Add sport selection and dynamic template for NBA, NFL, and MLB projections

app.py

@@ -28,9 +28,14 @@ tab1, tab2 = st.tabs(["Data Load", "Manage Portfolio"])
 with tab1:
     if st.button('Clear data', key='reset1'):
         st.session_state.clear()
-
+    sport_var = st.selectbox("Select Sport", ["NBA", "NFL", "MLB"])
     st.subheader("Projections File")
-    st.info("upload a projections file that has Data oriented in the following format:")
+    if sport_var == "NBA":
+        st.info("upload a projections file that has Data oriented in the following format: 'Player', 'Team', 'Opp', 'Position', 'Salary', 'Median', 'Minutes', 'Own'")
+    elif sport_var == "NFL":
+        st.info("upload a projections file that has Data oriented in the following format: 'Player', 'Team', 'Opp', 'Position', 'Salary', 'Median', 'Minutes', 'Own'")
+    elif sport_var == "MLB":
+        st.info("upload a projections file that has Data oriented in the following format: 'Player', 'Team', 'Opp', 'Position', 'Salary', 'Median', 'Minutes', 'Own'")
     
     # Create two columns for the uploader and template button
     upload_col, template_col = st.columns([3, 1])
@@ -39,8 +44,12 @@ with tab1:
         projections_file = st.file_uploader("Upload Projections File (CSV or Excel)", type=['csv', 'xlsx', 'xls'])
     
     with template_col:
-        # Create empty DataFrame with required columns
-        template_df = pd.DataFrame(columns=['player_names', 'position', 'team', 'salary', 'median', 'ownership', 'captain ownership'])
+        if sport_var == "NBA":
+            template_df = pd.DataFrame(columns=['Player', 'Team', 'Opp', 'Position', 'Salary', 'Median', 'Minutes', 'Own'])
+        elif sport_var == "NFL":
+            template_df = pd.DataFrame(columns=['Player', 'Team', 'Opp', 'Position', 'Salary', 'Median', 'Minutes', 'Own'])
+        elif sport_var == "MLB":
+            template_df = pd.DataFrame(columns=['Player', 'Team', 'Opp', 'Position', 'Salary', 'Median', 'Minutes', 'Own'])
         # Add download button for template
         st.download_button(
             label="Template",

function_hold/NBA_functions.py

@@ -0,0 +1,399 @@
+def DK_NBA_ROO_Build(projections_file):
+    total_sims = 1000
+    
+    projects_raw = projections_file.copy()
+    projects_raw = projects_raw.replace("", np_nan)
+    dk_df = projects_raw.sort_values(by='Median', ascending=False)
+    
+    basic_own_df = dk_df.copy()
+    basic_own_df['name_team'] = basic_own_df['Player'] + basic_own_df['Position']
+            
+    def calculate_ownership(df):
+        # Filter the dataframe based on the position
+        frame = df.copy()
+        
+        # Calculate Small Field Own%
+        frame['Base Own%'] = np_where(
+            (frame['Own'] - frame['Own'].mean() >= 0),
+            frame['Own'] * (5 * (frame['Own'] - (frame['Own'].mean() / 1.5)) / 100) + frame['Own'].mean(),
+            frame['Own']
+        )
+        frame['Base Own%'] = np_where(
+            frame['Base Own%'] > 85,
+            85,
+            frame['Base Own%']
+        )
+        
+        # Calculate Small Field Own%
+        frame['Small Field Own%'] = np_where(
+            (frame['Own'] - frame['Own'].mean() >= 0),
+            frame['Own'] * (6 * (frame['Own'] - frame['Own'].mean()) / 100) + frame['Own'].mean(),
+            frame['Own']
+        )
+        frame['Small Field Own%'] = np_where(
+            frame['Small Field Own%'] > 85,
+            85,
+            frame['Small Field Own%']
+        )
+
+        # Calculate Large Field Own%
+        frame['Large Field Own%'] = np_where(
