Refactor app.py to remove Google Sheets integration and streamline database connections. Update seed frame functions to utilize MongoDB collections for player name mapping, enhancing data retrieval for DraftKings and FanDuel simulations. Improve overall code clarity and maintainability.

app.py

@@ -2,52 +2,19 @@ import streamlit as st
 st.set_page_config(layout="wide")
 import numpy as np
 import pandas as pd
-import gspread
 import pymongo
 import time
 
 @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-----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\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["NFL_Database"]
-     
-        NFL_Data = st.secrets['NFL_Data']
-
-        gc = gspread.service_account_from_dict(credentials)
-        gc2 = gspread.service_account_from_dict(credentials2)
 
-        return gc, gc2, db, NFL_Data
+        return db
     
-gcservice_account, gcservice_account2, db, NFL_Data = init_conn()
+db = init_conn()
 
 percentages_format = {'Exposure': '{:.2%}'}
 freq_format = {'Exposure': '{:.2%}', 'Proj Own': '{:.2%}', 'Edge': '{:.2%}'}
@@ -55,49 +22,85 @@ dk_columns = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'sal
 fd_columns = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']
 
 @st.cache_data(ttl = 600)
-def init_DK_seed_frames(sharp_split):  
+def init_DK_seed_frames(sharp_split):
+
+    collection = db['DK_NFL_name_map']
+    cursor = collection.find()
+    raw_data = pd.DataFrame(list(cursor))
+    names_dict = dict(zip(raw_data['key'], raw_data['value']))
     
     collection = db["DK_NFL_seed_frame"] 
     cursor = collection.find().limit(sharp_split)
 
     raw_display = pd.DataFrame(list(cursor))
     raw_display = raw_display[['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
+    dict_columns = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST']
+    st.write("converting names")
+    for col in dict_columns:
+        raw_display[col] = raw_display[col].map(names_dict)
     DK_seed = raw_display.to_numpy()
 
     return DK_seed
 
 @st.cache_data(ttl = 600)
 def init_DK_Secondary_seed_frames(sharp_split):  
+
+    collection = db['DK_NFL_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_NFL_Secondary_seed_frame"] 
     cursor = collection.find().limit(sharp_split)
 
     raw_display = pd.DataFrame(list(cursor))
     raw_display = raw_display[['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
+    dict_columns = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST']
+    st.write("converting names")
+    for col in dict_columns:
+        raw_display[col] = raw_display[col].map(names_dict)
     DK_seed = raw_display.to_numpy()
 
     return DK_seed
 
 @st.cache_data(ttl = 599)
-def init_FD_seed_frames(sharp_split):  
+def init_FD_seed_frames(sharp_split): 
+
+    collection = db['FD_NFL_name_map']
+    cursor = collection.find()
+    raw_data = pd.DataFrame(list(cursor))
+    names_dict = dict(zip(raw_data['key'], raw_data['value']))
     
     collection = db["FD_NFL_seed_frame"] 
     cursor = collection.find().limit(sharp_split)
 
     raw_display = pd.DataFrame(list(cursor))
     raw_display = raw_display[['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
+    dict_columns = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST']
+    st.write("converting names")
+    for col in dict_columns:
+        raw_display[col] = raw_display[col].map(names_dict)
     FD_seed = raw_display.to_numpy()
 
     return FD_seed
 
 @st.cache_data(ttl = 599)
 def init_FD_Secondary_seed_frames(sharp_split):  
+
+    collection = db['FD_NFL_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_NFL_Secondary_seed_frame"] 
     cursor = collection.find().limit(sharp_split)
 
     raw_display = pd.DataFrame(list(cursor))
     raw_display = raw_display[['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
+    dict_columns = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST']
+    st.write("converting names")
+    for col in dict_columns:
+        raw_display[col] = raw_display[col].map(names_dict)
     FD_seed = raw_display.to_numpy()
 
     return FD_seed
@@ -359,11 +362,10 @@ with tab1:
                         'Team_map':dict(zip(raw_baselines.Player,raw_baselines.Team)),
                         'STDev_map':dict(zip(raw_baselines.Player,raw_baselines.STDev))
                         }
+                
                 Sim_Winners = sim_contest(1000, st.session_state.working_seed, st.session_state.maps_dict, Contest_Size)
                 Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners))
                 
-                #st.table(Sim_Winner_Frame)
-                            
                 # Initial setup
                 Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners), columns=column_names + ['Fantasy'])
                 Sim_Winner_Frame['GPP_Proj'] = (Sim_Winner_Frame['proj'] + Sim_Winner_Frame['Fantasy']) / 2
@@ -409,6 +411,7 @@ with tab1:
                         'Team_map':dict(zip(raw_baselines.Player,raw_baselines.Team)),
                         'STDev_map':dict(zip(raw_baselines.Player,raw_baselines.STDev))
                         }
+                
                 Sim_Winners = sim_contest(1000, st.session_state.working_seed, st.session_state.maps_dict, Contest_Size)
                 Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners))