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| import streamlit as st | |
| import numpy as np | |
| from itertools import compress | |
| from PIL import Image | |
| from pages.Functions.Assessment_functions import CLIP_single_object_classifier, CLIP_multi_object_recognition_DSwrapper, CLIP_object_negation | |
| # Create dictionary to hold functions | |
| fun_dict = { | |
| 'Multiple object types':CLIP_multi_object_recognition_DSwrapper, | |
| 'Single object':CLIP_single_object_classifier, | |
| 'Negation':CLIP_object_negation} | |
| st.title('Automated Assessment') | |
| st.write('On this page you can use automated assessment algorithms to assess how good uploaded images match their respective prompts.') | |
| st.write(' ') | |
| st.sidebar.image('Graphics/IL_Logo.png') | |
| try: | |
| # Create necessary variables | |
| prompt_dir = st.session_state['prompt_dir'] | |
| curr_eval_df = st.session_state['eval_df'] | |
| curr_eval_df['Picture_index']=curr_eval_df.index.values | |
| # Assess how many images are available for automatic assessment | |
| automated_eval_available = sum(curr_eval_df['automated_eval']) | |
| # Add task name to eval_df | |
| temp_prompt_dir=prompt_dir[['ID','Representations','Task_specific_label']] | |
| temp_prompt_dir['Prompt_no']=temp_prompt_dir['ID'].astype('str') | |
| curr_eval_df = curr_eval_df.merge(temp_prompt_dir,on='Prompt_no') | |
| except KeyError: | |
| automated_eval_available = 0 | |
| # If images for assessment available: create form to start assessment | |
| # Else: Note to upload images for assessment | |
| if automated_eval_available > 0: | |
| with st.form("auto_assessment_form",clear_on_submit=True): | |
| # Form info statment | |
| st.write('Select tasks to assess with the automated assessment:') | |
| # Add selection for available categories | |
| assess_multi_object = st.checkbox( | |
| 'Multiple object types ({0} images available)'.format( | |
| len(curr_eval_df.loc[ | |
| (curr_eval_df['automated_eval']==True)& | |
| (curr_eval_df['Task']=='Multiple object types')]) | |
| )) | |
| assess_single_object = st.checkbox( | |
| 'Single object type ({0} images available)'.format( | |
| len(curr_eval_df.loc[ | |
| (curr_eval_df['automated_eval']==True)& | |
| (curr_eval_df['Task']=='Single object')]) | |
| )) | |
| negation = st.checkbox( | |
| 'Negation ({0} images available)'.format( | |
| len(curr_eval_df.loc[ | |
| (curr_eval_df['automated_eval']==True)& | |
| (curr_eval_df['Task']=='Negation')]) | |
| )) | |
| submitted = st.form_submit_button("Start automated assessment") | |
| if submitted: | |
| # Create list for tasks which were selected for assessment | |
| selected_tasks = list( | |
| compress( | |
| ['Multiple object types','Single object','Negation'], | |
| [assess_multi_object,assess_single_object,negation])) | |
| # Create dataset to loop over with assessment | |
| assessed_df = curr_eval_df.loc[ | |
| (curr_eval_df['automated_eval']==True)& | |
| (curr_eval_df['Task'].isin(selected_tasks))] | |
| results_column = [] | |
| for row in assessed_df.itertuples(): | |
| # Apply task based classifier and safe in list | |
| temp_image = Image.open(st.session_state['uploaded_img'][row.Picture_index]) | |
| temp_result = fun_dict[row.Task]( | |
| temp_image,row.Representations,row.Task_specific_label) | |
| results_column.append(temp_result) | |
| assessed_df['Score']=results_column | |
| st.session_state['auto_eval_df']=assessed_df[['File_name','Prompt_no','Picture_index','Task','Score']] | |
| st.write('Completed assessment. Access results on the summary page.') | |
| else: | |
| st.write('Upload files on dashboard starting page to start automated assessment.') | |
| #st.write(st.session_state['auto_eval_df']) |