+            (frame['Own'] - frame['Own'].mean() >= 0),
+            frame['Own'] * (2.5 * (frame['Own'] - frame['Own'].mean()) / 100) + frame['Own'].mean(),
+            frame['Own']
+        )
+        frame['Large Field Own%'] = np_where(
+            frame['Large Field Own%'] > 85,
+            85,
+            frame['Large Field Own%']
+        )
+
+        # Calculate Cash Own%
+        frame['Cash Own%'] = np_where(
+            (frame['Own'] - frame['Own'].mean() >= 0),
+            frame['Own'] * (8 * (frame['Own'] - frame['Own'].mean()) / 100) + frame['Own'].mean(),
+            frame['Own']
+        )
+        frame['Cash Own%'] = np_where(
+            frame['Cash Own%'] > 85,
+            85,
+            frame['Cash Own%']
+        )
+
+        return frame
+
+    # Apply the function to each dataframe
+    basic_own_df = calculate_ownership(basic_own_df)
+    
+    own_norm_var_reg = 800 / basic_own_df['Own'].sum()
+    own_norm_var_small = 800 / basic_own_df['Small Field Own%'].sum()
+    own_norm_var_large = 800 / basic_own_df['Large Field Own%'].sum()
+    own_norm_var_cash = 800 / basic_own_df['Cash Own%'].sum()
+    basic_own_df['Own'] = basic_own_df['Own'] * own_norm_var_reg
+    basic_own_df['Small_Own'] = basic_own_df['Small Field Own%'] * own_norm_var_small
+    basic_own_df['Large_Own'] = basic_own_df['Large Field Own%'] * own_norm_var_large
+    basic_own_df['Cash_Own'] = basic_own_df['Cash Own%'] * own_norm_var_cash
+    
+    basic_own_df['Own'] = np_where(basic_own_df['Own'] > 90, 90, basic_own_df['Own'])
+    
+    # Apply the function to each dataframe
+    basic_own_df = calculate_ownership(basic_own_df)
+    
+    own_norm_var_reg = 800 / basic_own_df['Own'].sum()
+    own_norm_var_small = 800 / basic_own_df['Small Field Own%'].sum()
+    own_norm_var_large = 800 / basic_own_df['Large Field Own%'].sum()
+    own_norm_var_cash = 800 / basic_own_df['Cash Own%'].sum()
+    basic_own_df['Own'] = basic_own_df['Own'] * own_norm_var_reg
+    basic_own_df['Small_Own'] = basic_own_df['Small Field Own%'] * own_norm_var_small
+    basic_own_df['Large_Own'] = basic_own_df['Large Field Own%'] * own_norm_var_large
+    basic_own_df['Cash_Own'] = basic_own_df['Cash Own%'] * own_norm_var_cash
+
+    own_dict = dict(zip(basic_own_df.Player, basic_own_df.Own))
+    small_own_dict = dict(zip(basic_own_df.Player, basic_own_df['Small Field Own%']))
+    large_own_dict = dict(zip(basic_own_df.Player, basic_own_df['Large Field Own%']))
+    cash_own_dict = dict(zip(basic_own_df.Player, basic_own_df['Cash Own%']))
+    team_dict = dict(zip(basic_own_df.name_team, basic_own_df.Team))
+    opp_dict = dict(zip(basic_own_df.Player, basic_own_df.Opp))
+    min_dict = dict(zip(basic_own_df.Player, basic_own_df.Minutes))
+    
+    flex_file = basic_own_df[['Player', 'Position', 'Salary', 'Median', 'Minutes']]
+    flex_file = flex_file.rename(columns={"Agg": "Median"})
+    flex_file['Floor'] = (flex_file['Median'] * .25) + (flex_file['Minutes'] * .25)
+    flex_file['Ceiling'] = flex_file['Median'] + 10 + (flex_file['Minutes'] * .25)
+    flex_file['STD'] = (flex_file['Median']/4)
+    flex_file = flex_file[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD']]
+    flex_file = flex_file.reset_index(drop=True)
+    hold_file = flex_file.copy()
+    overall_file = flex_file.copy()
+    salary_file = flex_file.copy()
+    
+    try:    
+        overall_median_gpu = np_array(overall_file['Median'])
+        overall_std_gpu = np_array(overall_file['STD'])
+        overall_salary_gpu = np_array(overall_file['Salary'])
