Delete pages

pages/1_💾_Data.py

@@ -1,51 +0,0 @@
-# Read
-import streamlit as st
-import sys,os
-
-
-sys.path.append(f'{os.getcwd()}/utils')
-from utils.data_users import get_developer_page_layout,get_product_dev_page_layout
-
-
-print(os.getcwd())
-# st.write(st.session_state.user_group)
-USER_GROUPS = ["Developer", "Manager", "Practitioner"]
-
-st.set_page_config(layout="wide")
-
-if 'user_group' not in st.session_state:
-    index_tmp = 0
-else: 
-    index_tmp = USER_GROUPS.index(st.session_state['user_group'])
-
-#Sidebar for USER GROUPS
-st.sidebar.title("USER GROUPS")
-backend = st.sidebar.selectbox(
-            "Select User-Group ", USER_GROUPS, index=index_tmp
-        )
-
-st.session_state['user_group'] = backend
-
-# #     with st.sidebar:
-# st.sidebar.title("🎈Explore Data Panel")
-
-st.title("Explore Data Panel for OCT Image Analysis")
-
-st.write(
-        """
-        ##
-        Examining the data is the key factor for better understanding of the AI system.Therefore,this panel introduces deta-centric approach by providing details regarding forllowing aspect of the data . 
-        """)
-
-
-list_test = """<ul>
-                    <li>Data Source Information: This tab contains information regarding following aspect of the data: 
-                        modality,format, domain, ethical considerations including license and data version. </li>
-                    <li>Exploratory Data Stats: This tab includes exploratory data analysis info covering following aspects: train/validation/test data division,summary statistics, sample visualization from each category.</li>
-                    <li>Data Onboarding: This tab provides details of data pre-processing and post-processing steps applied over the dataset before traning, and data augmentations.</li>
-                 </ul>"""
-st.markdown(list_test, unsafe_allow_html=True)
-
-
-if backend == "Developer":
-    get_product_dev_page_layout()

pages/1_💾_Data_Panel.py

@@ -1,57 +0,0 @@
-# Read
-import streamlit as st
-import sys,os
-
-
-sys.path.append(f'{os.getcwd()}/utils')
-from utils.data_users import get_product_dev_page_layout,get_product_manager_page_layout,get_product_practitioner_page_layout
-
-
-print(os.getcwd())
-# st.write(st.session_state.user_group)
-USER_GROUPS = ["Developer", "Manager", "Practitioner"]
-
-st.set_page_config(layout="wide")
-
-if 'user_group' not in st.session_state:
-    index_tmp = 0
-else: 
-    index_tmp = USER_GROUPS.index(st.session_state['user_group'])
-
-#Sidebar for USER GROUPS
-st.sidebar.title("USER GROUPS")
-backend = st.sidebar.selectbox(
-            "Select User-Group ", USER_GROUPS, index=index_tmp
-        )
-
-st.session_state['user_group'] = backend
-
-# #     with st.sidebar:
-# st.sidebar.title("🎈Explore Data Panel")
-
-st.title("Data Panel for OCT Image Analysis")
-
-st.write(
-        """
-        ##
-        To gain a comprehensive understanding of the AI system, examining the data has a crucial role. The Data Panel adopts a data-centric approach, providing detailed information about the following aspects of the data:
-        """)
-
-
-list_test = """<ul>
-                <li>Data Source Information contains information related to the modality, format, domain, ethical considerations, including licensing and data version. </li>
-                <li>Exploratory Data Stats presents exploratory data analysis information covering train/validation/test data division, summary statistics, and sample visualization from each category. </li>
-                <li>Data Onboarding provides information about the data pre-processing and post-processing steps applied to the dataset before training, as well as any data augmentations that were used.</li>
- </ul>"""
-st.markdown(list_test, unsafe_allow_html=True)
-
-
-if backend == "Developer":
-    get_product_dev_page_layout()
-
-
-if backend == "Manager":
-    get_product_manager_page_layout()
-
-if backend == "Practitioner":
-    get_product_practitioner_page_layout()

