ss

app.py

@@ -53,24 +53,41 @@ def valuation_predictor(goals, assists, y_cards, r_cards,
 
 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)"])
+df_present = pd.DataFrame(columns=["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:
+for index, row in df.iterrows():
     league = ""
     if row['league_es1']:
-        league == "La Liga"
+        league = "La Liga"
     elif row['league_fr1']:
-        league == "League 1"
+        league = "League 1"
     elif row['league_gb1']: 
-        league == "Premier league"
+        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"
-        )
+        league = "Serie A"
+    elif row['league_de1']:
+        league = "Bundesliga"
+
+    new_row = {
+        "player_name": row["player_name"],
+        "league": league,
+        "goals": row["goals"],
+        "assists": row["assists"],
+        "yellow_cards": row["yellow_cards"],
+        "red_cards": row["red_cards"],
+        "minutes_played": row["minutes_played"],
+        "height_in_cm": row["height_in_cm"],
+        "age": row["age"],
+        "months_left": row["months_left"],
+        "own_goals": row["own_goals"],
+        "opponent_goals": row["opponent_goals"],
+        "clean_sheets": row["clean_sheets"],
+        "market_value_in_eur": row["market_value_in_eur"],
+        "predicted_value": "123123123"  # Replace this with your actual predicted value
+    }
+
+    # Append new row to df_present
+    df_present = df_present.append(new_row, ignore_index=True)
     
     predicted_value = valuation_predictor()