Implement initial Streamlit application with MongoDB integration and data simulation features

app.py

@@ -0,0 +1,514 @@
+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():
+        
+        uri = st.secrets['mongo_uri']
+        client = pymongo.MongoClient(uri, retryWrites=True, serverSelectionTimeoutMS=500000)
+        db = client["League_of_Legends_Database"]
+
+        return db
+    
+db = init_conn()
+
+percentages_format = {'Exposure': '{:.2%}'}
+freq_format = {'Exposure': '{:.2%}', 'Proj Own': '{:.2%}', 'Edge': '{:.2%}'}
+
+dk_columns = ['CPT', 'TOP', 'JNG', 'MID', 'ADC', 'SUP', 'TEAM', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']
+fd_columns = ['CPT', 'TOP', 'JNG', 'MID', 'ADC', 'SUP', 'TEAM', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']
+
+@st.cache_data(ttl = 599)
+def init_DK_seed_frames(league):  
+
+        if league == 'LCK':
+            collection = db['LOL_LEC_seed_frame'] 
+        elif league =='LEC':
+             collection = db['LOL_LEC_seed_frame']
+        elif league =='LTA':
+             collection = db['LOL_LTA_seed_frame']
+        cursor = collection.find()
+    
+        raw_display = pd.DataFrame(list(cursor))
+        raw_display = raw_display[['CPT', 'TOP', 'JNG', 'MID', 'ADC', 'SUP', 'TEAM', 'salary', 'proj', 'Team', 'Team_count', 'Secondary', 'Secondary_count', 'Own']]
+        DK_seed = raw_display.to_numpy()
+
+        return DK_seed
+
+@st.cache_data(ttl = 599)
+def init_baselines():
+        
+    collection = db['Player_Range_of_Outcomes']
+    cursor = collection.find()
+
+    raw_display = pd.DataFrame(list(cursor))
+
+    raw_display['Player'] = raw_display['Player'].astype(str)
+    raw_display['STDev'] = raw_display['Median'] / 4
+    load_display = raw_display.drop_duplicates(subset=['Player'], keep='first')
+    
+    dk_raw = load_display.dropna(subset=['Median'])
+
+    return dk_raw
+
+@st.cache_data
+def convert_df(array):
+    array = pd.DataFrame(array, columns=column_names)
+    return array.to_csv().encode('utf-8')
+
+@st.cache_data
+def calculate_DK_value_frequencies(np_array):
+    unique, counts = np.unique(np_array[:, :6], return_counts=True)
+    frequencies = counts / len(np_array)  # Normalize by the number of rows 
+    combined_array = np.column_stack((unique, frequencies))  
+    return combined_array 
+
+@st.cache_data
+def calculate_FD_value_frequencies(np_array):
+    unique, counts = np.unique(np_array[:, :6], return_counts=True)
+    frequencies = counts / len(np_array)  # Normalize by the number of rows 
+    combined_array = np.column_stack((unique, frequencies))  
+    return combined_array
+
+@st.cache_data
+def sim_contest(Sim_size, seed_frame, maps_dict, sharp_split, Contest_Size):
+    SimVar = 1
+    Sim_Winners = []
+    fp_array = seed_frame[:sharp_split, :]
+    
+    # Pre-vectorize functions
+    vec_projection_map = np.vectorize(maps_dict['Projection_map'].__getitem__)
+    vec_stdev_map = np.vectorize(maps_dict['STDev_map'].__getitem__)
+    
+    st.write('Simulating contest on frames')
+    
+    while SimVar <= Sim_size:
+        fp_random = fp_array[np.random.choice(fp_array.shape[0], Contest_Size)]
+            
+        sample_arrays1 = np.c_[
+            fp_random, 
+            np.sum(np.random.normal(
+                loc=vec_projection_map(fp_random[:, :-7]),
+                scale=vec_stdev_map(fp_random[:, :-7])),
+            axis=1)
+        ]
+
+        sample_arrays = sample_arrays1
+
+        final_array = sample_arrays[sample_arrays[:, 7].argsort()[::-1]]
+        best_lineup = final_array[final_array[:, -1].argsort(kind='stable')[::-1][:1]]
+        Sim_Winners.append(best_lineup)
+        SimVar += 1
+        
+    return Sim_Winners
+
+DK_seed = init_DK_seed_frames('LCK')
+dk_raw = init_baselines()
+
+tab1, tab2 = st.tabs(['Contest Sims', 'Data Export'])
+with tab2:
+    col1, col2 = st.columns([1, 7])
+    with col1:
+        if st.button("Load/Reset Data", key='reset1'):
+              st.cache_data.clear()
+              for key in st.session_state.keys():
