Update app.py

app.py

@@ -27,7 +27,7 @@ st.sidebar.markdown('Using transformer model')
 
 ## Loading in dataset
 #df = pd.read_csv('mtsamples_small.csv',index_col=0)
-df = pd.read_csv("shpi_w_rouge21Nov.csv")
+df = pd.read_csv("demo_shpi_w_rouge25Nov.csv")
 #df.shape
 df['HADM_ID'] = df['HADM_ID'].astype(str).apply(lambda x: x.replace('.0',''))
 
@@ -50,7 +50,7 @@ st.sidebar.header("Search for Patient:")
 patientid = df['Patient_ID']
 patient = st.sidebar.selectbox('Select Patient ID:', patientid)
 admissionid = df['Admission_ID'].loc[df['Patient_ID'] == patient]
-HospitalAdmission = st.sidebar.selectbox('', admissionid) 
+HospitalAdmission = st.sidebar.selectbox(' ', admissionid) 
 
 #Another way to for filter selection 
 #patient = st.sidebar.multiselect(
@@ -113,6 +113,7 @@ runtext =st.text_area('Input Clinical Note here:', str(original_text2), height=3
 reference_text = original_text['Reference_text'].values
 
 
+
 #===== Pysummarization =====
 from pysummarization.nlpbase.auto_abstractor import AutoAbstractor
 from pysummarization.tokenizabledoc.simple_tokenizer import SimpleTokenizer
@@ -324,4 +325,6 @@ def run_model(input_text):
 if st.button('Submit'):
     run_model(runtext)
     
+   
     st.text_area('Reference text', str(reference_text))
+