most filtering is done

app.py

@@ -1,9 +1,9 @@
 import gradio as gr
 import pandas as pd
+import numpy as np
 
 # Load the spaces.parquet file as a dataframe
 df = pd.read_parquet("spaces.parquet")
-
 """
 Todos:
     Create tabbed interface for filtering and graphs
@@ -14,10 +14,40 @@ Todos:
     Plotly graph of hardware
     Investigate README lengths
     bar chart of the number of spaces per author
+    Is there a correlation between pinning a space and the number of likes?
+    Is a correlation between the emoji and the number of likes?
+    distribution of python versions
+    what models are most used
+    what organizations are most popular in terms of their models and datasets being used
+    most duplicated spaces
+
+        "id",
+        "author",
+        "created_at",
+        "last_modified",
+        "subdomain",
+        "host",
+        "likes",
+        "sdk",
+        "tags",
+        "readme_size",
+        "python_version",
+        "license",
+        "duplicated_from",
+        "models",
+        "datasets",
+        "emoji",
+        "colorFrom",
+        "colorTo",
+        "pinned",
+        "stage",
+        "hardware",
+        "devMode",
+        "custom_domains",    
 """
 
 
-def filtered_df(emoji, likes):
+def filtered_df(emoji, likes, author, hardware, tags, models, datasets):
     _df = df
     # if emoji is not none, filter the dataframe with it
     if emoji:
@@ -25,11 +55,50 @@ def filtered_df(emoji, likes):
     # if likes is not none, filter the dataframe with it
     if likes:
         _df = _df[_df["likes"] >= likes]
+    if author:
+        _df = _df[_df["author"].isin(author)]
+    if hardware:
+        _df = _df[_df["hardware"].isin(hardware)]
+    # check to see if the array of sdk_tags contains any of the selected tags
+    if tags:
+        _df = _df[_df["sdk_tags"].apply(lambda x: any(tag in x for tag in tags))]
+    if models:
+        _df = _df[
+            _df["models"].apply(
+                lambda x: (
+                    any(model in x for model in models) if x is not None else False
+                )
+            )
+        ]
+    if datasets:
+        _df = _df[
+            _df["datasets"].apply(
+                lambda x: (
+                    any(dataset in x for dataset in datasets)
+                    if x is not None
+                    else False
+                )
+            )
+        ]
     return _df
 
 
 with gr.Blocks() as demo:
     df = df[df["stage"] == "RUNNING"]
+    # combine the sdk and tags columns, one of which is a string and the other is an array of strings
+    # first convert the sdk column to an array of strings
+    df["sdk"] = df["sdk"].apply(lambda x: np.array([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[0], x[1])), axis=1
+    )
+
+    # 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"],
+    )
     emoji = gr.Dropdown(
         df["emoji"].unique().tolist(), label="Search by Emoji 🤗", multiselect=True
     )  # Dropdown to select the emoji
@@ -42,13 +111,87 @@ with gr.Blocks() as demo:
     hardware = gr.Dropdown(
         df["hardware"].unique().tolist(), label="Search by Hardware", multiselect=True
     )
+    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
+    )
+    # create a gradio checkbox group for hardware
+    hardware = gr.CheckboxGroup(
+        df["hardware"].unique().tolist(), label="Filter by Hardware"
+    )
+
+    space_license = gr.CheckboxGroup(
+        df["license"].unique().tolist(), label="Filter by license"
+    )
+
+    # Assuming df is your dataframe and 'array_column' is the column containing np.array of strings
+    array_column_as_lists = df["models"].apply(
+        lambda x: np.array(["None"]) if np.ndim(x) == 0 else x
+    )
+    # Now, flatten all arrays into one list
+    flattened_strings = np.concatenate(array_column_as_lists.values)
+    # Get unique strings
+    unique_strings = np.unique(flattened_strings)
+    # Convert to a list if needed
+    unique_strings_list = unique_strings.tolist()
+    models = gr.Dropdown(
+        unique_strings_list,
+        label="Search by Model",
+        multiselect=True,
+    )
+
+    # Assuming df is your dataframe and 'array_column' is the column containing np.array of strings
+    array_column_as_lists = df["datasets"].apply(
+        lambda x: np.array(["None"]) if np.ndim(x) == 0 else x
+    )
+
+    # Now, flatten all arrays into one list
+    flattened_strings = np.concatenate(array_column_as_lists.values)
+    # Get unique strings
+    unique_strings = np.unique(flattened_strings)
+    # Convert to a list if needed
+    unique_strings_list = unique_strings.tolist()
+    datasets = gr.Dropdown(
+        unique_strings_list,
+        label="Search by Model",
+        multiselect=True,
+    )
+
     devMode = gr.Checkbox(value=False, label="DevMode Enabled")
     clear = gr.ClearButton(components=[emoji])
 
-    df = pd.DataFrame(df[["emoji", "host", "likes", "hardware"]])
-    df["host"] = df["host"].apply(lambda x: f"<a href={x}>{x}</a>")
-    gr.DataFrame(filtered_df, inputs=[emoji, likes], datatype=["str", "html"])
+    df = pd.DataFrame(
+        df[
+            [
+                "id",
+                "emoji",
+                "author",
+                "url",
+                "likes",
+                "hardware",
+                "sdk_tags",
+                "models",
+                "datasets",
+            ]
+        ]
+    )
+    df["url"] = df["url"].apply(
+        lambda x: (
+            f"<a target='_blank' href=https://huggingface.co/spaces/{x}>{x}</a>"
+            if x is not None and "/" in x
+            else f"<a target='_blank' href=https://{x[0]}>{x[0]}</a>"
+        )
+    )
+    gr.DataFrame(
+        filtered_df,
+        inputs=[emoji, likes, author, hardware, sdk_tags, models, datasets],
+        datatype="html",
+    )
 
 
-print(df.head())
 demo.launch()