaded_file, show_raw_data, plot_type, selected_columns):\n",
        "    if uploaded_file is None:\n",
        "        return \"Please upload a CSV file.\", None, \"Please upload a CSV file.\"\n",
        "\n",
        "    data = pd.read_csv(uploaded_file.name)\n",
        "    column_choices = list(data.columns)\n",
        "\n",
        "    if show_raw_data:\n",
        "        raw_data_output = data\n",
        "    else:\n",
        "        raw_data_output = \"Raw data is not displayed.\"\n",
        "\n",
        "    if selected_columns:\n",
        "        selected_columns = selected_columns.split(',')\n",
        "        fig, ax = plt.subplots()\n",
        "        if plot_type == \"Line Plot\":\n",
        "            data[selected_columns].plot(kind='line', ax=ax)\n",
        "        elif plot_type == \"Bar Plot\":\n",
        "            data[selected_columns].plot(kind='bar', ax=ax)\n",
        "        elif plot_type == \"Scatter Plot\" and len(selected_columns) >= 2:\n",
        "            ax.scatter(data[selected_columns[0]], data[selected_columns[1]])\n",
        "            plt.ylim([0,100])\n",
        "            plt.xlim([0,365])\n",
        "            plt.xlabel(selected_columns[0])\n",
        "            plt.ylabel(selected_columns[1])\n",
        "            plt.legend()\n",
        "\n",
        "        return raw_data_output, plt, \"Plot generated.\", column_choices\n",
        "    else:\n",
        "        return raw_data_output, None, \"Select columns and plot type to generate plot.\", column_choices\n",
        "\n",
        "# Define the Gradio interface\n",
        "interface = gr.Interface(\n",
        "    fn=visualize_csv,\n",
        "    inputs=[\n",
        "        gr.File(label=\"Choose a CSV file\"),\n",
        "        gr.Checkbox(label=\"Show raw data\"),\n",
        "        gr.Radio([\"Line Plot\", \"Bar Plot\", \"Scatter Plot\"], label=\"Choose the type of plot\"),\n",
        "        gr.Textbox(label=\"Select columns for plotting (comma-separated)\")\n",
        "    ],\n",
        "    outputs=[\n",
        "        \"text\",\n",
        "        gr.Plot(),\n",
        "        \"text\",\n",
        "        gr.Dropdown()\n",
        "    ],\n",
        "    title=\"CSV Visualizer\",\n",
        "    live=True\n",
        ")\n",
        "\n",
        "\n",
        "# Launch the interface\n",
        "interface.launch(share=True)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 591
        },
        "id": "i5v_jQrJ1pUb",
        "outputId": "c7e76244-6851-4e77-8d63-960eb9eb059e"
      },
      "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://e350a44f9c14b09d04.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://e350a44f9c14b09d04.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": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Applications\n",
        "## **Hosuing Data Explorer**"
      ],
      "metadata": {
        "id": "S0sEj7_7btNd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import gradio as gr\n",
        "import pandas as pd\n",
        "\n",
        "def load_data():\n",
        "    url = 'https://raw.githubusercontent.com/ua-datalab/DataEngineering/main/datasets/az_housing_data.csv'\n",
        "    data = pd.read_csv(url, usecols=['zip', 'period_begin', 'period_end', 'county', 'median_sale_price', 'homes_sold', 'inventory', 'irs_estimated_population', 'longitude', 'latitude'])\n",
        "    data = data.dropna()\n",
        "    data['period_begin'] = pd.to_datetime(data['period_begin'])\n",
        "    data['period_end'] = pd.to_datetime(data['period_end'])\n",
        "    int_columns = ['median_sale_price', 'homes_sold', 'inventory', 'irs_estimated_population']\n",
        "    data[int_columns] = data[int_columns].astype(float)\n",
        "    data = data[data['median_sale_price'] >= 150000]\n",
        "    return data\n",
        "\n",
        "data = load_data()\n",
        "\n",
        "def filter_data(select_by, selected_counties, selected_zips, max_price):\n",
        "    if select_by == 'County':\n",
        "        filtered_data = data[data['county'].isin(selected_counties)]\n",
        "    elif select_by == 'Zip':\n",
        "        filtered_data = data[data['zip'].isin(selected_zips)]\n",
        "\n",
        "    filtered_data = filtered_data[filtered_data['median_sale_price'] <= max_price]\n",
        "    return filtered_data\n",
        "\n",
        "# Define the Gradio interface\n",
        "interface = gr.Interface(\n",
        "    fn=filter_data,\n",
        "    inputs=[\n",
        "        gr.Radio([\"County\", \"Zip\"], label=\"Select Location Filter:\"),\n",
        "        gr.CheckboxGroup(choices=list(data['county'].unique()), label=\"Select Counties\"),\n",
        "        gr.CheckboxGroup(choices=list(map(str, data['zip'].unique())), label=\"Select Zip Codes\"),\n",
