Merge pull request #204 from posit-dev/patch-http

Use `pyodide_http.patch_all()` and remove custom Seaborn data shim

Author: wch

src/hooks/usePyodide.tsx

@@ -170,173 +170,13 @@ def _mock_ipykernel():
     m.CommManager = CommManager
     mods["ipykernel.comm"] = m
 
-
-# A shim for the seaborn.load_dataset() function to work in Pyodide. Normally
-# that function won't work in Pyodide because it uses the urllib module, which
-# doesn't work in Pyodide. This shim replaces the uses of urllib with a wrapper
-# function around JS's synchronous XMLHttpRequest.
-def _shim_seaborn_load_dataset():
-    import importlib
-    import importlib.abc
-    import io
-    import os
-    import sys
-    import pyodide.code
-
-    # ==========================================================================
-    # Python wrapper to get a URL synchronously using a JS XMLHttpRequest.
-    # ==========================================================================
-    get_url = pyodide.code.run_js(
-        """
-    (url) => {
-      const request = new XMLHttpRequest();
-      request.open("GET", url, false); // false for synchronous request
-      request.send(null);
-      if (request.status === 200) {
-          return request.responseText;
-      } else {
-          return "";
-      }
-    }
-    """
-    )
-
-    # ==========================================================================
-    # Substitutes for functions from seaborn.utils
-    # ==========================================================================
-    DATASET_SOURCE = "https://raw.githubusercontent.com/mwaskom/seaborn-data/master"
-    DATASET_NAMES_URL = f"{DATASET_SOURCE}/dataset_names.txt"
-
-    def urlopen(url: str) -> io.BytesIO:
-        res = get_url(url)
-        return io.BytesIO(res.encode("utf-8"))
-
-    def urlretrieve(url: str, filename: str) -> None:
-        res = get_url(url)
-        with open(filename, "w") as file:
-            file.write(res)
-
-    def get_dataset_names():
-        with urlopen(DATASET_NAMES_URL) as resp:
-            txt = resp.read()
-
-        dataset_names = [name.strip() for name in txt.decode().split("\\n")]
-        return list(filter(None, dataset_names))
-
-    def load_dataset(name, cache=True, data_home=None, **kws):
-        # Load these dynamically with importlib instead of with 'import pandas'
-        # because if we do a normal import, then Pyodide will detect and
-        # automatically download the files for these packages and all their
-        # dependencies when it starts, which is slow.
-        pd = importlib.import_module("pandas")
-        seaborn = importlib.import_module("seaborn")
-
-        if isinstance(name, pd.DataFrame):
-            err = (
-                "This function accepts only strings (the name of an example dataset). "
-                "You passed a pandas DataFrame. If you have your own dataset, "
-                "it is not necessary to use this function before plotting."
-            )
-            raise TypeError(err)
-
-        url = f"{DATASET_SOURCE}/{name}.csv"
-
-        if cache:
-            cache_path = os.path.join(
-                seaborn.utils.get_data_home(data_home), os.path.basename(url)
-            )
-            if not os.path.exists(cache_path):
-                if name not in get_dataset_names():
-                    raise ValueError(f"'{name}' is not one of the example datasets.")
-                urlretrieve(url, cache_path)
-            full_path = cache_path
-        else:
-            full_path = url
-
-        df = pd.read_csv(full_path, **kws)
-
-        if df.iloc[-1].isnull().all():
-            df = df.iloc[:-1]
-
-        if name == "tips":
-            df["day"] = pd.Categorical(df["day"], ["Thur", "Fri", "Sat", "Sun"])
-            df["sex"] = pd.Categorical(df["sex"], ["Male", "Female"])
-            df["time"] = pd.Categorical(df["time"], ["Lunch", "Dinner"])
-            df["smoker"] = pd.Categorical(df["smoker"], ["Yes", "No"])
-
-        elif name == "flights":
-            months = df["month"].str[:3]
-            df["month"] = pd.Categorical(months, months.unique())
-
-        elif name == "exercise":
-            df["time"] = pd.Categorical(df["time"], ["1 min", "15 min", "30 min"])
-            df["kind"] = pd.Categorical(df["kind"], ["rest", "walking", "running"])
-            df["diet"] = pd.Categorical(df["diet"], ["no fat", "low fat"])
-
-        elif name == "titanic":
-            df["class"] = pd.Categorical(df["class"], ["First", "Second", "Third"])
-            df["deck"] = pd.Categorical(df["deck"], list("ABCDEFG"))
-
-        elif name == "penguins":
-            df["sex"] = df["sex"].str.title()
-
-        elif name == "diamonds":
-            df["color"] = pd.Categorical(
-                df["color"],
-                ["D", "E", "F", "G", "H", "I", "J"],
-            )
-            df["clarity"] = pd.Categorical(
-                df["clarity"],
-                ["IF", "VVS1", "VVS2", "VS1", "VS2", "SI1", "SI2", "I1"],
-            )
-            df["cut"] = pd.Categorical(
-                df["cut"],
-                ["Ideal", "Premium", "Very Good", "Good", "Fair"],
-            )
-
-        elif name == "taxis":
-            df["pickup"] = pd.to_datetime(df["pickup"])
-            df["dropoff"] = pd.to_datetime(df["dropoff"])
-
-        elif name == "seaice":
-            df["Date"] = pd.to_datetime(df["Date"])
-
-        elif name == "dowjones":
-            df["Date"] = pd.to_datetime(df["Date"])
-
-        return df
-
-    # ======================================================================================
-    # Import hook to inject shim when seaborn is loaded
-    # ======================================================================================
-    class PostImportFinder(importlib.abc.MetaPathFinder):
-        def __init__(self):
-            self.is_loading = False
-
-        def find_module(self, fullname, path=None):
-            if fullname == "seaborn" and not self.is_loading:
-                return self
-
-        def load_module(self, fullname):
-            self.is_loading = True
-            # Load the actual seaborn module
-            seaborn = importlib.import_module("seaborn")
-
-            # Apply the shimmed version of load_dataset
-            seaborn.utils.load_dataset = load_dataset
-            seaborn.load_dataset = load_dataset
-
-            return seaborn
-
-    sys.meta_path.insert(0, PostImportFinder())
-
 _mock_multiprocessing()
 _mock_ipykernel()
-_shim_seaborn_load_dataset()
 
 def _pyodide_env_init():
     import os
     import sys
+    import pyodide_http
 
     # We don't use ssl in this function, but this is needed for Shiny to load.
     import ssl
@@ -348,6 +188,9 @@ def _pyodide_env_init():
     # Add current directory to Python path.
     sys.path.insert(0, "")
 
+    # Patch http libraries to work with Pyodide
+    pyodide_http.patch_all()
+
 _pyodide_env_init()
 
 # Function for saving a set of files so we can load them as a module.