Update app.py

app.py

@@ -17,6 +17,10 @@ def predict(inning, game_id):
         inning = 7
     elif inning == "Eight":
         inning = 8
+    elif inning == "Five":
+        inning = 5
+    elif inning == "Six":
+        inning = 6
     
     df = data_retrieve(inning, game_id)
     # print(df)
@@ -33,6 +37,9 @@ def predict(inning, game_id):
     df_main = df_main.drop(columns=['Opp_LOB'])
     df_main = df_main[(df_main['Inning'] <= inning)]
 
+    df_main = df_main[df_main['Team_Name'].isin(team_names)]
+    df_main = df_main[df_main['Opposition_Team'].isin(team_names)]
+
     df_main = df_main[(df_main['Team_Name'] != 'American League All-Stars') & (df_main['Team_Name'] != 'National League All-Stars')]
     df_main = df_main.dropna()
     df_main = df_main.drop_duplicates()
@@ -74,10 +81,14 @@ def predict(inning, game_id):
 
     # print(len(df.columns))
     if inning == 8:
-        model = xgb.XGBRegressor()
-        model.load_model('xgbr_ts_inn8_exp3_model.json')
+        model = tf.keras.models.load_model('CONVR_ts_inn8_exp6_model.keras')
     elif inning ==7:
-        model = tf.keras.models.load_model('ANNR_ts_inn7_exp5_model.keras')
+        model = tf.keras.models.load_model('CONVR_ts_inn7_exp9_model.keras')
+    elif inning ==6:
+        model = tf.keras.models.load_model('CONVR_ts_inn6_exp6_model.keras')
+    elif inning ==5:
+        model = tf.keras.models.load_model('CONVR_ts_inn5_exp2_model.keras')
+    
 
 
     # with open('pca_model4.pkl', 'rb') as f:
@@ -92,9 +103,8 @@ def predict(inning, game_id):
     # return
     score_1 = model.predict(pivoted_df_home)
     score_2 = model.predict(pivoted_df_away)
-    if inning == 7:
-        score_1 = [item for sublist in score_1 for item in sublist]
-        score_2 = [item for sublist in score_2 for item in sublist]
+    score_1 = [item for sublist in score_1 for item in sublist]
+    score_2 = [item for sublist in score_2 for item in sublist]
 
     score_1 = np.round(score_1[0],1)
     score_2 = np.round(score_2[0],1)
@@ -113,6 +123,12 @@ def predict(inning, game_id):
     elif inning == 7:
         home_runs = list(pivoted_df_home['Runs_7'])[0]
         away_runs = list(pivoted_df_away['Runs_7'])[0]
+    elif inning == 6:
+        home_runs = list(pivoted_df_home['Runs_6'])[0]
+        away_runs = list(pivoted_df_away['Runs_6'])[0]
+    elif inning == 5:
+        home_runs = list(pivoted_df_home['Runs_5'])[0]
+        away_runs = list(pivoted_df_away['Runs_5'])[0]
 
     if score_1 < home_runs:
         score_1 = home_runs
@@ -226,7 +242,7 @@ with gr.Blocks() as demo:
     #             </center>
     #             """)
     with gr.Row():
-        inning = gr.Radio(["Seven", "Eight"], label="Inning", scale=1)
+        inning = gr.Radio(["Five", "Six", "Seven", "Eight"], label="Inning", scale=1)
         game_id = gr.Number(None, minimum=0, label="Game_ID", scale=1)