pages/2_📝_Model.py

@@ -1,46 +0,0 @@
-import streamlit as st
-import sys,os
-
-
-sys.path.append(f'{os.getcwd()}/utils')
-from utils.model_users import get_product_dev_page_layout
-
-# st.write(st.session_state.user_group)
-USER_GROUPS = ["Developer", "Manager", "Practitioner"]
-
-st.set_page_config(layout="wide")
-
-if 'user_group' not in st.session_state:
-    index_tmp = 0
-else: 
-    index_tmp = USER_GROUPS.index(st.session_state['user_group'])
-
-#Sidebar for USER GROUPS
-st.sidebar.title("USER GROUPS")
-backend = st.sidebar.selectbox(
-            "Select User-Group ", USER_GROUPS, index=index_tmp
-        )
-
-st.session_state['user_group'] = backend
-
-
-
-st.title("Explore Model Panel for OCT Image Analysis")
-st.write(
-        """
-       The users can find following information regarding the AI model developed for the task: what the mode does, how it was developed and how it is used. Thus, we provide answers 
-       to those questions via below described tabs.
-        """)
-
-list_test = """<ul>
-                <li>Model generic information: This tab contains summary of the model’s details, including its main features, capabilities, and intended use. 
-                    It also hihglights the model’s behaviour, such as the type of data inputs(image, feature etc) it can handle and the types of outputs it produces.</li>
-                <li>Model development information: The tab includes information on the hyperparameters, model development framework such as Tensorflow or PyTorch, and other technical details. It also includes a complete analysis of the model’s inference performance, such as the model size, 
-                        hardware-specific (GPU and CPU) inference time,and speed, as well as reproducibility check list.</li>
-                <li>Model deployment information: This tab provides information on how the model is used in production, including details on the inference speed and latency,  
-                        and how users can access and interact with the model in production.</li>
-                </ul>"""
-st.markdown(list_test, unsafe_allow_html=True)
-
-if backend == "Developer":
-    get_product_dev_page_layout()

pages/2_📝_Model_Panel.py

@@ -1,52 +0,0 @@
-import streamlit as st
-import sys,os
-
-
-sys.path.append(f'{os.getcwd()}/utils')
-from utils.model_users import get_product_dev_page_layout,get_product_manager_page_layout,get_product_practitioner_page_layout
-
-# st.write(st.session_state.user_group)
-USER_GROUPS = ["Developer", "Manager", "Practitioner"]
-
-st.set_page_config(layout="wide")
-
-if 'user_group' not in st.session_state:
-    index_tmp = 0
-else: 
-    index_tmp = USER_GROUPS.index(st.session_state['user_group'])
-
-#Sidebar for USER GROUPS
-st.sidebar.title("USER GROUPS")
-backend = st.sidebar.selectbox(
-            "Select User-Group ", USER_GROUPS, index=index_tmp
-        )
-
-st.session_state['user_group'] = backend
-
-
-
-st.title("Model Panel for OCT Image Analysis")
-st.write(
-        """
-       The users can find following information regarding the AI model developed for the task: what is the purpose of the model, how it was developed and how it is used. Thus,answers 
-       to these questions areprovided via the tabs described below.
-        """)
-
-list_test = """<ul>
-                <li>Model Generic information contains a summary of the model’s details, including its main features, capabilities, and intended use. 
-                    It also highlights the model’s behaviour, such as the type of data inputs(image, feature etc) it can handle and the types of outputs it produces.</li>
-                <li>Model Development Information includes information on the hyperparameters, model development framework/library such as Tensorflow or PyTorch, and other technical details. It also includes a complete analysis of the model’s inference performance, such as the model size, 
-                        hardware-specific (GPU and CPU) inference time,and speed, as well as reproducibility check list.</li>
-                <li>Model Deployment Information provides information on how the model is used in production, including details on the inference speed and latency,  
-                        and how users can access and interact with the model in production.</li>
-                </ul>"""
-st.markdown(list_test, unsafe_allow_html=True)
-
-if backend == "Developer":
-    get_product_dev_page_layout()
-
-if backend == "Manager":
-    get_product_manager_page_layout()
-
-if backend == "Practitioner":
-    get_product_practitioner_page_layout()

pages/3_🎯_Evaluation_Panel.py

@@ -1,41 +0,0 @@
-import streamlit as st
-import sys,os
-
-sys.path.append(f'{os.getcwd()}/utils')
-
-from utils.eval_users import get_product_dev_page_layout
-
-# st.write(st.session_state.user_group)
-USER_GROUPS = ["Developer", "Manager", "Practitioner"]
-
-st.set_page_config(layout="wide")
-
-if 'user_group' not in st.session_state:
-    index_tmp = 0
-else: 
-    index_tmp = USER_GROUPS.index(st.session_state['user_group'])
-
-#Sidebar for USER GROUPS
-st.sidebar.title("USER GROUPS")
-backend = st.sidebar.selectbox(
-            "Select User-Group ", USER_GROUPS, index=index_tmp
-        )
-
-st.session_state['user_group'] = backend
-
-st.title("Explore Performance Panel for OCT Image Analysis")
-st.write(
-        """
-       This panel provides information on the evaluation of the AI model’s performance, including details on the metrics used and the results of the evaluation. USerrs can also find 
-       our notes regarding the issues. The performance metric visualizations and samples of failure and success cases are given in in this panel as well.""")
-
-list_test = """<ul>
-                    <li>Evaluation Metrics: This tab explains the details of the performance metrics and how each metric is calculated. 
-                        Users can also find the characteristics of the evaluation data set. </li>
-                    <li>Performance Summary: This tab includes visualizations of the performance metrics over test set.</li>
-                    <li>Limitations: This tab provides examples of observed failure and success cases, along with visualizations and any possible observations behind the failure cases.</li>
-                </ul>"""
-st.markdown(list_test, unsafe_allow_html=True)
-
-if backend == "Developer":
-    get_product_dev_page_layout()

