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import json
import gradio as gr
from outcome_switch import OutcomeSwitchingDetector, get_sections_text
from outcome_switch.visual import (
get_article_markdown,
get_highlighted_text,
get_registry_dataframe,
get_sankey_diagram,
)
_CALCULATED_COSINE_THRESHOLD = 0.44
_app_description = open("front/app-description.md").read()
_article_id_examples = json.load(open("front/examples.json"))
_pmcid_start_value = _article_id_examples[0]
config = json.load(open('./config.json', 'r'))
# Load Detector (ner and sim model)
osd = OutcomeSwitchingDetector(
config["ner_path"],
config["sim_path"],
config["ner_label2id"]
)
def controller(article_id:str):
# clean input and run detection
article_id = str(article_id).strip()
output = osd.detect(article_id)
# init outputs
article_markdown=None
article_highlighted_text=None
registry_df=None
similarity_diagram=None
# check whether article markdown can be displayed
if output["db"] is None :
gr.Warning(f"Wrong format for input id : {article_id}")
return None, None, None, None
elif output["article_sections"] is None or output["filtered_sections"] is None:
gr.Warning(f"Could not retrieve text for id {article_id} (id not found in database or abstract/fulltext unavailable on PubMed/PMC)")
return None, None, None, None
else :
article_markdown = get_article_markdown(article_id, output["article_sections"], output["filtered_sections"])
# check whether annotations can be displayed
if output["raw_entities"] is not None and output["filtered_sections"] is not None:
original_text = get_sections_text(output["filtered_sections"])
article_highlighted_text = get_highlighted_text(output["raw_entities"], original_text)
else :
gr.Warning("Could not extract any outcomes entities in article text")
# check whether registry outcomes can be displayed
if output["ctgov_outcomes"] is not None:
registry_df = get_registry_dataframe(output["ctgov_outcomes"])
else:
gr.Warning("ClinicalTrials.Gov outcomes were not found (either no NCTID detected or no outcomes declared in registry)")
return article_markdown, article_highlighted_text, registry_df, similarity_diagram
# check whether similarity diagram can be displayed
if (output["connections"] is not None and output["raw_entities"] is not None and
output["ctgov_outcomes"] is not None and output["article_outcomes"] is not None):
registry_outcomes_tup = [(outcome["type"], outcome["measure"] + " , " + outcome["timeFrame"])
for outcome in output["ctgov_outcomes"]]
similarity_diagram = get_sankey_diagram(
registry_outcomes_tup,
output["article_outcomes"],
output["connections"],
output["raw_entities"],
_CALCULATED_COSINE_THRESHOLD
)
else:
gr.Warning("Could not compute similarity diagram (missing registry or article outcomes)")
return article_markdown, article_highlighted_text, registry_df, similarity_diagram
def clean():
return None, None, None, None
with gr.Blocks() as blocks:
with gr.Column():
gr.Markdown('# Outcome Switching Detection \n' + _app_description )
with gr.Row():
with gr.Column():
with gr.Row():
pmid_input = gr.Textbox(value=_pmcid_start_value, label="PMID or PMCID (PMCID must be preceded by 'PMC' prefix)")
with gr.Row():
clear_button = gr.ClearButton()
detect_button = gr.Button(value="Detect", variant="primary")
gr.Examples(examples = _article_id_examples, inputs=pmid_input)
gr.Markdown("## Results \n")
with gr.Tabs():
with gr.TabItem("Article Useful Sections"):
filtered_article = gr.Markdown()
with gr.TabItem("Article Detected Outcomes"):
ner_output = gr.HighlightedText(
color_map={"primary": "lightcoral", "secondary": "lightgreen"},
show_legend=True,
combine_adjacent=True,
)
with gr.TabItem("Registry Outcomes"):
ctgov_output = gr.DataFrame()
with gr.TabItem("Similarity"):
similarity_output = gr.Plot(show_label=False)
# OUTPUTS AND BUTTONS
outputs = [filtered_article, ner_output, ctgov_output, similarity_output]
clear_button.add([pmid_input]+outputs)
detect_button.click(fn=controller, inputs=pmid_input, outputs=outputs)
blocks.launch()
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