Create app.py

app.py

@@ -0,0 +1,79 @@
+import gradio as gr
+from joblib import load
+import pandas as pd
+
+path='./huggingface_final_df_deployment.csv'
+df=pd.read_csv(path)
+df['DATE']=pd.to_datetime(df['DATE'])
+
+y_values = ['L', 'W']
+y=pd.Series(y_values)
+y=y.astype('category')
+
+model=load('./model_SVM.joblib')
+#aquí indicamos cuál es el valor de las clases
+label_encoding=load('./label_classes.joblib') # aquí traemos el orden de clasificación de las categoría
+idx_labels=label_encoding.transform(y.cat.categories) # reordeno mis categorías con esos índices
+class_names=y.cat.categories[idx_labels] # aquí damos los valores de las categorías en el orden que teníamos guardados
+def run_my_model(Home_Team, Away_Team): #aquí ponemos que es solo una entrada pero es UNA TABLA la entrada
+    
+    #Paso1: Función de HomeTeam
+    def home_team_recent_record(Home_Team):
+        filtered_df=df[df['TEAM_ABBREVIATION']==Home_Team]
+        most_recent_record=filtered_df[filtered_df['DATE']==filtered_df['DATE'].max()]
+    
+        return most_recent_record
+
+     #Paso2: Función de AwayTeam
+    def away_team_recent_record(Away_Team):
+        filtered_df=df[df['TEAM_ABBREVIATION']==Away_Team]
+        most_recent_record=filtered_df[filtered_df['DATE']==filtered_df['DATE'].max()]
+    
+        return most_recent_record
+    
+    #Paso3: Dataframe con últimos records de HomeTeam
+    df_home_team_latest_record=home_team_recent_record(Home_Team)
+    df_home_team_latest_record=df_home_team_latest_record.reset_index()
+    
+    #Paso4: Dataframe con últimos records de AwayTeam
+    df_away_team_latest_record=away_team_recent_record(Away_Team)
+    df_away_team_latest_record=df_away_team_latest_record.reset_index()
+    
+    #Paso5: Cálculos
+    HOME_AWAY="HOME"
+    WINS_DIFFERENTIAL=df_home_team_latest_record.loc[0,"W"]-df_away_team_latest_record.loc[0,"W"]
+    WINNING_PERCENTAGE_DIFFERENTIAL=(df_home_team_latest_record.loc[0,'W_PCT']-df_away_team_latest_record.loc[0,'W_PCT'])*100
+    AWAY_WINS_DIFFERENTIAL=df_home_team_latest_record.loc[0,'AWAY_WINS']-df_away_team_latest_record.loc[0,'AWAY_WINS']
+    WINS_IN_LAST_10_GAMES=df_home_team_latest_record.loc[0,'LAST_10_GAME_WINS']
+    WINS_IN_LAST_5_GAMES=df_home_team_latest_record.loc[0,'LAST_5_GAME_WINS']
+    BACK_TO_BACK="NO_B2B"
+    
+    #Paso6: Convierto a un input dataframe
+    data = {
+            'HOME/AWAY': [HOME_AWAY],
+            'WINS_DIFFERENTIAL': [WINS_DIFFERENTIAL],
+            'WINNING PERCENTAGE DIFFERENTIAL': [WINNING_PERCENTAGE_DIFFERENTIAL],
+            'AWAY WINS DIFFERENTIAL': [AWAY_WINS_DIFFERENTIAL],
+            'WINS IN LAST 10 GAMES': [WINS_IN_LAST_10_GAMES],
+            'WINS IN LAST 5 GAMES': [WINS_IN_LAST_5_GAMES],
+            'BACK-TO-BACK': [BACK_TO_BACK]}
+
+    input_df = pd.DataFrame(data)
+    
+    #Paso7: Creación del If
+    if class_names[model.predict(input_df)][0]=="W":
+        Winning_Team=Home_Team
+    else:
+        Winning_Team=Away_Team
+    
+    return "The winning team is: " + Winning_Team
+
+gui= gr.Interface(
+    fn=run_my_model,
+    inputs=["text","text"],
+    outputs="text",
+    live=False,
+    title='NBA Regular games outcome prediction AI Oracle'
+)
+
+gui.launch()