Add files via upload

Author: galiph

data/ner/streamlit_app.py

@@ -0,0 +1,162 @@
+import streamlit as st
+st.set_page_config(
+    layout="centered",  # Can be "centered" or "wide". In the future also "dashboard", etc.
+    initial_sidebar_state="auto",  # Can be "auto", "expanded", "collapsed"
+    page_title='Detect tumor characteristics',  # String or None. Strings get appended with "• Streamlit".
+    page_icon='../../resources/favicon.png',  # String, anything supported by st.image, or None.
+)
+import pandas as pd
+import numpy as np
+import os
+os.environ["JAVA_HOME"] = "/usr/lib/jvm/java-8-openjdk-amd64"
+import sys
+sys.path.append(os.path.abspath('./NER_COLAB'))
+#sys.path.append(os.path.abspath('../../'))
+import streamlit_apps_config as config
+from streamlit_ner_output import show_html2, jsl_display_annotations, get_color
+
+## Marking down NER Style
+st.markdown(config.STYLE_CONFIG, unsafe_allow_html=True)
+
+root_path = config.project_path
+
+
+########## To Remove the Main Menu Hamburger ########
+
+hide_menu_style = """
+        <style>
+        #MainMenu {visibility: hidden;}
+        </style>
+        """
+st.markdown(hide_menu_style, unsafe_allow_html=True)
+
+########## Side Bar ########
+
+## loading logo(newer version with href)
+import base64
+@st.cache(allow_output_mutation=True)
+def get_base64_of_bin_file(bin_file):
+    with open(bin_file, 'rb') as f:
+        data = f.read()
+    return base64.b64encode(data).decode()
+
+@st.cache(allow_output_mutation=True)
+def get_img_with_href(local_img_path, target_url):
+    img_format = os.path.splitext(local_img_path)[-1].replace('.', '')
+    bin_str = get_base64_of_bin_file(local_img_path)
+    html_code = f'''
+        <a href="{target_url}">
+            <img height="90%" width="90%" src="data:image/{img_format};base64,{bin_str}" />
+        </a>'''
+    return html_code
+
+logo_html = get_img_with_href('./NER_COLAB/resources/jsl-logo.png', 'https://www.johnsnowlabs.com/')
+st.sidebar.markdown(logo_html, unsafe_allow_html=True)
+
+
+## loading logo (older version without href)
+# st.sidebar.image('../../resources/jsl-logo.png', use_column_width=True)
+
+### loading description file
+descriptions = pd.read_json('./NER_COLAB/streamlit_apps_descriptions.json')
+
+## Getting NER model list
+descriptions = descriptions[descriptions['app'] == 'NER_TUMOR']
+model_names = descriptions['model_name'].values
+
+## Displaying Availabe models
+st.sidebar.title("Choose the pretrained model to test")
+test_data = pd.read_json('./test.json')
+
+selected_model = st.sidebar.selectbox("", test_data.columns[1:])
+
+
+######## Main Page #########
+
+#### displaying selected model's output
+#app_title = descriptions[descriptions['model_name'] == selected_model]['title'].iloc[0]
+#app_description = descriptions[descriptions['model_name'] == selected_model]['description'].iloc[0]
+#st.title('Spark NLP NER')
+#st.title(app_title)
+#st.markdown("<h2>"+app_description+"</h2>", unsafe_allow_html=True)
+#st.subheader("")
+#### Loading Files
+
+#TEXT_FILE_PATH = os.path.join(os.getcwd(),'inputs/'+selected_model)
+#RESULT_FILE_PATH = os.path.join(os.getcwd(),'outputs/'+selected_model)
+
+#input_files = sorted(os.listdir(TEXT_FILE_PATH))
+#input_files_paths = [os.path.join(TEXT_FILE_PATH, fname) for fname in input_files]
+#result_files_paths = [os.path.join(RESULT_FILE_PATH, fname.split('.')[0]+'.json') for fname in input_files]
+
+#title_text_mapping_dict={}
+#title_json_mapping_dict={}
+#for index, file_path in enumerate(input_files_paths):
+#  lines = open(file_path, 'r', encoding='utf-8').readlines()
+#  title = lines[0]
+#  text = ''.join(lines[1:])
+#  title_text_mapping_dict[title] = text
+#  title_json_mapping_dict[title] = result_files_paths[index]
+
+
+
+#### Displaying Available examples
+
+
+import SessionState
+
+ss = SessionState.get(x=1)
+
+selected_title = list(zip(test_data.index ,test_data.Insight))[ss.x]
+
+c1, c2 = st.columns(2)
+
+with c1:
+  if st.button("Previous Insight"):
+      ss.x = ss.x - 1
+      selected_title = list(zip(test_data.index ,test_data.Insight))[ss.x]
+
+with c2:
+  if st.button("Next Insight"):
+      ss.x = ss.x + 1
+      selected_title = list(zip(test_data.index ,test_data.Insight))[ss.x]
+
+#if st.button("Next Insight"):
+#    ss.x = ss.x + 1
+#    selected_title = list(zip(test_data.index ,test_data.Insight))[ss.x]
+
+#if st.button("Previous Insight"):
+#    ss.x = ss.x - 1
+#    selected_title = list(zip(test_data.index ,test_data.Insight))[ss.x]
+
+widget = st.empty()
+ss.x = widget.slider('Position', 0, max(test_data.index), ss.x)
+
+#slider = st.slider("Select Insight", 0, max(test_data.index))
+
+#title = st.selectbox("Choose Sample Text", zip(test_data.index ,test_data.Insight))#input_files_list)
+#selected = st.button('Go to selected text')
+
+#if selected:
+#    selected_title = title
+#    df = pd.DataFrame({"ner_chunk"  : test_data[selected_model][selected_title[0]]})
+
+## selected example
+
+labels_set = set()
+
+df = pd.DataFrame({"ner_chunk"  : test_data[selected_model][selected_title[0]]})
+
+for i in test_data[selected_model][selected_title[0]]:
+    i[4]['entity'] = i[4]['entity'].strip().upper()
+    labels_set.add(i[4]['entity'])
+
+labels_set = list(labels_set)
+
+labels = st.sidebar.multiselect(
+        "Entity labels", options=labels_set, default=list(labels_set)
+    )
+
+### Show Entities
+#selected_text = title_text_mapping_dict[selected_title]
+show_html2(test_data.Insight[ss.x], df, labels, "Text annotated with identified Named Entities")