Update app.py

app.py

@@ -106,7 +106,7 @@ def main(myargs):
 		if st.session_state['input_type'] == 'File' and "embeddings_all" in st.session_state and st.session_state.embeddings_plot in ["2D", "3D"]:
 			indices = [x for x in range(st.session_state.data_df[st.session_state.input_column].values.shape[0])]
 			if "selected_indices" in st.session_state:
-				if len(st.session_state.selected_indices) >4:
+				if len(st.session_state.selected_indices) >=4:
 					l = st.session_state.selected_indices
 					l.sort()
 					indices = l
@@ -164,7 +164,7 @@ def main(myargs):
 					plot = plotpx.scatter(
 								df_embeddings, x='x', y='y',
 								color='severity', labels={'color': 'severity'},
-								hover_data=['text','all_predictions','data_index'],title = 'BERT Embeddings Visualization - Please select rows (at least 5) to display specific examples'
+								hover_data=['text','all_predictions','data_index'],title = 'BERT Embeddings Visualization - Please select rows (at least 4) to display specific examples'
 					)
 
 				else:
@@ -172,7 +172,7 @@ def main(myargs):
 					plot = plotpx.scatter_3d(
 								df_embeddings, x='x', y='y', z='z',
 								color='severity', labels={'color': 'severity'},
-								hover_data=['text','all_predictions','data_index'],title = 'BERT Embeddings Visualization - Please select rows (at least 5) to display specific examples'
+								hover_data=['text','all_predictions','data_index'],title = 'BERT Embeddings Visualization - Please select rows (at least 4) to display specific examples'
 					)
 
 				st.plotly_chart(plot,use_container_width=True,)
@@ -223,7 +223,7 @@ def main(myargs):
 	st.sidebar.image(os.path.join(project_dir,"imgs/doctor.png"),use_column_width=False)
 
 	# Designing the interface
-	st.markdown("<h1 style='text-align: center; color: black;'>HCSBC: Hierarchical Classification System for Breast Cancer</h1>", unsafe_allow_html=True)
+	st.markdown("<h1 style='text-align: center; color: black;'>HCSBC: Hierarchical Classification System for Breast Cancer Specimen Report</h1>", unsafe_allow_html=True)
 	st.markdown("System Pipeline: Pathology Emory Pubmed BERT + 6 independent Machine Learning discriminators")
 	# For newline
 	st.write('\n')
@@ -241,7 +241,7 @@ def main(myargs):
 	# File selection
 	st.sidebar.title("Data Selection")
 			
-	st.session_state['input_type'] = st.sidebar.radio("Input Selection", ('File', 'Text'), key="data_format")
+	st.session_state['input_type'] = st.sidebar.radio("Input Selection", ('File', 'Text'), key="data_format",index=1)
 	if "prev_input_type" not in st.session_state:
 		st.session_state['prev_input_type'] = st.session_state.input_type
 
@@ -287,7 +287,7 @@ def main(myargs):
 		st.session_state['prev_input_type'] = "Text"
 
 		input_column = "Input"
-		data = st.sidebar.text_area("Please enter a breast cancer pathology diagnose")
+		data = st.sidebar.text_area("Please enter a breast cancer pathology diagnose",value="BRWIRE Left wire directed segmntal mastectomy; short suture, superior; long suture, lateral   breast, left, wire-directed segmental mastectomy:   - infiltrating ductal carcinoma, nottingham grade i, 0.8 cm in maximum gross dimension.   - ductal carcinoma in situ, low nuclear grade, solid and cribriform types, associated with  microcalcifications and partially involving a small intraductal papilloma (0.2 cm). - invasive and in situ carcinoma extend to within 0.2 cm of the anterior specimen edge  separately submitted margin specimen below).  - no angiolymphatic invasion identifie  - adjacent breast with biopsy site changes, a small intraductal papilloma (0.2 cm), and fibrocystic changes.      - see synoptic report.")
 		if "user_input" in st.session_state:
 			if data != st.session_state.user_input:
 				delete_var_session(keys=["data_df","input_column","user_input","hg_df","all_l","highlight_samples","selected_indices","json_output","bert_lime_output","embeddings_all"])