Removing need for NFL_Data source and using strictly mongo

app.py

@@ -38,7 +38,7 @@ def init_conn():
         
         uri = st.secrets['mongo_uri']
         client = pymongo.MongoClient(uri, retryWrites=True, serverSelectionTimeoutMS=500000)
-        db = client["testing_db"]
+        db = client["NFL_Database"]
      
         NFL_Data = st.secrets['NFL_Data']
 
@@ -57,49 +57,66 @@ fd_columns = ['QB', 'RB1', 'RB2', 'WR1', 'WR2', 'WR3', 'TE', 'FLEX', 'DST', 'sal
 @st.cache_data(ttl = 600)
 def init_DK_seed_frames():  
     
-        collection = db["DK_NFL_seed_frame"] 
-        cursor = collection.find()
+    collection = db["DK_NFL_seed_frame"] 
+    cursor = collection.find()
+
+    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']]
+    DK_seed = raw_display.to_numpy()
+
+    return DK_seed
+
+@st.cache_data(ttl = 600)
+def init_DK_Secondary_seed_frames():  
     
-        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']]
-        DK_seed = raw_display.to_numpy()
+    collection = db["DK_NFL_Secondary_seed_frame"] 
+    cursor = collection.find()
+
+    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']]
+    DK_seed = raw_display.to_numpy()
 
-        return DK_seed
+    return DK_seed
 
 @st.cache_data(ttl = 599)
 def init_FD_seed_frames():  
     
-        collection = db["FD_NFL_seed_frame"] 
-        cursor = collection.find()
+    collection = db["FD_NFL_seed_frame"] 
+    cursor = collection.find()
+
+    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']]
+    FD_seed = raw_display.to_numpy()
+
+    return FD_seed
+
+@st.cache_data(ttl = 599)
+def init_FD_Secondary_seed_frames():  
     
-        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']]
-        FD_seed = raw_display.to_numpy()
+    collection = db["FD_NFL_Secondary_seed_frame"] 
+    cursor = collection.find()
 
-        return FD_seed
+    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']]
+    FD_seed = raw_display.to_numpy()
+
+    return FD_seed
 
 @st.cache_data(ttl = 599)
 def init_baselines():
-    try:
-        sh = gcservice_account.open_by_url(NFL_Data)
-    except:
-        sh = gcservice_account2.open_by_url(NFL_Data)
-    
-    worksheet = sh.worksheet('DK_ROO')
-    load_display = pd.DataFrame(worksheet.get_all_records())
-    load_display.replace('', np.nan, inplace=True)
-    load_display['STDev'] = load_display['Median'] / 4
-    load_display = load_display.drop_duplicates(subset=['Player'], keep='first')
-    
-    dk_raw = load_display.dropna(subset=['Median'])
-    
-    worksheet = sh.worksheet('FD_ROO')
-    load_display = pd.DataFrame(worksheet.get_all_records())
-    load_display.replace('', np.nan, inplace=True)
-    load_display['STDev'] = load_display['Median'] / 4
-    load_display = load_display.drop_duplicates(subset=['Player'], keep='first')
-    
-    fd_raw = load_display.dropna(subset=['Median'])
+    collection = db["DK_NFL_ROO"] 
+    cursor = collection.find()
+
+    raw_display = pd.DataFrame(list(cursor))
+    dk_raw = raw_display[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Cei', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%',
+                          'Own', 'Small_Field_Own', 'Large_Field_Own', 'Cash_Field_Own', 'CPT_Own', 'LevX', 'version', 'slate', 'timestamp', 'player_id', 'site']]
+
+    collection = db["FD_NFL_ROO"] 
+    cursor = collection.find()
+
+    raw_display = pd.DataFrame(list(cursor))
+    fd_raw = raw_display[['Player', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Cei', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '2x%', '3x%', '4x%',
+                          'Own', 'Small_Field_Own', 'Large_Field_Own', 'Cash_Field_Own', 'CPT_Own', 'LevX', 'version', 'slate', 'timestamp', 'player_id', 'site']]
 
     return dk_raw, fd_raw