Spaces:
Sleeping
Sleeping
| import gradio as gr | |
| import pandas as pd | |
| import numpy as np | |
| import plotly.express as px | |
| from datasets import load_dataset | |
| def load_transform_data(): | |
| """ | |
| Load and transform data from a parquet file. | |
| Returns: | |
| pandas.DataFrame: Transformed dataframe. | |
| """ | |
| spaces_dataset = 'jsulz/space-stats' | |
| dataset = load_dataset(spaces_dataset) | |
| df = dataset['train'].to_pandas() | |
| # combine the sdk and tags columns, one of which is a string and the other is an array of strings | |
| df["sdk"] = df["sdk"].apply(lambda x: np.array([str(x)])) | |
| df["licenses"] = df["license"].apply( | |
| lambda x: np.array([str(x)]) if x is None else x | |
| ) | |
| # then combine the sdk and tags columns so that their elements are together | |
| df["sdk_tags"] = df[["sdk", "tags"]].apply( | |
| lambda x: np.concatenate((x.iloc[0], x.iloc[1])), axis=1 | |
| ) | |
| # Fill the NaN values with an empty string | |
| df['emoji'] = np.where(df['emoji'].isnull(), '', df['emoji']) | |
| # where the custom_domains column is not null, use that as the url, otherwise, use the host column | |
| df["url"] = np.where( | |
| df["custom_domains"].isnull(), | |
| df["id"], | |
| df["custom_domains"], | |
| ) | |
| # Build up a pretty url that's clickable with the emoji | |
| df["url"] = df[["url", "emoji"]].apply( | |
| lambda x: ( | |
| f"<a target='_blank' href=https://huggingface.co/spaces/{x.iloc[0]}>{str(x.iloc[1]) + " " + x.iloc[0]}</a>" | |
| if x.iloc[0] is not None and "/" in x.iloc[0] | |
| else f"<a target='_blank' href=https://{x.iloc[0][0]}>{str(x.iloc[1]) + " " + x.iloc[0][0]}</a>" | |
| ), | |
| axis=1, | |
| ) | |
| # Prep the models, datasets, and licenses columns for display | |
| df["r_models"] = [ | |
| ", ".join(models) if models is not None else "" for models in df["models"] | |
| ] | |
| df["r_sdk_tags"] = [ | |
| ", ".join(sdk_tags) if sdk_tags is not None else "" | |
| for sdk_tags in df["sdk_tags"] | |
| ] | |
| df["r_datasets"] = [ | |
| ", ".join(datasets) if datasets is not None else "" | |
| for datasets in df["datasets"] | |
| ] | |
| df["r_licenses"] = [ | |
| ", ".join(licenses) if licenses is not None else "" | |
| for licenses in df["licenses"] | |
| ] | |
| return df | |
| def filtered_df( | |
| filtered_emojis, | |
| filtered_likes, | |
| filtered_author, | |
| filtered_hardware, | |
| filtered_tags, | |
| filtered_models, | |
| filtered_datasets, | |
| space_licenses, | |
| filtered_devmode, | |
| ): | |
| """ | |
| Filter the dataframe based on the given criteria. | |
| Args: | |
| filtered_emojis (list): List of emojis to filter the dataframe by. | |
| filtered_likes (int): Minimum number of likes to filter the dataframe by. | |
| filtered_author (list): List of authors to filter the dataframe by. | |
| filtered_hardware (list): List of hardware to filter the dataframe by. | |
| filtered_tags (list): List of tags to filter the dataframe by. | |
| filtered_models (list): List of models to filter the dataframe by. | |
| filtered_datasets (list): List of datasets to filter the dataframe by. | |
| space_licenses (list): List of licenses to filter the dataframe by. | |
| Returns: | |
| pandas.DataFrame: Filtered dataframe with the following columns: "URL", "Likes", "Models", "Datasets", "Licenses". | |
| """ | |
| _df = df | |
| if filtered_emojis: | |
| _df = _df[_df["emoji"].isin(filtered_emojis)] | |
| if filtered_likes: | |
| _df = _df[_df["likes"] >= filtered_likes] | |
| if filtered_author: | |
| _df = _df[_df["author"].isin(filtered_author)] | |
| if filtered_hardware: | |
| _df = _df[_df["hardware"].isin(filtered_hardware)] | |
| if filtered_tags: | |
| _df = _df[ | |
| _df["sdk_tags"].apply(lambda x: any(tag in x for tag in filtered_tags)) | |
| ] | |