+                  del st.session_state[key]
+              DK_seed = init_DK_seed_frames('LCK')
+              dk_raw = init_baselines()
+              
+        slate_var1 = st.radio("Which data are you loading?", ('LCK', 'LEC', 'LTA'))
+        site_var1 = st.radio("What site are you working with?", ('Draftkings'))
+        if site_var1 == 'Draftkings':
+            raw_baselines = dk_raw.copy()
+            column_names = dk_columns
+            
+            team_var1 = st.radio("Do you want a frame with specific teams?", ('Full Slate', 'Specific Teams'), key='team_var1')
+            if team_var1 == 'Specific Teams':
+                    team_var2 = st.multiselect('Which teams do you want?', options = dk_raw['Team'].unique())
+            elif team_var1 == 'Full Slate':
+                    team_var2 = dk_raw.Team.values.tolist()
+            
+            stack_var1 = st.radio("Do you want a frame with specific stack sizes?", ('Full Slate', 'Specific Stack Sizes'), key='stack_var1')
+            if stack_var1 == 'Specific Stack Sizes':
+                    stack_var2 = st.multiselect('Which stack sizes do you want?', options = [4, 3, 2, 1, 0])
+            elif stack_var1 == 'Full Slate':
+                    stack_var2 = [4, 3, 2, 1, 0]
+        
+        if st.button("Prepare data export", key='data_export'):
+                data_export = st.session_state.working_seed.copy()
+                st.download_button(
+                    label="Export optimals set",
+                    data=convert_df(data_export),
+                    file_name='LOL_optimals_export.csv',
+                    mime='text/csv',
+                )
+            
+    with col2:
+        if st.button("Load Data", key='load_data'):
+            if site_var1 == 'Draftkings':
+                if 'working_seed' in st.session_state:
+                    st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 8], team_var2)]
+                    st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 9], stack_var2)]
+                    st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:1000], columns=column_names)
+                elif 'working_seed' not in st.session_state:
+                    st.session_state.working_seed = init_DK_seed_frames(slate_var1)
+                    st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 8], team_var2)]
+                    st.session_state.working_seed = st.session_state.working_seed[np.isin(st.session_state.working_seed[:, 9], stack_var2)]
+                    st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:1000], columns=column_names)
+                
+        with st.container():
+            if 'data_export_display' in st.session_state:
+                st.dataframe(st.session_state.data_export_display.style.format(freq_format, precision=2), use_container_width = True)
+            
+with tab1:
+    col1, col2 = st.columns([1, 7])
+    with col1:
+        if st.button("Load/Reset Data", key='reset2'):
+              st.cache_data.clear()
+              for key in st.session_state.keys():
+                  del st.session_state[key]
+              DK_seed = init_DK_seed_frames('LCK')
+              dk_raw = init_baselines()
+        sim_slate_var1 = st.radio("Which data are you loading?", ('LCK', 'LEC', 'LTA'), key='sim_slate_var1')
+
+        sim_site_var1 = st.radio("What site are you working with?", ('Draftkings'), key='sim_site_var1')
+        if sim_site_var1 == 'Draftkings':
+            raw_baselines = dk_raw.copy()
+            column_names = dk_columns
+            
+        contest_var1 = st.selectbox("What contest size are you simulating?", ('Small', 'Medium', 'Large', 'Custom'))
+        if contest_var1 == 'Small':
+            Contest_Size = 1000
+
+        elif contest_var1 == 'Medium':
+            Contest_Size = 5000
+        elif contest_var1 == 'Large':
+            Contest_Size = 10000
+        elif contest_var1 == 'Custom':
+            Contest_Size = st.number_input("Insert contest size", value=100, placeholder="Type a number under 10,000...")
