diff --git "a/app.py" "b/app.py" --- "a/app.py" +++ "b/app.py" @@ -1,37 +1,221 @@ import streamlit as st -import yaml, os, json, random, time, shutil +import yaml, os, json, random, time, re, torch, random, warnings, shutil, sys +import seaborn as sns import plotly.graph_objs as go from itertools import chain from PIL import Image +import pandas as pd from io import BytesIO from streamlit_extras.let_it_rain import rain +from annotated_text import annotated_text from vouchervision.LeafMachine2_Config_Builder import write_config_file -from vouchervision.VoucherVision_Config_Builder import build_VV_config , TestOptionsGPT, TestOptionsPalm, check_if_usable -from vouchervision.vouchervision_main import voucher_vision -from vouchervision.general_utils import summarize_expense_report, validate_dir -from vouchervision.utils import upload_to_drive, image_to_base64, setup_streamlit_config, save_uploaded_file, check_prompt_yaml_filename - +from vouchervision.VoucherVision_Config_Builder import build_VV_config, run_demo_tests_GPT, run_demo_tests_Palm , TestOptionsGPT, TestOptionsPalm, check_if_usable, run_api_tests +from vouchervision.vouchervision_main import voucher_vision, voucher_vision_OCR_test +from vouchervision.general_utils import test_GPU, get_cfg_from_full_path, summarize_expense_report, create_google_ocr_yaml_config, validate_dir +from vouchervision.model_maps import ModelMaps +from vouchervision.API_validation import APIvalidation +from vouchervision.utils_hf import upload_to_drive, image_to_base64, setup_streamlit_config, save_uploaded_file, check_prompt_yaml_filename ######################################################################################################## -### Constants #### +### ADDED FOR HUGGING FACE #### ######################################################################################################## -PROMPTS_THAT_NEED_DOMAIN_KNOWLEDGE = ["Version 1","Version 1 PaLM 2"] -# LLM_VERSIONS = ["GPT 4", "GPT 3.5", "Azure GPT 4", "Azure GPT 3.5", "PaLM 2"] -COLORS_EXPENSE_REPORT = { - 'GPT_4': '#8fff66', # Bright Green - 'GPT_3_5': '#006400', # Dark Green - 'PALM2': '#66a8ff' # blue - } +if 'uploader_idk' not in st.session_state: + st.session_state['uploader_idk'] = 1 +if 'input_list_small' not in st.session_state: + st.session_state['input_list_small'] = [] +if 'input_list' not in st.session_state: + st.session_state['input_list'] = [] +if 'user_clicked_load_prompt_yaml' not in st.session_state: + st.session_state['user_clicked_load_prompt_yaml'] = None +if 'new_prompt_yaml_filename' not in st.session_state: + st.session_state['new_prompt_yaml_filename'] = None + MAX_GALLERY_IMAGES = 50 GALLERY_IMAGE_SIZE = 128 -N_OVERALL_STEPS = 6 +def content_input_images_hf(): + st.write('---') + col1, col2 = st.columns([2,8]) + + with col1: + st.header('Run name') + st.session_state.config['leafmachine']['project']['run_name'] = st.text_input("Run name", st.session_state.config['leafmachine']['project'].get('run_name', ''), + label_visibility='collapsed',key=995) + st.write("Run name will be the name of the final zipped folder.") + + st.write('---') + st.header('Input Images') + st.session_state['dir_uploaded_images'] = os.path.join(st.session_state.dir_home,'uploads') + st.session_state['dir_uploaded_images_small'] = os.path.join(st.session_state.dir_home,'uploads_small') + uploaded_files = st.file_uploader("Upload Images", type=['jpg', 'jpeg'], accept_multiple_files=True, key=st.session_state['uploader_idk']) + if uploaded_files: + # Clear input image gallery and input list + clear_image_gallery() + + # Process the new iamges + for uploaded_file in uploaded_files: + file_path = save_uploaded_file(st.session_state['dir_uploaded_images'], uploaded_file) + st.session_state['input_list'].append(file_path) + + img = Image.open(file_path) + img.thumbnail((GALLERY_IMAGE_SIZE, GALLERY_IMAGE_SIZE), Image.Resampling.LANCZOS) + file_path_small = save_uploaded_file(st.session_state['dir_uploaded_images_small'], uploaded_file, img) + st.session_state['input_list_small'].append(file_path_small) + print(uploaded_file.name) + + # Set the local images to the uploaded images + st.session_state.config['leafmachine']['project']['dir_images_local'] = st.session_state['dir_uploaded_images'] + + n_images = len([f for f in os.listdir(st.session_state.config['leafmachine']['project']['dir_images_local']) if os.path.isfile(os.path.join(st.session_state.config['leafmachine']['project']['dir_images_local'], f))]) + st.session_state['processing_add_on'] = f" {n_images} Images" + uploaded_files = None + st.session_state['uploader_idk'] += 1 + st.info(f"Processing **{n_images}** images from {st.session_state.config['leafmachine']['project']['dir_images_local']}") + + + st.button("Use Test Image",help="This will clear any uploaded images and load the 1 provided test image.",on_click=use_test_image) + + with col2: + if st.session_state['input_list_small']: + st.subheader('Image Gallery') + if len(st.session_state['input_list_small']) > MAX_GALLERY_IMAGES: + # Only take the first 100 images from the list + images_to_display = st.session_state['input_list_small'][:MAX_GALLERY_IMAGES] + else: + # If there are less than 100 images, take them all + images_to_display = st.session_state['input_list_small'] + st.image(images_to_display) + + + + + +def create_download_button(zip_filepath): + with open(zip_filepath, 'rb') as f: + bytes_io = BytesIO(f.read()) + st.download_button( + label=f"Download Results for{st.session_state['processing_add_on']}",type='primary', + data=bytes_io, + file_name=os.path.basename(zip_filepath), + mime='application/zip' + ) + + +def delete_directory(dir_path): + try: + shutil.rmtree(dir_path) + st.session_state['input_list'] = [] + st.session_state['input_list_small'] = [] + # st.success(f"Deleted previously uploaded images, making room for new images: {dir_path}") + except OSError as e: + st.error(f"Error: {dir_path} : {e.strerror}") + + +def clear_image_gallery(): + delete_directory(st.session_state['dir_uploaded_images']) + delete_directory(st.session_state['dir_uploaded_images_small']) + validate_dir(st.session_state['dir_uploaded_images']) + validate_dir(st.session_state['dir_uploaded_images_small']) + + +def use_test_image(): + st.info(f"Processing images from {os.path.join(st.session_state.dir_home,'demo','demo_images')}") + st.session_state.config['leafmachine']['project']['dir_images_local'] = os.path.join(st.session_state.dir_home,'demo','demo_images') + n_images = len([f for f in os.listdir(st.session_state.config['leafmachine']['project']['dir_images_local']) if os.path.isfile(os.path.join(st.session_state.config['leafmachine']['project']['dir_images_local'], f))]) + st.session_state['processing_add_on'] = f" {n_images} Images" + clear_image_gallery() + st.session_state['uploader_idk'] += 1 + + +def create_download_button_yaml(file_path, selected_yaml_file): + file_label = f"Download {selected_yaml_file}" + with open(file_path, 'rb') as f: + st.download_button( + label=file_label, + data=f, + file_name=os.path.basename(file_path), + mime='application/x-yaml',use_container_width=True + ) + + +def upload_local_prompt_to_server(dir_prompt): + uploaded_file = st.file_uploader("Upload a custom prompt file", type=['yaml']) + if uploaded_file is not None: + # Check the file extension + file_name = uploaded_file.name + if file_name.endswith('.yaml'): + file_path = os.path.join(dir_prompt, file_name) + + # Save the file + with open(file_path, 'wb') as f: + f.write(uploaded_file.getbuffer()) + st.success(f"Saved file {file_name} in {dir_prompt}") + else: + st.error("Please upload a .yaml file that you previously created using this Prompt Builder tool.") + + +def refresh(): + st.session_state['uploader_idk'] += 1 + st.write('') + + + +# def display_image_gallery(): +# # Initialize the container +# con_image = st.empty() + +# # Start the div for the image grid +# img_grid_html = """ +#
+# """ + +# # Loop through each image in the input list +# # with con_image.container(): +# for image_path in st.session_state['input_list']: +# # Open the image and create a thumbnail +# img = Image.open(image_path) +# img.thumbnail((120, 120), Image.Resampling.LANCZOS) + +# # Convert the image to base64 +# base64_image = image_to_base64(img) + +# # Append the image to the grid HTML +# # img_html = f""" +# #
+# # Image +# #
+# # """ +# img_html = f""" +# Image +# """ +# img_grid_html += img_html +# # st.markdown(img_html, unsafe_allow_html=True) + + +# # Close the div for the image grid +# img_grid_html += "
" + +# # Display the image grid in the container +# with con_image.container(): +# st.markdown(img_grid_html, unsafe_allow_html=True) + +# # The CSS to make the images display inline and be responsive +# css = """ +# +# """ +# # Apply the CSS +# st.markdown(css, unsafe_allow_html=True) +######################################################################################################## ######################################################################################################## -### Progress bar #### ######################################################################################################## class ProgressReport: def __init__(self, overall_bar, batch_bar, text_overall, text_batch): @@ -70,17 +254,184 @@ class ProgressReport: self.current_overall_step = 0 self.overall_bar.progress(0) self.text_overall.text(step_name) - + def get_n_images(self): return self.n_images def get_n_overall(self): return self.total_overall_steps + +class JSONReport: + def __init__(self, col_updates, col_json, col_json_WFO, col_json_GEO, col_json_map): + self.plant_list = [':evergreen_tree:', ':deciduous_tree:',':palm_tree:', + ':maple_leaf:',':fallen_leaf:',':mushroom:',':leaves:', + ':cactus:',':seedling:',':tulip:',':sunflower:',':hibiscus:', + ':cherry_blossom:',':rose:',] + self.location_list = [':earth_africa:',':earth_americas:',':earth_asia:',] + self.book_list = [':bookmark_tabs:',':ledger:',':notebook:',':clipboard:',':scroll:', + ':notebook_with_decorative_cover:',':green_book:',':blue_book:', + ':open_book:',':closed_book:',':book:', + ':orange_book:',':books:',':memo:',':pencil:', + ] + + # Create placeholders for each JSON component + self.col_updates = col_updates + self.col_json = col_json + self.col_json_WFO = col_json_WFO + self.col_json_GEO = col_json_GEO + self.col_json_map = col_json_map + + self.update_main = col_updates.empty() + + self.update_left = col_json.empty() + self.header_json = col_json.empty() + self.json_placeholder = col_json.empty() + + self.update_middle = col_json_WFO.empty() + self.header_json_WFO = col_json_WFO.empty() + self.json_WFO_placeholder = col_json_WFO.empty() + + self.update_right = col_json_GEO.empty() + self.header_json_GEO = col_json_GEO.empty() + self.json_GEO_placeholder = col_json_GEO.empty() + + self.update_map = col_json_map.empty() + self.header_json_map = col_json_map.empty() + self.json_map = col_json_map.empty() + + + self.json = None + self.json_WFO = None + self.json_GEO = None + + self.text_main = '' + self.text_middle = '' + self.text_right = '' + + self.header_text_main = None + self.header_text_middle = None + self.header_text_right = None + + def set_JSON(self, json_main, json_WFO, json_GEO): + i_plant = random.randint(0,len(self.plant_list)-1) + i_location = random.randint(0,len(self.location_list)-1) + i_book = random.randint(0,len(self.book_list)-1) + self.json = json_main + self.json_WFO = json_WFO + self.json_GEO = json_GEO + + # Update placeholders with new JSON data + self.header_text_main = None + self.header_text_middle = None + self.header_text_right = None + + self.update_main.subheader(f':loudspeaker: {self.text_main}') + self.update_left.subheader(f'{self.book_list[i_book]}', divider='rainbow') + self.update_middle.subheader(f'{self.plant_list[i_plant]}', divider='rainbow') + self.update_right.subheader(f'{self.location_list[i_location]}', divider='rainbow') + self.update_map.subheader(f':world_map:', divider='rainbow') + + self.header_json.markdown('**LLM-derived information from the OCR text**') + self.header_json_WFO.markdown('World Flora Online') + self.header_json_GEO.markdown('Geolocate') + self.header_json_map.markdown(f':large_purple_circle: :violet[Geolocated] :large_green_circle: :green[From OCR Text]') + + self.json_placeholder.json(self.json) + self.json_WFO_placeholder.json(self.json_WFO) + self.json_GEO_placeholder.json(self.json_GEO) + + # If GEO data is available, plot on the map + # Clear the existing content in the map placeholder + # Clear the existing content in the map placeholder + self.json_map.empty() + map_points = [] + map_data = [] + # Function to safely convert to float + def safe_float_convert(value): + try: + return float(value) + except (ValueError, TypeError): + return None + + # Check and process first point's data + lat = safe_float_convert(self.json_GEO.get("GEO_decimal_lat")) if self.json_GEO else None + lon = safe_float_convert(self.json_GEO.get("GEO_decimal_long")) if self.json_GEO else None + + if lat is not None and lon is not None: + map_points.append({'lat': lat, 'lon': lon, 'color': '#8800ff' , 'size': [50000]}) + + # Check and process second point's data + lat_verbatim = safe_float_convert(self.json.get("decimalLatitude")) if self.json else None + lon_verbatim = safe_float_convert(self.json.get("decimalLongitude")) if self.json else None + + if lat_verbatim is not None and lon_verbatim is not None: + map_points.append({'lat': lat_verbatim, 'lon': lon_verbatim, 'color': '#00c227' , 'size': [25000]}) + + # Convert the list of points to a DataFrame + map_data = pd.DataFrame(map_points) + + # Display the map if map_data is not empty + if not map_data.empty: + with self.json_map: + st.map(map_data, zoom=4, size='size', color='color') + + def set_text(self, text_main=None, text_middle=None, text_right=None): + if text_main: + self.text_main = text_main + self.update_main.subheader(f':loudspeaker: {self.text_main}') + if text_middle: + self.text_middle = text_middle + self.update_middle.subheader('', divider='rainbow') + if text_right: + self.text_right = text_right + self.update_right.subheader(self.text_right, divider='rainbow') + + def clear_JSON(self): + self.json = None + self.json_WFO = None + self.json_GEO = None + + # Clear the content in the placeholders + self.json_placeholder.empty() + self.json_WFO_placeholder.empty() + self.json_GEO_placeholder.empty() + + def