import streamlit as st
import pandas as pd
from io import StringIO
import json
from transformers import pipeline # 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.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')