diff --git a/backend/src/dashboard/main.py b/backend/src/dashboard/main.py
index 50b53a8..cf89b57 100644
--- a/backend/src/dashboard/main.py
+++ b/backend/src/dashboard/main.py
@@ -8,6 +8,9 @@ import os
 BUCKET = "retrofit-data-dev"
 PREFIX = "hubspot_insight/"
 
+# -------------------------
+# S3 Helpers
+# -------------------------
 def get_latest_s3_file(bucket: str, prefix: str = "") -> str:
     s3 = boto3.client("s3")
     response = s3.list_objects_v2(Bucket=bucket, Prefix=prefix)
@@ -46,63 +49,69 @@ def download_and_read_latest(bucket: str, prefix: str = "", download_dir="downlo
 
 
 # -------------------------
-# Load S3 JSON
+# Build DataFrame from S3
 # -------------------------
-latest_key, file_path, data = download_and_read_latest(BUCKET, PREFIX)
+def load_dataframe():
+    latest_key, file_path, data = download_and_read_latest(BUCKET, PREFIX)
 
-# -------------------------
-# Build expanded table
-# -------------------------
-records = []
+    records = []
+    for entry in data:
+        p = entry["deal_properties"]
+        line_items = entry.get("line_items", [])
 
-for entry in data:
-    p = entry["deal_properties"]
-    line_items = entry.get("line_items", [])
-
-    if not line_items:
-        records.append({
-            "Expected Commencement Date": p.get("expected_commencement_date"),
-            "Deal ID": p.get("deal_id"),
-            "Line Item Name": "Missing Line Item",
-            "Line Item Amount": 0
-        })
-    else:
-        for li in line_items:
+        if not line_items:
             records.append({
                 "Expected Commencement Date": p.get("expected_commencement_date"),
                 "Deal ID": p.get("deal_id"),
-                "Line Item Name": li.get("name"),
-                "Line Item Amount": li.get("amount", 0)
+                "Line Item Name": "Missing Line Item",
+                "Line Item Amount": 0
             })
+        else:
+            for li in line_items:
+                records.append({
+                    "Expected Commencement Date": p.get("expected_commencement_date"),
+                    "Deal ID": p.get("deal_id"),
+                    "Line Item Name": li.get("name"),
+                    "Line Item Amount": li.get("amount", 0)
+                })
 
-df = pd.DataFrame(records)
+    df = pd.DataFrame(records)
+    df["Line Item Amount"] = pd.to_numeric(df["Line Item Amount"], errors="coerce").fillna(0)
+
+    return df
 
-# Convert numeric fields
-df["Line Item Amount"] = pd.to_numeric(df["Line Item Amount"], errors="coerce").fillna(0)
 
 # -------------------------
-# Unique date list
+# Initial Load
 # -------------------------
-unique_dates = sorted(df["Expected Commencement Date"].dropna().unique())
-unique_dates = ["All Dates"] + list(unique_dates)
+df = load_dataframe()
+
 
 # -------------------------
 # Dash App
 # -------------------------
 app = Dash(__name__)
-server = app.server   # ← REQUIRED for Render/Gunicorn
+server = app.server   # required for Render
 
 app.layout = html.Div([
     html.H1("Line Items by Expected Commencement Date", style={"textAlign": "center"}),
 
     dcc.Dropdown(
         id="date-filter",
-        options=[{"label": d, "value": d} for d in unique_dates],
+        options=[{"label": "All Dates", "value": "All Dates"}] +
+                [{"label": d, "value": d} for d in sorted(df["Expected Commencement Date"].dropna().unique())],
         value="All Dates",
         clearable=False,
         style={"width": "300px", "margin": "20px auto"}
     ),
 
+    html.Button(
+        "Refresh Data",
+        id="refresh-btn",
+        n_clicks=0,
+        style={"margin": "10px", "padding": "10px 20px"}
+    ),
+
     dash_table.DataTable(
         id="lineitem-table",
         columns=[
@@ -118,15 +127,23 @@ app.layout = html.Div([
     )
 ])
 
+
 # -------------------------
-# Callback for filtering
+# Callback (filter + refresh)
 # -------------------------
 @app.callback(
     Output("lineitem-table", "data"),
-    Input("date-filter", "value")
+    Input("date-filter", "value"),
+    Input("refresh-btn", "n_clicks")
 )
-def update_table(selected_date):
+def update_table(selected_date, n_clicks):
+    global df
 
+    # Refresh DF from AWS when button clicked
+    if n_clicks > 0:
+        df = load_dataframe()
+
+    # Filter data
     if selected_date == "All Dates":
         dff = df.copy()
     else:
@@ -141,14 +158,12 @@ def update_table(selected_date):
            .reset_index()
     )
 
-    # Final rename to match DataTable column IDs
     grouped = grouped.rename(columns={
         "Total_Deals": "Total Deals",
         "Total_Amount": "Total Amount (£)"
     })
 
     grouped = grouped.sort_values("Total Amount (£)", ascending=False)
-
     return grouped.to_dict("records")