+        strength_var1 = st.selectbox("How sharp is the field in the contest?", ('Very', 'Above Average', 'Average', 'Below Average', 'Not Very'))
+        if strength_var1 == 'Not Very':
+            sharp_split = 500000
+        elif strength_var1 == 'Below Average':
+            sharp_split = 400000
+        elif strength_var1 == 'Average':
+            sharp_split = 300000
+        elif strength_var1 == 'Above Average':
+            sharp_split = 200000
+        elif strength_var1 == 'Very':
+            sharp_split = 100000
+
+    
+    with col2:
+        if st.button("Run Contest Sim"):
+            if 'working_seed' in st.session_state:
+                maps_dict = {
+                        'Projection_map':dict(zip(raw_baselines.Player,raw_baselines.Median)),
+                        'Salary_map':dict(zip(raw_baselines.Player,raw_baselines.Salary)),
+                        'Pos_map':dict(zip(raw_baselines.Player,raw_baselines.Position)),
+                        'Own_map':dict(zip(raw_baselines.Player,raw_baselines['Own'])),
+                        '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, maps_dict, sharp_split, Contest_Size)
+                Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners))
+                            
+                # 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
+                Sim_Winner_Frame['unique_id'] = Sim_Winner_Frame['proj'].astype(str) + Sim_Winner_Frame['salary'].astype(str) + Sim_Winner_Frame['Team'].astype(str) + Sim_Winner_Frame['Secondary'].astype(str)
+                Sim_Winner_Frame = Sim_Winner_Frame.assign(win_count=Sim_Winner_Frame['unique_id'].map(Sim_Winner_Frame['unique_id'].value_counts()))
+                
+                # Type Casting
+                type_cast_dict = {'salary': int, 'proj': np.float16, 'Fantasy': np.float16, 'GPP_Proj': np.float32, 'Own': np.float32}
+                Sim_Winner_Frame = Sim_Winner_Frame.astype(type_cast_dict)
+                
+                # Sorting
+                st.session_state.Sim_Winner_Frame = Sim_Winner_Frame.sort_values(by=['win_count', 'GPP_Proj'], ascending= [False, False]).copy().drop_duplicates(subset='unique_id').head(100)
+                st.session_state.Sim_Winner_Frame.drop(columns='unique_id', inplace=True)
+                
+                # Data Copying
+                st.session_state.Sim_Winner_Export = Sim_Winner_Frame.copy()
+                
+                # Data Copying
+                st.session_state.Sim_Winner_Display = Sim_Winner_Frame.copy()
+                
+            else:
+                if sim_site_var1 == 'Draftkings':
+                    st.session_state.working_seed = init_DK_seed_frames(sim_slate_var1)
+                maps_dict = {
+                        'Projection_map':dict(zip(raw_baselines.Player,raw_baselines.Median)),
+                        'Salary_map':dict(zip(raw_baselines.Player,raw_baselines.Salary)),
+                        'Pos_map':dict(zip(raw_baselines.Player,raw_baselines.Position)),
+                        'Own_map':dict(zip(raw_baselines.Player,raw_baselines['Own'])),
+                        '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, maps_dict, sharp_split, Contest_Size)
+                Sim_Winner_Frame = pd.DataFrame(np.concatenate(Sim_Winners))
+                            
+                # 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
+                Sim_Winner_Frame['unique_id'] = Sim_Winner_Frame['proj'].astype(str) + Sim_Winner_Frame['salary'].astype(str) + Sim_Winner_Frame['Team'].astype(str) + Sim_Winner_Frame['Secondary'].astype(str)
+                Sim_Winner_Frame = Sim_Winner_Frame.assign(win_count=Sim_Winner_Frame['unique_id'].map(Sim_Winner_Frame['unique_id'].value_counts()))
+                
+                # Type Casting
+                type_cast_dict = {'salary': int, 'proj': np.float16, 'Fantasy': np.float16, 'GPP_Proj': np.float32, 'Own': np.float32}
+                Sim_Winner_Frame = Sim_Winner_Frame.astype(type_cast_dict)
+                
+                # Sorting
+                st.session_state.Sim_Winner_Frame = Sim_Winner_Frame.sort_values(by=['win_count', 'GPP_Proj'], ascending= [False, False]).copy().drop_duplicates(subset='unique_id').head(100)
+                st.session_state.Sim_Winner_Frame.drop(columns='unique_id', inplace=True)
+                
+               # Data Copying
+                st.session_state.Sim_Winner_Export = Sim_Winner_Frame.copy()
+                
+                # Data Copying
+                st.session_state.Sim_Winner_Display = Sim_Winner_Frame.copy()
+                freq_copy = st.session_state.Sim_Winner_Display
+                
+            if sim_site_var1 == 'Draftkings':
+                freq_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,0:7].values, return_counts=True)),
+                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+            freq_working['Freq'] = freq_working['Freq'].astype(int)
