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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('NER Visualizer')
##################################
#### 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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