Updated the main file

app.py

@@ -13,6 +13,9 @@ def fetch_data(user_id, mmr, comf_1, comf_2, comf_3, comf_4, comf_5):
 
     money = pd.read_csv("result_money.csv")    
 
+    print()
+    print(f"Reading player {player_id}.  Starting now")
+
     print(money)
     print(money.values)
 
@@ -32,6 +35,9 @@ def fetch_data(user_id, mmr, comf_1, comf_2, comf_3, comf_4, comf_5):
     # We also need to load the money values from the generated csv file
     # df_gen(draft, league_money(captains, data_type), data_type)
 
+    print(f"Done reading player {player_id}")
+    print()
+
     return player_id
 
 demo = gr.Interface(
@@ -44,16 +50,6 @@ demo = gr.Interface(
         gr.Slider(1, 5, value=1, step=1, label="Comfort (Pos 3)"),
         gr.Slider(1, 5, value=1, step=1, label="Comfort (Pos 4)"),
         gr.Slider(1, 5, value=1, step=1, label="Comfort (Pos 5)")
-        # gr.Image(type="filepath"),
-        # gr.Dropdown(["Pinch", "Spiral", "Shift Up", "Bulge", "Volcano"], value="Bulge", label="Function"), 
-        # gr.Checkbox(label="Randomize inputs?"),
-        # gr.Slider(0, 0.5, value=0.25, label="Radius (as fraction of image size)"),
-        # gr.Slider(0, 1, value=0.5, label="Center X"),
-        # gr.Slider(0, 1, value=0.5, label="Center Y"),
-        # gr.Slider(0, 1, value=0.5, label="Strength"),
-        # gr.Slider(0, 1, value=0.5, label="Edge Smoothness"),
-        # gr.Slider(0, 0.5, value=0.1, label="Center Smoothness")
-        # gr.Checkbox(label="Reverse Gradient Direction"),
     ],
     # examples=[
     #     [np.asarray(Image.open("examples/1500_maze.jpg")), "Bulge", True, 0.25, 0.5, 0.5, 0.5],
@@ -63,16 +59,6 @@ demo = gr.Interface(
     # ],
     outputs=[
         "text"
-        # gr.Image(label="Transformed Image"),
-        # gr.Image(label="bulge_model Model Classification"),
-        # gr.Image(label="yolov8n Model Classification"),
-        # gr.Image(label="yolov8x Model Classification"),
-        # gr.Label(),
-        # gr.Label(),
-        # gr.Image(label="Gradient Vector Field"),
-        # gr.Image(label="Inverse Gradient"),
-        # gr.Image(label="Inverted Vector Field"),
-        # gr.Image(label="Fixed Image")
     ],
     title="RD2L Pricing Prediction",
     article="Uhhhhh this is the article",