+        
+        data_shape = (len(overall_file['Player']), total_sims)  # Example: 1000 rows
+        salary_array = np_zeros(data_shape)
+        sim_array = np_zeros(data_shape)
+        
+        for x in range(0, total_sims):    
+            result_gpu = overall_salary_gpu
+            salary_array[:, x] = result_gpu 
+        cupy_array = salary_array
+        
+        salary_file = salary_file.reset_index(drop=True)
+        salary_cupy = DataFrame(cupy_array, columns=list(range(0, total_sims)))
+        salary_check_file = pd_concat([salary_file, salary_cupy], axis=1)
+    except:
+        for x in range(0,total_sims):    
+            salary_file[x] = salary_file['Salary']
+        salary_check_file = salary_file.copy()
+
+    salary_file=salary_check_file.drop(['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
+
+    salary_file = salary_file.div(1000)
+    
+    try:
+        for x in range(0, total_sims):    
+            result_gpu = np_random.normal(overall_median_gpu, overall_std_gpu)
+            sim_array[:, x] = result_gpu 
+        add_array = sim_array
+        
+        overall_file = overall_file.reset_index(drop=True)
+        df2 = DataFrame(add_array, columns=list(range(0, total_sims)))
+        check_file = pd_concat([overall_file, df2], axis=1)
+    except:
+        for x in range(0,total_sims):    
+            overall_file[x] = np_random.normal(overall_file['Median'],overall_file['STD'])
+        check_file = overall_file.copy()
+        
+    overall_file=check_file.drop(['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
+
+    players_only = hold_file[['Player']]
+    raw_lineups_file = players_only
+
+    for x in range(0,total_sims):
+        maps_dict = {'proj_map':dict(zip(hold_file.Player,overall_file[x]))}
+        raw_lineups_file[x] = sum([raw_lineups_file['Player'].map(maps_dict['proj_map'])])
+        players_only[x] = raw_lineups_file[x].rank(ascending=False)
+
+    players_only=players_only.drop(['Player'], axis=1)
+    
+    salary_4x_check = (overall_file - (salary_file*4))
+    salary_5x_check = (overall_file - (salary_file*5))
+    salary_6x_check = (overall_file - (salary_file*6))
+    gpp_check = (overall_file - ((salary_file*5)+10))
+
+    players_only['Average_Rank'] = players_only.mean(axis=1)
+    players_only['Top_finish'] = players_only[players_only == 1].count(axis=1)/total_sims
+    players_only['Top_5_finish'] = players_only[players_only <= 5].count(axis=1)/total_sims
+    players_only['Top_10_finish'] = players_only[players_only <= 10].count(axis=1)/total_sims
+    players_only['20+%'] = overall_file[overall_file >= 20].count(axis=1)/float(total_sims)
+    players_only['4x%'] = salary_4x_check[salary_4x_check >= 1].count(axis=1)/float(total_sims)
+    players_only['5x%'] = salary_5x_check[salary_5x_check >= 1].count(axis=1)/float(total_sims)
+    players_only['6x%'] = salary_6x_check[salary_6x_check >= 1].count(axis=1)/float(total_sims)
+    players_only['GPP%'] = gpp_check[gpp_check >= 1].count(axis=1)/float(total_sims)
+
+    players_only['Player'] = hold_file[['Player']]
+
+    final_outcomes = players_only[['Player', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '4x%', '5x%', '6x%', 'GPP%']]
+
+    final_Proj = pd_merge(hold_file, final_outcomes, on="Player")
+    final_Proj = final_Proj[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '4x%', '5x%', '6x%', 'GPP%']]
+    
+    final_Proj['name_team'] = final_Proj['Player'] + final_Proj['Position']
+    final_Proj['Own'] = final_Proj['Player'].map(own_dict)
+    final_Proj['Small_Own'] = final_Proj['Player'].map(small_own_dict)