pages/3_🎯_Performance_Evaluation_Panel.py

@@ -1,42 +0,0 @@
-import streamlit as st
-import sys,os
-
-sys.path.append(f'{os.getcwd()}/utils')
-
-from utils.eval_users import get_product_dev_page_layout
-
-# st.write(st.session_state.user_group)
-USER_GROUPS = ["Developer", "Manager", "Practitioner"]
-
-st.set_page_config(layout="wide")
-
-if 'user_group' not in st.session_state:
-    index_tmp = 0
-else: 
-    index_tmp = USER_GROUPS.index(st.session_state['user_group'])
-
-#Sidebar for USER GROUPS
-st.sidebar.title("USER GROUPS")
-backend = st.sidebar.selectbox(
-            "Select User-Group ", USER_GROUPS, index=index_tmp
-        )
-
-st.session_state['user_group'] = backend
-
-st.title("Performance Evaluation Panel for OCT Image Analysis")
-st.write(
-        """
-       This panel provides information on the evaluation of the AI model’s performance, including details on the metrics used and the results of the evaluation. Users can also find 
-       our notes regarding the encountered issues. The performance metric visualizations and samples of failure and success cases are given in this panel as well.""")
-
-list_test = """<ul>
-                    <li>Evaluation Metrics explains the details of the performance metrics and how each metric is calculated. </li>
-                    <li>Performance Summary includes visualizations of the performance metrics over the test set. Users can also find the characteristics of the evaluation data set</li>
-                    <li>Issues and Limitations provides examples of observed failure cases, along with visualizations and any possible observations behind the failure cases.</li>
-                </ul>"""
-st.markdown(list_test, unsafe_allow_html=True)
-
-
-get_product_dev_page_layout()
-# if backend == "Developer":
-#     get_product_dev_page_layout()

pages/4_🔬_Decision_Exploration_Panel.py

@@ -1,48 +0,0 @@
-import streamlit as st
-import sys,os
-
-sys.path.append(f'{os.getcwd()}/utils')
-
-from utils.decisions_users import get_product_dev_page_layout
-
-# st.write(st.session_state.user_group)
-USER_GROUPS = ["Developer", "Manager", "Practitioner"]
-
-st.set_page_config(layout="wide")
-
-if 'user_group' not in st.session_state:
-    index_tmp = 0
-else: 
-    index_tmp = USER_GROUPS.index(st.session_state['user_group'])
-
-#Sidebar for USER GROUPS
-st.sidebar.title("USER GROUPS")
-backend = st.sidebar.selectbox(
-            "Select User-Group ", USER_GROUPS, index=index_tmp
-        )
-
-st.session_state['user_group'] = backend
-
-st.title("Decision Exploration Panel for OCT Image Analysis")
-st.write(
-        """
-       This panel provides provides both local and global explanations.
-       """)
-
-list_test = """<ul>
-                <li>Global Explanations: this tab illustrates both representative samples and task-specific borderline samples in the training data from each category in the classification task. These samples enable users
-                to understand which inputs contribute to the model’s decisions. On the other
-                hand, borderline cases can be used to highlight potential failure cases. Also, one can see a manifold visualization
-                of both representative and borderline samples here.</li>
-                <li>Instance Level Explanations: this panel is intended for model sensitivity analysis, decision correction, and decision highlighting using both local expla-
-                nation methods and presenting samples similar to the target sample from the
-                representative samples of the training set. [GradCAM](https://arxiv.org/abs/1610.02391) method is used as a post-hoc explanation technique to highlight aregion of interest.</li>
-                    </ul>"""
-st.markdown(list_test, unsafe_allow_html=True)
-
-# if backend == "Developer":
-#     get_product_dev_page_layout()
-if backend == "Manager":
-    get_product_dev_page_layout(user_type ="Manager")
-else:
-    get_product_dev_page_layout()