file upload

app.py

@@ -1,5 +1,5 @@
 import streamlit as st
-import yaml, os, json, random, time, re
+import yaml, os, json, random, time, re, shutil
 import matplotlib.pyplot as plt
 import plotly.graph_objs as go
 import numpy as np
@@ -276,7 +276,20 @@ def create_space_saver():
         st.session_state.config['leafmachine']['project']['delete_temps_keep_VVE'] = st.checkbox("Delete Temporary Files (KEEP files required for VoucherVisionEditor)", st.session_state.config['leafmachine']['project'].get('delete_temps_keep_VVE', False))
         st.session_state.config['leafmachine']['project']['delete_all_temps'] = st.checkbox("Keep only the final transcription file", st.session_state.config['leafmachine']['project'].get('delete_all_temps', False),help="*WARNING:* This limits your ability to do quality assurance. This will delete all folders created by VoucherVision, leaving only the `transcription.xlsx` file.")
 
+def save_uploaded_file(directory, img_file):
+    if not os.path.exists(directory):
+        os.makedirs(directory)
+    # Assuming the uploaded file is an image
+    with Image.open(img_file) as image:
+        # Save the image with a jpg extension
+        image.save(os.path.join(directory, img_file.name), "JPG")
 
+def delete_directory(dir_path):
+    try:
+        shutil.rmtree(dir_path)
+        st.success(f"Deleted folder: {dir_path}")
+    except OSError as e:
+        st.error(f"Error: {dir_path} : {e.strerror}")
 
 
 # def create_private_file():
@@ -1051,7 +1064,7 @@ def content_tab_settings():
     
     ### Input Images Local
     with col_local_1:
-        st.session_state.config['leafmachine']['project']['dir_images_local'] = st.text_input("Input images directory", st.session_state.config['leafmachine']['project'].get('dir_images_local', ''))
+        st.session_state.config['leafmachine']['project']['dir_images_local'] = st.session_state['dir_uploaded_images'] #st.text_input("Input images directory", st.session_state.config['leafmachine']['project'].get('dir_images_local', ''))
         st.session_state.config['leafmachine']['project']['continue_run_from_partial_xlsx'] = st.text_input("Continue run from partially completed project XLSX", st.session_state.config['leafmachine']['project'].get('continue_run_from_partial_xlsx', ''), disabled=True)
         st.write("---")
         st.subheader('LLM Version')
@@ -1073,29 +1086,16 @@ def content_tab_settings():
             st.session_state.config['leafmachine']['project']['prompt_version'] = st.selectbox("Prompt Version", versions, index=versions.index(selected_version))
 
     with col_local_2:
-        uploaded_files = st.file_uploader("Upload Images", accept_multiple_files=True)
-        for uploaded_file in uploaded_files:
-            print(uploaded_file)
-            bytes_data = uploaded_file.read()
-            st.write("filename:", uploaded_file.name)
-            st.write(bytes_data)
-        # if st.session_state.config['leafmachine']['LLM_version'] in ["GPT 4", "GPT 3.5", "Azure GPT 4", "Azure GPT 3.5",]:
-        #     st.session_state.config['leafmachine']['project']['prompt_version'] = st.selectbox("Prompt Version", ["Version 1", "Version 1 No Domain Knowledge", "Version 2"], index=["Version 1", "Version 1 No Domain Knowledge", "Version 2"].index(st.session_state.config['leafmachine']['project'].get('prompt_version', "Version 2")))
-        # elif st.session_state.config['leafmachine']['LLM_version'] in ["PaLM 2",]:
-        #     st.session_state.config['leafmachine']['project']['prompt_version'] = st.selectbox("Prompt Version", ["Version 1 PaLM 2", "Version 1 PaLM 2 No Domain Knowledge", "Version 2 PaLM 2"], index=["Version 1 PaLM 2", "Version 1 PaLM 2 No Domain Knowledge", "Version 2 PaLM 2"].index(st.session_state.config['leafmachine']['project'].get('prompt_version', "Version 2 PaLM 2")))
-
-    ### Modules
-    # with col_m1:
-    #     st.session_state.config['leafmachine']['modules']['specimen_crop'] = st.checkbox("Specimen Close-up", st.session_state.config['leafmachine']['modules'].get('specimen_crop', True),disabled=True)
-
-    ### cropped_components
-    # with col_cropped_1:
-    #     st.session_state.config['leafmachine']['cropped_components']['do_save_cropped_annotations'] = st.checkbox("Save cropped components as images", st.session_state.config['leafmachine']['cropped_components'].get('do_save_cropped_annotations', True), disabled=True)
-    #     st.session_state.config['leafmachine']['cropped_components']['save_per_image'] = st.checkbox("Save cropped components grouped by specimen", st.session_state.config['leafmachine']['cropped_components'].get('save_per_image', False), disabled=True)
-    #     st.session_state.config['leafmachine']['cropped_components']['save_per_annotation_class'] = st.checkbox("Save cropped components grouped by type", st.session_state.config['leafmachine']['cropped_components'].get('save_per_annotation_class', True), disabled=True)
-    #     st.session_state.config['leafmachine']['cropped_components']['binarize_labels'] = st.checkbox("Binarize labels", st.session_state.config['leafmachine']['cropped_components'].get('binarize_labels', False), disabled=True)
-    #     st.session_state.config['leafmachine']['cropped_components']['binarize_labels_skeletonize'] = st.checkbox("Binarize and skeletonize labels", st.session_state.config['leafmachine']['cropped_components'].get('binarize_labels_skeletonize', False), disabled=True)
-    
+        st.session_state['dir_uploaded_images'] = os.path.join(st.session_state.dir_home,'uploads')
+        uploaded_files = st.file_uploader("Upload Images", type=['jpg', 'jpeg'], accept_multiple_files=True)
+        if uploaded_files:
+            delete_directory(st.session_state['dir_uploaded_images'])
+            st.session_state['dir_uploaded_images'] = os.path.join(st.session_state.dir_home,'uploads')
+            validate_dir(st.session_state['dir_uploaded_images'])
+            for uploaded_file in uploaded_files:
+                print(uploaded_file)
+                save_uploaded_file(st.session_state['dir_uploaded_images'], uploaded_file)
+        
     with col_cropped_1:
         default_crops = st.session_state.config['leafmachine']['cropped_components'].get('save_cropped_annotations', ['leaf_whole'])
         st.write("Prior to transcription, use LeafMachine2 to crop all labels from input images to create label collages for each specimen image. (Requires GPU)")