Refactor app.py: streamline connection initialization by removing unused Google Sheets credentials and enhance lineup functions to support multiple slate designations for improved flexibility

app.py

@@ -9,46 +9,14 @@ st.set_page_config(layout="wide")
 
 @st.cache_resource
 def init_conn():
-        scope = ['https://spreadsheets.google.com/feeds', 'https://www.googleapis.com/auth/drive']
-
-        credentials = {
-          "type": "service_account",
-          "project_id": "model-sheets-connect",
-          "private_key_id": st.secrets['model_sheets_connect_pk'],
-          "private_key": "-----BEGIN PRIVATE KEY-----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\nF/j9eUXfXjAlb042MphoF3ZC\n-----END PRIVATE KEY-----\n",
-          "client_email": "[email protected]",
-          "client_id": "100369174533302798535",
-          "auth_uri": "https://accounts.google.com/o/oauth2/auth",
-          "token_uri": "https://oauth2.googleapis.com/token",
-          "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
-          "client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/gspread-connection%40model-sheets-connect.iam.gserviceaccount.com"
-        }
-        
-        credentials2 = {
-          "type": "service_account",
-          "project_id": "sheets-api-connect-378620",
-          "private_key_id": st.secrets['sheets_api_connect_pk'],
-          "private_key": "-----BEGIN PRIVATE KEY-----\nMIIEvQIBADANBgkqhkiG9w0BAQEFAASCBKcwggSjAgEAAoIBAQCtKa01beXwc88R\nnPZVQTNPVQuBnbwoOfc66gW3547ja/UEyIGAF112dt/VqHprRafkKGmlg55jqJNt\na4zceLKV+wTm7vBu7lDISTJfGzCf2TrxQYNqwMKE2LOjI69dBM8u4Dcb4k0wcp9v\ntW1ZzLVVuwTvmrg7JBHjiSaB+x5wxm/r3FOiJDXdlAgFlytzqgcyeZMJVKKBQHyJ\njEGg/1720A0numuOCt71w/2G0bDmijuj1e6tH32MwRWcvRNZ19K9ssyDz2S9p68s\nYDhIxX69OWxwScTIHLY6J2t8txf/XMivL/636fPlDADvBEVTdlT606n8CcKUVQeq\npUVdG+lfAgMBAAECggEAP38SUA7B69eTfRpo658ycOs3Amr0JW4H/bb1rNeAul0K\nZhwd/HnU4E07y81xQmey5kN5ZeNrD5EvqkZvSyMJHV0EEahZStwhjCfnDB/cxyix\nZ+kFhv4y9eK+kFpUAhBy5nX6T0O+2T6WvzAwbmbVsZ+X8kJyPuF9m8ldcPlD0sce\ntj8NwVq1ys52eosqs7zi2vjt+eMcaY393l4ls+vNq8Yf27cfyFw45W45CH/97/Nu\n5AmuzlCOAfFF+z4OC5g4rei4E/Qgpxa7/uom+BVfv9G0DIGW/tU6Sne0+37uoGKt\nW6DzhgtebUtoYkG7ZJ05BTXGp2lwgVcNRoPwnKJDxQKBgQDT5wYPUBDW+FHbvZSp\nd1m1UQuXyerqOTA9smFaM8sr/UraeH85DJPEIEk8qsntMBVMhvD3Pw8uIUeFNMYj\naLmZFObsL+WctepXrVo5NB6RtLB/jZYxiKMatMLUJIYtcKIp+2z/YtKiWcLnwotB\nWdCjVnPTxpkurmF2fWP/eewZ+wKBgQDRMtJg7etjvKyjYNQ5fARnCc+XsI3gkBe1\nX9oeXfhyfZFeBXWnZzN1ITgFHplDznmBdxAyYGiQdbbkdKQSghviUQ0igBvoDMYy\n1rWcy+a17Mj98uyNEfmb3X2cC6WpvOZaGHwg9+GY67BThwI3FqHIbyk6Ko09WlTX\nQpRQjMzU7QKBgAfi1iflu+q0LR+3a3vvFCiaToskmZiD7latd9AKk2ocsBd3Woy9\n+hXXecJHPOKV4oUJlJgvAZqe5HGBqEoTEK0wyPNLSQlO/9ypd+0fEnArwFHO7CMF\nycQprAKHJXM1eOOFFuZeQCaInqdPZy1UcV5Szla4UmUZWkk1m24blHzXAoGBAMcA\nyH4qdbxX9AYrC1dvsSRvgcnzytMvX05LU0uF6tzGtG0zVlub4ahvpEHCfNuy44UT\nxRWW/oFFaWjjyFxO5sWggpUqNuHEnRopg3QXx22SRRTGbN45li/+QAocTkgsiRh1\nqEcYZsO4mPCsQqAy6E2p6RcK+Xa+omxvSnVhq0x1AoGAKr8GdkCl4CF6rieLMAQ7\nLNBuuoYGaHoh8l5E2uOQpzwxVy/nMBcAv+2+KqHEzHryUv1owOi6pMLv7A9mTFoS\n18B0QRLuz5fSOsVnmldfC9fpUc6H8cH1SINZpzajqQA74bPwELJjnzrCnH79TnHG\nJuElxA33rFEjbgbzdyrE768=\n-----END PRIVATE KEY-----\n",
-          "client_email": "gspread-connection@sheets-api-connect-378620.iam.gserviceaccount.com",
-          "client_id": "106625872877651920064",
-          "auth_uri": "https://accounts.google.com/o/oauth2/auth",
-          "token_uri": "https://oauth2.googleapis.com/token",
-          "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
-          "client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/gspread-connection%40sheets-api-connect-378620.iam.gserviceaccount.com"
-        }
 