+            freq_working['Position'] = freq_working['Player'].map(maps_dict['Pos_map'])
+            freq_working['Salary'] = freq_working['Player'].map(maps_dict['Salary_map'])
+            freq_working['Proj Own'] = freq_working['Player'].map(maps_dict['Own_map']) / 100
+            freq_working['Exposure'] = freq_working['Freq']/(1000)
+            freq_working['Edge'] = freq_working['Exposure'] - freq_working['Proj Own']
+            freq_working['Team'] = freq_working['Player'].map(maps_dict['Team_map'])
+            st.session_state.player_freq = freq_working.copy()
+
+            if sim_site_var1 == 'Draftkings':
+                cpt_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,0:1].values, return_counts=True)),
+                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+            cpt_working['Freq'] = cpt_working['Freq'].astype(int)
+            cpt_working['Position'] = cpt_working['Player'].map(maps_dict['Pos_map'])
+            cpt_working['Salary'] = cpt_working['Player'].map(maps_dict['Salary_map'])
+            cpt_working['Proj Own'] = cpt_working['Player'].map(maps_dict['Own_map']) / 600
+            cpt_working['Exposure'] = cpt_working['Freq']/(1000)
+            cpt_working['Edge'] = cpt_working['Exposure'] - cpt_working['Proj Own']
+            cpt_working['Team'] = cpt_working['Player'].map(maps_dict['Team_map'])
+            st.session_state.cpt_freq = cpt_working.copy()
+            
+            if sim_site_var1 == 'Draftkings':
+                top_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,1:2].values, return_counts=True)),
+                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+            top_working['Freq'] = top_working['Freq'].astype(int)
+            top_working['Position'] = top_working['Player'].map(maps_dict['Pos_map'])
+            top_working['Salary'] = top_working['Player'].map(maps_dict['Salary_map'])
+            top_working['Proj Own'] = top_working['Player'].map(maps_dict['Own_map']) / 105
+            top_working['Exposure'] = top_working['Freq']/(1000)
+            top_working['Edge'] = top_working['Exposure'] - top_working['Proj Own']
+            top_working['Team'] = top_working['Player'].map(maps_dict['Team_map'])
+            st.session_state.top_freq = top_working.copy()
+            
+            if sim_site_var1 == 'Draftkings':
+                jng_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,2:3].values, return_counts=True)),
+                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+            jng_working['Freq'] = jng_working['Freq'].astype(int)
+            jng_working['Position'] = jng_working['Player'].map(maps_dict['Pos_map'])
+            jng_working['Salary'] = jng_working['Player'].map(maps_dict['Salary_map'])
+            jng_working['Proj Own'] = jng_working['Player'].map(maps_dict['Own_map']) / 135
+            jng_working['Exposure'] = jng_working['Freq']/(1000)
+            jng_working['Edge'] = jng_working['Exposure'] - jng_working['Proj Own']
+            jng_working['Team'] = jng_working['Player'].map(maps_dict['Team_map'])
+            st.session_state.jng_freq = jng_working.copy()
+            
+            if sim_site_var1 == 'Draftkings':
+                mid_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,3:4].values, return_counts=True)),
+                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+            mid_working['Freq'] = mid_working['Freq'].astype(int)
+            mid_working['Position'] = mid_working['Player'].map(maps_dict['Pos_map'])
+            mid_working['Salary'] = mid_working['Player'].map(maps_dict['Salary_map'])
+            mid_working['Proj Own'] = mid_working['Player'].map(maps_dict['Own_map']) / 120
+            mid_working['Exposure'] = mid_working['Freq']/(1000)
+            mid_working['Edge'] = mid_working['Exposure'] - mid_working['Proj Own']
+            mid_working['Team'] = mid_working['Player'].map(maps_dict['Team_map'])
+            st.session_state.mid_freq = mid_working.copy()
+            
+            if sim_site_var1 == 'Draftkings':
+                adc_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,4:5].values, return_counts=True)),
+                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+            adc_working['Freq'] = adc_working['Freq'].astype(int)
+            adc_working['Position'] = adc_working['Player'].map(maps_dict['Pos_map'])
+            adc_working['Salary'] = adc_working['Player'].map(maps_dict['Salary_map'])
+            adc_working['Proj Own'] = adc_working['Player'].map(maps_dict['Own_map']) / 135
+            adc_working['Exposure'] = adc_working['Freq']/(1000)
+            adc_working['Edge'] = adc_working['Exposure'] - adc_working['Proj Own']
+            adc_working['Team'] = adc_working['Player'].map(maps_dict['Team_map'])
+            st.session_state.adc_freq = adc_working.copy()
+            
+            if sim_site_var1 == 'Draftkings':
