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import streamlit as st
from annotated_text import annotated_text
import pandas as pd
import yaml
import os
from src.negation import *
from src.app_utils import *
from src.inference import inference
from src.trainers import eval_spacy
#### Loading configuration and models ####
with open('./st_config.yaml', "r") as yamlfile:
args = yaml.load(yamlfile, Loader=yaml.FullLoader)
if args['model_dir'] is None:
model_names_dir = []
elif os.path.exists(args['model_dir']):
model_names_dir = os.listdir(args['model_dir'])
else:
model_names_dir = []
model_names = model_names_dir + args['default_models'] if args['default_models'] is not None else model_names_dir
st.title('Radiology NER')
st.markdown('This app is to experiment on using NER to extract span of text from radiological notes that will to indicate the current condition of the patient. The targeted extraction includes \n 1) the symptoms of the disease \n 2) the location of the organs affected the symptops \n 3) and the progress of the disease. \nen_ner_bc5cdr_md is the base model already trained to detect diseases and chemicals. en_Radiology_ner_bc5cdr_md is fine tuned on the base model with additional entities to indicate "Existence or Worsening" of symptoms and "Absence or recovering" of symptoms. This will help practitioners to quickly note the key words in the clinical report as well as the tabulated result can be use for analysis for other downstream task')
##################################
#### sidebar (Chose Model) ######
##################################
model_name= st.sidebar.selectbox("Select a model", options=model_names)
print(model_name)
if len(model_names) > 0:
models = load_models(model_names,args, model_names_dir)
print(models)
selected_model = models[model_name]
print(selected_model)
##################################
#### sidebar (Chose Example) ####
##################################
st.sidebar.markdown('###')
if args['examples'] is not None:
chosen_note = st.sidebar.selectbox("Select an example text", options=args['examples'].keys())
else:
chosen_note = None
if chosen_note == "radiology_eval_dataset":
text_input = pd.read_csv("./eval_35.csv", converters={'entities': ast.literal_eval})
text_input = text_input.to_dict('records')
# set colors for each entity
if len(model_names) > 0:
ents_available = selected_model.get_pipe('ner').labels
print(ents_available)
ent_colors_map = dict(map(lambda i,j : (i,j) , ents_available,args['colors_palette'][:len(ents_available)]))
##################
### Text area ###
##################
if chosen_note != "radiology_eval_dataset":
text_input = st.text_area("Type notes in the box below",
value=args['examples'][chosen_note] if args['examples'] is not None else '')
st.markdown("---")
############################
### Side bar (Load Files)###
############################
st.sidebar.info('For csv & json files, name the text columns to be infered as "text". Annotated labels as "entities" Format of json text as below')
st.sidebar.json([{"text":"example","entities":[[5,6,"do"],[8,11,"dx"]]},{"text":"example2","entities":[[5,6,"do"],[8,11,"dx"]]}],expanded=False)
uploaded_file = st.sidebar.file_uploader("Upload a file", type=["csv","json","pdf", "txt"])
text_input = process_files(uploaded_file, text_input)
#################################
### Side bar (Select Entities)###
#################################
selected_entities = st.sidebar.multiselect(
"Select the entities you want to view",
options=ents_available if len(model_names)> 0 else [],
default=ents_available if len(model_names)> 0 else [],
)
##########################
### Text Area (Slider)###
##########################
if (len(text_input)> 1) & (isinstance(text_input,(list,dict))):
sample = st.slider('Select Example', min_value=1, max_value=len(text_input))
else:
sample = None
# Process documents to tokens
if len(model_names)>0:
infer_input = text_input[sample-1]["text"] if sample is not None else text_input
doc = selected_model(infer_input)
textcol_negate, textcol_compare = st.columns([1, 1])
# checkboxes for negation
negate = textcol_negate.checkbox('Check for Negation')
##########################################
### Checkboxes for Compare with labels ###
##########################################
if (isinstance(text_input,(dict,list))):
if 'entities' in text_input[0].keys():
state_compare = False
compare = textcol_compare.checkbox('Compare between predictions and labels',disabled=state_compare)
else:
state_compare, compare = True, False
else:
state_compare, compare = True, False
###############################
### Processing for negation ###
###############################
if negate:
neg_ent = {"ent_types":list(selected_model.get_pipe('ner').labels)}
neg = negation(selected_model, neg_ent)
doc = infer_negation(neg,selected_model,infer_input,doc)
selected_entities += ['NEG']
ent_colors_map.update({'NEG': '#C7C7C7'})
################################
### Processing for Comparision##
################################
if compare & (isinstance(text_input,(dict,list))):
infer_input = text_input[sample-1]
tokens_compare = process_text_compare(infer_input,selected_entities,colors=ent_colors_map)
tokens = process_text(doc, selected_entities,colors=ent_colors_map)
st.markdown('##')
# Display results
st.markdown('#### Predictions')
annotated_text(*tokens)
if compare & (isinstance(text_input,(dict,list))):
st.markdown('#### Labels')
annotated_text(*tokens_compare)
st.markdown("---")
data = pd.DataFrame.from_dict([{'label': entity.label_, 'text': entity.text, 'start': entity.start, 'end': entity.end} \
for entity in doc.ents])
if data.shape[1]>0:
st.table(data['label'].value_counts())
myexpander = st.expander('Details on text')
myexpander.table(data)
###################################
#### Inference on whole dataset####
###################################
infer_whole_dataset = st.checkbox('Inference on whole dataset')
if (isinstance(text_input,(dict,list))) & (infer_whole_dataset):
texts = []
for text in text_input:
texts.append(text['text'])
st.markdown('### Prediction on whole dataset')
inference_data = inference(selected_model,texts)
### Applying negation to whole dataset
if negate:
neg_ent = {"ent_types":list(selected_model.get_pipe('ner').labels)}
neg = negation(selected_model, neg_ent)
docs = selected_model.pipe(texts,batch_size=8)
records = []
for no,doc in enumerate(docs):
doc = infer_negation(neg,selected_model,texts[no],doc)
if len(doc.ents)>0:
records.append([{'id':no+1,'text':doc.text,'span': entity.text,
'entity': entity.label_, 'start': entity.start, 'end': entity.end}
for entity in doc.ents])
else:
records.append([{'id':no+1,'text':doc.text,'span': None,
'entity': None, 'start':None, 'end': None}])
inference_data = pd.DataFrame.from_dict(sum(records,[])).set_index(['text','id'])
st.download_button(
label="Download Prediction as CSV",
data=inference_data.to_csv().encode('utf-8'),
file_name='inference_data.csv',
mime='text/csv',
)
########################################
### Expander for dataframe and report###
########################################
report_expander = st.expander('Report on Evaluation Results')
results_metrics = eval_spacy(selected_model,text_input)
overall_score = pd.DataFrame.from_dict({'Type':['Overall'],'Precision': [results_metrics['ents_p']],
'Recall': [results_metrics['ents_r']],
'F1': [results_metrics['ents_f']]})
overall_score = overall_score.set_index('Type')
entities_score = pd.DataFrame.from_dict(results_metrics['ents_per_type']).T
entities_score = entities_score.rename(columns={'p':'Precision','r':'Recall','f':'F1'})
report_expander.table(overall_score)
report_expander.table(entities_score)
df_expander = st.expander('Inference Table')
df_expander.write(inference_data.to_html(), unsafe_allow_html=True)
#df_expander.table(inference_data)
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