         uri = st.secrets['mongo_uri']
         client = pymongo.MongoClient(uri, retryWrites=True, serverSelectionTimeoutMS=500000)
         db = client["NBA_DFS"]
-     
-        NBA_Data = st.secrets['NBA_Data']
-
-        gc = gspread.service_account_from_dict(credentials)
-        gc2 = gspread.service_account_from_dict(credentials2)
 
-        return gc, gc2, db, NBA_Data
+        return db
     
-gcservice_account, gcservice_account2, db, NBA_Data = init_conn()
+db = init_conn()
 
 dk_columns = ['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'FLEX', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']
 fd_columns = ['PG1', 'PG2', 'SG1', 'SG2', 'SF1', 'SF2', 'PF1', 'PF2', 'C1', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']
@@ -148,16 +116,33 @@ def load_overall_stats():
     return dk_raw, fd_raw, dk_raw_sec, fd_raw_sec, roo_raw, timestamp
 
 @st.cache_data(ttl = 60)
-def init_DK_lineups():  
+def init_DK_lineups(slate_desig: str):  
         
-        collection = db['DK_NBA_name_map']
-        cursor = collection.find()
-        raw_data = pd.DataFrame(list(cursor))
-        names_dict = dict(zip(raw_data['key'], raw_data['value']))
-    
-        collection = db["DK_NBA_seed_frame"] 
-        cursor = collection.find().limit(10000)
-    
+        if slate_desig == 'Main Slate':
+            collection = db['DK_NBA_name_map']
+            cursor = collection.find()
+            raw_data = pd.DataFrame(list(cursor))
+            names_dict = dict(zip(raw_data['key'], raw_data['value']))
+        
+            collection = db["DK_NBA_seed_frame"] 
+            cursor = collection.find().limit(10000)
+        elif slate_desig == 'Secondary':
+            collection = db['DK_NBA_Secondary_name_map']
+            cursor = collection.find()
+            raw_data = pd.DataFrame(list(cursor))
+            names_dict = dict(zip(raw_data['key'], raw_data['value']))
+        
+            collection = db["DK_NBA_Secondary_seed_frame"] 
+            cursor = collection.find().limit(10000)
+        elif slate_desig == 'Auxiliary':
+            collection = db['DK_NBA_Auxiliary_name_map']
+            cursor = collection.find()
+            raw_data = pd.DataFrame(list(cursor))
+            names_dict = dict(zip(raw_data['key'], raw_data['value']))
+        
+            collection = db["DK_NBA_Auxiliary_seed_frame"] 
+            cursor = collection.find().limit(10000) 
+
         raw_display = pd.DataFrame(list(cursor))
         raw_display = raw_display[['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'FLEX', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
         dict_columns = ['PG', 'SG', 'SF', 'PF', 'C', 'G', 'F', 'FLEX']
@@ -168,16 +153,51 @@ def init_DK_lineups():
         return DK_seed
 
 @st.cache_data(ttl = 60)
-def init_FD_lineups():  
-        
-        collection = db['FD_NBA_name_map']
-        cursor = collection.find()
-        raw_data = pd.DataFrame(list(cursor))
-        names_dict = dict(zip(raw_data['key'], raw_data['value']))
-    
-        collection = db["FD_NBA_seed_frame"] 
+def init_DK_SD_lineups(slate_desig: str):
+
+        if slate_desig == 'Main Slate':
+            collection = db["DK_NBA_SD_seed_frame"] 
+        elif slate_desig == 'Secondary':
+            collection = db["DK_NBA_Secondary_SD_seed_frame"] 
+        elif slate_desig == 'Auxiliary':
+            collection = db["DK_NBA_Auxiliary_SD_seed_frame"] 
+
         cursor = collection.find().limit(10000)
     
