Add frontend

app/components/image.py

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+import streamlit as st
+import uuid
+
+def display_img(column, path, final_data, name, available_colors):
+
+    with column:
+        with st.container():
+
+            st.pills('', ['Flag', 'Relabel', 'Remove'], key=path + name, selection_mode='single')
+            with st.popover('Image Path'):
+                st.write(f'``{path}``')
+            with st.container(horizontal=True):
+                st.badge(final_data['label'].values[0], color=available_colors[final_data['label'].values[0]])
+                #st.space()
+                st.write(f'Patient ID: ``{final_data['patient'].values[0]}``')
+            st.image(image=path, caption=final_data['description'].values[0])

app/main.py

@@ -0,0 +1,161 @@
+import streamlit as st
+from components.image import display_img
+import os
+import pandas as pd
+
+
+# HEAD ------------------------------------------------------------------------------------------------------
+
+data_context = None
+colors = ['red', 'green', 'yellow', 'violet', 'orange', 'blue', 'gray']
+colors_i = 1
+if 'available_labels' not in st.session_state.keys():
+    st.session_state['available_labels'] = {
+        'default': 'red'
+    }
+
+MAX_IMG_PER_PAGE=12
+if 'imgs' not in st.session_state.keys():
+   st.session_state['imgs'] = [[]]
+
+if 'chat_history' not in st.session_state.keys():
+   st.session_state['chat_history'] = [{'name': 'ai', 'content': 'Hello! How can I help you with labeling this dataset?'}]
+
+st.set_page_config(layout='wide')
+
+# HEADER --------------------------------------------------------------------------------------------------
+
+st.header('Googol')
+
+'---'
+# FILE UPLOAD AREA ----------------------------------------------------------------------------------------
+
+# imgs = st.file_uploader('Upload Dataset  Folder / Images', type=['jpg', 'jpeg', 'png', 'svg'], accept_multiple_files='directory')
+
+with st.expander('# 📁 Add Files', width='stretch'):
+    folder_path = st.text_input('Please choose a folder path:')
+    consider_folder_as_patient = st.checkbox('Consider Subfolder As Patient ID')
+    consider_folder_as_label = st.checkbox('Consider Subfolder As Label')
+    confirmed = st.button('Confirm')
+    ALLOWED_EXTENSIONS = ('jpg', 'jpeg', 'png', 'svg')
+    df_data = {'label': [], 'description': [], 'path': [], 'patient': []}
+    if folder_path and confirmed:
+        current_page = 0
+        file_num = 0
+        iterator = os.walk(folder_path)
+        data = next(iterator, None)
+        st.session_state['imgs'] = [[]]
+        while data is not None:
+            dirpath, dirnames, filenames = data
+
+            folder_name = dirpath.split('/')[-1]
+
+            for filename in filenames:
+                # Check if file format is appropriate
+                if filename.endswith(ALLOWED_EXTENSIONS):
+
+                    # If there's no space in the current page, add a new page
+                    if file_num == MAX_IMG_PER_PAGE:
+                        current_page += 1
+                        st.session_state['imgs'].append([])
+                        file_num = 0
+
+                    # Add to current page
+                    file_path = os.path.join(dirpath, filename)
+                    st.session_state['imgs'][current_page].append(file_path)
+
+                    df_data['label'].append(folder_name if consider_folder_as_label else 'default')
+                    df_data['description'].append('No description provided.')
+                    df_data['path'].append(file_path)
+                    df_data['patient'].append(folder_name if consider_folder_as_patient else 'anonymous')
+                    file_num += 1
+
+            data = next(iterator, None)
+
+        st.session_state['final_data_df'] = pd.DataFrame(df_data)
+
+        # Setting labels
+        for label in st.session_state['final_data_df']['label'].unique():
+            st.session_state['available_labels'][label] = colors[colors_i % len(colors)]
+            colors_i += 1
+
+        print('Data collected: ', st.session_state['imgs'])
+
+# EXPORT & Statistics --------------------------------------------------------------------------------
+
+@st.dialog('Statistics', width='large')
+def show_statistics():
+    #st.bar_chart()
+    st.write('Data')
+
+    if 'final_data_df' in st.session_state is not None:
+
+        st.dataframe(st.session_state['final_data_df'])
+
+        st.write('Label Frequencies')
+        frequencies_df = st.session_state['final_data_df']['label'].value_counts()
+
+        st.bar_chart(frequencies_df, horizontal=True)
+    else:
+        st.error('Please choose a folder before viewing statistics.')
+
+
+export_and_st = st.columns(2)
+
+with export_and_st[0]:
+    if st.button('# 📦 Export Results', width='stretch'):
+        print('Exported')
+with export_and_st[1]:
+    if st.button('📊 View Statistics', width='stretch'):
+        show_statistics()
+
+# MAIN AREA (Where Images are Displayed) -------------------------------------------------------------
+
+columns = st.columns(3, gap='medium')
+
+if 'page_num' not in st.session_state:
+    st.session_state['page_num'] = 0
+with st.container(key='imgs_page'):
+
+    if len(st.session_state['imgs']) > 1:
+        last_page = len(st.session_state['imgs'])
+        st.session_state['page_num'] = st.select_slider('Page', options=range(last_page))
+
+    for i, img in enumerate(st.session_state['imgs'][st.session_state['page_num']]):
+        display_img(columns[i % 3], img, st.session_state['final_data_df'][st.session_state['final_data_df']['path'] == img], str(i), st.session_state['available_labels'])
+
+
+# SIDEBAR (Chatbot Zone) -----------------------------------------------------------------------------
+with st.sidebar:
+
+    with st.container(horizontal=True):
+        for label in st.session_state['available_labels'].keys():
+            st.badge(label, color=st.session_state['available_labels'][label])
+
+    with st.form('context'):
+        st.write('Write the medical context for your dataset:')
+        data_context = st.text_input('context')
+        submit_button = st.form_submit_button('Submit')
+
+    if submit_button:
+        print(data_context)
+
+    with st.container(key='chat_area'):
+        '---'
+        '# AI Chat'
+
+        for msg in st.session_state['chat_history']:
+            with st.chat_message(msg['name']):
+                st.write(msg['content'])
+
+        with st.chat_message('user'):
+            with st.form('user_msg_form', clear_on_submit=True):
+                user_input = st.text_input('You:', placeholder='Can you label these images according to anomalies found?', value='')
+                submit = st.form_submit_button('Send', icon='➡️')
+
+                if submit:
+                    st.session_state['chat_history'].append({'name': 'user', 'content': user_input})
+
+                    # This is where the API call will take place
+                    st.session_state['chat_history'].append({'name': 'ai', 'content': 'I\'m an AI reply'})
+                    st.rerun()