asd

app.py

@@ -26,9 +26,6 @@ fg = fs.get_or_create_feature_group(
     primary_key=['player_id', 'date']  # Replace with your primary key column name(s)
 )
 
-df = fg.read()
-print(df)
-
 def valuation_predictor(goals, assists, y_cards, r_cards, 
                     m_played, height, age, mo_left, 
                     t_made_goals, t_conceded_goals, t_clean_sheets,
@@ -54,10 +51,33 @@ def valuation_predictor(goals, assists, y_cards, r_cards,
     formatted_output_thousand = f"{round(estimated_valuation[0], -3):,} EUR"
     return formatted_output_thousand
 
+df = fg.read()
+df = df.drop(columns=["player_id", "date"])
+df_present = pd.DataFrame(["Player name", "League", "Goals", "Assists", "Yellow cards", "Red cards", "Minutes played", "Height (cm)", "Age", "Months left on contract", "Team made goals", "Team conceded goals", "Team clean sheets", "Actual market value (EUR)", "Model predicted market value (EUR)"])
+
+for row in df:
+    league = ""
+    if row['league_es1']:
+        league == "La Liga"
+    elif row['league_fr1']:
+        league == "League 1"
+    elif row['league_gb1']: 
+        league == "Premier league"
+    elif row['league_it1']:
+        league == "Serie A"
+    elif row['league_it1']:
+        league == "Bundesliga"    
+    
+    df_present = df_present.append(
+        row["player_name"], league, row["goals"], row["assists"], row["yellow_cards"], row["red_cards"], row["minutes_played"], row["height_in_cm"], row["age"], row["months_left"], row["own_goals"], row["opponent_goals"], row["clean_sheets"], row["market_value_in_eur"], "123123123"
+        )
+    
+    predicted_value = valuation_predictor()
 
 with gr.Blocks(theme=gr.themes.Soft()) as demo:
     with gr.Row():
         gr.Label(value="Latest predictions", show_label=False)
+        gr.DataFrame(value=df_present)