+    final_Proj['Large_Own'] = final_Proj['Player'].map(large_own_dict)
+    final_Proj['Cash_Own'] = final_Proj['Player'].map(cash_own_dict)
+    final_Proj['Minutes Proj'] = final_Proj['Player'].map(min_dict)
+    final_Proj['Team'] = final_Proj['name_team'].map(team_dict)
+    final_Proj['Opp'] = final_Proj['Player'].map(opp_dict)
+    final_Proj['CPT_Own'] = final_Proj['Own'] / 6
+    final_Proj['LevX'] = ((final_Proj[['Top_finish', '6x%', 'Top_5_finish']].mean(axis=1))*100) - final_Proj['Own']
+    final_Proj['ValX'] = ((final_Proj[['5x%', '6x%']].mean(axis=1))*100) + final_Proj['LevX']
+
+    final_Proj = final_Proj[['Player', 'Minutes Proj', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '4x%', '5x%', '6x%', 'GPP%', 'Own', 'Small_Own', 'Large_Own', 'Cash_Own', 'CPT_Own', 'LevX', 'ValX']]
+    final_Proj = final_Proj.sort_values(by='Median', ascending=False)
+    
+    return final_Proj.copy()
+
+def FD_NBA_ROO_Build(projections_file):
+    total_sims = 1000
+        
+    projects_raw = projections_file.copy()
+    fd_df = projects_raw.sort_values(by='Median', ascending=False)
+    
+    basic_own_df = fd_df.copy()
+    basic_own_df['name_team'] = basic_own_df['Player'] + basic_own_df['Position']
+            
+    def calculate_ownership(df):
+        # Filter the dataframe based on the position
+        frame = df.copy()
+        
+        # Calculate Small Field Own%
+        frame['Base Own%'] = np_where(
+            (frame['Own'] - frame['Own'].mean() >= 0),
+            frame['Own'] * (5 * (frame['Own'] - (frame['Own'].mean() / 1.5)) / 100) + frame['Own'].mean(),
+            frame['Own']
+        )
+        frame['Base Own%'] = np_where(
+            frame['Base Own%'] > 85,
+            85,
+            frame['Base Own%']
+        )
+        
+        # Calculate Small Field Own%
+        frame['Small Field Own%'] = np_where(
+            (frame['Own'] - frame['Own'].mean() >= 0),
+            frame['Own'] * (6 * (frame['Own'] - frame['Own'].mean()) / 100) + frame['Own'].mean(),
+            frame['Own']
+        )
+        frame['Small Field Own%'] = np_where(
+            frame['Small Field Own%'] > 85,
+            85,
+            frame['Small Field Own%']
+        )
+
+        # Calculate Large Field Own%
+        frame['Large Field Own%'] = np_where(
+            (frame['Own'] - frame['Own'].mean() >= 0),
+            frame['Own'] * (2.5 * (frame['Own'] - frame['Own'].mean()) / 100) + frame['Own'].mean(),
+            frame['Own']
+        )
+        frame['Large Field Own%'] = np_where(
+            frame['Large Field Own%'] > 85,
+            85,
+            frame['Large Field Own%']
+        )
+
+        # Calculate Cash Own%
+        frame['Cash Own%'] = np_where(
+            (frame['Own'] - frame['Own'].mean() >= 0),
+            frame['Own'] * (8 * (frame['Own'] - frame['Own'].mean()) / 100) + frame['Own'].mean(),
+            frame['Own']
+        )
+        frame['Cash Own%'] = np_where(
+            frame['Cash Own%'] > 85,
+            85,
+            frame['Cash Own%']
+        )
+
+        return frame
+
+    # Apply the function to each dataframe
+    basic_own_df = calculate_ownership(basic_own_df)
+    
+    own_norm_var_reg = 900 / basic_own_df['Own'].sum()
+    own_norm_var_small = 900 / basic_own_df['Small Field Own%'].sum()
+    own_norm_var_large = 900 / basic_own_df['Large Field Own%'].sum()
+    own_norm_var_cash = 900 / basic_own_df['Cash Own%'].sum()
+    basic_own_df['Own'] = basic_own_df['Own'] * own_norm_var_reg