+                sup_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,5:6].values, return_counts=True)),
+                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+            sup_working['Freq'] = sup_working['Freq'].astype(int)
+            sup_working['Position'] = sup_working['Player'].map(maps_dict['Pos_map'])
+            sup_working['Salary'] = sup_working['Player'].map(maps_dict['Salary_map'])
+            sup_working['Proj Own'] = sup_working['Player'].map(maps_dict['Own_map']) / 105
+            sup_working['Exposure'] = sup_working['Freq']/(1000)
+            sup_working['Edge'] = sup_working['Exposure'] - sup_working['Proj Own']
+            sup_working['Team'] = sup_working['Player'].map(maps_dict['Team_map'])
+            st.session_state.sup_freq = sup_working.copy()
+            
+            if sim_site_var1 == 'Draftkings':
+                team_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,6:7].values, return_counts=True)),
+                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+            team_working['Freq'] = team_working['Freq'].astype(int)
+            team_working['Position'] = team_working['Player'].map(maps_dict['Pos_map'])
+            team_working['Salary'] = team_working['Player'].map(maps_dict['Salary_map'])
+            team_working['Proj Own'] = team_working['Player'].map(maps_dict['Own_map']) / 100
+            team_working['Exposure'] = team_working['Freq']/(1000)
+            team_working['Edge'] = team_working['Exposure'] - team_working['Proj Own']
+            team_working['Team'] = team_working['Player'].map(maps_dict['Team_map'])
+            st.session_state.team_freq = team_working.copy()
+
+            if sim_site_var1 == 'Draftkings':
+                stack_working = pd.DataFrame(np.column_stack(np.unique(freq_copy.iloc[:,9:10].values, return_counts=True)),
+                                                columns=['Player','Freq']).sort_values('Freq', ascending=False).reset_index(drop=True)
+            stack_working['Freq'] = stack_working['Freq'].astype(int)
+            stack_working['Exposure'] = stack_working['Freq']/(1000)
+            st.session_state.stack_freq = stack_working.copy()
+            
+        with st.container():
+            if st.button("Reset Sim", key='reset_sim'):
+                for key in st.session_state.keys():
+                    del st.session_state[key]
+            if 'player_freq' in st.session_state: 
+                player_split_var2 = st.radio("Are you wanting to isolate any lineups with specific players?", ('Full Players', 'Specific Players'), key='player_split_var2')
+                if player_split_var2 == 'Specific Players':
+                          find_var2 = st.multiselect('Which players must be included in the lineups?', options = st.session_state.player_freq['Player'].unique())
+                elif player_split_var2 == 'Full Players':
+                          find_var2 = st.session_state.player_freq.Player.values.tolist()
+    
+                if player_split_var2 == 'Specific Players':
+                          st.session_state.Sim_Winner_Display = st.session_state.Sim_Winner_Frame[np.equal.outer(st.session_state.Sim_Winner_Frame.to_numpy(), find_var2).any(axis=1).all(axis=1)]
+                if player_split_var2 == 'Full Players':
+                          st.session_state.Sim_Winner_Display = st.session_state.Sim_Winner_Frame
+            if 'Sim_Winner_Display' in st.session_state:
+                st.dataframe(st.session_state.Sim_Winner_Display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), use_container_width = True)
+            if 'Sim_Winner_Export' in st.session_state:
+                st.download_button(
+                    label="Export Full Frame",
+                    data=st.session_state.Sim_Winner_Export.to_csv().encode('utf-8'),
+                    file_name='LOL_consim_export.csv',
+                    mime='text/csv',
+                )  
+                
+        with st.container():
+            tab1, tab2, tab3, tab4, tab5, tab6, tab7, tab8, tab9 = st.tabs(['Stack Exposures', 'Overall Exposures', 'CPT Exposures', 'TOP Exposures', 'JNG Exposures', 'MID Exposures', 'ADC Exposures', 'SUP Exposures', 'Team Exposures'])
+            
+            with tab1:
+                if 'stack_freq' in st.session_state and st.session_state.stack_freq is not None:
+                    
+                    st.dataframe(st.session_state.stack_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(percentages_format, precision=2), use_container_width = True)
+                    st.download_button(
+                        label="Export Exposures",
+                        data=st.session_state.stack_freq.to_csv().encode('utf-8'),
+                        file_name='stack_freq.csv',
+                        mime='text/csv',
+                        key='stack'
+                    )
+
+            with tab2:
