diff --git a/app.py b/app.py
new file mode 100644
index 0000000..bee03c3
--- /dev/null
+++ b/app.py
@@ -0,0 +1,52 @@
+import pandas as pd
+from flask import Flask, render_template
+
+from download_digital import construct_dataframe
+
+app = Flask(__name__)
+
+
+@app.route("/")
+def tables(
+    url: str = "https://beschaeftigtenbefragung.verdi.de/",
+    tag: str = "bez_data_2",
+):
+    df = construct_dataframe(url=url, tag=tag, grouped=False)
+
+    output_str = []
+
+    def _print_as_html(df: pd.DataFrame):
+        df = df.astype({"Digitale Befragung": "Int32"})
+        with pd.option_context("display.max_rows", None):
+            table = df.to_html(
+                index_names=False,
+                justify="left",
+                index=False,
+                classes="sortable dataframe",
+            )
+
+            tfoot = [
+                "  <tfoot>",
+                "      <td>Gesamt</td>",
+            ]
+            for i in range(len(df.columns) - 2):
+                tfoot.append("      <td/>")
+            tfoot.extend(
+                [
+                    f"      <td>{df['Digitale Befragung'].sum()}</td>",
+                    "    </tr>",
+                    "  </tfoot>",
+                ]
+            )
+            tfoot = "\n".join(tfoot)
+            idx = table.index("</table>")
+            output_str.append(table[: idx - 1])
+            output_str.append(tfoot)
+            output_str.append(table[idx:])
+
+    _print_as_html(df)
+    _print_as_html(
+        df.groupby("Bundesland", as_index=False)[["Digitale Befragung"]].sum()
+    )
+
+    return render_template("base.html", tables="\n".join(output_str))
diff --git a/download_digital.py b/download_digital.py
index b3d36a5..a8bd597 100644
--- a/download_digital.py
+++ b/download_digital.py
@@ -71,13 +71,11 @@ bundesland_dict = {
 }
 
 
-def main(
+def construct_dataframe(
     url: str = "https://beschaeftigtenbefragung.verdi.de/",
     tag: str = "bez_data_2",
-    dry_run: bool = False,
     grouped: bool = False,
-    html: bool = False,
-) -> pd.DataFrame:
+):
     r = requests.get(url)
     soup = BeautifulSoup(r.text, "html.parser")
 
@@ -109,49 +107,19 @@ def main(
     if grouped:
         df = df.groupby("Bundesland", as_index=False)[["Digitale Befragung"]].sum()
 
-    if dry_run or html:
-        if html:
-            print("<link rel='stylesheet' href='style.css'>")
-            print("<script src='sorttable.js'></script>")
+    return df
 
-        def _print_as_html(df: pd.DataFrame):
-            df = df.astype({"Digitale Befragung": "Int32"})
-            with pd.option_context("display.max_rows", None):
-                if html:
-                    table = df.to_html(
-                        index_names=False,
-                        justify="left",
-                        index=False,
-                        classes="sortable dataframe",
-                    )
 
-                    tfoot = [
-                        "  <tfoot>",
-                        "      <td>Gesamt</td>",
-                    ]
-                    for i in range(len(df.columns) - 2):
-                        tfoot.append("      <td/>")
-                    tfoot.extend(
-                        [
-                            f"      <td>{df['Digitale Befragung'].sum()}</td>",
-                            "    </tr>",
-                            "  </tfoot>",
-                        ]
-                    )
-                    tfoot = "\n".join(tfoot)
-                    idx = table.index("</table>")
-                    print(table[: idx - 1])
-                    print(tfoot)
-                    print(table[idx:])
-                else:
-                    print(df)
-
-        _print_as_html(df)
-        if html:
-            _print_as_html(
-                df.groupby("Bundesland", as_index=False)[["Digitale Befragung"]].sum()
-            )
+def main(
+    url: str = "https://beschaeftigtenbefragung.verdi.de/",
+    tag: str = "bez_data_2",
+    dry_run: bool = False,
+    grouped: bool = False,
+) -> None:
+    df = construct_dataframe(url=url, tag=tag, grouped=grouped)
 
+    if dry_run:
+        print(df)
     else:
         filename = f"data/{datetime.today().strftime('%Y-%m-%d')}_data.ods"
         if Path(filename).exists():