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Intel-Generative-Image-Dashboard / pages /3_📊Assessment summary.py

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updated to v0.0.5

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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