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James McCool
Enhance app.py with dual database connections and implement caching for baseline data and lineups. Added functionality for data export and player frequency analysis, improving user interaction with new UI elements.
e353ca4
| import streamlit as st | |
| st.set_page_config(layout="wide") | |
| for name in dir(): | |
| if not name.startswith('_'): | |
| del globals()[name] | |
| import numpy as np | |
| import pandas as pd | |
| import streamlit as st | |
| import gspread | |
| import pymongo | |
| def init_conn(): | |
| uri = st.secrets['mongo_uri'] | |
| client = pymongo.MongoClient(uri, retryWrites=True, serverSelectionTimeoutMS=500000) | |
| db = client["MLB_Database"] | |
| db2 = client["MLB_DFS"] | |
| return db, db2 | |
| db, db2 = init_conn() | |
| player_roo_format = {'Top_finish': '{:.2%}','Top_5_finish': '{:.2%}', 'Top_10_finish': '{:.2%}', '20+%': '{:.2%}', '2x%': '{:.2%}', '3x%': '{:.2%}', | |
| '4x%': '{:.2%}'} | |
| dk_columns = ['FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'FLEX6', 'salary', 'proj', 'Own'] | |
| fd_columns = ['FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'FLEX6', 'salary', 'proj', 'Own'] | |
| st.markdown(""" | |
| <style> | |
| /* Tab styling */ | |
| .stTabs [data-baseweb="tab-list"] { | |
| gap: 8px; | |
| padding: 4px; | |
| } | |
| .stTabs [data-baseweb="tab"] { | |
| height: 50px; | |
| white-space: pre-wrap; | |
| background-color: #DAA520; | |
| color: white; | |
| border-radius: 10px; | |
| gap: 1px; | |
| padding: 10px 20px; | |
| font-weight: bold; | |
| transition: all 0.3s ease; | |
| } | |
| .stTabs [aria-selected="true"] { | |
| background-color: #DAA520; | |
| border: 3px solid #FFD700; | |
| color: white; | |
| } | |
| .stTabs [data-baseweb="tab"]:hover { | |
| background-color: #FFD700; | |
| cursor: pointer; | |
| } | |
| </style>""", unsafe_allow_html=True) | |
| def init_baselines(): | |
| collection = db["Player_Range_Of_Outcomes"] | |
| cursor = collection.find() | |
| player_frame = pd.DataFrame(cursor) | |
| timestamp = player_frame['Timestamp'][0] | |
| roo_data = player_frame.drop(columns=['_id', 'index', 'timestamp']) | |
| roo_data['Salary'] = roo_data['Salary'].astype(int) | |
| collection = db["Player_SD_Range_Of_Outcomes"] | |
| cursor = collection.find() | |
| player_frame = pd.DataFrame(cursor) | |
| sd_roo_data = player_frame.drop(columns=['_id', 'index']) | |
| sd_roo_data['Salary'] = sd_roo_data['Salary'].astype(int) | |
| collection = db["Scoring_Percentages"] | |
| cursor = collection.find() | |
| team_frame = pd.DataFrame(cursor) | |
| scoring_percentages = team_frame.drop(columns=['_id', 'index']) | |
| return roo_data, sd_roo_data, scoring_percentages | |
| def init_DK_lineups(): | |
| collection = db['DK_MLB_SD1_seed_frame'] | |
| cursor = collection.find().limit(10000) | |
| raw_display = pd.DataFrame(list(cursor)) | |
| raw_display = raw_display[['FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'FLEX6', 'salary', 'proj', 'Own']] | |
| DK_seed = raw_display.to_numpy() | |
| return DK_seed | |
| def init_FD_lineups(): | |
| collection = db['FD_MLB_SD1_seed_frame'] | |
| cursor = collection.find().limit(10000) | |
| raw_display = pd.DataFrame(list(cursor)) | |
| raw_display = raw_display[['FLEX1', 'FLEX2', 'FLEX3', 'FLEX4', 'FLEX5', 'FLEX6', 'salary', 'proj', 'Own']] | |
| FD_seed = raw_display.to_numpy() | |
| return FD_seed | |
| def convert_df_to_csv(df): | |
| return df.to_csv().encode('utf-8') | |
| def convert_df(array): | |
| array = pd.DataFrame(array, columns=column_names) | |
| return array.to_csv().encode('utf-8') | |
| roo_data, sd_roo_data, scoring_percentages = init_baselines() | |
| hold_display = roo_data | |
| view_var = st.radio("Select view", ["Simple", "Advanced"]) | |
| tab1, tab2, tab3 = st.tabs(["Scoring Percentages", "Player ROO", "Optimals"]) | |
| with tab1: | |
| st.title("Scoring Percentages") | |
| st.dataframe(scoring_percentages) | |
| with tab2: | |