| if filtered_models: | |
| _df = _df[ | |
| _df["models"].apply( | |
| lambda x: ( | |
| any(model in x for model in filtered_models) | |
| if x is not None | |
| else False | |
| ) | |
| ) | |
| ] | |
| if filtered_datasets: | |
| _df = _df[ | |
| _df["datasets"].apply( | |
| lambda x: ( | |
| any(dataset in x for dataset in filtered_datasets) | |
| if x is not None | |
| else False | |
| ) | |
| ) | |
| ] | |
| if space_licenses: | |
| _df = _df[ | |
| _df["licenses"].apply( | |
| lambda x: ( | |
| any(space_license in x for space_license in space_licenses) | |
| if x is not None | |
| else False | |
| ) | |
| ) | |
| ] | |
| # rename the columns names to make them more readable | |
| _df = _df.rename( | |
| columns={ | |
| "url": "URL", | |
| "likes": "Likes", | |
| "r_models": "Models", | |
| "r_datasets": "Datasets", | |
| "r_licenses": "Licenses", | |
| } | |
| ) | |
| if filtered_devmode: | |
| _df = _df[ | |
| _df["devMode"] == filtered_devmode | |
| ] | |
| return _df[["URL", "Likes", "Models", "Datasets", "Licenses"]] | |
| def count_items(items): | |
| """ | |
| Count the occurrences of items and authors in a given list of items. | |
| Parameters: | |
| items (dataframe column): A dataframe column containing a list of items. | |
| Returns: | |
| tuple: A tuple containing two dictionaries. The first dictionary contains the count of each item, | |
| and the second dictionary contains the count of each author. | |
| """ | |
| items = np.concatenate([arr for arr in items.values if arr is not None]) | |
| item_count = {} | |
| item_author_count = {} | |
| for item in items: | |
| if item in item_count: | |
| item_count[item] += 1 | |
| else: | |
| item_count[item] = 1 | |
| author = item.split('/')[0] | |
| if author in item_author_count: | |
| item_author_count[author] += 1 | |
| else: | |
| item_author_count[author] = 1 | |
| return item_count, item_author_count | |
| def flatten_column(_df, column): | |
| """ | |
| Flattens a column in a DataFrame. | |
| Args: | |
| _df (pandas.DataFrame): The DataFrame containing the column. | |
| column (str): The name of the column to flatten. | |
| Returns: | |
| list: A list of unique values from the flattened column. | |
| """ | |
| column_to_list = _df[column].apply( | |
| lambda x: np.array(["None"]) if np.ndim(x) == 0 else x | |
| ) | |
| flattened = np.concatenate(column_to_list.values) | |
| uniques = np.unique(flattened) | |
| return uniques.tolist() | |
| with gr.Blocks(fill_width=True) as demo: | |
| df = load_transform_data() | |
| with gr.Tab(label="Spaces Overview"): | |
| # The Pandas dataframe has a datetime column. Plot the growth of spaces (row entries) over time. | |
| # The x-axis should be the date and the y-axis should be the cumulative number of spaces created up to that date . | |
| df = df.sort_values("created_at") | |
| df['cumulative_spaces'] = df['created_at'].rank(method='first').astype(int) | |
| fig1 = px.line( | |
| df, | |
| x="created_at", | |
| y="cumulative_spaces", | |
| title="Growth of Spaces Over Time", | |
| labels={"created_at": "Date", "cumulative_spaces": "Number of Spaces"}, | |
| template="plotly_dark", | |
| ) | |
| gr.Plot(fig1) | |
| with gr.Row(): | |
| # Create a pie charge showing the distribution of spaces by SDK | |
| fig2 = px.pie(df, names='sdk', title='Distribution of Spaces by SDK', template='plotly_dark') | |
| gr.Plot(fig2) | |
| # create a pie chart showing the distribution of spaces by emoji for the top 10 used emojis | |
| emoji_counts = df['emoji'].value_counts().head(10).reset_index() | |
| fig3 = px.pie(emoji_counts, names='emoji', values='count', title='Distribution of Spaces by Emoji', template='plotly_dark') | |
| gr.Plot(fig3) | |
| # Create a scatter plot showing the relationship between the number of likes and the number of spaces created by an author | |