+    basic_own_df['Small_Own'] = basic_own_df['Small Field Own%'] * own_norm_var_small
+    basic_own_df['Large_Own'] = basic_own_df['Large Field Own%'] * own_norm_var_large
+    basic_own_df['Cash_Own'] = basic_own_df['Cash Own%'] * own_norm_var_cash
+    
+    basic_own_df['Own'] = np_where(basic_own_df['Own'] > 90, 90, basic_own_df['Own'])
+    
+    # Apply the function to each dataframe
+    basic_own_df = calculate_ownership(basic_own_df)
+    
+    own_norm_var_reg = 900 / basic_own_df['Own'].sum()
+    own_norm_var_small = 900 / basic_own_df['Small Field Own%'].sum()
+    own_norm_var_large = 900 / basic_own_df['Large Field Own%'].sum()
+    own_norm_var_cash = 900 / basic_own_df['Cash Own%'].sum()
+    basic_own_df['Own'] = basic_own_df['Own'] * own_norm_var_reg
+    basic_own_df['Small_Own'] = basic_own_df['Small Field Own%'] * own_norm_var_small
+    basic_own_df['Large_Own'] = basic_own_df['Large Field Own%'] * own_norm_var_large
+    basic_own_df['Cash_Own'] = basic_own_df['Cash Own%'] * own_norm_var_cash
+
+    own_dict = dict(zip(basic_own_df.Player, basic_own_df.Own))
+    small_own_dict = dict(zip(basic_own_df.Player, basic_own_df['Small Field Own%']))
+    large_own_dict = dict(zip(basic_own_df.Player, basic_own_df['Large Field Own%']))
+    cash_own_dict = dict(zip(basic_own_df.Player, basic_own_df['Cash Own%']))
+    team_dict = dict(zip(basic_own_df.name_team, basic_own_df.Team))
+    opp_dict = dict(zip(basic_own_df.Player, basic_own_df.Opp))
+    min_dict = dict(zip(basic_own_df.Player, basic_own_df.Minutes))
+        
+    flex_file = basic_own_df[['Player', 'Position', 'Salary', 'Median', 'Minutes']]
+    flex_file = flex_file.rename(columns={"Agg": "Median"})
+    flex_file['Floor'] = (flex_file['Median'] * .25) + (flex_file['Minutes'] * .25)
+    flex_file['Ceiling'] = flex_file['Median'] + 10 + (flex_file['Minutes'] * .25)
+    flex_file['STD'] = (flex_file['Median']/4)
+    flex_file = flex_file[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD']]
+    flex_file = flex_file.reset_index(drop=True)
+    hold_file = flex_file.copy()
+    overall_file = flex_file.copy()
+    salary_file = flex_file.copy()
+    
+    try:    
+        overall_median_gpu = np_array(overall_file['Median'])
+        overall_std_gpu = np_array(overall_file['STD'])
+        overall_salary_gpu = np_array(overall_file['Salary'])
+        
+        data_shape = (len(overall_file['Player']), total_sims)  # Example: 1000 rows
+        salary_array = np_zeros(data_shape)
+        sim_array = np_zeros(data_shape)
+        
+        for x in range(0, total_sims):    
+            result_gpu = overall_salary_gpu
+            salary_array[:, x] = result_gpu 
+        cupy_array = salary_array
+        
+        salary_file = salary_file.reset_index(drop=True)
+        salary_cupy = DataFrame(cupy_array, columns=list(range(0, total_sims)))
+        salary_check_file = pd_concat([salary_file, salary_cupy], axis=1)
+    except:
+        for x in range(0,total_sims):    
+            salary_file[x] = salary_file['Salary']
+        salary_check_file = salary_file.copy()
+
+    salary_file=salary_check_file.drop(['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
+
+    salary_file = salary_file.div(1000)
+    
+    try:
+        for x in range(0, total_sims):    
+            result_gpu = np_random.normal(overall_median_gpu, overall_std_gpu)
+            sim_array[:, x] = result_gpu 
+        add_array = sim_array
+        
+        overall_file = overall_file.reset_index(drop=True)
+        df2 = DataFrame(add_array, columns=list(range(0, total_sims)))
