From dca4969004ad97de4b9fbdc8e972b9a7cef6dab4 Mon Sep 17 00:00:00 2001
From: barthes <nicolas.barthes@cefe.cnrs.fr>
Date: Tue, 26 Mar 2024 15:43:13 +0100
Subject: [PATCH] updated requirements.txt updated .gitignore for .streamlit
config folder
---
.gitignore | 1 -
app.py | 15 ++++++++-------
application_functions.py | 2 ++
requirements.txt | 3 ++-
4 files changed, 12 insertions(+), 9 deletions(-)
diff --git a/.gitignore b/.gitignore
index b2c0c60..56516f3 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,4 +1,3 @@
.idea/
-.streamlit/
GUI_Pinard.iml
__pycache__/
\ No newline at end of file
diff --git a/app.py b/app.py
index 708e629..8338188 100644
--- a/app.py
+++ b/app.py
@@ -70,7 +70,7 @@ with st.container():
categorical_variable_2 = settings_column.selectbox("Second Variable Select (hover data)", options = cat_cols)
plot_pc = scatter_column.plotly_chart(px.scatter(data_frame=pc_data, x=pc_1, y=pc_2, template="simple_white", height=800, color=categorical_variable, hover_data = [categorical_variable_2], hover_name=pc_data.index, title="PC plot of sample spectra"))
# Clustering method
- cluster_type = ['', 'k-means']
+ cluster_type = ['', 'k-means', 'umap']
# cluster_type = ['k-means', 'umap'] # uncomment if more clustering algorithms available
type_cluster = settings_column.selectbox("Clustering method: ", options=cluster_type, key=38)
# clustering via K-Means
@@ -130,7 +130,7 @@ with st.container():
pd.DataFrame(export).T.to_csv('./data/sample_selections/Samples_from_' + sselectx_csv.name + '_for_Chemical_Analysis.csv')
else:
scatter_column.write("_Please Choose a file_")
- # clustering via UMAP / HDBSCAN
+ # clustering via UMAP / HDBSCAN -- TO BE DONE !!!
if type_cluster == 'umap':
import hdbscan
# plot de pc with colored clusters and selected samples
@@ -204,16 +204,17 @@ with st.container():
model_column.write('Model Imported')
result = ''
if st.button("Predict"):
+ # use prediction function from application_functions.py to predict chemical values
result = prediction(NIRS_csv, qsep, qhdr, model_loaded)
st.write('Predicted values are: ')
st.dataframe(result)
pd.DataFrame(result).to_csv(export_name + '.csv')
st.write('Predictions exported to ' + export_name + '.csv')
- # # export to local drive
- # from urllib.request import urlretrieve
- # url = ('http://localhost:8501' + export_name[1:] + '.csv')
- # filename = export_name + '.csv'
- # urlretrieve(url, filename)
+ # export to local drive
+ from urllib.request import urlretrieve
+ url = ('http://localhost:8501' + export_name[1:] + '.csv')
+ filename = export_name + '.csv'
+ urlretrieve(url, filename)
# create a report with information on the prediction
## see https://stackoverflow.com/a/59578663
if type(result) is list:
diff --git a/application_functions.py b/application_functions.py
index d4ff907..caecb1c 100644
--- a/application_functions.py
+++ b/application_functions.py
@@ -193,4 +193,6 @@ def list_files(mypath, import_type):
from os import listdir
from os.path import isfile, join
list_files = [f for f in listdir(mypath) if isfile(join(mypath, f)) and f.endswith(import_type + '.pkl')]
+ if list_files == []:
+ list_files = ['Please, create a model before - no model available yet']
return list_files
\ No newline at end of file
diff --git a/requirements.txt b/requirements.txt
index b833c6c..6afe88c 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -4,4 +4,5 @@ Pillow>=8.4.0
protobuf>=3.19.0
watchdog>=2.1.8
pinard>=1.0
-julia>=0.6.2
\ No newline at end of file
+julia>=0.6.2
+plotly>=5.20.0
\ No newline at end of file
--
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