Py.Cafe
Powerlifting Awards Analysis
- app.py
- index.html
- requirements.txt
app.py
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import streamlit as st
import pandas as pd
import io
# ==============================================================================
# Main App Logic
# ==============================================================================
def process_data(uploaded_file):
"""
This function contains the core logic from your original script.
It takes an uploaded file, processes it, and returns the final awards CSV as a string.
"""
# --- PART 1: FILE FORMATTING ---
# The uploaded_file from Streamlit is already in a file-like format
df = pd.read_csv(uploaded_file)
# Check if all required columns exist in the uploaded file
required_columns = [
'Name', 'Gender', 'Raw/Equipped', 'Team', 'Awards Division',
'Body Weight (kg)', 'Weight Class', 'Squat 1', 'Squat 2', 'Squat 3', 'Best Squat',
'Bench 1', 'Bench 2', 'Bench 3', 'Best Bench', 'Subtotal',
'Deadlift 1', 'Deadlift 2', 'Deadlift 3', 'Best Deadlift', 'Total',
'IPF Points', 'Place'
]
# Find which columns are missing, if any
missing_cols = [col for col in required_columns if col not in df.columns]
if missing_cols:
# Raise an exception with a helpful error message
raise ValueError(f"The uploaded CSV is missing the following required columns: {', '.join(missing_cols)}")
df_formatted = df[required_columns].copy()
team_col_index = df_formatted.columns.get_loc('Team')
df_formatted.insert(team_col_index + 1, 'School', '')
def split_team_affiliation(row):
team_val = row['Team']
if isinstance(team_val, str) and '/' in team_val:
parts = team_val.split('/', 1)
row['Team'] = parts[0].strip()
row['School'] = parts[1].strip()
return row
df_formatted = df_formatted.apply(split_team_affiliation, axis=1)
final_column_order = [
'Name', 'Gender', 'Raw/Equipped', 'Team', 'School', 'Awards Division',
'Body Weight (kg)', 'Weight Class', 'Squat 1', 'Squat 2', 'Squat 3', 'Best Squat',
'Bench 1', 'Bench 2', 'Bench 3', 'Best Bench', 'Subtotal',
'Deadlift 1', 'Deadlift 2', 'Deadlift 3', 'Best Deadlift', 'Total',
'IPF Points', 'Place'
]
df_formatted = df_formatted[final_column_order]
df_formatted.rename(columns={'School': ''}, inplace=True)
# --- PART 2: AWARDS CALCULATION ---
df_awards = df_formatted.rename(columns={'': 'School'})
all_awards_dfs = []
# --- Section 1: Best Lifter Awards ---
df_best_lifter = df_awards[~df_awards['Awards Division'].str.contains("Guest", na=False)].copy()
df_best_lifter['IPF Points'] = pd.to_numeric(df_best_lifter['IPF Points'], errors='coerce')
best_lifters_idx = df_best_lifter.groupby(['Gender', 'Awards Division'])['IPF Points'].idxmax()
best_lifters = df_best_lifter.loc[best_lifters_idx].sort_values(by=['Gender', 'Awards Division'])
best_lifters_list_for_csv = []
for index, lifter in best_lifters.iterrows():
category_name = f"Best Lifter - {lifter['Gender'].title()}'s {lifter['Awards Division']}"
winner_name = f"1st - {lifter['Name']}"
best_lifters_list_for_csv.append([category_name, winner_name])
all_awards_dfs.append(pd.DataFrame([['--- Best Lifter Awards ---', '']]))
all_awards_dfs.append(pd.DataFrame(best_lifters_list_for_csv, columns=['Category', 'Winner']))
all_awards_dfs.append(pd.DataFrame([['', '']]))
# --- Section 2: Individual Standings ---
df_standings = df_awards[~df_awards['Awards Division'].str.contains("Guest", na=False)].copy()
df_standings['Place'] = pd.to_numeric(df_standings['Place'], errors='coerce')
df_standings = df_standings[df_standings['Place'].isin([1, 2, 3])]
df_standings['Weight Class Sortable'] = df_standings['Weight Class'].apply(lambda wc: float(str(wc).replace('+', '.1')))
df_standings = df_standings.sort_values(by=['Gender', 'Awards Division', 'Weight Class Sortable', 'Place'])
medal_map = {1: "Gold (1st)", 2: "Silver (2nd)", 3: "Bronze (3rd)"}
standings_list_for_csv = []
for division in df_standings['Awards Division'].unique():
gender = df_standings[df_standings['Awards Division'] == division]['Gender'].iloc[0].title()
division_df = df_standings[df_standings['Awards Division'] == division]
for wc in division_df['Weight Class Sortable'].unique():
wc_str = str(division_df[division_df['Weight Class Sortable']==wc]['Weight Class'].iloc[0])
wc_df = division_df[division_df['Weight Class Sortable'] == wc]
for _, row in wc_df.iterrows():
medal = medal_map.get(row['Place'], f"{int(row['Place'])}th")
standings_list_for_csv.append({
'Division': f"{gender}'s {division}",
'Weight Class': f"{wc_str}kg",
'Medal': medal,
'Name': row['Name'],
'Total (kg)': row['Total']
})
all_awards_dfs.append(pd.DataFrame([['--- Individual Standings ---', '', '', '', '']]))