format_json(self, json_obj): + try: + return json.dumps(json.loads(json_obj), indent=4, sort_keys=False) + except: + return json.dumps(json_obj, indent=4, sort_keys=False) + +def does_private_file_exist(): + dir_home = os.path.dirname(os.path.dirname(__file__)) + path_cfg_private = os.path.join(dir_home, 'PRIVATE_DATA.yaml') + return os.path.exists(path_cfg_private) + +def setup_streamlit_config(dir_home): + # Define the directory path and filename + dir_path = os.path.join(dir_home, ".streamlit") + file_path = os.path.join(dir_path, "config.toml") + + # Check if directory exists, if not create it + if not os.path.exists(dir_path): + os.makedirs(dir_path) + + # Create or modify the file with the provided content + config_content = f""" + [theme] + base = "dark" + primaryColor = "#00ff00" + + [server] + enableStaticServing = false + runOnSave = true + port = 8524 + """ + + with open(file_path, "w") as f: + f.write(config_content.strip()) -######################################################################################################## -### Streamlit helper functions #### -######################################################################################################## def display_scrollable_results(JSON_results, test_results, OPT2, OPT3): """ Display the results from JSON_results in a scrollable container. @@ -124,7 +475,6 @@ def display_scrollable_results(JSON_results, test_results, OPT2, OPT3): st.markdown(results_html, unsafe_allow_html=True) - def display_test_results(test_results, JSON_results, llm_version): if llm_version == 'gpt': OPT1, OPT2, OPT3 = TestOptionsGPT.get_options() @@ -162,6 +512,7 @@ def display_test_results(test_results, JSON_results, llm_version): # Close the custom container st.write('', unsafe_allow_html=True) + for idx, (test_name, result) in enumerate(sorted(test_results.items())): _, ind_opt1, ind_opt2, ind_opt3 = test_name.split('__') opt2_readable = "Use LeafMachine2" if OPT2[int(ind_opt2.split('-')[1])] else "Don't use LeafMachine2" @@ -188,13 +539,9 @@ def display_test_results(test_results, JSON_results, llm_version): # proportional_rain("🥇", success_count, "💔", failure_count, font_size=72, falling_speed=5, animation_length="infinite") rain_emojis(test_results) - - def add_emoji_delay(): time.sleep(0.3) - - def rain_emojis(test_results): # test_results = { # 'test1': True, # Test passed @@ -234,33 +581,260 @@ def rain_emojis(test_results): ) add_emoji_delay() - - +def format_json(json_obj): + try: + return json.dumps(json.loads(json_obj), indent=4, sort_keys=False) + except: + return json.dumps(json_obj, indent=4, sort_keys=False) + def get_prompt_versions(LLM_version): yaml_files = [f for f in os.listdir(os.path.join(st.session_state.dir_home, 'custom_prompts')) if f.endswith('.yaml')] - if LLM_version in ["GPT 4", "GPT 3.5", "Azure GPT 4", "Azure GPT 3.5"]: - versions = ["Version 1", "Version 1 No Domain Knowledge", "Version 2"] - return (versions + yaml_files, "Version 2") - elif LLM_version in ["PaLM 2",]: - versions = ["Version 1 PaLM 2", "Version 1 PaLM 2 No Domain Knowledge", "Version 2 PaLM 2"] - return (versions + yaml_files, "Version 2 PaLM 2") + return yaml_files + +def get_private_file(): + dir_home = os.path.dirname(os.path.dirname(__file__)) + path_cfg_private = os.path.join(dir_home, 'PRIVATE_DATA.yaml') + return get_cfg_from_full_path(path_cfg_private) + + + +# def create_private_file(): +# st.session_state.proceed_to_main = False + +# if st.session_state.private_file: +# cfg_private = get_private_file() +# create_private_file_0(cfg_private) +# else: +# st.title("VoucherVision") +# create_private_file_0() + +def create_private_file(): + st.session_state.proceed_to_main = False + st.title("VoucherVision") + col_private,_= st.columns([12,2]) + + if st.session_state.private_file: + cfg_private = get_private_file() else: - # Handle other cases or raise an error - return (yaml_files, None) + cfg_private = {} + cfg_private['openai'] = {} + cfg_private['openai']['OPENAI_API_KEY'] ='' + + cfg_private['openai_azure'] = {} + cfg_private['openai_azure']['openai_api_key'] = '' + cfg_private['openai_azure']['api_version'] = '' + cfg_private['openai_azure']['openai_api_base'] ='' + cfg_private['openai_azure']['openai_organization'] ='' + cfg_private['openai_azure']['openai_api_type'] ='' + + cfg_private['google_cloud'] = {} + cfg_private['google_cloud']['path_json_file'] ='' + + cfg_private['google_palm'] = {} + cfg_private['google_palm']['google_palm_api'] ='' + + with col_private: + st.header("Set API keys") + st.info("***Note:*** There is a known bug with tabs in Streamlit. If you update an input field it may take you back to the 'Project Settings' tab. Changes that you made are saved, it's just an annoying glitch. We are aware of this issue and will fix it as soon as we can.") + st.warning("To commit changes to API keys you must press the 'Set API Keys' button at the bottom of the page.") + st.write("Before using VoucherVision you must set your API keys. All keys are stored locally on your computer and are never made public.") + st.write("API keys are stored in `../VoucherVision/PRIVATE_DATA.yaml`.") + st.write("Deleting this file will allow you to reset API keys. Alternatively, you can edit the keys in the user interface.") + st.write("Leave keys blank if you do not intend to use that service.") + + st.write("---") + st.subheader("Google Vision (*Required*)") + st.markdown("VoucherVision currently uses [Google Vision API](https://cloud.google.com/vision/docs/ocr) for OCR. Generating an API key for this is more involved than the others. [Please carefully follow the instructions outlined here to create and setup your account.](https://cloud.google.com/vision/docs/setup) ") + st.markdown(""" + Once your account is created, [visit this page](https://console.cloud.google.com) and create a project. Then follow these instructions: + + - **Select your Project**: If you have multiple projects, ensure you select the one where you've enabled the Vision API. + - **Open the Navigation Menu**: Click on the hamburger menu (three horizontal lines) in the top left corner. + - **Go to IAM & Admin**: In the navigation pane, hover over "IAM & Admin" and then click on "Service accounts." + - **Locate Your Service Account**: Find the service account for which you wish to download the JSON key. If you haven't created a service account yet, you'll need to do so by clicking the "CREATE SERVICE ACCOUNT" button at the top. + - **Download the JSON Key**: + - Click on the three dots (actions menu) on the right side of your service account name. + - Select "Manage keys." + - In the pop-up window, click on the "ADD KEY" button and select "JSON." + - The JSON key file will automatically be downloaded to your computer. + - **Store Safely**: This file contains sensitive data that can be used to authenticate and bill your Google Cloud account. Never commit it to public repositories or expose it in any way. Always keep it safe and secure. + """) + with st.container(): + c_in_ocr, c_button_ocr = st.columns([10,2]) + with c_in_ocr: + google_vision = st.text_input(label = 'Full path to Google Cloud JSON API key file', value = cfg_private['google_cloud'].get('path_json_file', ''), + placeholder = 'e.g. C:/Documents/Secret_Files/google_API/application_default_credentials.json', + help ="This API Key is in the form of a JSON file. Please save the JSON file in a safe directory. DO NOT store the JSON key inside of the VoucherVision directory.", + type='password',key='924857298734590283750932809238') + with c_button_ocr: + st.empty() + + st.write("---") + st.subheader("OpenAI") + st.markdown("API key for first-party OpenAI API. Create an account with OpenAI [here](https://platform.openai.com/signup), then create an API key [here](https://platform.openai.com/account/api-keys).") + with st.container(): + c_in_openai, c_button_openai = st.columns([10,2]) + with c_in_openai: + openai_api_key = st.text_input("openai_api_key", cfg_private['openai'].get('OPENAI_API_KEY', ''), + help='The actual API key. Likely to be a string of 2 character, a dash, and then a 48-character string: sk-XXXXXXXX...', + placeholder = 'e.g. sk-XXXXXXXX...', + type='password') + with c_button_openai: + st.empty() + + st.write("---") + st.subheader("OpenAI - Azure") + st.markdown("This version OpenAI relies on Azure servers directly as is intended for private enterprise instances of OpenAI's services, such as [UM-GPT](https://its.umich.edu/computing/ai). Administrators will provide you with the following information.") + azure_openai_api_version = st.text_input("azure_openai_api_version", cfg_private['openai_azure'].get('api_version', ''), + help='API Version e.g. "2023-05-15"', + placeholder = 'e.g. 2023-05-15', + type='password') + azure_openai_api_key = st.text_input("azure_openai_api_key", cfg_private['openai_azure'].get('openai_api_key', ''), + help='The actual API key. Likely to be a 32-character string', + placeholder = 'e.g. 12333333333333333333333333333332', + type='password') + azure_openai_api_base = st.text_input("azure_openai_api_base", cfg_private['openai_azure'].get('openai_api_base', ''), + help='The base url for the API e.g. "https://api.umgpt.umich.edu/azure-openai-api"', + placeholder = 'e.g. https://api.umgpt.umich.edu/azure-openai-api', + type='password') + azure_openai_organization = st.text_input("azure_openai_organization", cfg_private['openai_azure'].get('openai_organization', ''), + help='Your organization code. Likely a short string', + placeholder = 'e.g. 123456', + type='password') + azure_openai_api_type = st.text_input("azure_openai_api_type", cfg_private['openai_azure'].get('openai_api_type', ''), + help='The API type. Typically "azure"', + placeholder = 'e.g. azure', + type='password') + with st.container(): + c_in_azure, c_button_azure = st.columns([10,2]) + with c_button_azure: + st.empty() + + st.write("---") + st.subheader("Google PaLM 2") + st.markdown('Follow these [instructions](https://developers.generativeai.google/tutorials/setup) to generate an API key for PaLM 2. You may need to also activate an account with [MakerSuite](https://makersuite.google.com/app/apikey) and enable "early access."') + with st.container(): + c_in_palm, c_button_palm = st.columns([10,2]) + with c_in_palm: + google_palm = st.text_input("Google PaLM 2 API Key", cfg_private['google_palm'].get('google_palm_api', ''), + help='The MakerSuite API key e.g. a 32-character string', + placeholder='e.g. SATgthsykuE64FgrrrrEervr3S4455t_geyDeGq', + type='password') + + with st.container(): + with c_button_ocr: + st.write("##") + st.button("Test OCR", on_click=test_API, args=['google_vision',c_in_ocr, cfg_private,openai_api_key,azure_openai_api_version,azure_openai_api_key, + azure_openai_api_base,azure_openai_organization,azure_openai_api_type,google_vision,google_palm]) + + with st.container(): + with c_button_openai: + st.write("##") + st.button("Test OpenAI", on_click=test_API, args=['openai',c_in_openai, cfg_private,openai_api_key,azure_openai_api_version,azure_openai_api_key, + azure_openai_api_base,azure_openai_organization,azure_openai_api_type,google_vision,google_palm]) + + with st.container(): + with c_button_azure: + st.write("##") + st.button("Test Azure OpenAI", on_click=test_API, args=['azure_openai',c_in_azure, cfg_private,openai_api_key,azure_openai_api_version,azure_openai_api_key, + azure_openai_api_base,azure_openai_organization,azure_openai_api_type,google_vision,google_palm]) + + with st.container(): + with c_button_palm: + st.write("##") + st.button("Test PaLM 2", on_click=test_API, args=['palm',c_in_palm, cfg_private,openai_api_key,azure_openai_api_version,azure_openai_api_key, + azure_openai_api_base,azure_openai_organization,azure_openai_api_type,google_vision,google_palm]) -def delete_directory(dir_path): - try: - shutil.rmtree(dir_path) - st.session_state['input_list'] = [] - st.session_state['input_list_small'] = [] - # st.success(f"Deleted previously uploaded images, making room for new images: {dir_path}") - except OSError as e: - st.error(f"Error: {dir_path} : {e.strerror}") + st.button("Set API Keys",type='primary', on_click=save_changes_to_API_keys, args=[cfg_private,openai_api_key,azure_openai_api_version,azure_openai_api_key, + azure_openai_api_base,azure_openai_organization,azure_openai_api_type,google_vision,google_palm]) + if st.button('Proceed to VoucherVision'): + st.session_state.proceed_to_private = False + st.session_state.proceed_to_main = True +def test_API(api, message_loc, cfg_private,openai_api_key,azure_openai_api_version,azure_openai_api_key, azure_openai_api_base,azure_openai_organization,azure_openai_api_type,google_vision,google_palm): + # Save the API keys + save_changes_to_API_keys(cfg_private,openai_api_key,azure_openai_api_version,azure_openai_api_key,azure_openai_api_base,azure_openai_organization,azure_openai_api_type,google_vision,google_palm) + + with st.spinner('Performing validation checks...'): + if api == 'google_vision': + print("*** Google Vision OCR API Key ***") + try: + demo_config_path = os.path.join(st.session_state.dir_home,'demo','validation_configs','google_vision_ocr_test.yaml') + demo_images_path = os.path.join(st.session_state.dir_home, 'demo', 'demo_images') + demo_out_path = os.path.join(st.session_state.dir_home, 'demo', 'demo_output','run_name') + create_google_ocr_yaml_config(demo_config_path, demo_images_path, demo_out_path) + voucher_vision_OCR_test(demo_config_path, st.session_state.dir_home, None, demo_images_path) + with message_loc: + st.success("Google Vision OCR API Key Valid :white_check_mark:") + return True + except Exception as e: + with message_loc: + st.error(f"Google Vision OCR API Key Failed! {e}") + return False + + elif api == 'openai': + print("*** OpenAI API Key ***") + try: + if run_api_tests('openai'): + with message_loc: + st.success("OpenAI API Key Valid :white_check_mark:") + else: + with message_loc: + st.error("OpenAI API Key Failed:exclamation:") + return False + except Exception as e: + with message_loc: + st.error(f"OpenAI API Key Failed:exclamation: {e}") + + elif api == 'azure_openai': + print("*** Azure OpenAI API Key ***") + try: + if run_api_tests('azure_openai'): + with message_loc: + st.success("Azure OpenAI API Key Valid :white_check_mark:") + else: + with message_loc: + st.error(f"Azure OpenAI API Key Failed:exclamation:") + return False + except Exception as e: + with message_loc: + st.error(f"Azure OpenAI API Key Failed:exclamation: {e}") + elif api == 'palm': + print("*** Google PaLM 2 API Key ***") + try: + if run_api_tests('palm'): + with message_loc: + st.success("Google PaLM 2 API Key Valid :white_check_mark:") + else: + with message_loc: + st.error("Google PaLM 2 API Key Failed:exclamation:") + return False + except Exception as e: + with message_loc: + st.error(f"Google PaLM 2 API Key Failed:exclamation: {e}") + + +def save_changes_to_API_keys(cfg_private,openai_api_key,azure_openai_api_version,azure_openai_api_key, + azure_openai_api_base,azure_openai_organization,azure_openai_api_type,google_vision,google_palm): + # Update the configuration dictionary with the new values + cfg_private['openai']['OPENAI_API_KEY'] = openai_api_key + + cfg_private['openai_azure']['api_version'] = azure_openai_api_version + cfg_private['openai_azure']['openai_api_key'] = azure_openai_api_key + cfg_private['openai_azure']['openai_api_base'] = azure_openai_api_base + cfg_private['openai_azure']['openai_organization'] = azure_openai_organization + cfg_private['openai_azure']['openai_api_type'] = azure_openai_api_type + + cfg_private['google_cloud']['path_json_file'] = google_vision + + cfg_private['google_palm']['google_palm_api'] = google_palm + # Call the function to write the updated configuration to the YAML file + write_config_file(cfg_private, st.session_state.dir_home, filename="PRIVATE_DATA.yaml") + st.session_state.private_file = does_private_file_exist() # Function to load a YAML file and update session_state def load_prompt_yaml(filename): @@ -269,33 +843,26 @@ def load_prompt_yaml(filename): st.session_state['prompt_info'] = yaml.safe_load(file) st.session_state['prompt_author'] = st.session_state['prompt_info'].get('prompt_author', st.session_state['default_prompt_author']) st.session_state['prompt_author_institution'] = st.session_state['prompt_info'].get('prompt_author_institution', st.session_state['default_prompt_author_institution']) + st.session_state['prompt_name'] = st.session_state['prompt_info'].get('prompt_name', st.session_state['default_prompt_name']) + st.session_state['prompt_version'] = st.session_state['prompt_info'].get('prompt_version', st.session_state['default_prompt_version']) st.session_state['prompt_description'] = st.session_state['prompt_info'].get('prompt_description', st.session_state['default_prompt_description']) - st.session_state['LLM'] = st.session_state['prompt_info'].get('LLM', 'gpt') st.session_state['instructions'] = st.session_state['prompt_info'].get('instructions', st.session_state['default_instructions']) st.session_state['json_formatting_instructions'] = st.session_state['prompt_info'].get('json_formatting_instructions', st.session_state['default_json_formatting_instructions'] ) st.session_state['rules'] = st.session_state['prompt_info'].get('rules', {}) st.session_state['mapping'] = st.session_state['prompt_info'].get('mapping', {}) - - st.session_state['prompt_info'] = { - 'prompt_author': st.session_state['prompt_author'], - 'prompt_author_institution': st.session_state['prompt_author_institution'], - 'prompt_description': st.session_state['prompt_description'], - 'LLM': st.session_state['LLM'], - 'instructions': st.session_state['instructions'], - 'json_formatting_instructions': st.session_state['json_formatting_instructions'], - 'rules': st.session_state['rules'], - 'mapping': st.session_state['mapping'], - } + st.session_state['LLM'] = st.session_state['prompt_info'].get('LLM', 'General Purpose') # Placeholder: st.session_state['assigned_columns'] = list(chain.from_iterable(st.session_state['mapping'].values())) - - -def save_prompt_yaml(filename, col_right_save): +### Updated to match HF version +# def save_prompt_yaml(filename): +def save_prompt_yaml(filename, col): yaml_content = { 'prompt_author': st.session_state['prompt_author'], 'prompt_author_institution': st.session_state['prompt_author_institution'], + 'prompt_name': st.session_state['prompt_name'], + 'prompt_version': st.session_state['prompt_version'], 'prompt_description': st.session_state['prompt_description'], 'LLM': st.session_state['LLM'], 'instructions': st.session_state['instructions'], @@ -312,33 +879,39 @@ def save_prompt_yaml(filename, col_right_save): st.success(f"Prompt saved as '{filename}.yaml'.") - upload_to_drive(filepath, filename) - - with col_right_save: - create_download_button_yaml(filepath, filename) - + upload_to_drive(filepath, filename) # added + with col: # added + create_download_button_yaml(filepath, filename) # added def check_unique_mapping_assignments(): + print(st.session_state['assigned_columns']) if len(st.session_state['assigned_columns']) != len(set(st.session_state['assigned_columns'])): st.error("Each column name must be assigned to only one category.") return False + elif not st.session_state['assigned_columns']: + st.error("No columns have been mapped.") + return False + elif len(st.session_state['assigned_columns']) != len(st.session_state['rules'].keys()): + incomplete = [item for item in list(st.session_state['rules'].keys()) if item not in st.session_state['assigned_columns']] + st.warning(f"These columns have been mapped: {st.session_state['assigned_columns']}") + st.error(f"However, these columns must be mapped before the prompt is complete: {incomplete}") + return False else: st.success("Mapping confirmed.") return True +def check_prompt_yaml_filename(fname): + # Check if the filename only contains letters, numbers, underscores, and dashes + pattern = r'^[\w-]+$' + + # The \w matches any alphanumeric character and is equivalent to the character class [a-zA-Z0-9_]. + # The hyphen - is literally matched. - -def create_download_button(zip_filepath): - with open(zip_filepath, 'rb') as f: - bytes_io = BytesIO(f.read()) - st.download_button( - label=f"Download Results for{st.session_state['processing_add_on']}",type='primary', - data=bytes_io, - file_name=os.path.basename(zip_filepath), - mime='application/zip' - ) - + if re.match(pattern, fname): + return True + else: + return False def btn_load_prompt(selected_yaml_file, dir_prompt): @@ -350,371 +923,92 @@ def btn_load_prompt(selected_yaml_file, dir_prompt): st.session_state['prompt_info'] = {} st.session_state['prompt_author'] = st.session_state['default_prompt_author'] st.session_state['prompt_author_institution'] = st.session_state['default_prompt_author_institution'] + st.session_state['prompt_name'] = st.session_state['prompt_name'] + st.session_state['prompt_version'] = st.session_state['prompt_version'] st.session_state['prompt_description'] = st.session_state['default_prompt_description'] st.session_state['instructions'] = st.session_state['default_instructions'] st.session_state['json_formatting_instructions'] = st.session_state['default_json_formatting_instructions'] st.session_state['rules'] = {} - st.session_state['LLM'] = 'gpt' + st.session_state['LLM'] = 'General Purpose' st.session_state['assigned_columns'] = [] st.session_state['prompt_info'] = { 'prompt_author': st.session_state['prompt_author'], 'prompt_author_institution': st.session_state['prompt_author_institution'], + 'prompt_name': st.session_state['prompt_name'], + 'prompt_version': st.session_state['prompt_version'], 'prompt_description': st.session_state['prompt_description'], - 'LLM': st.session_state['LLM'], 'instructions': st.session_state['instructions'], 'json_formatting_instructions': st.session_state['json_formatting_instructions'], 'rules': st.session_state['rules'], 'mapping': st.session_state['mapping'], + 'LLM': st.session_state['LLM'] } +def build_LLM_prompt_config(): + col_main1, col_main2 = st.columns([10,2]) + with col_main1: + st.session_state.logo_path = os.path.join(st.session_state.dir_home, 'img','logo.png') + st.session_state.logo = Image.open(st.session_state.logo_path) + st.image(st.session_state.logo, width=250) + with col_main2: + if st.button('Exit',key='exist button 2'): + st.session_state.proceed_to_build_llm_prompt = False + st.session_state.proceed_to_main = True + st.rerun() + st.session_state['assigned_columns'] = [] + st.session_state['default_prompt_author'] = 'unknown' + st.session_state['default_prompt_author_institution'] = 'unknown' + st.session_state['default_prompt_name'] = 'custom_prompt' + st.session_state['default_prompt_version'] = 'v-1-0' + st.session_state['default_prompt_author_institution'] = 'unknown' + st.session_state['default_prompt_description'] = 'unknown' + st.session_state['default_LLM'] = 'General Purpose' + st.session_state['default_instructions'] = """1. Refactor the unstructured OCR text into a dictionary based on the JSON structure outlined below. +2. Map the unstructured OCR text to the appropriate JSON key and populate the field given the user-defined rules. +3. JSON key values are permitted to remain empty strings if the corresponding information is not found in the unstructured OCR text. +4. Duplicate dictionary fields are not allowed. +5. Ensure all JSON keys are in camel case. +6. Ensure new JSON field values follow sentence case capitalization. +7. Ensure all key-value pairs in the JSON dictionary strictly adhere to the format and data types specified in the template. +8. Ensure output JSON string is valid JSON format. It should not have trailing commas or unquoted keys. +9. Only return a JSON dictionary represented as a string. You should not explain your answer.""" + st.session_state['default_json_formatting_instructions'] = """This section provides rules for formatting each JSON value organized by the JSON key.""" + + # Start building the Streamlit app + col_prompt_main_left, ___, col_prompt_main_right = st.columns([6,1,3]) -def refresh(): - st.session_state['uploader_idk'] += 1 - st.write('') - - - -def upload_local_prompt_to_server(dir_prompt): - uploaded_file = st.file_uploader("Upload a custom prompt file", type=['yaml']) - if uploaded_file is not None: - # Check the file extension - file_name = uploaded_file.name - if file_name.endswith('.yaml'): - file_path = os.path.join(dir_prompt, file_name) - - # Save the file - with open(file_path, 'wb') as f: - f.write(uploaded_file.getbuffer()) - st.success(f"Saved file {file_name} in {dir_prompt}") - else: - st.error("Please upload a .yaml file that you previously created using this Prompt Builder tool.") - - - -def create_download_button_yaml(file_path, selected_yaml_file): - file_label = f"Download {selected_yaml_file}" - with open(file_path, 'rb') as f: - st.download_button( - label=file_label, - data=f, - file_name=os.path.basename(file_path), - mime='application/x-yaml' - ) - - - -def clear_image_gallery(): - delete_directory(st.session_state['dir_uploaded_images']) - delete_directory(st.session_state['dir_uploaded_images_small']) - validate_dir(st.session_state['dir_uploaded_images']) - validate_dir(st.session_state['dir_uploaded_images_small']) - - - -def use_test_image(): - st.info(f"Processing images from {os.path.join(st.session_state.dir_home,'demo','demo_images')}") - st.session_state.config['leafmachine']['project']['dir_images_local'] = os.path.join(st.session_state.dir_home,'demo','demo_images') - n_images = len([f for f in os.listdir(st.session_state.config['leafmachine']['project']['dir_images_local']) if os.path.isfile(os.path.join(st.session_state.config['leafmachine']['project']['dir_images_local'], f))]) - st.session_state['processing_add_on'] = f" {n_images} Images" - clear_image_gallery() - st.session_state['uploader_idk'] += 1 - - - -######################################################################################################## -### Streamlit sections #### -######################################################################################################## -def create_space_saver(): - st.subheader("Space Saving Options") - col_ss_1, col_ss_2 = st.columns([2,2]) - with col_ss_1: - st.write("Several folders are created and populated with data during the VoucherVision transcription process.") - st.write("Below are several options that will allow you to automatically delete temporary files that you may not need for everyday operations.") - st.write("VoucherVision creates the following folders. Folders marked with a :star: are required if you want to use VoucherVisionEditor for quality control.") - st.write("`../[Run Name]/Archival_Components`") - st.write("`../[Run Name]/Config_File`") - st.write("`../[Run Name]/Cropped_Images` :star:") - st.write("`../[Run Name]/Logs`") - st.write("`../[Run Name]/Original_Images` :star:") - st.write("`../[Run Name]/Transcription` :star:") - with col_ss_2: - 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 show_available_APIs(): - st.session_state['has_key_openai'] = (os.getenv('OPENAI_API_KEY') is not None) and (os.getenv('OPENAI_API_KEY') != '') - st.session_state['has_key_google_OCR'] = (os.getenv('GOOGLE_APPLICATION_CREDENTIALS') is not None) and (os.getenv('GOOGLE_APPLICATION_CREDENTIALS') != '') - st.session_state['has_key_palm2'] = (os.getenv('PALM_API_KEY') is not None) and (os.getenv('PALM_API_KEY') != '') - st.session_state['has_key_azure'] = (os.getenv('AZURE_API_KEY') is not None) and (os.getenv('AZURE_API_KEY') != '') - - emoji_good = ":heavy_check_mark:" - emoji_bad = ":x:" - - table = { - 'Google Vision OCR API (required!)': emoji_good if st.session_state['has_key_google_OCR'] else emoji_bad, - 'OpenAI API': emoji_good if st.session_state['has_key_openai'] else emoji_bad, - 'PaLM 2 API': emoji_good if st.session_state['has_key_palm2'] else emoji_bad, - 'OpenAI API (Azure)': emoji_good if st.session_state['has_key_azure'] else emoji_bad, - } - for api_name, status in table.items(): - st.markdown(f"* {status} {api_name}") - -def display_image_gallery(): - # Initialize the container - con_image = st.empty() - - # Start the div for the image grid - img_grid_html = """ -
- """ - - # Loop through each image in the input list - # with con_image.container(): - for image_path in st.session_state['input_list']: - # Open the image and create a thumbnail - img = Image.open(image_path) - img.thumbnail((120, 120), Image.Resampling.LANCZOS) - - # Convert the image to base64 - base64_image = image_to_base64(img) - - # Append the image to the grid HTML - # img_html = f""" - #
- # Image - #
- # """ - img_html = f""" - Image - """ - img_grid_html += img_html - # st.markdown(img_html, unsafe_allow_html=True) - - - # Close the div for the image grid - img_grid_html += "