+        raw_display = pd.DataFrame(list(cursor))
+        raw_display = raw_display[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
+        DK_seed = raw_display.to_numpy()
+
+        return DK_seed
+
+@st.cache_data(ttl = 60)
+def init_FD_lineups(slate_desig: str):  
+        
+        if slate_desig == 'Main Slate':
+            collection = db['FD_NBA_name_map']
+            cursor = collection.find()
+            raw_data = pd.DataFrame(list(cursor))
+            names_dict = dict(zip(raw_data['key'], raw_data['value']))
+        
+            collection = db["FD_NBA_seed_frame"] 
+            cursor = collection.find().limit(10000)
+        elif slate_desig == 'Secondary':
+            collection = db['FD_NBA_Secondary_name_map']
+            cursor = collection.find()
+            raw_data = pd.DataFrame(list(cursor))
+            names_dict = dict(zip(raw_data['key'], raw_data['value']))
+        
+            collection = db["FD_NBA_Secondary_seed_frame"] 
+            cursor = collection.find().limit(10000)
+        elif slate_desig == 'Auxiliary':
+            collection = db['FD_NBA_Auxiliary_name_map']
+            cursor = collection.find()
+            raw_data = pd.DataFrame(list(cursor))
+            names_dict = dict(zip(raw_data['key'], raw_data['value']))
+        
+            collection = db["FD_NBA_Auxiliary_seed_frame"] 
+            cursor = collection.find().limit(10000) 
+    
         raw_display = pd.DataFrame(list(cursor))
         raw_display = raw_display[['PG1', 'PG2', 'SG1', 'SG2', 'SF1', 'SF2', 'PF1', 'PF2', 'C1', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
         dict_columns = ['PG1', 'PG2', 'SG1', 'SG2', 'SF1', 'SF2', 'PF1', 'PF2', 'C1']
@@ -187,6 +207,24 @@ def init_FD_lineups():
 
         return FD_seed
 
+@st.cache_data(ttl = 60)
+def init_FD_SD_lineups(slate_desig: str):
+
+        if slate_desig == 'Main Slate':
+            collection = db["FD_NBA_SD_seed_frame"] 
+        elif slate_desig == 'Secondary':
+            collection = db["FD_NBA_Secondary_SD_seed_frame"] 
+        elif slate_desig == 'Auxiliary':
+            collection = db["FD_NBA_Auxiliary_SD_seed_frame"] 
+
+        cursor = collection.find().limit(10000)
+    
+        raw_display = pd.DataFrame(list(cursor))
+        raw_display = raw_display[['CPT', 'FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
+        DK_seed = raw_display.to_numpy()
+
+        return DK_seed
+
 def convert_df_to_csv(df):
     return df.to_csv().encode('utf-8')
 
@@ -199,8 +237,8 @@ dk_raw, fd_raw, dk_raw_sec, fd_raw_sec, roo_raw, timestamp = load_overall_stats(
 salary_dict = dict(zip(roo_raw.Player, roo_raw.Salary))
 
 try:
-    dk_lineups = init_DK_lineups()
-    fd_lineups = init_FD_lineups()
+    dk_lineups = init_DK_lineups('Main Slate')
+    fd_lineups = init_FD_lineups('Main Slate')
 except:
     dk_lineups = pd.DataFrame(columns=dk_columns)
     fd_lineups = pd.DataFrame(columns=fd_columns)
@@ -223,8 +261,8 @@ with tab1:
                     st.cache_data.clear()
                     dk_raw, fd_raw, dk_raw_sec, fd_raw_sec, roo_raw, timestamp = load_overall_stats()
                     id_dict = dict(zip(roo_raw.Player, roo_raw.player_ID))
-                    dk_lineups = init_DK_lineups()
-                    fd_lineups = init_FD_lineups()
+                    dk_lineups = init_DK_lineups('Main Slate')
+                    fd_lineups = init_FD_lineups('Main Slate')
                     t_stamp = f"Last Update: " + str(timestamp) + f" CST"
                     for key in st.session_state.keys():
                         del st.session_state[key]
@@ -259,7 +297,7 @@ with tab1:
         with col1:
             low_salary = st.number_input('Enter Lowest Salary', min_value=3000, max_value=15000, value=3000, step=100, key='low_salary')
         with col2:
-            high_salary = st.number_input('Enter Highest Salary', min_value=3000, max_value=15000, value=15000, step=100, key='high_salary')
+            high_salary = st.number_input('Enter Highest Salary', min_value=3000, max_value=25000, value=25000, step=100, key='high_salary')
     
     display_container_1 = st.empty()
     display_dl_container_1 = st.empty()
@@ -300,9 +338,9 @@ with tab2:
     with st.expander("Info and Filters"):
         if st.button("Load/Reset Data", key='reset2'):
             st.cache_data.clear()
-            dk_raw, fd_raw, dk_raw_sec, fd_raw_sec, roo_raw, timestamp, roo_backlog = load_overall_stats()
-            dk_lineups = init_DK_lineups()
-            fd_lineups = init_FD_lineups()
+            dk_raw, fd_raw, dk_raw_sec, fd_raw_sec, roo_raw, timestamp = load_overall_stats()
+            dk_lineups = init_DK_lineups('Main Slate')
+            fd_lineups = init_FD_lineups('Main Slate')
             t_stamp = f"Last Update: " + str(timestamp) + f" CST"
             for key in st.session_state.keys():
                 del st.session_state[key]