| author_likes = df.groupby('author').agg({'likes': 'sum', 'id': 'count'}).reset_index() | |
| fig4 = px.scatter( | |
| author_likes, | |
| x="id", | |
| y="likes", | |
| title="Relationship between Number of Spaces Created and Number of Likes", | |
| labels={"id": "Number of Spaces Created", "likes": "Number of Likes"}, | |
| hover_data={"author": True}, | |
| template="plotly_dark", | |
| ) | |
| gr.Plot(fig4) | |
| # Create a scatter plot showing the relationship between the number of likes and the number of spaces created by an author | |
| emoji_likes = df.groupby('emoji').agg({'likes': 'sum', 'id': 'count'}).sort_values(by='likes', ascending=False).head(20).reset_index() | |
| fig10 = px.scatter( | |
| emoji_likes, | |
| x="id", | |
| y="likes", | |
| title="Relationship between Space Emoji and Number of Likes", | |
| labels={"id": "Number of Spaces Created", "likes": "Number of Likes"}, | |
| hover_data={"emoji": True}, | |
| template="plotly_dark", | |
| ) | |
| gr.Plot(fig10) | |
| # Create a bar chart of hardware in use | |
| hardware = df['hardware'].value_counts().reset_index() | |
| hardware.columns = ['Hardware', 'Number of Spaces'] | |
| fig5 = px.bar( | |
| hardware, | |
| x="Hardware", | |
| y="Number of Spaces", | |
| title="Hardware in Use", | |
| labels={ | |
| "Hardware": "Hardware", | |
| "Number of Spaces": "Number of Spaces (log scale)", | |
| }, | |
| color="Hardware", | |
| template="plotly_dark", | |
| ) | |
| fig5.update_layout(yaxis_type="log") | |
| gr.Plot(fig5) | |
| model_count, model_author_count = count_items(df['models']) | |
| model_author_count = pd.DataFrame(model_author_count.items(), columns=['Model Author', 'Number of Spaces']) | |
| fig8 = px.bar( | |
| model_author_count.sort_values("Number of Spaces", ascending=False).head( | |
| 20 | |
| ), | |
| x="Model Author", | |
| y="Number of Spaces", | |
| title="Most Popular Model Authors", | |
| labels={"Model": "Model", "Number of Spaces": "Number of Spaces"}, | |
| template="plotly_dark", | |
| ) | |
| gr.Plot(fig8) | |
| model_count = pd.DataFrame(model_count.items(), columns=['Model', 'Number of Spaces']) | |
| # then make a bar chart | |
| fig6 = px.bar( | |
| model_count.sort_values("Number of Spaces", ascending=False).head(20), | |
| x="Model", | |
| y="Number of Spaces", | |
| title="Most Used Models", | |
| labels={"Model": "Model", "Number of Spaces": "Number of Spaces"}, | |
| template="plotly_dark", | |
| ) | |
| gr.Plot(fig6) | |
| dataset_count, dataset_author_count = count_items(df['datasets']) | |
| dataset_count = pd.DataFrame(dataset_count.items(), columns=['Datasets', 'Number of Spaces']) | |
| dataset_author_count = pd.DataFrame(dataset_author_count.items(), columns=['Dataset Author', 'Number of Spaces']) | |
| fig9 = px.bar( | |
| dataset_author_count.sort_values("Number of Spaces", ascending=False).head( | |
| 20 | |
| ), | |
| x="Dataset Author", | |
| y="Number of Spaces", | |
| title="Most Popular Dataset Authors", | |
| labels={ | |
| "Dataset Author": "Dataset Author", | |
| "Number of Spaces": "Number of Spaces", | |
| }, | |
| template="plotly_dark", | |
| ) | |
| gr.Plot(fig9) | |
| # then make a bar chart | |
| fig7 = px.bar( | |
| dataset_count.sort_values("Number of Spaces", ascending=False).head(20), | |
| x="Datasets", | |
| y="Number of Spaces", | |
| title="Most Used Datasets", | |
| labels={"Datasets": "Datasets", "Number of Spaces": "Number of Spaces"}, | |
| template="plotly_dark", | |
| ) | |
| gr.Plot(fig7) | |
| with gr.Row(): | |
| # Get the most duplicated spaces | |
| duplicated_spaces = df['duplicated_from'].value_counts().head(20).reset_index() | |
| duplicated_spaces["duplicated_from"] = duplicated_spaces[ | |
| "duplicated_from" | |
| ].apply( | |