| st.title("Player ROO") | |
| st.dataframe(sd_roo_data) | |
| with tab3: | |
| with st.expander("Info and Filters"): | |
| if st.button("Load/Reset Data", key='reset2'): | |
| st.cache_data.clear() | |
| roo_data, sd_roo_data, scoring_percentages = init_baselines() | |
| hold_display = roo_data | |
| dk_lineups = init_DK_lineups() | |
| fd_lineups = init_FD_lineups() | |
| t_stamp = f"Last Update: " + str(timestamp) + f" CST" | |
| for key in st.session_state.keys(): | |
| del st.session_state[key] | |
| slate_var1 = st.radio("Which data are you loading?", ('Regular', 'Showdown')) | |
| site_var1 = st.radio("What site are you working with?", ('Draftkings', 'Fanduel')) | |
| if slate_var1 == 'Regular': | |
| if site_var1 == 'Draftkings': | |
| dk_lineups = init_DK_lineups() | |
| elif site_var1 == 'Fanduel': | |
| fd_lineups = init_FD_lineups() | |
| elif slate_var1 == 'Showdown': | |
| if site_var1 == 'Draftkings': | |
| dk_lineups = init_DK_lineups() | |
| elif site_var1 == 'Fanduel': | |
| fd_lineups = init_FD_lineups() | |
| lineup_num_var = st.number_input("How many lineups do you want to display?", min_value=1, max_value=1000, value=150, step=1) | |
| if slate_var1 == 'Regular': | |
| raw_baselines = roo_data | |
| elif slate_var1 == 'Showdown': | |
| raw_baselines = sd_roo_data | |
| if site_var1 == 'Draftkings': | |
| if slate_var1 == 'Regular': | |
| ROO_slice = raw_baselines[raw_baselines['Site'] == 'Draftkings'] | |
| player_salaries = dict(zip(ROO_slice['Player'], ROO_slice['Salary'])) | |
| elif slate_var1 == 'Showdown': | |
| player_salaries = dict(zip(raw_baselines['Player'], raw_baselines['Salary'])) | |
| # Get the minimum and maximum ownership values from dk_lineups | |
| min_own = np.min(dk_lineups[:,8]) | |
| max_own = np.max(dk_lineups[:,8]) | |
| column_names = dk_columns | |
| player_var1 = st.radio("Do you want a frame with specific Players?", ('Full Slate', 'Specific Players'), key='player_var1') | |
| if player_var1 == 'Specific Players': | |
| player_var2 = st.multiselect('Which players do you want?', options = raw_baselines['Player'].unique()) | |
| elif player_var1 == 'Full Slate': | |
| player_var2 = raw_baselines.Player.values.tolist() | |
| elif site_var1 == 'Fanduel': | |
| raw_baselines = hold_display | |
| if slate_var1 == 'Regular': | |
| ROO_slice = raw_baselines[raw_baselines['Site'] == 'Fanduel'] | |
| player_salaries = dict(zip(ROO_slice['Player'], ROO_slice['Salary'])) | |
| elif slate_var1 == 'Showdown': | |
| player_salaries = dict(zip(raw_baselines['Player'], raw_baselines['Salary'])) | |
| min_own = np.min(fd_lineups[:,8]) | |
| max_own = np.max(fd_lineups[:,8]) | |
| column_names = fd_columns | |
| player_var1 = st.radio("Do you want a frame with specific Players?", ('Full Slate', 'Specific Players'), key='player_var1') | |
| if player_var1 == 'Specific Players': | |
| player_var2 = st.multiselect('Which players do you want?', options = raw_baselines['Player'].unique()) | |
| elif player_var1 == 'Full Slate': | |
| player_var2 = raw_baselines.Player.values.tolist() | |
| if st.button("Prepare data export", key='data_export'): | |
| data_export = st.session_state.working_seed.copy() | |
| # if site_var1 == 'Draftkings': | |
| # for col_idx in range(6): | |
| # data_export[:, col_idx] = np.array([id_dict.get(player, player) for player in data_export[:, col_idx]]) | |
| # elif site_var1 == 'Fanduel': | |
| # for col_idx in range(6): | |
| # data_export[:, col_idx] = np.array([id_dict.get(player, player) for player in data_export[:, col_idx]]) | |
| st.download_button( | |
| label="Export optimals set", | |
| data=convert_df(data_export), | |
| file_name='MLB_optimals_export.csv', | |
| mime='text/csv', | |
| ) | |
| if site_var1 == 'Draftkings': | |
| if 'working_seed' in st.session_state: | |
| st.session_state.working_seed = st.session_state.working_seed | |
| if player_var1 == 'Specific Players': | |
| st.session_state.working_seed = st.session_state.working_seed[np.equal.outer(st.session_state.working_seed, player_var2).any(axis=1).all(axis=1)] | |