        "        gr.Slider(minimum=data['median_sale_price'].min(), maximum=data['median_sale_price'].max(), label=\"Max Price\")\n",
        "    ],\n",
        "    outputs=gr.Dataframe(),\n",
        "    title=\"Arizona Housing Market Analysis Tool\",\n",
        "    # description=\n",
        "    description=\"\"\"\n",
        "    <div style='text-align:center;'>\n",
        "        <img src='https://t3.ftcdn.net/jpg/03/12/06/66/240_F_312066669_OIVg3OgYBPYDq35sJTiMwJGmCfQhcIuq.jpg' width='200'><br>\n",
        "    </div>\n",
        "    <p>This tool allows you to explore the Arizona housing market data. Use the controls to filter data based on location and price. Depending on your choice of location filter, use either the county or zip code checkboxes.</p>\n",
        "    \"\"\"\n",
        "    # \"This tool allows you to explore the Arizona housing market data. Use the controls to filter data based on location and price. Depending on your choice of location filter, use either the county or zip code checkboxes.\"\n",
        ")\n",
        "\n",
        "\n",
        "# Launch the interface\n",
        "interface.launch(share=True)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 591
        },
        "id": "GpOaRJSu1pZH",
        "outputId": "8c057323-c1f6-4951-ec0d-d892bb5ce0de"
      },
      "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://30d5004dc839406155.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://30d5004dc839406155.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": "markdown",
      "source": [
        "# **College Fest Registration Website**"
      ],
      "metadata": {
        "id": "yg7e1OyBd3y7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import gradio as gr\n",
        "import pandas as pd\n",
        "import uuid\n",
        "\n",
        "def register(name, email, age, college, selected_events, need_accommodation, dietary_preferences):\n",
        "    # Load existing data\n",
        "    filename = 'registrations.csv'\n",
        "    try:\n",
        "        data = pd.read_csv(filename)\n",
        "    except FileNotFoundError:\n",
        "        data = pd.DataFrame(columns=['ID', 'Name', 'Email', 'Age', 'College', 'Events', 'Accommodation', 'Dietary Preferences'])\n",
        "\n",
        "    # Generate a unique ID\n",
        "    unique_id = str(uuid.uuid4())[:8]\n",
        "\n",
        "    # Add data to DataFrame\n",
        "    new_data = pd.DataFrame({\n",
        "        'ID': [unique_id],\n",
        "        'Name': [name],\n",
        "        'Email': [email],\n",
        "        'Age': [age],\n",
        "        'College': [college],\n",
        "        'Events': [', '.join(selected_events)],\n",
        "        'Accommodation': ['Yes' if need_accommodation else 'No'],\n",
        "        'Dietary Preferences': [dietary_preferences]\n",
        "    })\n",
        "    data = data.append(new_data, ignore_index=True)\n",
        "\n",
        "    # Save data to CSV\n",
        "    data.to_csv(filename, index=False)\n",
        "\n",
        "    return f\"Thank you {name} for registering! Your registration ID is {unique_id}\"\n",
        "\n",
        "events = ['Technical Symposium', 'Cultural Fest', 'Sports Meet', 'Workshops', 'Guest Lectures']\n",
        "\n",
        "interface = gr.Interface(\n",
        "    fn=register,\n",
        "    inputs=[\n",
        "        gr.Textbox(label=\"Full Name\"),\n",
        "        gr.Textbox(label=\"Email\"),\n",
        "        gr.Number(label=\"Age\", minimum=18, maximum=100),\n",
        "        gr.Textbox(label=\"College Name\"),\n",
        "        gr.CheckboxGroup(choices=events, label=\"Select Events\"),\n",
        "        gr.Checkbox(label=\"Do you need accommodation?\"),\n",
        "        gr.Radio(choices=['No Preference', 'Vegetarian', 'Vegan', 'Gluten-Free'], label=\"Dietary Preferences\")\n",
        "    ],\n",
        "    outputs=\"text\",\n",
        "    title=\"IVY SPECTRUM - Student Registration\",\n",
        "    description=\"\"\"\n",
        "    <div style='text-align:center;'>\n",
        "        <img src='https://raw.githubusercontent.com/ua-datalab/DataEngineering/main/images/dalle3_ivy_spectrum.png' width='200' style='margin: auto; display: block;'><br>\n",
        "    </div>\n",
        "    <p>Please fill out the registration details.</p>\n",
        "    \"\"\"\n",
        "\n",
        "    # \"Please fill out the registration details.\"\n",
        ")\n",
        "\n",
        "interface.launch(share=True)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 591
        },
        "id": "K2cPcg73d8dx",
        "outputId": "4c81e07c-b1d1-480a-9451-93217be68694"
      },
      "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://2e90a43a935bb67a1b.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://2e90a43a935bb67a1b.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": 21
        }
      ]
    }
  ]
}