| lambda x: f"<a target='_blank' href=https://huggingface.co/spaces/{x}>{x}</a>" | |
| ) | |
| duplicated_spaces.columns = ["Space", "Number of Duplicates"] | |
| gr.DataFrame(duplicated_spaces, datatype="html" ) | |
| # Get the most liked spaces | |
| liked_spaces = df[['id', 'likes']].sort_values(by='likes', ascending=False).head(20) | |
| liked_spaces["id"] = liked_spaces["id"].apply( | |
| lambda x: f"<a target='_blank' href=https://huggingface.co/spaces/{x}>{x}</a>" | |
| ) | |
| liked_spaces.columns = ['Space', 'Number of Likes'] | |
| gr.DataFrame(liked_spaces, datatype="html") | |
| with gr.Row(): | |
| # Create a dataframe with the top 10 authors and the number of spaces they have created | |
| author_counts = df['author'].value_counts().head(20).reset_index() | |
| author_counts["author"] = author_counts["author"].apply( | |
| lambda x: f"<a target='_blank' href=https://huggingface.co/{x}>{x}</a>" | |
| ) | |
| author_counts.columns = ["Author", "Number of Spaces"] | |
| gr.DataFrame(author_counts, datatype="html") | |
| # create a dataframe where we groupby author and sum their likes | |
| author_likes = df.groupby('author').agg({'likes': 'sum'}).reset_index() | |
| author_likes = author_likes.sort_values(by='likes', ascending=False).head(20) | |
| author_likes["author"] = author_likes["author"].apply( | |
| lambda x: f"<a target='_blank' href=https://huggingface.co/{x}>{x}</a>" | |
| ) | |
| author_likes.columns = ["Author", "Number of Likes"] | |
| gr.DataFrame(author_likes, datatype="html") | |
| with gr.Tab(label="Spaces Search"): | |
| df = df[df['stage'] == 'RUNNING'] | |
| # Layout | |
| with gr.Row(): | |
| emoji = gr.Dropdown( | |
| df["emoji"].unique().tolist(), label="Search by Emoji 🤗", multiselect=True | |
| ) # Dropdown to select the emoji | |
| likes = gr.Slider( | |
| minimum=df["likes"].min(), | |
| maximum=df["likes"].max(), | |
| step=1, | |
| label="Filter by Likes", | |
| ) # Slider to filter by likes | |
| with gr.Row(): | |
| author = gr.Dropdown( | |
| df["author"].unique().tolist(), label="Search by Author", multiselect=True | |
| ) | |
| # get the list of unique strings in the sdk_tags column | |
| sdk_tags = np.unique(np.concatenate(df["sdk_tags"].values)) | |
| # create a dropdown for the sdk_tags | |
| sdk_tags = gr.Dropdown( | |
| sdk_tags.tolist(), label="Filter by SDK/Tags", multiselect=True | |
| ) | |
| with gr.Row(): | |
| # create a gradio checkbox group for hardware | |
| hardware = gr.CheckboxGroup( | |
| df["hardware"].unique().tolist(), label="Filter by Hardware" | |
| ) | |
| licenses = np.unique(np.concatenate(df["licenses"].values)) | |
| space_license = gr.Dropdown(licenses.tolist(), label="Filter by license") | |
| with gr.Row(): | |
| models = gr.Dropdown( | |
| flatten_column(df, "models"), | |
| label="Search by Model", | |
| multiselect=True, | |
| ) | |
| datasets = gr.Dropdown( | |
| flatten_column(df, "datasets"), | |
| label="Search by Dataset", | |
| multiselect=True, | |
| ) | |
| devmode = gr.Checkbox(label="Show Dev Mode Spaces") | |
| clear = gr.ClearButton(components=[ | |
| emoji, | |
| author, | |
| hardware, | |
| sdk_tags, | |
| models, | |
| datasets, | |
| space_license | |
| ]) | |
| df = pd.DataFrame( | |
| df[ | |
| [ | |
| "id", | |
| "emoji", | |
| "author", | |
| "url", | |
| "likes", | |
| "hardware", | |
| "sdk_tags", | |
| "models", | |
| "datasets", | |
| "licenses", | |
| "r_sdk_tags", | |
| "r_models", | |
| "r_datasets", | |
| "r_licenses", | |
| 'devMode' | |
| ] | |
| ] | |
| ) | |
| gr.DataFrame( | |
| filtered_df, | |
| inputs=[ | |
| emoji, | |
| likes, | |
| author, | |
| hardware, | |
| sdk_tags, | |
| models, | |
| datasets, | |
| space_license, | |
| devmode, | |
| ], | |
| datatype="html", | |
| wrap=True, | |
| column_widths=["25%", "5%", "25%", "25%", "20%"] | |
| ) | |
| demo.launch() | |