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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()
#@st.cache
#def convert_df_to_csv(df):
# IMPORTANT: Cache the conversion to prevent computation on every rerun
# return df[['File_name','Prompt_no','Task','Score']].to_csv().encode('utf-8')
def df_to_csv_download(df, added_version_code='vNone'):
# IMPORTANT: Cache the conversion to prevent computation on every rerun
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')
###### Manual assessment visualisation ############################
st.header('Manual assessment')
try:
if sum(st.session_state['eval_df']['manual_eval_completed'])>0:
# Display file uploader
manual_file_upload = st.file_uploader("Upload .csv with saved manual assessment for model comparison", accept_multiple_files=True)
# Create dataset for manual summary plots
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
# Add plots / tables to page
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')
###### Automated assessment visualisation ############################
st.write(' ')
st.header('Automated assessment')
try:
# Create dataset for automated summary plots
auto_eval_df = st.session_state['auto_eval_df']
auto_eval_df['Model']='Automated assessment'
assessment_result_frames['Automated assessment'] = auto_eval_df
# Display file uploader
auto_file_upload = st.file_uploader("Upload .csv with saved automated assessment for model comparison", accept_multiple_files=True)
# Add plots / tables to page
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')
###### Gallery ############################
try:
# Start gallery
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.')
# Create needed info frames
gallery_df = assessment_result_frames[assessment_method_selected]
curr_prompt_dir = st.session_state['prompt_dir']
# Select task
tasks_available = gallery_df.Task.unique().tolist()
task_selected = st.selectbox('Select task type',tasks_available)
# Select image type
type_selected = st.selectbox(
'Select image type',
('Correctly generated images', 'Incorrectly generated images'))
type_selected_dict = {'Correctly generated images':True, 'Incorrectly generated images':False}
# Create df for presented images
gallery_df_print = gallery_df.loc[
(gallery_df['Score']==type_selected_dict[type_selected])&
(gallery_df['Task']==task_selected)]
# Select presented image and prompt
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()
# Plot prompt and image
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)
#st.write(auto_df_print)
except IndexError:
st.write('There is no image availabe in your selected category.')
except KeyError:
pass
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