|
import streamlit as st |
|
import pandas as pd |
|
import seaborn as sns |
|
import matplotlib.pyplot as plt |
|
from PIL import Image |
|
from pages.Functions.Dashboard_functions import pre_assessment_visualisation, multi_comparison_plotI, print_results_tabs |
|
from Dashboard_setup import sidebar_information, dashboard_version_code |
|
sidebar_information() |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def df_to_csv_download(df, added_version_code='vNone'): |
|
|
|
df['Dashboard_version']= added_version_code |
|
return df[['File_name','Prompt_no','Task','Score','Dashboard_version']].to_csv().encode('utf-8') |
|
|
|
assessment_result_frames = {} |
|
st.title('Assessment Summary') |
|
|
|
|
|
|
|
|
|
st.header('Manual assessment') |
|
try: |
|
if sum(st.session_state['eval_df']['manual_eval_completed'])>0: |
|
|
|
manual_file_upload = st.file_uploader("Upload .csv with saved manual assessment for model comparison", accept_multiple_files=True) |
|
|
|
manual_eval_df = st.session_state['eval_df'] |
|
manual_eval_df['Score'] = manual_eval_df['manual_eval_task_score'].map({'Yes':True, 'No':False}) |
|
manual_results_df = manual_eval_df.loc[ |
|
(manual_eval_df['manual_eval']==True)& |
|
~(manual_eval_df['manual_eval_task_score'].isna())] |
|
manual_results_df['Model']='Manual assessment' |
|
assessment_result_frames['Manual assessment'] = manual_results_df |
|
|
|
|
|
print_results_tabs(file_upload=manual_file_upload, results_df=manual_results_df) |
|
|
|
st.download_button( |
|
label="Download manual assessment data", |
|
data=df_to_csv_download(manual_results_df, added_version_code=dashboard_version_code), |
|
file_name='manual_assessment.csv', |
|
mime='text/csv', |
|
) |
|
else: |
|
pre_assessment_visualisation(type_str='manual') |
|
except KeyError: |
|
pre_assessment_visualisation(type_str='manual') |
|
|
|
|
|
|
|
st.write(' ') |
|
st.header('Automated assessment') |
|
try: |
|
|
|
auto_eval_df = st.session_state['auto_eval_df'] |
|
auto_eval_df['Model']='Automated assessment' |
|
assessment_result_frames['Automated assessment'] = auto_eval_df |
|
|
|
|
|
auto_file_upload = st.file_uploader("Upload .csv with saved automated assessment for model comparison", accept_multiple_files=True) |
|
|
|
|
|
print_results_tabs(file_upload=auto_file_upload, results_df=auto_eval_df) |
|
|
|
st.download_button( |
|
label="Download automated assessment data", |
|
data=df_to_csv_download(auto_eval_df, added_version_code=dashboard_version_code), |
|
file_name='automated_assessment.csv', |
|
mime='text/csv', |
|
) |
|
except KeyError: |
|
pre_assessment_visualisation(type_str='automated') |
|
|
|
|
|
|
|
|
|
try: |
|
|
|
st.header('Assessment gallery') |
|
|
|
assessment_method_selected = st.selectbox( |
|
'Select generation method', |
|
assessment_result_frames.keys()) |
|
|
|
if len(assessment_result_frames.keys())<1: |
|
st.write('Complete manual or automated assessment to access images in the gallery.') |
|
|
|
|
|
gallery_df = assessment_result_frames[assessment_method_selected] |
|
curr_prompt_dir = st.session_state['prompt_dir'] |
|
|
|
|
|
tasks_available = gallery_df.Task.unique().tolist() |
|
task_selected = st.selectbox('Select task type',tasks_available) |
|
|
|
type_selected = st.selectbox( |
|
'Select image type', |
|
('Correctly generated images', 'Incorrectly generated images')) |
|
type_selected_dict = {'Correctly generated images':True, 'Incorrectly generated images':False} |
|
|
|
gallery_df_print = gallery_df.loc[ |
|
(gallery_df['Score']==type_selected_dict[type_selected])& |
|
(gallery_df['Task']==task_selected)] |
|
|
|
generation_number = st.number_input('Generation number',min_value=1, max_value=len(gallery_df_print), step=1) |
|
gallery_row_print = gallery_df_print.iloc[int(generation_number-1)] |
|
curr_Prompt_no = gallery_row_print.Prompt_no |
|
curr_Prompt = curr_prompt_dir[curr_prompt_dir['ID']==int(curr_Prompt_no)].Prompt |
|
curr_Picture_index = gallery_row_print.Picture_index.item() |
|
|
|
st.write('File name: '+gallery_row_print.File_name) |
|
st.write('Prompt: '+curr_Prompt.item()) |
|
st.image(st.session_state['uploaded_img'][curr_Picture_index],width=350) |
|
|
|
|
|
except IndexError: |
|
st.write('There is no image availabe in your selected category.') |
|
except KeyError: |
|
pass |
|
|
|
|