diff --git a/src/dev_streamlit.py b/src/dev_streamlit.py
index e2779d3..675ebf7 100644
--- a/src/dev_streamlit.py
+++ b/src/dev_streamlit.py
@@ -58,6 +58,12 @@
 # User input location
 location = st.text_input("Surf Spot", placeholder="Enter surf spot!")
 
+graph_type = st.radio(
+    "Choose graph type",
+    ["Height/Period :ocean:", "Direction :world_map:"],
+    index=None,
+)
+
 # Checks if location has been entered.
 # If True, gathers surf report and displays map
 if location:
@@ -72,8 +78,8 @@
     # Writes the GPT response
     if gpt_response is not None:
         st.write(gpt_response)
-
+    
     # Displays the line graph
     st.write("# Surf Conditions")
-    df = sl_help.graph_data(report_dict)
+    df = sl_help.graph_data(report_dict, graph_type)
     st.line_chart(df.rename(columns={"date": "index"}).set_index("index"))
diff --git a/src/streamlit_helper.py b/src/streamlit_helper.py
index 2695165..c0e12ea 100644
--- a/src/streamlit_helper.py
+++ b/src/streamlit_helper.py
@@ -57,7 +57,7 @@ def map_data(lat, long):
     return m
 
 
-def graph_data(report_dict):
+def graph_data(report_dict, graph_type="Height/Period :ocean:"):
     """
     Gathers the forecasted dates, heights, period and stores them in a pandas
     dataframe. Will be used to display the line chart
@@ -71,11 +71,23 @@ def graph_data(report_dict):
     forecasted_periods = [
         forecast["period"] for forecast in report_dict["Forecast"]
     ]
+    forecasted_directions = [
+        forecast["direction"] for forecast in report_dict["Forecast"]
+    ]
     # table
-    df = pd.DataFrame({
-        "date": forecasted_dates,
-        "heights": forecasted_heights,
-        "periods": forecasted_periods,
-    })
+    if graph_type == "Height/Period :ocean:" or graph_type == None:
+        df = pd.DataFrame({
+            "date": forecasted_dates,
+            "heights": forecasted_heights,
+            "periods": forecasted_periods,
+        })
+    else:
+        df = pd.DataFrame({
+            "date": forecasted_dates,
+            "directions": forecasted_directions
+        })
 
     return df
+
+
+