| elif player_var1 == 'Full Slate': | |
| st.session_state.working_seed = dk_lineups.copy() | |
| st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names) | |
| elif 'working_seed' not in st.session_state: | |
| st.session_state.working_seed = dk_lineups.copy() | |
| st.session_state.working_seed = st.session_state.working_seed | |
| if player_var1 == 'Specific Players': | |
| st.session_state.working_seed = st.session_state.working_seed[np.equal.outer(st.session_state.working_seed, player_var2).any(axis=1).all(axis=1)] | |
| elif player_var1 == 'Full Slate': | |
| st.session_state.working_seed = dk_lineups.copy() | |
| st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names) | |
| elif site_var1 == 'Fanduel': | |
| if 'working_seed' in st.session_state: | |
| st.session_state.working_seed = st.session_state.working_seed | |
| if player_var1 == 'Specific Players': | |
| st.session_state.working_seed = st.session_state.working_seed[np.equal.outer(st.session_state.working_seed, player_var2).any(axis=1).all(axis=1)] | |
| elif player_var1 == 'Full Slate': | |
| st.session_state.working_seed = fd_lineups.copy() | |
| st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names) | |
| elif 'working_seed' not in st.session_state: | |
| st.session_state.working_seed = fd_lineups.copy() | |
| st.session_state.working_seed = st.session_state.working_seed | |
| if player_var1 == 'Specific Players': | |
| st.session_state.working_seed = st.session_state.working_seed[np.equal.outer(st.session_state.working_seed, player_var2).any(axis=1).all(axis=1)] | |
| elif player_var1 == 'Full Slate': | |
| st.session_state.working_seed = fd_lineups.copy() | |
| st.session_state.data_export_display = pd.DataFrame(st.session_state.working_seed[0:lineup_num_var], columns=column_names) | |
| export_file = st.session_state.data_export_display.copy() | |
| # if site_var1 == 'Draftkings': | |
| # for col_idx in range(6): | |
| # export_file.iloc[:, col_idx] = export_file.iloc[:, col_idx].map(id_dict) | |
| # elif site_var1 == 'Fanduel': | |
| # for col_idx in range(6): | |
| # export_file.iloc[:, col_idx] = export_file.iloc[:, col_idx].map(id_dict) | |
| with st.container(): | |
| if st.button("Reset Optimals", key='reset3'): | |
| for key in st.session_state.keys(): | |
| del st.session_state[key] | |
| if site_var1 == 'Draftkings': | |
| st.session_state.working_seed = dk_lineups.copy() | |
| elif site_var1 == 'Fanduel': | |
| st.session_state.working_seed = fd_lineups.copy() | |
| if 'data_export_display' in st.session_state: | |
| st.dataframe(st.session_state.data_export_display.style.background_gradient(axis=0).background_gradient(cmap='RdYlGn').format(precision=2), height=500, use_container_width = True) | |
| st.download_button( | |
| label="Export display optimals", | |
| data=convert_df(export_file), | |
| file_name='MLB_display_optimals.csv', | |
| mime='text/csv', | |
| ) | |
| with st.container(): | |
| if 'working_seed' in st.session_state: | |
| # Create a new dataframe with summary statistics | |
| if site_var1 == 'Draftkings': | |
| summary_df = pd.DataFrame({ | |
| 'Metric': ['Min', 'Average', 'Max', 'STDdev'], | |
| 'Salary': [ | |
| np.min(st.session_state.working_seed[:,6]), | |
| np.mean(st.session_state.working_seed[:,6]), | |
| np.max(st.session_state.working_seed[:,6]), | |
| np.std(st.session_state.working_seed[:,6]) | |
| ], | |
| 'Proj': [ | |
| np.min(st.session_state.working_seed[:,7]), | |
| np.mean(st.session_state.working_seed[:,7]), | |
| np.max(st.session_state.working_seed[:,7]), | |
| np.std(st.session_state.working_seed[:,7]) | |
| ], | |
| 'Own': [ | |
| np.min(st.session_state.working_seed[:,8]), | |
| np.mean(st.session_state.working_seed[:,8]), | |
| np.max(st.session_state.working_seed[:,8]), | |
| np.std(st.session_state.working_seed[:,8]) | |
| ] | |
| }) | |
| elif site_var1 == 'Fanduel': | |
| summary_df = pd.DataFrame({ | |
| 'Metric': ['Min', 'Average', 'Max', 'STDdev'], | |