" - - # Display the image grid in the container - with con_image.container(): - st.markdown(img_grid_html, unsafe_allow_html=True) - - # The CSS to make the images display inline and be responsive - css = """ - - """ - # Apply the CSS - st.markdown(css, unsafe_allow_html=True) - -def show_header_welcome(): - st.session_state.logo_path = os.path.join(st.session_state.dir_home, 'img','logo.png') - st.session_state.logo = Image.open(st.session_state.logo_path) - st.image(st.session_state.logo, width=250) - - - -######################################################################################################## -### Sidebar for Expense Report #### -######################################################################################################## -def render_expense_report_summary(): - cost_labels = [] - cost_values = [] - total_images = 0 - cost_per_image_dict = {} - st.header('Expense Report Summary') - - if st.session_state.expense_summary: - st.metric(label="Total Cost", value=f"${round(st.session_state.expense_summary['total_cost_sum'], 4):,}") - col1, col2 = st.columns(2) - - # Run count and total costs - with col1: - st.metric(label="Run Count", value=st.session_state.expense_summary['run_count']) - st.metric(label="Tokens In", value=f"{st.session_state.expense_summary['tokens_in_sum']:,}") - - # Token information - with col2: - st.metric(label="Total Images", value=st.session_state.expense_summary['n_images_sum']) - st.metric(label="Tokens Out", value=f"{st.session_state.expense_summary['tokens_out_sum']:,}") - - # Calculate cost proportion per image for each API version - st.subheader('Average Cost per Image by API Version') - - # Iterate through the expense report to accumulate costs and image counts - for index, row in st.session_state.expense_report.iterrows(): - api_version = row['api_version'] - total_cost = row['total_cost'] - n_images = row['n_images'] - total_images += n_images # Keep track of total images processed - if api_version not in cost_per_image_dict: - cost_per_image_dict[api_version] = {'total_cost': 0, 'n_images': 0} - cost_per_image_dict[api_version]['total_cost'] += total_cost - cost_per_image_dict[api_version]['n_images'] += n_images - - api_versions = list(cost_per_image_dict.keys()) - colors = [COLORS_EXPENSE_REPORT[version] if version in COLORS_EXPENSE_REPORT else '#DDDDDD' for version in api_versions] - - # Calculate the cost per image for each API version - for version, cost_data in cost_per_image_dict.items(): - total_cost = cost_data['total_cost'] - n_images = cost_data['n_images'] - - # Calculate the cost per image for this version - cost_per_image = total_cost / n_images if n_images > 0 else 0 - cost_labels.append(version) - cost_values.append(cost_per_image) - - # Generate the pie chart - cost_pie_chart = go.Figure(data=[go.Pie(labels=cost_labels, values=cost_values, hole=.3)]) - - # Update traces for custom text in hoverinfo, displaying cost with a dollar sign and two decimal places - cost_pie_chart.update_traces( - marker=dict(colors=colors), - text=[f"${value:.2f}" for value in cost_values], - textinfo='percent+label', - hoverinfo='label+percent+text' - ) - st.plotly_chart(cost_pie_chart, use_container_width=True) - - st.subheader('Proportion of Total Cost by API Version') - cost_labels = [] - cost_proportions = [] - total_cost_by_version = {} - - # Sum the total cost for each API version - for index, row in st.session_state.expense_report.iterrows(): - api_version = row['api_version'] - total_cost = row['total_cost'] - if api_version not in total_cost_by_version: - total_cost_by_version[api_version] = 0 - total_cost_by_version[api_version] += total_cost - - # Calculate the combined total cost for all versions - combined_total_cost = sum(total_cost_by_version.values()) - - # Calculate the proportion of total cost for each API version - for version, total_cost in total_cost_by_version.items(): - proportion = (total_cost / combined_total_cost) * 100 if combined_total_cost > 0 else 0 - cost_labels.append(version) - cost_proportions.append(proportion) - - # Generate the pie chart - cost_pie_chart = go.Figure(data=[go.Pie(labels=cost_labels, values=cost_proportions, hole=.3)]) - - # Update traces for custom text in hoverinfo - cost_pie_chart.update_traces( - marker=dict(colors=colors), - text=[f"${cost:.2f}" for cost in total_cost_by_version.values()], - textinfo='percent+label', - hoverinfo='label+percent+text' - ) - st.plotly_chart(cost_pie_chart, use_container_width=True) - - # API version usage percentages pie chart - st.subheader('Runs by API Version') - api_versions = list(st.session_state.expense_summary['api_version_percentages'].keys()) - percentages = [st.session_state.expense_summary['api_version_percentages'][version] for version in api_versions] - pie_chart = go.Figure(data=[go.Pie(labels=api_versions, values=percentages, hole=.3)]) - pie_chart.update_layout(margin=dict(t=0, b=0, l=0, r=0)) - pie_chart.update_traces(marker=dict(colors=colors),) - st.plotly_chart(pie_chart, use_container_width=True) - - else: - st.error('No expense report data available.') - - - -def sidebar_content(): - if not os.path.exists(os.path.join(st.session_state.dir_home,'expense_report')): - validate_dir(os.path.join(st.session_state.dir_home,'expense_report')) - expense_report_path = os.path.join(st.session_state.dir_home, 'expense_report', 'expense_report.csv') - - if os.path.exists(expense_report_path): - # File exists, proceed with summarization - st.session_state.expense_summary, st.session_state.expense_report = summarize_expense_report(expense_report_path) - render_expense_report_summary() - else: - st.session_state.expense_summary, st.session_state.expense_report = None, None - st.header('Expense Report Summary') - st.write('Available after first run...') - - - -######################################################################################################## -### Config Builder #### -######################################################################################################## -def build_LLM_prompt_config(): - st.session_state['assigned_columns'] = [] - st.session_state['default_prompt_author'] = 'unknown' - st.session_state['default_prompt_author_institution'] = 'unknown' - st.session_state['default_prompt_description'] = 'unknown' - st.session_state['default_instructions'] = """1. Refactor the unstructured OCR text into a dictionary based on the JSON structure outlined below. -2. You should map the unstructured OCR text to the appropriate JSON key and then populate the field based on its rules. -3. Some JSON key fields are permitted to remain empty if the corresponding information is not found in the unstructured OCR text. -4. Ignore any information in the OCR text that doesn't fit into the defined JSON structure. -5. Duplicate dictionary fields are not allowed. -6. Ensure that all JSON keys are in lowercase. -7. Ensure that new JSON field values follow sentence case capitalization. -8. Ensure all key-value pairs in the JSON dictionary strictly adhere to the format and data types specified in the template. -9. Ensure the output JSON string is valid JSON format. It should not have trailing commas or unquoted keys. -10. Only return a JSON dictionary represented as a string. You should not explain your answer.""" - st.session_state['default_json_formatting_instructions'] = """The next section of instructions outlines how to format the JSON dictionary. The keys are the same as those of the final formatted JSON object. -For each key there is a format requirement that specifies how to transcribe the information for that key. -The possible formatting options are: -1. "verbatim transcription" - field is populated with verbatim text from the unformatted OCR. -2. "spell check transcription" - field is populated with spelling corrected text from the unformatted OCR. -3. "boolean yes no" - field is populated with only yes or no. -4. "boolean 1 0" - field is populated with only 1 or 0. -5. "integer" - field is populated with only an integer. -6. "[list]" - field is populated from one of the values in the list. -7. "yyyy-mm-dd" - field is populated with a date in the format year-month-day. -The desired null value is also given. Populate the field with the null value of the information for that key is not present in the unformatted OCR text.""" - - # Start building the Streamlit app - col_prompt_main_left, ___, col_prompt_main_right = st.columns([6,1,3]) - - with col_prompt_main_left: - st.title("Custom LLM Prompt Builder") - st.subheader('About') - st.write("This form allows you to craft a prompt for your specific task.") - st.subheader('For Hugging Face Spaces') - st.write("If you create a prompt with the Hugging Face Spaces implementation of VoucherVision, make sure that you download the prompt immediately after you have 'Saved' the prompt. Default storage space on HF Spaces is not persistant, so if you refresh the page your prompt will probably disappear.") - st.write("You can submit your prompt using this link and we will add it to our library so it will always be available.") - - st.subheader('How it works') - st.write("1. Edit this page until you are happy with your instructions. We recommend looking at the basic structure, writing down your prompt inforamtion in a Word document so that it does not randomly disappear, and then copying and pasting that info into this form once your whole prompt structure is defined.") - st.write("2. After you enter all of your prompt instructions, click 'Save' and give your file a name.") - st.write("3. This file will be saved as a yaml configuration file in the `..VoucherVision/custom_prompts` folder.") - st.write("4. When you go back the main VoucherVision page you will now see your custom prompt available in the 'Prompt Version' dropdown menu.") - st.write("5. Select your custom prompt. Note, your prompt will only be available for the LLM that you set when filling out the form below.") + + with col_prompt_main_left: + + st.title("Custom LLM Prompt Builder") + st.subheader('About') + st.write("This form allows you to craft a prompt for your specific task. You can also edit the JSON yaml files directly, but please try loading the prompt back into this form to ensure that the formatting is correct. If this form cannot load your manually edited JSON yaml file, then it will not work in VoucherVision.") + st.subheader(':rainbow[How it Works]') + st.write("1. Edit this page until you are happy with your instructions. We recommend looking at the basic structure, writing down your prompt inforamtion in a Word document so that it does not randomly disappear, and then copying and pasting that info into this form once your whole prompt structure is defined.") + st.write("2. After you enter all of your prompt instructions, click 'Save' and give your file a name.") + st.write("3. This file will be saved as a yaml configuration file in the `..VoucherVision/custom_prompts` folder.") + st.write("4. When you go back the main VoucherVision page you will now see your custom prompt available in the 'Prompt Version' dropdown menu.") + st.write("5. The LLM ***only*** sees information from the 'instructions', 'rules', and 'json_formatting_instructions' sections. All other information is for versioning and integration with VoucherVisionEditor.") + + st.write("---") + st.header('Load an Existing Prompt Template') + st.write("By default, this form loads the minimum required transcription fields but does not provide rules for each field. You can also load an existing prompt as a template, editing or deleting values as needed.") dir_prompt = os.path.join(st.session_state.dir_home, 'custom_prompts') yaml_files = [f for f in os.listdir(dir_prompt) if f.endswith('.yaml')] - col_upload_yaml, col_upload_yaml_2 = st.columns([4,4]) - with col_upload_yaml: - # Upload a prompt from your computer - upload_local_prompt_to_server(dir_prompt) - - col_select_yaml, col_upload_btn, col_download_btn = st.columns([6,2,2]) - with col_select_yaml: - # Dropdown for selecting a YAML file + col_load_text, col_load_btn, col_load_btn2 = st.columns([8,2,2]) + with col_load_text: + # Dropdown for selecting a YAML file st.session_state['selected_yaml_file'] = st.selectbox('Select a prompt .YAML file to load:', [''] + yaml_files) - - with col_upload_btn: + with col_load_btn: st.write('##') # Button to load the selected prompt - st.button('Load Selected Prompt into Builder', on_click=btn_load_prompt, args=[st.session_state['selected_yaml_file'] , dir_prompt]) - - with col_download_btn: + st.button('Load Prompt', on_click=btn_load_prompt, args=[st.session_state['selected_yaml_file'], dir_prompt],use_container_width=True) + + with col_load_btn2: if st.session_state['selected_yaml_file']: # Construct the full path to the file download_file_path = os.path.join(dir_prompt, st.session_state['selected_yaml_file'] ) @@ -723,127 +1017,209 @@ The desired null value is also given. Populate the field with the null value of create_download_button_yaml(download_file_path, st.session_state['selected_yaml_file'] ) # Prompt Author Information + st.write("---") st.header("Prompt Author Information") st.write("We value community contributions! Please provide your name(s) (or pseudonym if you prefer) for credit. If you leave this field blank, it will say 'unknown'.") - st.session_state['prompt_author'] = st.text_input("Enter names of prompt author(s)", value=st.session_state['prompt_info'].get('prompt_author', st.session_state['default_prompt_author'])) + if 'prompt_author' not in st.session_state:# != st.session_state['default_prompt_author']: + st.session_state['prompt_author'] = st.text_input("Enter names of prompt author(s)", value=st.session_state['default_prompt_author'],key=1111) + else: + st.session_state['prompt_author'] = st.text_input("Enter names of prompt author(s)", value=st.session_state['prompt_author'],key=1112) + # Institution st.write("Please provide your institution name. If you leave this field blank, it will say 'unknown'.") - st.session_state['prompt_author_institution'] = st.text_input("Enter name of institution", value=st.session_state['prompt_info'].get('prompt_author_institution', st.session_state['default_prompt_author_institution'])) + if 'prompt_author_institution' not in st.session_state: + st.session_state['prompt_author_institution'] = st.text_input("Enter name of institution", value=st.session_state['default_prompt_author_institution'],key=1113) + else: + st.session_state['prompt_author_institution'] = st.text_input("Enter name of institution", value=st.session_state['prompt_author_institution'],key=1114) + # Prompt name + st.write("Please provide a simple name for your prompt. If you leave this field blank, it will say 'custom_prompt'.") + if 'prompt_name' not in st.session_state: + st.session_state['prompt_name'] = st.text_input("Enter prompt name", value=st.session_state['default_prompt_name'],key=1115) + else: + st.session_state['prompt_name'] = st.text_input("Enter prompt name", value=st.session_state['prompt_name'],key=1116) + + # Prompt verion + st.write("Please provide a version identifier for your prompt. If you leave this field blank, it will say 'v-1-0'.") + if 'prompt_version' not in st.session_state: + st.session_state['prompt_version'] = st.text_input("Enter prompt version", value=st.session_state['default_prompt_version'],key=1117) + else: + st.session_state['prompt_version'] = st.text_input("Enter prompt version", value=st.session_state['prompt_version'],key=1118) + + st.write("Please provide a description of your prompt and its intended task. Is it designed for a specific collection? Taxa? Database structure?") - st.session_state['prompt_description'] = st.text_input("Enter description of prompt", value=st.session_state['prompt_info'].get('prompt_description', st.session_state['default_prompt_description'])) - - # Input for new file name - st.write('---') - st.header("Prompt Name") - st.write('Provide a name for your custom prompt. It can only conatin letters, numbers, and underscores. No spaces, dashes, or special characters.') - st.session_state['new_prompt_yaml_filename'] = st.text_input("Enter filename to save your prompt as a configuration YAML:", value=None, placeholder='my_prompt_name') - - # Define the options for the LLM Model Type dropdown + if 'prompt_description' not in st.session_state: + st.session_state['prompt_description'] = st.text_input("Enter description of prompt", value=st.session_state['default_prompt_description'],key=1119) + else: + st.session_state['prompt_description'] = st.text_input("Enter description of prompt", value=st.session_state['prompt_description'],key=11111) + st.write('---') st.header("Set LLM Model Type") - llm_options = ['gpt', 'palm'] + # Define the options for the dropdown + llm_options_general = ["General Purpose", + "OpenAI GPT Models","Google PaLM2 Models","Google Gemini Models","MistralAI Models",] + llm_options_all = ModelMaps.get_models_gui_list() + + if 'LLM' not in st.session_state: + st.session_state['LLM'] = st.session_state['default_LLM'] + + if st.session_state['LLM']: + llm_options = llm_options_general + llm_options_all + [st.session_state['LLM']] + else: + llm_options = llm_options_general + llm_options_all # Create the dropdown and set the value to session_state['LLM'] - st.write("Which LLM is this prompt designed for? This will not restrict its use to a specific LLM, but some prompts will behave in different ways across models.") - st.write("For example, VoucherVision will automatically add multiple JSON formatting blocks to all PaLM 2 prompts to coax PaLM 2 to return a valid JSON object.") - st.session_state['LLM'] = st.selectbox('Set LLM', llm_options, index=llm_options.index(st.session_state.get('LLM', 'gpt'))) - + st.write("Which LLM is this prompt designed for? This will not restrict its use to a specific LLM, but some prompts will behave differently across models.") + st.write("SLTPvA prompts have been validated with all supported LLMs, but perfornce may vary. If you design a prompt to work best with a specific model, then you can indicate the model here.") + st.write("For general purpose prompts (like the SLTPvA prompts) just use the 'General Purpose' option.") + st.session_state['LLM'] = st.selectbox('Set LLM', llm_options, index=llm_options.index(st.session_state.get('LLM', 'General Purpose'))) + st.write('---') # Instructions Section st.header("Instructions") st.write("These are the general instructions that guide the LLM through the transcription task. We recommend using the default instructions unless you have a specific reason to change them.") - st.session_state['instructions'] = st.text_area("Enter instructions:", value=st.session_state['default_instructions'].strip(), height=350, disabled=True) + if 'instructions' not in st.session_state: + st.session_state['instructions'] = st.text_area("Enter guiding instructions", value=st.session_state['default_instructions'].strip(), height=350,key=111112) + else: + st.session_state['instructions'] = st.text_area("Enter guiding instructions", value=st.session_state['instructions'].strip(), height=350,key=111112) + st.write('---') # Column Instructions Section st.header("JSON Formatting Instructions") st.write("The following section tells the LLM how we want to structure the JSON dictionary. We do not recommend changing this section because it would likely result in unstable and inconsistent behavior.") - st.session_state['json_formatting_instructions'] = st.text_area("Enter column instructions:", value=st.session_state['default_json_formatting_instructions'], height=350, disabled=True) + if 'json_formatting_instructions' not in st.session_state: + st.session_state['json_formatting_instructions'] = st.text_area("Enter general JSON guidelines", value=st.session_state['default_json_formatting_instructions'],key=111114) + else: + st.session_state['json_formatting_instructions'] = st.text_area("Enter general JSON guidelines", value=st.session_state['json_formatting_instructions'],key=111115) + + + + + st.write('---') col_left, col_right = st.columns([6,4]) + + null_value_rules = '' + c_name = "EXAMPLE_COLUMN_NAME" + c_value = "REPLACE WITH DESCRIPTION" + with col_left: st.subheader('Add/Edit Columns') + st.markdown("The pre-populated fields are REQUIRED for downstream validation steps. They must be in all prompts.") # Initialize rules in session state if not already present if 'rules' not in st.session_state or not st.session_state['rules']: - st.session_state['rules']['Dictionary'] = { - "catalog_number": { - "format": "verbatim transcription", - "null_value": "", - "description": "The barcode identifier, typically a number with at least 6 digits, but fewer than 30 digits." - } - } - st.session_state['rules']['SpeciesName'] = { - "taxonomy": ["Genus_species"] - } - - new_column_name = st.text_input("Enter a new column name:") - - if st.button("Add New Column") and new_column_name: - if new_column_name not in st.session_state['rules']['Dictionary']: - st.session_state['rules']['Dictionary'][new_column_name] = {"format": "", "null_value": "", "description": ""} - st.success(f"New column '{new_column_name}' added. Now you can edit its properties.") + for required_col in st.session_state['required_fields']: + st.session_state['rules'][required_col] = c_value + + + + + # Layout for adding a new column name + # col_text, col_textbtn = st.columns([8, 2]) + # with col_text: + st.session_state['new_column_name'] = st.text_input("Enter a new column name:") + # with col_textbtn: + # st.write('##') + if st.button("Add New Column") and st.session_state['new_column_name']: + if st.session_state['new_column_name'] not in st.session_state['rules']: + st.session_state['rules'][st.session_state['new_column_name']] = c_value + st.success(f"New column '{st.session_state['new_column_name']}' added. Now you can edit its properties.") + st.session_state['new_column_name'] = '' else: st.error("Column name already exists. Please enter a unique column name.") + st.session_state['new_column_name'] = '' + - # Get columns excluding the protected "catalog_number" + # Get columns excluding the protected "catalogNumber" st.write('#') - editable_columns = [col for col in st.session_state['rules']['Dictionary'] if col != "catalog_number"] - column_name = st.selectbox("Select a column to edit:", [""] + editable_columns) + # required_columns = [col for col in st.session_state['rules'] if col not in st.session_state['required_fields']] + editable_columns = [col for col in st.session_state['rules'] if col not in ["catalogNumber"]] + removable_columns = [col for col in st.session_state['rules'] if col not in st.session_state['required_fields']] + + st.session_state['current_rule'] = st.selectbox("Select a column to edit:", [""] + editable_columns) + # column_name = st.selectbox("Select a column to edit:", editable_columns) - # Handle rules editing - current_rule = st.session_state['rules']['Dictionary'].get(column_name, { - "format": "", - "null_value": "", - "description": "" - }) + + + # if 'current_rule' not in st.session_state: + # st.session_state['current_rule'] = current_rule + + + - if 'selected_column' not in st.session_state: - st.session_state['selected_column'] = column_name # Form for input fields with st.form(key='rule_form'): - format_options = ["verbatim transcription", "spell check transcription", "boolean yes no", "boolean 1 0", "integer", "[list]", "yyyy-mm-dd"] - current_rule["format"] = st.selectbox("Format:", format_options, index=format_options.index(current_rule["format"]) if current_rule["format"] else 0) - current_rule["null_value"] = st.text_input("Null value:", value=current_rule["null_value"]) - current_rule["description"] = st.text_area("Description:", value=current_rule["description"]) + # format_options = ["verbatim transcription", "spell check transcription", "boolean yes no", "boolean 1 0", "integer", "[list]", "yyyy-mm-dd"] + # current_rule["format"] = st.selectbox("Format:", format_options, index=format_options.index(current_rule["format"]) if current_rule["format"] else 0) + # current_rule["null_value"] = st.text_input("Null value:", value=current_rule["null_value"]) + if st.session_state['current_rule']: + current_rule_description = st.text_area("Description of category:", value=st.session_state['rules'][st.session_state['current_rule']]) + else: + current_rule_description = '' commit_button = st.form_submit_button("Commit Column") - default_rule = { - "format": format_options[0], # default format - "null_value": "", # default null value - "description": "", # default description - } - if st.session_state['selected_column'] != column_name: - # Column has changed. Update the session_state selected column. - st.session_state['selected_column'] = column_name - # Reset the current rule to the default for this new column, or a blank rule if not set. - current_rule = st.session_state['rules']['Dictionary'].get(column_name, default_rule.copy()) + # default_rule = { + # "format": format_options[0], # default format + # "null_value": "", # default null value + # "description": "", # default description + # } + # if st.session_state['current_rule'] != st.session_state['current_rule']: + # # Column has changed. Update the session_state selected column. + # st.session_state['current_rule'] = st.session_state['current_rule'] + # # Reset the current rule to the default for this new column, or a blank rule if not set. + # current_rule = st.session_state['rules'][st.session_state['current_rule']].get(current_rule, c_value) # Handle commit action - if commit_button and column_name: + if commit_button and st.session_state['current_rule']: # Commit the rules to the session state. - st.session_state['rules']['Dictionary'][column_name] = current_rule.copy() - st.success(f"Column '{column_name}' added/updated in rules.") + st.session_state['rules'][st.session_state['current_rule']] = current_rule_description + st.success(f"Column '{st.session_state['current_rule']}' added/updated in rules.") # Force the form to reset by clearing the fields from the session state - st.session_state.pop('selected_column', None) # Clear the selected column to force reset + st.session_state.pop('current_rule', None) # Clear the selected column to force reset + + # st.session_state['rules'][column_name] = current_rule + # st.success(f"Column '{column_name}' added/updated in rules.") + + # # Reset current_rule to default values for the next input + # current_rule["format"] = default_rule["format"] + # current_rule["null_value"] = default_rule["null_value"] + # current_rule["description"] = default_rule["description"] + + # # To ensure that the form fields are reset, we can clear them from the session state + # for key in current_rule.keys(): + # st.session_state[key] = default_rule[key] # Layout for removing an existing column - delete_column_name = st.selectbox("Select a column to delete:", [""] + editable_columns, key='delete_column') + # del_col, del_colbtn = st.columns([8, 2]) + # with del_col: + delete_column_name = st.selectbox("Select a column to delete:", [""] + removable_columns) + # with del_colbtn: + # st.write('##') if st.button("Delete Column") and delete_column_name: del st.session_state['rules'][delete_column_name] st.success(f"Column '{delete_column_name}' removed from rules.") + + + with col_right: # Display the current state of the JSON rules st.subheader('Formatted Columns') - st.json(st.session_state['rules']['Dictionary']) + st.json(st.session_state['rules']) + + # st.subheader('All Prompt Info') + # st.json(st.session_state['prompt_info']) + st.write('---') + + col_left_mapping, col_right_mapping = st.columns([6,4]) with col_left_mapping: st.header("Mapping") @@ -852,7 +1228,7 @@ The desired null value is also given. Populate the field with the null value of # Dynamically create a list of all column names that can be assigned # This assumes that the column names are the keys in the dictionary under 'rules' - all_column_names = list(st.session_state['rules']['Dictionary'].keys()) + all_column_names = list(st.session_state['rules'].keys()) categories = ['TAXONOMY', 'GEOGRAPHY', 'LOCALITY', 'COLLECTING', 'MISCELLANEOUS'] if ('mapping' not in st.session_state) or (st.session_state['mapping'] == {}): @@ -901,42 +1277,75 @@ The desired null value is also given. Populate the field with the null value of st.subheader('Formatted Column Maps') st.json(st.session_state['mapping']) + + col_left_save, col_right_save = st.columns([6,4]) + with col_left_save: + # Input for new file name + new_filename = st.text_input("Enter filename to save your prompt as a configuration YAML:",placeholder='my_prompt_name') + # Button to save the new YAML file + if st.button('Save YAML', type='primary'): + if new_filename: + if check_unique_mapping_assignments(): + if check_prompt_yaml_filename(new_filename): + save_prompt_yaml(new_filename) + else: + st.error("File name can only contain letters, numbers, underscores, and dashes. Cannot contain spaces.") + else: + st.error("Mapping contains an error. Make sure that each column is assigned to only ***one*** category.") + else: + st.error("Please enter a filename.") + + if st.button('Exit'): + st.session_state.proceed_to_build_llm_prompt = False + st.session_state.proceed_to_main = True + st.rerun() + + + st.write('---') st.header("Save and Download Custom Prompt") st.write('Once you click save, validation checks will verify the formatting and then a download button will appear so that you can ***save a local copy of your custom prompt.