Update pages/upload_file.py

pages/upload_file.py

@@ -1,2 +1,77 @@
 import streamlit as st
-st.header("Work in Progress")
+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([1,1.5,1])
+col1.subheader("SBS code V2.0")
+col2.subheader("SBS description v2.0")
+col3.subheader("Similarity score")
+
+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')