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import streamlit as st
import pandas as pd
from io import StringIO
import json
from transformers import pipeline
#from transformers import AutoTokenizer, AutoModelForTokenClassification
def on_click():
st.session_state.user_input = ""
#@st.cache
def convert_df(df:pd.DataFrame):
return df.to_csv(index=False).encode('utf-8')
#@st.cache
def convert_json(df:pd.DataFrame):
result = df.to_json(orient="index")
parsed = json.loads(result)
json_string = json.dumps(parsed)
#st.json(json_string, expanded=True)
return json_string
#st.title("📘SBS mapper")
st.header("Work in Progress")
uploaded_file = st.file_uploader(label = "Upload single csv file")
if uploaded_file is not None:
stringio = StringIO(uploaded_file.getvalue().decode("utf-8"))
string_data = stringio.read()
st.success('Your file input is: '+ string_data, icon="✅")
#my_model_results = pipeline("ner", model= "checkpoint-92")
HuggingFace_model_results = pipeline("ner", model = "blaze999/Medical-NER")
createNER_button = st.button("Map to SBS codes")
col1, col2, col3 = st.columns([1,1,2.5])
col1.subheader("Score")
col2.subheader("SBS code")
col3.subheader("SBS description V2.0")
dictA = {"Score": [], "SBS Code": [], "SBS Description V2.0": []}
if uploaded_file is not None and createNER_button == True:
dict1 = {"word": [], "entity": []}
dict2 = {"word": [], "entity": []}
#stringio = StringIO(uploaded_file.getvalue().decode("utf-8"))
#string_data = stringio.read()
#st.write("Your input is: ", string_data)
#with col1:
# #st.write(my_model_results(string_data))
# #col1.subheader("myDemo Model")
# #for result in my_model_results(string_data):
# # st.write(result['word'], result['entity'])
# # dict1["word"].append(result['word']), dict1["entity"].append(result['entity'])
# #df1 = pd.DataFrame.from_dict(dict1)
# #st.write(df1)
with col2:
#st.write(HuggingFace_model_results(string_data))
#col2.subheader("Hugging Face Model")
for result in HuggingFace_model_results(string_data):
st.write(result['word'], result['entity'])
dict2["word"].append(result['word']), dict2["entity"].append(result['entity'])
df2 = pd.DataFrame.from_dict(dict2)
#st.write(df2)
cs, c1, c2, c3, cLast = st.columns([0.75, 1.5, 1.5, 1.5, 0.75])
with c1:
#csvbutton = download_button(results, "results.csv", "📥 Download .csv")
csvbutton = st.download_button(label="📥 Download .csv", data=convert_df(df1), file_name= "results.csv", mime='text/csv', key='csv')
with c2:
#textbutton = download_button(results, "results.txt", "📥 Download .txt")
textbutton = st.download_button(label="📥 Download .txt", data=convert_df(df1), file_name= "results.text", mime='text/plain', key='text')
with c3:
#jsonbutton = download_button(results, "results.json", "📥 Download .json")
jsonbutton = st.download_button(label="📥 Download .json", data=convert_json(df1), file_name= "results.json", mime='application/json', key='json') |