all_awards_dfs.append(pd.DataFrame(standings_list_for_csv))
all_awards_dfs.append(pd.DataFrame([['', '']]))
# --- Section 3: Best Team & School Awards ---
def get_team_points(place):
place = int(place)
points_map = {1: 12, 2: 9, 3: 8, 4: 7, 5: 6, 6: 5, 7: 4, 8: 3, 9: 2}
return points_map.get(place, 1)
df_team_awards = df_awards[df_awards['Total'] > 0].copy()
df_team_awards['Place'] = pd.to_numeric(df_team_awards['Place'], errors='coerce').fillna(0)
df_team_awards = df_team_awards[df_team_awards['Place'] > 0]
df_team_awards['Team Points'] = df_team_awards['Place'].apply(get_team_points)
df_teams = df_team_awards[df_team_awards['Team'].str.lower() != 'individual'].copy()
team_scores = df_teams.groupby(['Awards Division', 'Team'])['Team Points'].apply(lambda x: x.nlargest(5).sum()).reset_index()
top_teams = team_scores.groupby('Awards Division').apply(lambda x: x.nlargest(3, 'Team Points')).reset_index(drop=True)
team_list_for_csv = []
for division in top_teams['Awards Division'].unique():
division_teams = top_teams[top_teams['Awards Division'] == division]
for i, (index, row) in enumerate(division_teams.iterrows()):
team_name = row['Team']
lifters = df_teams[(df_teams['Awards Division'] == division) & (df_teams['Team'] == team_name)]['Name'].tolist()
team_list_for_csv.append({
'Division': f"Best Team - {division}",
'Rank': i + 1,
'Team': team_name,
'Points': row['Team Points'],
'Contributing Lifters': ", ".join(lifters)
})
all_awards_dfs.append(pd.DataFrame([['--- Best Team Awards ---', '', '', '', '']]))
all_awards_dfs.append(pd.DataFrame(team_list_for_csv))
all_awards_dfs.append(pd.DataFrame([['', '']]))
df_schools = df_team_awards[df_team_awards['School'].notna() & (df_team_awards['School'] != '')].copy()
school_list_for_csv = []
school_categories = {
"Men's College": df_schools[df_schools['Awards Division'].str.contains("College") & (df_schools['Gender'] == 'MALE')],
"Women's College": df_schools[df_schools['Awards Division'].str.contains("College") & (df_schools['Gender'] == 'FEMALE')],
"Men's High School": df_schools[df_schools['Awards Division'].str.contains("High School") & (df_schools['Gender'] == 'MALE')],
"Women's High School": df_schools[df_schools['Awards Division'].str.contains("High School") & (df_schools['Gender'] == 'FEMALE')]
}
def process_school_awards(df, category_name):
if df.empty:
return []
school_scores = df.groupby('School')['Team Points'].apply(lambda x: x.nlargest(5).sum()).reset_index()
top_schools = school_scores.nlargest(3, 'Team Points')
awards_list = []
for i, (index, row) in enumerate(top_schools.iterrows()):
school_name = row['School']
lifters = df[df['School'] == school_name]['Name'].tolist()
awards_list.append({
'Category': f"Best School - {category_name}",
'Rank': i + 1,
'School': school_name,
'Points': row['Team Points'],
'Contributing Lifters': ", ".join(lifters)
})
return awards_list
all_awards_dfs.append(pd.DataFrame([['--- Best School Awards ---', '', '', '', '']]))
for name, df_cat in school_categories.items():
school_awards_data = process_school_awards(df_cat, name)
if school_awards_data:
school_list_for_csv.extend(school_awards_data)
all_awards_dfs.append(pd.DataFrame(school_list_for_csv))
# --- Final CSV Export ---
final_awards_df = pd.concat(all_awards_dfs).fillna('')
return final_awards_df.to_csv(index=False, header=False).encode('utf-8')
# ==============================================================================
# Streamlit UI
# ==============================================================================
st.set_page_config(layout="wide")
st.title('Powerlifting Championships Awards Generator')
st.write("""
Upload the unformatted results CSV file to automatically calculate and generate the awards report.
The CSV must contain the following columns: 'Name', 'Gender', 'Team', 'Awards Division', 'Body Weight (kg)', 'Weight Class', 'Best Squat', 'Best Bench', 'Best Deadlift', 'Total', 'IPF Points', and 'Place'.
""")
uploaded_file = st.file_uploader("Choose a CSV file", type="csv")
if uploaded_file is not None:
if st.button('Generate Awards Report'):
with st.spinner('Calculating awards... Please wait.'):
try:
# When the button is clicked, process the data
awards_csv_data = process_data(uploaded_file)
st.balloons()
st.success('Awards calculation complete!')
# Provide the download button for the generated file
st.download_button(
label="Download Awards CSV",
data=awards_csv_data,
file_name='powerlifting_awards.csv',
mime='text/csv',
)
except Exception as e:
# If any error occurs during processing, show it in the app
st.error(f"An error occurred: {e}")