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import difflib
from collections import Counter
import streamlit as st
import pandas as pd
import srsly
def search(query):
results = []
for grant in grants:
if query in grant["tags"]:
results.append({"title": grant["title"], "tags": grant["tags"]})
st.session_state["results"] = results
st.header("Search 🔎 grants using MeSH 🔖")
st.sidebar.header("Information ℹ")
st.sidebar.write(
"A complete list of MeSH tags can be found here https://meshb.nlm.nih.gov/treeView"
)
st.sidebar.write("The grants data can be found at [https://www.threesixtygiving.org/](https://data.threesixtygiving.org/). They are published under a [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) license.")
st.sidebar.write(
"The model used to tag grants is https://huggingface.co/Wellcome/WellcomeBertMesh"
)
st.sidebar.header("Parameters")
nb_results = st.sidebar.slider(
"Number of results to display", value=20, min_value=1, max_value=100
)
if "grants" not in st.session_state:
st.session_state["grants"] = list(srsly.read_jsonl("tagged_grants.jsonl"))
grants = st.session_state["grants"]
if "tags" not in st.session_state:
st.session_state["tags"] = list(set([tag for grant in grants for tag in grant["tags"]]))
tags = st.session_state["tags"]
query = st.text_input("", value="Malaria")
st.button("Search 🔎", on_click=search, kwargs={"query": query})
if "results" in st.session_state:
st.caption("Related MeSH terms")
if st.session_state["results"]:
retrieved_tags = [tag for res in st.session_state["results"] for tag in res["tags"]]
most_common_tags = [tag for tag, _ in Counter(retrieved_tags).most_common(20)]
else:
most_common_tags = difflib.get_close_matches(query, tags, n=20)
columns = st.columns(5)
for row_i in range(3):
for col_i, col in enumerate(columns):
with col:
tag_i = row_i * 5 + col_i
if tag_i < len(most_common_tags):
tag = most_common_tags[tag_i]
st.button(tag, on_click=search, kwargs={"query": tag})
results = st.session_state["results"]
st.caption(f"Found {len(results)}. Displaying {nb_results}")
st.download_button(
"Download results",
data=pd.DataFrame(results).to_csv(),
file_name="results.csv",
mime="text/csv",
)
st.table(results[:nb_results])
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