+                if 'player_freq' in st.session_state and st.session_state.player_freq is not None:
+                    
+                    st.dataframe(st.session_state.player_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
+                    st.download_button(
+                        label="Export Exposures",
+                        data=st.session_state.player_freq.to_csv().encode('utf-8'),
+                        file_name='player_freq_export.csv',
+                        mime='text/csv',
+                        key='overall'
+                    )
+
+            with tab3:
+                if 'cpt_freq' in st.session_state and st.session_state.cpt_freq is not None:
+
+                    st.dataframe(st.session_state.cpt_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width=True)
+                    st.download_button(
+                        label="Export Exposures",
+                        data=st.session_state.cpt_freq.to_csv().encode('utf-8'),
+                        file_name='cpt_freq.csv',
+                        mime='text/csv',
+                        key='cpt'
+                    )
+
+            with tab4:
+                if 'top_freq' in st.session_state and st.session_state.top_freq is not None:
+                    
+                    st.dataframe(st.session_state.top_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
+                    st.download_button(
+                        label="Export Exposures",
+                        data=st.session_state.top_freq.to_csv().encode('utf-8'),
+                        file_name='top_freq.csv',
+                        mime='text/csv',
+                        key='top'
+                    )
+
+            with tab5:
+                if 'jng_freq' in st.session_state and st.session_state.jng_freq is not None:
+                    
+                    st.dataframe(st.session_state.jng_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
+                    st.download_button(
+                        label="Export Exposures",
+                        data=st.session_state.jng_freq.to_csv().encode('utf-8'),
+                        file_name='jng_freq.csv',
+                        mime='text/csv',
+                        key='jng'
+                    )
+
+            with tab6:
+                if 'mid_freq' in st.session_state and st.session_state.mid_freq is not None:
+                    
+                    st.dataframe(st.session_state.mid_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
+                    st.download_button(
+                        label="Export Exposures",
+                        data=st.session_state.mid_freq.to_csv().encode('utf-8'),
+                        file_name='mid_freq.csv',
+                        mime='text/csv',
+                        key='mid'
+                    )
+
+            with tab7:
+                if 'adc_freq' in st.session_state and st.session_state.adc_freq is not None:
+                    
+                    st.dataframe(st.session_state.adc_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
+                    st.download_button(
+                        label="Export Exposures",
+                        data=st.session_state.adc_freq.to_csv().encode('utf-8'),
+                        file_name='adc_freq.csv',
+                        mime='text/csv',
+                        key='adc'
+                    )
+
+            with tab8:
+                if 'sup_freq' in st.session_state and st.session_state.sup_freq is not None:
+                    
+                    st.dataframe(st.session_state.sup_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
+                    st.download_button(
+                        label="Export Exposures",
+                        data=st.session_state.sup_freq.to_csv().encode('utf-8'),
+                        file_name='sup_freq.csv',
+                        mime='text/csv',
+                        key='sup'
+                    )
+
+            with tab9:
+                if 'team_freq' in st.session_state and st.session_state.team_freq is not None:
+                    
+                    st.dataframe(st.session_state.team_freq.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(freq_format, precision=2), use_container_width = True)
+                    st.download_button(
+                        label="Export Exposures",
+                        data=st.session_state.team_freq.to_csv().encode('utf-8'),
+                        file_name='team_freq.csv',
+                        mime='text/csv',
+                        key='team'
+                    )
+            

app.yaml

@@ -0,0 +1,10 @@
+runtime: python
+env: flex
+
+runtime_config:
+  python_version: 3
+
+entrypoint: streamlit run streamlit-app.py --server.port $PORT
+
+automatic_scaling:
+  max_num_instances: 1000

requirements.txt

@@ -0,0 +1,10 @@
+streamlit
+gspread
+openpyxl
+matplotlib
+streamlit-aggrid
+pulp
+docker
+plotly
+scipy
+pymongo