Update app.py

app.py

@@ -1,151 +1,67 @@
-from pathlib import Path
-from typing import List, Dict, Tuple
-import matplotlib.colors as mpl_colors
+import requests
+from flask import Flask, request, redirect, url_for, session
+from google.oauth2 import id_token
+from google.auth.transport import requests as grequests
+
+app = Flask(__name__)
+app.secret_key = "your_secret_key"
+
+CLIENT_ID = "your_google_client_id"
+
+# Google API endpoints
+DISCOVERY_URL = "https://accounts.google.com/.well-known/openid-configuration"
+TOKEN_ENDPOINT = "https://oauth2.googleapis.com/token"
+DOCS_API_ENDPOINT = "https://docs.googleapis.com/v1/documents/{documentId}"
+
+@app.route("/")
+def index():
+    return "Welcome to the Google Docs Shiny app!"
+
+@app.route("/login")
+def login():
+    # Generate Google login URL
+    authorization_endpoint = requests.get(DISCOVERY_URL).json()["authorization_endpoint"]
+    redirect_uri = request.base_url + "/callback"
+    login_url = f"{authorization_endpoint}?response_type=code&client_id={CLIENT_ID}&redirect_uri={redirect_uri}&scope=email%20profile%20openid&access_type=offline"
+    return redirect(login_url)
+
+@app.route("/login/callback")
+def login_callback():
+    # Exchange authorization code for access token
+    code = request.args.get("code")
+    token_request_data = {
+        "code": code,
+        "client_id": CLIENT_ID,
+        "client_secret": "your_client_secret",
+        "redirect_uri": request.base_url,
+        "grant_type": "authorization_code"
+    }
+    token_response = requests.post(TOKEN_ENDPOINT, data=token_request_data)
+    access_token = token_response.json().get("access_token")
+    
+    if access_token:
+        session["access_token"] = access_token
+        return redirect(url_for("fetch_comments"))
+    else:
+        return "Failed to obtain access token."
+
+@app.route("/fetch_comments")
+def fetch_comments():
+    if "access_token" in session:
+        # Fetch comments from Google Docs API
+        document_id = "your_document_id"
+        headers = {"Authorization": f"Bearer {session['access_token']}"}
+        response = requests.get(DOCS_API_ENDPOINT.format(documentId=document_id), headers=headers)
+        
+        if response.status_code == 200:
+            # Parse comments from response
+            comments = response.json().get("comments", [])
+            return str(comments)
+        else:
+            return f"Failed to fetch comments. Status code: {response.status_code}"
+    else:
+        return "User not authenticated."
+
+if __name__ == "__main__":
+    app.run(debug=True)
 
-import pandas as pd
-import seaborn as sns
-import shinyswatch
-
-from shiny import App, Inputs, Outputs, Session, reactive, render, req, ui
-
-sns.set_theme()
-
-www_dir = Path(__file__).parent.resolve() / "www"
-
-df = pd.read_csv(Path(__file__).parent / "penguins.csv", na_values="NA")
-numeric_cols: List[str] = df.select_dtypes(include=["float64"]).columns.tolist()
-species: List[str] = df["Species"].unique().tolist()
-species.sort()
-
-app_ui = ui.page_fillable(
-    shinyswatch.theme.minty(),
-    ui.layout_sidebar(
-        ui.sidebar(
-            # Artwork by @allison_horst
-            ui.input_selectize(
-                "xvar",
-                "X variable",
-                numeric_cols,
-                selected="Bill Length (mm)",
-            ),
-            ui.input_selectize(
-                "yvar",
-                "Y variable",
-                numeric_cols,
-                selected="Bill Depth (mm)",
-            ),
-            ui.input_checkbox_group(
-                "species", "Filter by species", species, selected=species
-            ),
-            ui.hr(),
-            ui.input_switch("by_species", "Show species", value=True),
-            ui.input_switch("show_margins", "Show marginal plots", value=True),
-        ),
-        ui.output_ui("value_boxes"),
-        ui.output_plot("scatter", fill=True),
-        ui.help_text(
-            "Artwork by ",
-            ui.a("@allison_horst", href="https://twitter.com/allison_horst"),
-            class_="text-end",
-        ),
-    ),
-)
-
-
-def server(input: Inputs, output: Outputs, session: Session):
-    @reactive.Calc
-    def filtered_df() -> pd.DataFrame:
-        """Returns a Pandas data frame that includes only the desired rows"""
-
-        # This calculation "req"uires that at least one species is selected
-        req(len(input.species()) > 0)
-
-        # Filter the rows so we only include the desired species
-        return df[df["Species"].isin(input.species())]
-
-    @output
-    @render.plot
-    def scatter():
-        """Generates a plot for Shiny to display to the user"""
-
-        # The plotting function to use depends on whether margins are desired
-        plotfunc = sns.jointplot if input.show_margins() else sns.scatterplot
-
-        plotfunc(
-            data=filtered_df(),
-            x=input.xvar(),
-            y=input.yvar(),
-            palette=palette,
-            hue="Species" if input.by_species() else None,
-            hue_order=species,
-            legend=False,
-        )
-
-    @output
-    @render.ui
-    def value_boxes():
-        df = filtered_df()
-
-        def penguin_value_box(title: str, count: int, bgcol: str, showcase_img: str):
-            return ui.value_box(
-                title,
-                count,
-                {"class_": "pt-1 pb-0"},
-                showcase=ui.fill.as_fill_item(
-                    ui.tags.img(
-                        {"style": "object-fit:contain;"},
-                        src=showcase_img,
-                    )
-                ),
-                theme_color=None,
-                style=f"background-color: {bgcol};",
-            )
-
-        if not input.by_species():
-            return penguin_value_box(
-                "Penguins",
-                len(df.index),
-                bg_palette["default"],
-                # Artwork by @allison_horst
-                showcase_img="penguins.png",
-            )
-
-        value_boxes = [
-            penguin_value_box(
-                name,
-                len(df[df["Species"] == name]),
-                bg_palette[name],
-                # Artwork by @allison_horst
-                showcase_img=f"{name}.png",
-            )
-            for name in species
-            # Only include boxes for _selected_ species
-            if name in input.species()
-        ]
-
-        return ui.layout_column_wrap(*value_boxes, width = 1 / len(value_boxes))
-
-
-# "darkorange", "purple", "cyan4"
-colors = [[255, 140, 0], [160, 32, 240], [0, 139, 139]]
-colors = [(r / 255.0, g / 255.0, b / 255.0) for r, g, b in colors]
-
-palette: Dict[str, Tuple[float, float, float]] = {
-    "Adelie": colors[0],
-    "Chinstrap": colors[1],
-    "Gentoo": colors[2],
-    "default": sns.color_palette()[0],  # type: ignore
-}
-
-bg_palette = {}
-# Use `sns.set_style("whitegrid")` to help find approx alpha value
-for name, col in palette.items():
-    # Adjusted n_colors until `axe` accessibility did not complain about color contrast
-    bg_palette[name] = mpl_colors.to_hex(sns.light_palette(col, n_colors=7)[1])  # type: ignore
-
-
-app = App(
-    app_ui,
-    server,
-    static_assets=str(www_dir),
-)