Delete pages/upload_file.py

pages/upload_file.py

@@ -1,86 +0,0 @@
-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')