***') col_left_save, col_right_save, _ = st.columns([2,2,8]) with col_left_save: # Button to save the new YAML file - if st.button('Save YAML', type='primary'): - if st.session_state['new_prompt_yaml_filename']: + if st.button('Save YAML', type='primary',key=3450798): + if st.session_state['prompt_name']: if check_unique_mapping_assignments(): - if check_prompt_yaml_filename(st.session_state['new_prompt_yaml_filename']): - save_prompt_yaml(st.session_state['new_prompt_yaml_filename'], col_right_save) + if check_prompt_yaml_filename(st.session_state['prompt_name']): + save_prompt_yaml(st.session_state['prompt_name'], col_right_save) else: st.error("File name can only contain letters, numbers, underscores, and dashes. Cannot contain spaces.") else: st.error("Mapping contains an error. Make sure that each column is assigned to only ***one*** category.") else: st.error("Please enter a filename.") - - st.write('---') - st.header("Return to VoucherVision") - if st.button('Exit'): - st.session_state.proceed_to_build_llm_prompt = False - st.session_state.proceed_to_main = True - st.rerun() + # with col_prompt_main_right: + # st.subheader('All Prompt Components') + # st.session_state['prompt_info'] = { + # 'prompt_author': st.session_state['prompt_author'], + # 'prompt_author_institution': st.session_state['prompt_author_institution'], + # 'prompt_name': st.session_state['prompt_name'], + # 'prompt_version': st.session_state['prompt_version'], + # 'prompt_description': st.session_state['prompt_description'], + # 'LLM': st.session_state['LLM'], + # 'instructions': st.session_state['instructions'], + # 'json_formatting_instructions': st.session_state['json_formatting_instructions'], + # 'rules': st.session_state['rules'], + # 'mapping': st.session_state['mapping'], + # } + # st.json(st.session_state['prompt_info']) with col_prompt_main_right: if st.session_state['user_clicked_load_prompt_yaml'] is None: # see if user has loaded a yaml to edit st.session_state['show_prompt_name_e'] = f"Prompt Status :arrow_forward: Building prompt from scratch" - if st.session_state['new_prompt_yaml_filename']: - st.session_state['show_prompt_name_w'] = f"New Prompt Name :arrow_forward: {st.session_state['new_prompt_yaml_filename']}.yaml" + if st.session_state['prompt_name']: + st.session_state['show_prompt_name_w'] = f"New Prompt Name :arrow_forward: {st.session_state['prompt_name']}.yaml" else: st.session_state['show_prompt_name_w'] = f"New Prompt Name :arrow_forward: [PLEASE SET NAME]" else: st.session_state['show_prompt_name_e'] = f"Prompt Status: Editing :arrow_forward: {st.session_state['selected_yaml_file']}" - if st.session_state['new_prompt_yaml_filename']: - st.session_state['show_prompt_name_w'] = f"New Prompt Name :arrow_forward: {st.session_state['new_prompt_yaml_filename']}.yaml" + if st.session_state['prompt_name']: + st.session_state['show_prompt_name_w'] = f"New Prompt Name :arrow_forward: {st.session_state['prompt_name']}.yaml" else: st.session_state['show_prompt_name_w'] = f"New Prompt Name :arrow_forward: [PLEASE SET NAME]" @@ -947,6 +1356,8 @@ The desired null value is also given. Populate the field with the null value of st.session_state['prompt_info'] = { 'prompt_author': st.session_state['prompt_author'], 'prompt_author_institution': st.session_state['prompt_author_institution'], + 'prompt_name': st.session_state['prompt_name'], + 'prompt_version': st.session_state['prompt_version'], 'prompt_description': st.session_state['prompt_description'], 'LLM': st.session_state['LLM'], 'instructions': st.session_state['instructions'], @@ -956,337 +1367,997 @@ The desired null value is also given. Populate the field with the null value of } st.json(st.session_state['prompt_info']) -def content_header(): - # Header section, run, quick start, API report - col_run_1, col_run_2, col_run_3, col_run_4 = st.columns([2,2,2,2]) +def show_header_welcome(): + st.session_state.logo_path = os.path.join(st.session_state.dir_home, 'img','logo.png') + st.session_state.logo = Image.open(st.session_state.logo_path) + st.image(st.session_state.logo, width=250) + +def determine_n_images(): + try: + # Check if 'dir_uploaded_images' key exists and it is not empty + if 'dir_uploaded_images' in st and st['dir_uploaded_images']: + dir_path = st['dir_uploaded_images'] # This would be the path to the directory + return len([f for f in os.listdir(dir_path) if os.path.isfile(os.path.join(dir_path, f))]) + else: + return None + except: + return None + +def save_api_status(present_keys, missing_keys, date_of_check): + with open(os.path.join(st.session_state.dir_home,'api_status.yaml'), 'w') as file: + yaml.dump({'present_keys': present_keys, 'missing_keys': missing_keys, "date": date_of_check}, file) + +def load_api_status(): + try: + with open(os.path.join(st.session_state.dir_home,'api_status.yaml'), 'r') as file: + status = yaml.safe_load(file) + return status.get('present_keys', []), status.get('missing_keys', []), status.get('date', []) + except FileNotFoundError: + return None, None, None + +def display_api_key_status(): + if not st.session_state['API_checked']: + present_keys, missing_keys, date_of_check = load_api_status() + if present_keys is None and missing_keys is None: + st.session_state['API_checked'] = False + else: + # Convert keys to annotations (similar to what you do in check_api_key_status) + present_annotations = [(key, " ", "#059c1b") for key in present_keys] # Adjust as needed + missing_annotations = [(key, " ", "#525252") for key in missing_keys] # Adjust as needed + + st.session_state['present_annotations'] = present_annotations + st.session_state['missing_annotations'] = missing_annotations + st.session_state['date_of_check'] = date_of_check + st.session_state['API_checked'] = True + + # Check if the API status has already been retrieved + if 'API_checked' not in st.session_state or not st.session_state['API_checked'] or st.session_state['API_rechecked']: + st.session_state['present_annotations'], st.session_state['missing_annotations'], st.session_state['date_of_check'] = check_api_key_status() + st.session_state['API_checked'] = True + st.session_state['API_rechecked'] = False + + st.markdown(f"Last checked on {st.session_state['date_of_check']}") + # Display present keys horizontally + if 'present_annotations' in st.session_state and st.session_state['present_annotations']: + annotated_text(*st.session_state['present_annotations']) + + # Display missing keys horizontally + if 'missing_annotations' in st.session_state and st.session_state['missing_annotations']: + annotated_text(*st.session_state['missing_annotations']) + + +def check_api_key_status(): + path_cfg_private = os.path.join(st.session_state.dir_home, 'PRIVATE_DATA.yaml') + cfg_private = get_cfg_from_full_path(path_cfg_private) + + API_Validator = APIvalidation(cfg_private, st.session_state.dir_home) + present_keys, missing_keys, date_of_check = API_Validator.report_api_key_status() # Assuming this function returns two lists + + # Prepare annotations for present keys + present_annotations = [] + missing_annotations = [] + for key in present_keys: + if "Valid" in key: + show_text = key.split('(')[0] + present_annotations.append((show_text, "ready!", "#059c1b")) # Green for valid + elif "Invalid" in key: + show_text = key.split('(')[0] + present_annotations.append((show_text, "error", "#870307")) # Red for invalid + + # Prepare annotations for missing keys + for key in missing_keys: + show_text = key.split('(')[0] + missing_annotations.append((show_text, "n/a", " ", "#c4c4c4")) # Red for invalid + + # Save API key status + save_api_status(present_keys, missing_keys, date_of_check) + + return present_annotations, missing_annotations, date_of_check + + +def convert_cost_dict_to_table(cost, name): + # Convert the dictionary to a pandas DataFrame for nicer display + df = pd.DataFrame.from_dict(cost, orient='index') + df.reset_index(inplace=True) + df.columns = [str(name), 'Input', 'Output'] + + + # Apply color gradient + cm = sns.light_palette("green", as_cmap=True) + styled_df = df.style.background_gradient(cmap=cm, subset=['Input', 'Output']) + return styled_df + +def get_all_cost_tables(): + warnings.filterwarnings('ignore', message=".*is_sparse is deprecated.*") + CostMap = ModelMaps + cost_names = CostMap.get_all_mapping_cost() + + path_api_cost = os.path.join(st.session_state.dir_home,'api_cost','api_cost.yaml') + with open(path_api_cost, 'r') as file: + cost_data = yaml.safe_load(file) + + cost_openai = {} + cost_azure = {} + cost_google = {} + cost_mistral = {} + cost_local = {} + for key, value in cost_names.items(): + parts = value.split("_") + if 'LOCAL' in parts: + cost_local[key] = cost_data.get(value,'') + elif 'AZURE' in parts: + cost_azure[key] = cost_data.get(value,'') + elif 'GPT' in parts: + cost_openai[key] = cost_data.get(value,'') + elif 'PALM2' in parts or 'GEMINI' in parts: + cost_google[key] = cost_data.get(value,'') + elif 'MISTRAL' in parts: + cost_mistral[key] = cost_data.get(value,'') + + styled_cost_openai = convert_cost_dict_to_table(cost_openai, "OpenAI") + styled_cost_azure = convert_cost_dict_to_table(cost_azure, "OpenAI (Azure Endpoints)") + styled_cost_google = convert_cost_dict_to_table(cost_google, "Google (VertexAI)") + styled_cost_mistral = convert_cost_dict_to_table(cost_mistral, "MistralAI") + styled_cost_local = convert_cost_dict_to_table(cost_local, "Local Models") + + return cost_openai, styled_cost_openai, cost_azure, styled_cost_azure, cost_google, styled_cost_google, cost_mistral, styled_cost_mistral, cost_local, styled_cost_local + + +def content_header(): + col_logo, col_run_1, col_run_2, col_run_3, col_run_4, col_run_5 = st.columns([2,2,2,2,2,2]) + + + col_test = st.container() + + st.subheader("Overall Progress") + col_run_info_1 = st.columns([1])[0] + col_updates_1, col_updates_2 = st.columns([5,1]) + col_json, col_json_WFO, col_json_GEO, col_json_map = st.columns([2, 2, 2, 2]) + + with col_run_info_1: + # Progress + # Progress + # st.subheader('Project') + # bar = st.progress(0) + # new_text = st.empty() # Placeholder for current step name + # progress_report = ProgressReportVV(bar, new_text, n_images=10) + + # Progress + overall_progress_bar = st.progress(0) + text_overall = st.empty() # Placeholder for current step name + st.subheader('Transcription Progress') + batch_progress_bar = st.progress(0) + text_batch = st.empty() # Placeholder for current step name + progress_report = ProgressReport(overall_progress_bar, batch_progress_bar, text_overall, text_batch) + json_report = JSONReport(col_updates_1, col_json, col_json_WFO, col_json_GEO, col_json_map) + + + with col_logo: + show_header_welcome() + + with col_run_1: + # st.subheader('Run VoucherVision') + N_STEPS = 6 + + if determine_n_images(): + st.session_state['processing_add_on'] = f" {determine_n_images()} Images" + else: + st.session_state['processing_add_on'] = '' + + if check_if_usable(): + if st.button(f"Start Processing{st.session_state['processing_add_on']}", type='primary',use_container_width=True): + st.session_state['formatted_json'] = None + st.session_state['formatted_json_WFO'] = None + st.session_state['formatted_json_GEO'] = None + # Define number of overall steps + progress_report.set_n_overall(N_STEPS) + progress_report.update_overall(f"Starting VoucherVision...") + + # First, write the config file. + write_config_file(st.session_state.config, st.session_state.dir_home, filename="VoucherVision.yaml") + + path_custom_prompts = os.path.join(st.session_state.dir_home,'custom_prompts',st.session_state.config['leafmachine']['project']['prompt_version']) + # Call the machine function. + st.session_state['formatted_json'], st.session_state['formatted_json_WFO'], st.session_state['formatted_json_GEO'], total_cost, n_failed_OCR, n_failed_LLM_calls = voucher_vision(None, + st.session_state.dir_home, + path_custom_prompts, + None, + progress_report, + json_report, + path_api_cost=os.path.join(st.session_state.dir_home,'api_cost','api_cost.yaml'), + is_hf = st.session_state['is_hf'], + is_real_run=True) + + if n_failed_OCR > 0: + st.error(f"Caution:heavy_exclamation_mark: :loudspeaker: {n_failed_LLM_calls} images had a no extractable OCR text :eyes:") + + if n_failed_LLM_calls > 0: + st.error(f"Caution:heavy_exclamation_mark: :loudspeaker: {n_failed_LLM_calls} images had a failed LLM API call :eyes:") + st.error(f"Make sure that you have access to the chosen LLM API model. Sometimes certain OpenAI accounts do not have access to all models, for example") + + if total_cost: + st.success(f":money_with_wings: This run cost :heavy_dollar_sign:{total_cost:.4f}") + else: + st.info(f":money_with_wings: This run cost :heavy_dollar_sign:{total_cost:.4f}") + st.balloons() + + else: + st.button("Start Processing", type='primary', disabled=True) + st.error(":heavy_exclamation_mark: Required API keys not set. Please visit the 'API Keys' tab and set the Google Vision OCR API key and at least one LLM key.") + + if st.session_state['formatted_json']: + json_report.set_JSON(st.session_state['formatted_json'], st.session_state['formatted_json_WFO'], st.session_state['formatted_json_GEO']) + + with col_run_5: + with st.expander("View Messages and Updates"): + st.info("***Note:*** If you use VoucherVision frequently, you can change the default values that are auto-populated in the form below. In a text editor or IDE, edit the first few rows in the file `../VoucherVision/vouchervision/VoucherVision_Config_Builder.py`") + + + + with col_run_1: + ct_left, ct_right = st.columns([1,1]) + with ct_left: + st.button("Refresh", on_click=refresh, use_container_width=True) + with ct_right: + if st.button('FAQs', use_container_width=True): + pass + + # with col_run_2: + # if st.button("Test