+        check_file = pd_concat([overall_file, df2], axis=1)
+    except:
+        for x in range(0,total_sims):    
+            overall_file[x] = np_random.normal(overall_file['Median'],overall_file['STD'])
+        check_file = overall_file.copy()
+        
+    overall_file=check_file.drop(['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'STD'], axis=1)
+
+    players_only = hold_file[['Player']]
+    raw_lineups_file = players_only
+
+    for x in range(0,total_sims):
+        maps_dict = {'proj_map':dict(zip(hold_file.Player,overall_file[x]))}
+        raw_lineups_file[x] = sum([raw_lineups_file['Player'].map(maps_dict['proj_map'])])
+        players_only[x] = raw_lineups_file[x].rank(ascending=False)
+
+    players_only=players_only.drop(['Player'], axis=1)
+        
+    salary_4x_check = (overall_file - (salary_file*4))
+    salary_5x_check = (overall_file - (salary_file*5))
+    salary_6x_check = (overall_file - (salary_file*6))
+    gpp_check = (overall_file - ((salary_file*5)+10))
+
+    players_only['Average_Rank'] = players_only.mean(axis=1)
+    players_only['Top_finish'] = players_only[players_only == 1].count(axis=1)/total_sims
+    players_only['Top_5_finish'] = players_only[players_only <= 5].count(axis=1)/total_sims
+    players_only['Top_10_finish'] = players_only[players_only <= 10].count(axis=1)/total_sims
+    players_only['20+%'] = overall_file[overall_file >= 20].count(axis=1)/float(total_sims)
+    players_only['4x%'] = salary_4x_check[salary_4x_check >= 1].count(axis=1)/float(total_sims)
+    players_only['5x%'] = salary_5x_check[salary_5x_check >= 1].count(axis=1)/float(total_sims)
+    players_only['6x%'] = salary_6x_check[salary_6x_check >= 1].count(axis=1)/float(total_sims)
+    players_only['GPP%'] = gpp_check[gpp_check >= 1].count(axis=1)/float(total_sims)
+
+    players_only['Player'] = hold_file[['Player']]
+
+    final_outcomes = players_only[['Player', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '4x%', '5x%', '6x%', 'GPP%']]
+
+    final_Proj = pd_merge(hold_file, final_outcomes, on="Player")
+    final_Proj = final_Proj[['Player', 'Position', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '4x%', '5x%', '6x%', 'GPP%']]
+    
+    final_Proj['name_team'] = final_Proj['Player'] + final_Proj['Position']
+    final_Proj['Own'] = final_Proj['Player'].map(own_dict)
+    final_Proj['Small_Own'] = final_Proj['Player'].map(small_own_dict)
+    final_Proj['Large_Own'] = final_Proj['Player'].map(large_own_dict)
+    final_Proj['Cash_Own'] = final_Proj['Player'].map(cash_own_dict)
+    final_Proj['Minutes Proj'] = final_Proj['Player'].map(min_dict)
+    final_Proj['Team'] = final_Proj['name_team'].map(team_dict)
+    final_Proj['Opp'] = final_Proj['Player'].map(opp_dict)
+    final_Proj['CPT_Own'] = final_Proj['Own'] / 6
+    final_Proj['LevX'] = ((final_Proj[['Top_finish', '6x%', 'Top_5_finish']].mean(axis=1))*100) - final_Proj['Own']
+    final_Proj['ValX'] = ((final_Proj[['5x%', '6x%']].mean(axis=1))*100) + final_Proj['LevX']
+
+    final_Proj = final_Proj[['Player', 'Minutes Proj', 'Position', 'Team', 'Opp', 'Salary', 'Floor', 'Median', 'Ceiling', 'Top_finish', 'Top_5_finish', 'Top_10_finish', '20+%', '4x%', '5x%', '6x%', 'GPP%', 'Own', 'Small_Own', 'Large_Own', 'Cash_Own', 'CPT_Own', 'LevX', 'ValX']]
+    final_Proj['Salary'] = final_Proj['Salary'].astype(int)
+    final_Proj = final_Proj.sort_values(by='Median', ascending=False)
+    
+    return final_Proj.copy()

requirements.txt

@@ -2,7 +2,7 @@ streamlit
 gspread
 openpyxl
 matplotlib
-streamlit-aggrid
+fuzzywuzzy
 pulp
 docker
 plotly