Update app.py

app.py

@@ -37,22 +37,27 @@ with st.form("prediction_form"):
     submitted = st.form_submit_button("Predict")
 
     if submitted:
-        # Organize the user inputs into a DataFrame that matches the training data
-        input_df = pd.DataFrame([[gp, min, pts, fgm, fga, fg_percent, threep_made, threepa, 
-                                  threep_percent, ftm, fta, ft_percent, oreb, dreb, reb, 
-                                  ast, stl, blk, tov]],
-                                columns=['GP', 'MIN', 'PTS', 'FGM', 'FGA', 'FG%', '3P Made', 
-                                         '3PA', '3P%', 'FTM', 'FTA', 'FT%', 'OREB', 'DREB', 
-                                         'REB', 'AST', 'STL', 'BLK', 'TOV'])
-
-        # Ensure the input DataFrame structure matches the model's expected format
-        # (You might need to adjust this part based on how the model was trained)
-
-        # Predict using the loaded model
+    # Assuming the rest of the code remains the same for collecting inputs
+    
+    # Create the DataFrame without considering the index as a feature
+    input_df = pd.DataFrame([[
+        gp, min, pts, fgm, fga, fg_percent, threep_made, threepa, threep_percent,
+        ftm, fta, ft_percent, oreb, dreb, reb, ast, stl, blk, tov
+    ]], columns=[
+        'GP', 'MIN', 'PTS', 'FGM', 'FGA', 'FG%', '3P Made', '3PA', '3P%', 
+        'FTM', 'FTA', 'FT%', 'OREB', 'DREB', 'REB', 'AST', 'STL', 'BLK', 'TOV'
+    ])
+
+    # Ensure there's no unexpected index that could be interpreted as a feature
+    input_df.reset_index(drop=True, inplace=True)
+
+    # Attempt prediction
+    try:
         prediction = model.predict(input_df)[0]
         
-        # Output the prediction
         if prediction == 1:
             st.success('The rookie is likely to have a successful career spanning at least 5 years!')
         else:
-            st.error('The rookie might not have a very successful career lasting at least 5 years.')
+            st.error('The rookie might not have a very successful career lasting at least 5 years.')
+    except ValueError as e:
+        st.error(f'Error making prediction: {e}')