| 'Salary': [ | |
| np.min(st.session_state.working_seed[:,6]), | |
| np.mean(st.session_state.working_seed[:,6]), | |
| np.max(st.session_state.working_seed[:,6]), | |
| np.std(st.session_state.working_seed[:,6]) | |
| ], | |
| 'Proj': [ | |
| np.min(st.session_state.working_seed[:,7]), | |
| np.mean(st.session_state.working_seed[:,7]), | |
| np.max(st.session_state.working_seed[:,7]), | |
| np.std(st.session_state.working_seed[:,7]) | |
| ], | |
| 'Own': [ | |
| np.min(st.session_state.working_seed[:,8]), | |
| np.mean(st.session_state.working_seed[:,8]), | |
| np.max(st.session_state.working_seed[:,8]), | |
| np.std(st.session_state.working_seed[:,8]) | |
| ] | |
| }) | |
| # Set the index of the summary dataframe as the "Metric" column | |
| summary_df = summary_df.set_index('Metric') | |
| # Display the summary dataframe | |
| st.subheader("Optimal Statistics") | |
| st.dataframe(summary_df.style.format({ | |
| 'Salary': '{:.2f}', | |
| 'Proj': '{:.2f}', | |
| 'Own': '{:.2f}' | |
| }).background_gradient(cmap='RdYlGn', axis=0, subset=['Salary', 'Proj', 'Own']), use_container_width=True) | |
| with st.container(): | |
| tab1, tab2 = st.tabs(["Display Frequency", "Seed Frame Frequency"]) | |
| with tab1: | |
| if 'data_export_display' in st.session_state: | |
| if site_var1 == 'Draftkings': | |
| player_columns = st.session_state.data_export_display.iloc[:, :6] | |
| elif site_var1 == 'Fanduel': | |
| player_columns = st.session_state.data_export_display.iloc[:, :6] | |
| # Flatten the DataFrame and count unique values | |
| value_counts = player_columns.values.flatten().tolist() | |
| value_counts = pd.Series(value_counts).value_counts() | |
| percentages = (value_counts / lineup_num_var * 100).round(2) | |
| # Create a DataFrame with the results | |
| summary_df = pd.DataFrame({ | |
| 'Player': value_counts.index, | |
| 'Frequency': value_counts.values, | |
| 'Percentage': percentages.values | |
| }) | |
| # Sort by frequency in descending order | |
| summary_df['Salary'] = summary_df['Player'].map(player_salaries) | |
| summary_df = summary_df[['Player', 'Salary', 'Frequency', 'Percentage']] | |
| summary_df = summary_df.sort_values('Frequency', ascending=False) | |
| summary_df = summary_df.set_index('Player') | |
| # Display the table | |
| st.write("Player Frequency Table:") | |
| st.dataframe(summary_df.style.format({'Percentage': '{:.2f}%'}), height=500, use_container_width=True) | |
| st.download_button( | |
| label="Export player frequency", | |
| data=convert_df_to_csv(summary_df), | |
| file_name='MLB_player_frequency.csv', | |
| mime='text/csv', | |
| ) | |
| with tab2: | |
| if 'working_seed' in st.session_state: | |
| if site_var1 == 'Draftkings': | |
| player_columns = st.session_state.working_seed[:, :6] | |
| elif site_var1 == 'Fanduel': | |
| player_columns = st.session_state.working_seed[:, :6] | |
| # Flatten the DataFrame and count unique values | |
| value_counts = player_columns.flatten().tolist() | |
| value_counts = pd.Series(value_counts).value_counts() | |
| percentages = (value_counts / len(st.session_state.working_seed) * 100).round(2) | |
| # Create a DataFrame with the results | |
| summary_df = pd.DataFrame({ | |
| 'Player': value_counts.index, | |
| 'Frequency': value_counts.values, | |
| 'Percentage': percentages.values | |
| }) | |
| # Sort by frequency in descending order | |
| summary_df['Salary'] = summary_df['Player'].map(player_salaries) | |
| summary_df = summary_df[['Player', 'Salary', 'Frequency', 'Percentage']] | |
| summary_df = summary_df.sort_values('Frequency', ascending=False) | |
| summary_df = summary_df.set_index('Player') | |
| # Display the table | |
| st.write("Seed Frame Frequency Table:") | |
| st.dataframe(summary_df.style.format({'Percentage': '{:.2f}%'}), height=500, use_container_width=True) | |
| st.download_button( | |
| label="Export seed frame frequency", | |
| data=convert_df_to_csv(summary_df), | |
| file_name='MLB_seed_frame_frequency.csv', | |
| mime='text/csv', | |
| ) | |