| import streamlit as st | |
| import pandas as pd | |
| from io import StringIO | |
| import json | |
| from transformers import pipeline # AutoTokenizer, AutoModelForTokenClassification | |
| #for k, v in st.session_state.items(): | |
| # st.session_state[k] = v | |
| 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.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="✅") | |
| #df_topics = filter_chapters_env(df_topics, "chapter_name") | |
| #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') |