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import streamlit as st
import pandas as pd
import os
import fnmatch
import json
class MultiURLData:
def __init__(self):
self.data = self.process_data()
def process_data(self):
dataframes = []
def find_files(directory, pattern):
for root, dirs, files in os.walk(directory):
for basename in files:
if fnmatch.fnmatch(basename, pattern):
filename = os.path.join(root, basename)
yield filename
for filename in find_files('results', 'results*.json'):
model_name = filename.split('/')[2]
with open(filename) as f:
data = json.load(f)
df = pd.DataFrame(data['results']).T
df = df.rename(columns={'acc': model_name})
df.index = df.index.str.replace('hendrycksTest-', '')
df.index = df.index.str.replace('harness\\|', '')
dataframes.append(df[[model_name]])
data = pd.concat(dataframes, axis=1)
data = data.transpose()
data['Model Name'] = data.index
cols = data.columns.tolist()
cols = cols[-1:] + cols[:-1]
data = data[cols]
return data
def get_top_performing_models(self, column_name, top_n):
sorted_data = self.data.sort_values(by=column_name, ascending=False)
return sorted_data['Model Name'].head(top_n).tolist()
def get_data(self, selected_models):
filtered_data = self.data[self.data['Model Name'].isin(selected_models)]
return filtered_data
data_provider = MultiURLData()
# Get top 20 performing models
top_models = data_provider.get_top_performing_models('harness|arc:challenge|25', 20)
# Initialize selected models and columns
selected_models = top_models
selected_columns = data_provider.data.columns.tolist()
# Create placeholders for the dataframe and multiselects
df_placeholder = st.empty()
models_multiselect = st.empty()
columns_multiselect = st.empty()
# Function to display dataframe
def display_dataframe(models, columns):
filtered_data = data_provider.get_data(models)
filtered_data = filtered_data[columns]
df_placeholder.dataframe(filtered_data)
# Function to display multiselects
def display_multiselects():
models = models_multiselect.multiselect(
'Select Models',
data_provider.data['Model Name'].tolist(),
default=selected_models
)
columns = columns_multiselect.multiselect(
'Select Columns',
data_provider.data.columns.tolist(),
default=selected_columns
)
return models, columns
# Display dataframe initially
display_dataframe(selected_models, selected_columns)
# Display multiselects initially
selected_models, selected_columns = display_multiselects()
# If the user clicks the "Update" button, update the selected models and columns
# and redisplay the dataframe
if st.button('Update'):
display_dataframe(selected_models, selected_columns)
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