GPT"): + # progress_report.set_n_overall(TestOptionsGPT.get_length()) + # test_results, JSON_results = run_demo_tests_GPT(progress_report) + # with col_test: + # display_test_results(test_results, JSON_results, 'gpt') + # st.balloons() + + # if st.button("Test PaLM2"): + # progress_report.set_n_overall(TestOptionsPalm.get_length()) + # test_results, JSON_results = run_demo_tests_Palm(progress_report) + # with col_test: + # display_test_results(test_results, JSON_results, 'palm') + # st.balloons() + + + with col_run_2: + if st.button('Save Current Settings',use_container_width=True): + if st.session_state.settings_filename: + config_file_path = os.path.join(st.session_state.dir_home, 'settings', st.session_state['settings_filename'] + '.yaml') + with open(config_file_path, 'w') as file: + yaml.dump(st.session_state.config, file, default_flow_style=False) + with col_run_4: + st.success(f'Current settings saved to {config_file_path}') + else: + with col_run_4: + st.error('Missing settings file name. Settings not saved.') + # st.session_state.config + with col_run_3: + st.session_state['settings_filename'] = st.text_input('Setting File Name',placeholder="Settings fileame",label_visibility='collapsed',value=None) + + + + with col_run_2: + if st.button('Load Settings',use_container_width=True): + if st.session_state['loaded_settings_filename']: + path_load_settings = os.path.join(st.session_state['dir_settings'],st.session_state['loaded_settings_filename']) + if os.path.exists(path_load_settings) and not None: + with open(path_load_settings, 'r') as file: + loaded_config = yaml.safe_load(file) + st.session_state.config, st.session_state.dir_home = build_VV_config(loaded_cfg=loaded_config) + with col_run_4: + st.success(f'Loaded settings from {path_load_settings}') + else: + st.error(f'Path to settings file does not exist: {path_load_settings}') + else: + with col_run_4: + st.warning(f'Filename not selected') + + + with col_run_3: + st.session_state['settings_choice_null'] = 'Select previous settings...' + st.session_state['dir_settings'] = os.path.join(st.session_state.dir_home, 'settings') + all_settings_files = [st.session_state['settings_choice_null']] + [f for f in os.listdir(st.session_state['dir_settings']) if f.endswith('.yaml')] + settings_choice = st.selectbox('Load Previous Settings', all_settings_files,label_visibility='collapsed') + if settings_choice != st.session_state['settings_choice_null']: + st.session_state['loaded_settings_filename'] = settings_choice + + + with col_run_2: + if st.button("Check GPU Status",use_container_width=True): + success, info = test_GPU() + + if success: + st.balloons() + with col_run_4: + for message in info: + st.success(message) + else: + with col_run_4: + for message in info: + st.error(message) + + + + + + + + + + + +def content_project_settings(): + + st.header('Project Settings') + col_project_1, col_project_2 = st.columns([11,1]) + ### Project + with col_project_1: + st.session_state.config['leafmachine']['project']['run_name'] = st.text_input("Run name", st.session_state.config['leafmachine']['project'].get('run_name', ''),key=63456) + st.session_state.config['leafmachine']['project']['dir_output'] = st.text_input("Output directory", st.session_state.config['leafmachine']['project'].get('dir_output', '')) + + + +def content_input_images(): + st.header('Input Images') + col_local_1, col_local_2 = st.columns([11,1]) + with col_local_1: + ### Input Images Local + 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']['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) + + + + +def content_llm_cost(): + st.write("---") + st.header('LLM Cost Calculator') + # ( n_in/1000 * Input + n_out/1000 * Output ) * n_img = COST + calculator_1,calculator_2,calculator_3,calculator_4,calculator_5 = st.columns([1,1,1,1,1]) + + st.subheader('Cost Matrix') + st.markdown('The table shows the cost of each LLM API per 1,000 tokens. An average VoucherVision call uses 2,000 input tokens and receives 500 output tokens.') + col_cost_1, col_cost_2, col_cost_3, col_cost_4, col_cost_5 = st.columns([1,1,1,1,1]) + + # Load all cost tables if not already done + if 'all_llm_cost' not in st.session_state: + st.session_state['all_llm_cost'] = True + st.session_state['cost_openai'], st.session_state['styled_cost_openai'], st.session_state['cost_azure'], st.session_state['styled_cost_azure'], st.session_state['cost_google'], st.session_state['styled_cost_google'], st.session_state['cost_mistral'], st.session_state['styled_cost_mistral'], st.session_state['cost_local'], st.session_state['styled_cost_local'] = get_all_cost_tables() + + with calculator_1: + # Combine all model names into a single list + model_names = [] + for df in [st.session_state['cost_openai'], st.session_state['cost_azure'], st.session_state['cost_google'], st.session_state['cost_mistral'], st.session_state['cost_local']]: + for key in df.keys(): + model_names.append(key) + + # Create a dropdown for model selection + selected_model = st.selectbox("Select a model", options=model_names) + + with calculator_2: + # Create input fields for n_in, n_out, n_img + n_in = st.number_input("Tokens In", min_value=0, value=2000, step=50) + with calculator_3: + n_out = st.number_input("Tokens Out", min_value=0, value=500, step=50) + with calculator_4: + n_img = st.number_input("Number of Images", min_value=0, value=1000, step=100) + + # Function to find the model's Input and Output values + def find_model_values(model, all_dfs): + for df in all_dfs: + if model in df.keys(): + return df[model]['in'], df[model]['out'] + return None, None + + # Calculate and display cost when button is pressed + input_value, output_value = find_model_values(selected_model, + [st.session_state['cost_openai'], st.session_state['cost_azure'], st.session_state['cost_google'], st.session_state['cost_mistral'], st.session_state['cost_local']]) + if input_value is not None and output_value is not None: + cost = (n_in/1000 * input_value + n_out/1000 * output_value) * n_img + with calculator_5: + st.text_input("Total Cost", f"${round(cost,2)}") # selected_model + + with col_cost_1: + rounding = 4 + st.dataframe(st.session_state.styled_cost_openai.format(precision=rounding), hide_index=True,) + with col_cost_2: + st.dataframe(st.session_state.styled_cost_azure.format(precision=rounding), hide_index=True,) + with col_cost_3: + st.dataframe(st.session_state.styled_cost_google.format(precision=rounding), hide_index=True,) + with col_cost_4: + st.dataframe(st.session_state.styled_cost_mistral.format(precision=rounding), hide_index=True,) + with col_cost_5: + st.dataframe(st.session_state.styled_cost_local.format(precision=rounding), hide_index=True,) + + + +def content_prompt_and_llm_version(): + st.header('Prompt Version') + col_prompt_1, col_prompt_2 = st.columns([4,2]) + with col_prompt_1: + available_prompts = get_prompt_versions(st.session_state.config['leafmachine']['LLM_version']) + + + if available_prompts: + default_version = available_prompts[0] ######### Can be configured by user ################################################################# + selected_version = st.session_state.config['leafmachine']['project'].get('prompt_version', default_version) + if selected_version not in available_prompts: + selected_version = default_version + st.session_state.config['leafmachine']['project']['prompt_version'] = st.selectbox("Prompt Version", available_prompts, index=available_prompts.index(selected_version),label_visibility='collapsed') + + with col_prompt_2: + if st.button("Build Custom LLM Prompt"): + st.session_state.proceed_to_build_llm_prompt = True + st.rerun() + + st.header('LLM Version') + col_llm_1, col_llm_2 = st.columns([4,2]) + + with col_llm_1: + GUI_MODEL_LIST = ModelMaps.get_models_gui_list() + st.session_state.config['leafmachine']['LLM_version'] = st.selectbox("LLM version", GUI_MODEL_LIST, index=GUI_MODEL_LIST.index(st.session_state.config['leafmachine'].get('LLM_version', ModelMaps.MODELS_GUI_DEFAULT))) + + + +def content_api_check(): + # In your Streamlit layout + # Create two columns for the header and the button + col_llm_2a, col_llm_2b = st.columns([6, 2]) # Adjust the ratio as needed + + # Place the header in the first column + with col_llm_2a: + st.header('Available APIs') + + # Display API key status + display_api_key_status() + + # Place the button in the second column, right-justified + # with col_llm_2b: + if st.button("Re-Check API Keys"): + st.session_state['API_checked'] = False + st.session_state['API_rechecked'] = True + # with col_llm_2c: + if st.button("Edit API Keys"): + st.session_state.proceed_to_private = True + st.rerun() + + + + + +def content_collage_overlay(): + st.write("---") + st.header('LeafMachine2 Label Collage') + col_cropped_1, col_cropped_2 = st.columns([4,4]) + + st.write("---") + st.header('OCR Overlay Image') + col_ocr_1, col_ocr_2 = st.columns([4,4]) + + demo_text_h = f"Google_OCR_Handwriting:\nHERBARIUM OF MARCUS W. LYON , JR . Tracaulon sagittatum Indiana : Porter Co. incal Springs edge wet subdunal woods 1927 TX 11 Ilowers pink UNIVERSITE HERBARIUM MICH University of Michigan Herbarium 1439649 copyright reserved PERSICARIA FEB 2 6 1965 cm " + demo_text_tr = f"trOCR:\nherbarium of marcus w. lyon jr. : : : tracaulon sagittatum indiana porter co. incal springs TX 11 Ilowers pink 1439649 copyright reserved D H U Q " + demo_text_p = f"Google_OCR_Printed:\nTracaulon sagittatum Indiana : Porter Co. incal Springs edge wet subdunal woods 1927 Ilowers pink 1439649 copyright reserved PERSICARIA FEB 2 6 1965 cm " + demo_text_b = demo_text_h + '\n' + demo_text_p + demo_text_trb = demo_text_h + '\n' + demo_text_p + '\n' + demo_text_tr + demo_text_trh = demo_text_h + '\n' + demo_text_tr + demo_text_trp = demo_text_p + '\n' + demo_text_tr + + with col_cropped_1: + default_crops = st.session_state.config['leafmachine']['cropped_components']['save_cropped_annotations'] + st.write("Prior to transcription, use LeafMachine2 to crop all labels from input images to create label collages for each specimen image. (Requires GPU)") + st.session_state.config['leafmachine']['use_RGB_label_images'] = st.checkbox("Use LeafMachine2 label collage for transcriptions", st.session_state.config['leafmachine'].get('use_RGB_label_images', False)) + + + option_selected_crops = st.multiselect(label="Components to crop", + options=['ruler', 'barcode','label', 'colorcard','map','envelope','photo','attached_item','weights', + 'leaf_whole', 'leaf_partial', 'leaflet', 'seed_fruit_one', 'seed_fruit_many', 'flower_one', 'flower_many', 'bud','specimen','roots','wood'],default=default_crops) + st.session_state.config['leafmachine']['cropped_components']['save_cropped_annotations'] = option_selected_crops + with col_cropped_2: + # Load the image only if it's not already in the session state + if "demo_collage" not in st.session_state: + # ba = os.path.join(st.session_state.dir_home, 'demo', 'ba', 'ba2.png') + ba = os.path.join(st.session_state.dir_home, 'demo', 'ba', 'ba2.jpg') + st.session_state["demo_collage"] = Image.open(ba) + + # Display the image + # st.image(st.session_state["demo_collage"], caption='LeafMachine2 Collage', output_format="PNG") + st.image(st.session_state["demo_collage"], caption='LeafMachine2 Collage', output_format="JPEG") + + + + with col_ocr_1: + options = [":rainbow[Printed + Handwritten]", "Printed", "Use both models"] + captions = [ + "Works well for both printed and handwritten text", + "Works for printed text", + "Adds both OCR versions to the LLM prompt" + ] + + st.write('This will plot bounding boxes around all text that Google Vision was able to detect. If there are no boxes around text, then the OCR failed, so that missing text will not be seen by the LLM when it is creating the JSON object. The created image will be viewable in the VoucherVisionEditor.') + + do_create_OCR_helper_image = st.checkbox("Create image showing an overlay of the OCR detections",value=st.session_state.config['leafmachine']['do_create_OCR_helper_image']) + st.session_state.config['leafmachine']['do_create_OCR_helper_image'] = do_create_OCR_helper_image + + + do_use_trOCR = st.checkbox("Supplement Google Vision OCR with trOCR (handwriting OCR) via 'microsoft/trocr-large-handwritten'", value=st.session_state.config['leafmachine']['project']['do_use_trOCR'],disabled=st.session_state['lacks_GPU']) + st.session_state.config['leafmachine']['project']['do_use_trOCR'] = do_use_trOCR + + # Get the current OCR option from session state + OCR_option = st.session_state.config['leafmachine']['project']['OCR_option'] + + # Map the OCR option to the index in options list + # You need to define the mapping based on your application's logic + option_to_index = { + 'hand': 0, + 'normal': 1, + 'both': 2, + } + default_index = option_to_index.get(OCR_option, 0) # Default to 0 if option not found + + # Create the radio button + OCR_option_select = st.radio( + "Select the Google Vision OCR version.", + options, + index=default_index, + help="",captions=captions, + ) + st.session_state.config['leafmachine']['project']['OCR_option'] = OCR_option_select + + if OCR_option_select == ":rainbow[Printed + Handwritten]": + OCR_option = 'hand' + elif OCR_option_select == "Printed": + OCR_option = 'normal' + elif OCR_option_select == "Use both models": + OCR_option = 'both' + else: + raise + + st.session_state.config['leafmachine']['project']['OCR_option'] = OCR_option + st.markdown("Below is an example of what the LLM would see given the choice of OCR ensemble. One, two, or three version of OCR can be fed into the LLM prompt. Typically, 'printed + handwritten' works well. If you have a GPU then you can enable trOCR.") + if (OCR_option == 'hand') and not do_use_trOCR: + st.text_area(label='HandwrittenPrinted',placeholder=demo_text_h,disabled=True, label_visibility='visible') + elif (OCR_option == 'normal') and not do_use_trOCR: + st.text_area(label='Printed',placeholder=demo_text_p,disabled=True, label_visibility='visible') + elif (OCR_option == 'both') and not do_use_trOCR: + st.text_area(label='HandwrittenPrinted + Printed',placeholder=demo_text_b,disabled=True, label_visibility='visible') + elif (OCR_option == 'both') and do_use_trOCR: + st.text_area(label='HandwrittenPrinted + Printed + trOCR',placeholder=demo_text_trb,disabled=True, label_visibility='visible') + elif (OCR_option == 'normal') and do_use_trOCR: + st.text_area(label='Printed + trOCR',placeholder=demo_text_trp,disabled=True, label_visibility='visible') + elif (OCR_option == 'hand') and do_use_trOCR: + st.text_area(label='HandwrittenPrinted + trOCR',placeholder=demo_text_trh,disabled=True, label_visibility='visible') + + with col_ocr_2: + if "demo_overlay" not in st.session_state: + # ocr = os.path.join(st.session_state.dir_home,'demo', 'ba','ocr.png') + ocr = os.path.join(st.session_state.dir_home,'demo', 'ba','ocr.jpg') + st.session_state["demo_overlay"] = Image.open(ocr) + + # st.image(st.session_state["demo_overlay"], caption='OCR Overlay Images', output_format = "PNG") + st.image(st.session_state["demo_overlay"], caption='OCR Overlay Images', output_format = "JPEG") + - # Progress bar - col_run_info_1 = st.columns([1])[0] - with col_run_info_1: - # Progress - st.subheader("Overall Progress") - overall_progress_bar = st.progress(0) - text_overall = st.empty() # Placeholder for current step name - - st.subheader('Transcription Progress') - batch_progress_bar = st.progress(0) - text_batch = st.empty() # Placeholder for current step name +def content_archival_components(): + st.write("---") + st.header('Archival Components') + ACD_version = st.selectbox("Archival Component Detector (ACD) Version", ["Version 2.1", "Version 2.2"]) + + ACD_confidence_default = int(st.session_state.config['leafmachine']['archival_component_detector']['minimum_confidence_threshold'] * 100) + ACD_confidence = st.number_input("ACD Confidence Threshold (%)", min_value=0, max_value=100,value=ACD_confidence_default) + st.session_state.config['leafmachine']['archival_component_detector']['minimum_confidence_threshold'] = float(ACD_confidence/100) - progress_report = ProgressReport(overall_progress_bar, batch_progress_bar, text_overall, text_batch) + st.session_state.config['leafmachine']['archival_component_detector']['do_save_prediction_overlay_images'] = st.checkbox("Save Archival Prediction Overlay Images", st.session_state.config['leafmachine']['archival_component_detector'].get('do_save_prediction_overlay_images', True)) + + st.session_state.config['leafmachine']['archival_component_detector']['ignore_objects_for_overlay'] = st.multiselect("Hide Archival Components in Prediction Overlay Images", + ['ruler', 'barcode','label', 'colorcard','map','envelope','photo','attached_item','weights',], + default=[]) + + # Depending on the selected version, set the configuration + if ACD_version == "Version 2.1": + st.session_state.config['leafmachine']['archival_component_detector']['detector_type'] = 'Archival_Detector' + st.session_state.config['leafmachine']['archival_component_detector']['detector_version'] = 'PREP_final' + st.session_state.config['leafmachine']['archival_component_detector']['detector_iteration'] = 'PREP_final' + st.session_state.config['leafmachine']['archival_component_detector']['detector_weights'] = 'best.pt' + elif ACD_version == "Version 2.2": #TODO update this to version 2.2 + st.session_state.config['leafmachine']['archival_component_detector']['detector_type'] = 'Archival_Detector' + st.session_state.config['leafmachine']['archival_component_detector']['detector_version'] = 'PREP_final' + st.session_state.config['leafmachine']['archival_component_detector']['detector_iteration'] = 'PREP_final' + st.session_state.config['leafmachine']['archival_component_detector']['detector_weights'] = 'best.pt' + + + +def content_processing_options(): + st.write("---") + st.header('Processing Options') + col_processing_1, col_processing_2 = st.columns([2,2,]) + with col_processing_1: + st.subheader('Compute Options') + st.session_state.config['leafmachine']['project']['num_workers'] = st.number_input("Number of CPU workers", value=st.session_state.config['leafmachine']['project'].get('num_workers', 1), disabled=False) + st.session_state.config['leafmachine']['project']['batch_size'] = st.number_input("Batch size", value=st.session_state.config['leafmachine']['project'].get('batch_size', 500), help='Sets the batch size for the LeafMachine2 cropping. If computer RAM is filled, lower this value to ~100.') + with col_processing_2: + st.subheader('Filename Prefix Handling') + st.session_state.config['leafmachine']['project']['prefix_removal'] = st.text_input("Remove prefix from catalog number", st.session_state.config['leafmachine']['project'].get('prefix_removal', ''),placeholder="e.g. MICH-V-") + st.session_state.config['leafmachine']['project']['suffix_removal'] = st.text_input("Remove suffix from catalog number", st.session_state.config['leafmachine']['project'].get('suffix_removal', ''),placeholder="e.g. _B") + st.session_state.config['leafmachine']['project']['catalog_numerical_only'] = st.checkbox("Require 'Catalog Number' to be numerical only", st.session_state.config['leafmachine']['project'].get('catalog_numerical_only', True)) + + ### Logging and Image Validation - col_v1 + st.write("---") + st.header('Logging and Image Validation') + col_v1, col_v2 = st.columns(2) + with col_v1: + option_check_illegal = st.checkbox("Check for illegal filenames", value=st.session_state.config['leafmachine']['do']['check_for_illegal_filenames']) + st.session_state.config['leafmachine']['do']['check_for_illegal_filenames'] = option_check_illegal + + st.session_state.config['leafmachine']['do']['check_for_corrupt_images_make_vertical'] = st.checkbox("Check for corrupt images", st.session_state.config['leafmachine']['do'].get('check_for_corrupt_images_make_vertical', True),disabled=True) - st.info("***Note:*** There is a known bug with tabs in Streamlit. If you update an input field it may take you back to the 'Project Settings' tab. Changes that you made are saved, it's just an annoying glitch. We are aware of this issue and will fix it as soon as we can.") - st.write("If you use VoucherVision frequently, you can change the default values that are auto-populated in the form below. In a text editor or IDE, edit the first few rows in the file `../VoucherVision/vouchervision/VoucherVision_Config_Builder.py`") + st.session_state.config['leafmachine']['print']['verbose'] = st.checkbox("Print verbose", st.session_state.config['leafmachine']['print'].get('verbose', True)) + st.session_state.config['leafmachine']['print']['optional_warnings'] = st.checkbox("Show optional warnings", st.session_state.config['leafmachine']['print'].get('optional_warnings', True)) - with col_run_1: - show_header_welcome() - st.subheader('Run VoucherVision') + with col_v2: + log_level = st.session_state.config['leafmachine']['logging'].get('log_level', None) + log_level_display = log_level if log_level is not None else 'default' + selected_log_level = st.selectbox("Logging Level", ['default', 'DEBUG', 'INFO', 'WARNING', 'ERROR'], index=['default', 'DEBUG', 'INFO', 'WARNING', 'ERROR'].index(log_level_display)) - if not check_if_usable(): - st.button("Start Processing", type='primary', disabled=True) - # st.error(":heavy_exclamation_mark: Required API keys not set. Please visit the 'API Keys' tab and set the Google Vision OCR API key and at least one LLM key.") - st.error(":heavy_exclamation_mark: Required API keys not set. Please set the API keys as 'Secrets' for your Hugging Face Space. Visit the 'Settings' tab at the top of the page.") + if selected_log_level == 'default': + st.session_state.config['leafmachine']['logging']['log_level'] = None else: - if st.button(f"Start Processing{st.session_state['processing_add_on']}", type='primary'): - - # First, write the config file. - write_config_file(st.session_state.config, st.session_state.dir_home, filename="VoucherVision.yaml") + st.session_state.config['leafmachine']['logging']['log_level'] = selected_log_level - path_custom_prompts = os.path.join(st.session_state.dir_home,'custom_prompts',st.session_state.config['leafmachine']['project']['prompt_version']) - # Define number of overall steps - progress_report.set_n_overall(N_OVERALL_STEPS) - progress_report.update_overall(f"Starting VoucherVision...") - # Call the machine function. - last_JSON_response, total_cost, st.session_state['zip_filepath'] = voucher_vision(None, st.session_state.dir_home, path_custom_prompts, None, progress_report,path_api_cost=os.path.join(st.session_state.dir_home,'api_cost','api_cost.yaml'), is_real_run=True) - - if total_cost: - st.success(f":money_with_wings: This run cost :heavy_dollar_sign:{total_cost:.4f}") - - # Format the JSON string for display. - if last_JSON_response is None: - st.markdown(f"Last JSON object in the batch: NONE") - else: - try: - formatted_json = json.dumps(json.loads(last_JSON_response), indent=4, sort_keys=False) - except: - formatted_json = json.dumps(last_JSON_response, indent=4, sort_keys=False) - st.markdown(f"Last JSON object in the batch:\n```\n{formatted_json}\n```") - st.balloons() +def content_tab_domain(): + st.write("---") + st.header('Embeddings Database') + col_emb_1, col_emb_2 = st.columns([4,2]) + with col_emb_1: + st.markdown( + """ + VoucherVision includes the option of using domain knowledge inside of the dynamically generated prompts. The OCR text is queried against a database of existing label transcriptions. The most similar existing transcriptions act as an example of what the LLM should emulate and are shown to the LLM as JSON objects. VoucherVision uses cosine similarity search to return the most similar existing transcription. + - Note: Using domain knowledge may increase the chance that foreign text is included in the final transcription + - Disabling this feature will show the LLM multiple examples of an empty JSON skeleton structure instead + - Enabling this option requires a GPU with at least 8GB of VRAM + - The domain knowledge files can be located in the directory "../VoucherVision/domain_knowledge". On first run the embeddings database must be created, which takes time. If the database creation runs each time you use VoucherVision, then something is wrong. + """ + ) - if st.session_state['zip_filepath']: - create_download_button(st.session_state['zip_filepath']) - st.button("Refresh", on_click=refresh) + st.write(f"Domain Knowledge is only available for the following prompts:") + for available_prompts in ModelMaps.PROMPTS_THAT_NEED_DOMAIN_KNOWLEDGE: + st.markdown(f"- {available_prompts}") + + if st.session_state.config['leafmachine']['project']['prompt_version'] in ModelMaps.PROMPTS_THAT_NEED_DOMAIN_KNOWLEDGE: + st.session_state.config['leafmachine']['project']['use_domain_knowledge'] = st.checkbox("Use domain knowledge", True, disabled=True) + else: + st.session_state.config['leafmachine']['project']['use_domain_knowledge'] = st.checkbox("Use domain knowledge", False, disabled=True) - with col_run_2: - st.subheader('Quick Start') - st.write('1. We include a single image for testing. Without uploading your own images, you can select options below and press "Start Processing" to try VoucherVision.') - st.write('2. Name your run --- If the same name already exist, VV will append the date to the run name.') - st.write('3. Choose a LLM version --- Only LLMs with valid keys will appear in the dropdown list.') - st.write('4. Select a prompt version --- Start with "Version 2". Custom Prompts will include ".yaml" in the name. You can build your own Custom Prompt in the Prompt Builder.') - st.markdown('5. Upload images --- Up to ~100 images can be uploaded in the Hugging Face Spaces implementation. If you want to process more images at once (and have more control in general) then use the [GitHub version](https://github.com/Gene-Weaver/VoucherVision). If you pay for persistent storage for your HF Space, then you may be able to process more too.') - with col_run_3: - st.subheader('') - st.write('6. LeafMachine2 collage --- If selected, LeafMachine2 will isolate all text from the image and create a label collage, which will be sent to the OCR algorithm instead of the full image. This improves OCR detection for small or finely written text.') - st.write('7. OCR overlay images --- If selected, VoucherVision will overlay the OCR detections onto the input image. This is useful for debugging transcription errors to see if the OCR failed or if the LLM failed.') - st.write('8. Start processing --- Wait for VoucherVision to finish.') - st.write('9. Download results --- Click the "Download Results" button to save the VoucherVision output to your computer. ***Output files will disappear if you start a new run or restart the Space.***') - st.write('10. Editing the LLM transcriptions --- Use the VoucherVisionEditor to revise and correct any mistakes or ommissions.') + st.write("") + if st.session_state.config['leafmachine']['project']['use_domain_knowledge']: + st.session_state.config['leafmachine']['project']['embeddings_database_name'] = st.text_input("Embeddings database name (only use underscores)", st.session_state.config['leafmachine']['project'].get('embeddings_database_name', '')) + st.session_state.config['leafmachine']['project']['build_new_embeddings_database'] = st.checkbox("Build *new* embeddings database", st.session_state.config['leafmachine']['project'].get('build_new_embeddings_database', False)) + st.session_state.config['leafmachine']['project']['path_to_domain_knowledge_xlsx'] = st.text_input("Path to domain knowledge CSV file (will be used to create new embeddings database)", st.session_state.config['leafmachine']['project'].get('path_to_domain_knowledge_xlsx', '')) + else: + st.session_state.config['leafmachine']['project']['embeddings_database_name'] = st.text_input("Embeddings database name (only use underscores)", st.session_state.config['leafmachine']['project'].get('embeddings_database_name', ''), disabled=True) + st.session_state.config['leafmachine']['project']['build_new_embeddings_database'] = st.checkbox("Build *new* embeddings database", st.session_state.config['leafmachine']['project'].get('build_new_embeddings_database', False), disabled=True) + st.session_state.config['leafmachine']['project']['path_to_domain_knowledge_xlsx'] = st.text_input("Path to domain knowledge CSV file (will be used to create new embeddings database)", st.session_state.config['leafmachine']['project'].get('path_to_domain_knowledge_xlsx', ''), disabled=True) - with col_run_4: - st.subheader('Available LLMs and APIs') - show_available_APIs() - st.info('Until the end of 2023, Azure OpenAI models will be available for anyone to use here. Then only PaLM 2 will be available. To use all services, duplicate this Space and provide your own API keys.') +def content_space_saver(): + st.write("---") + st.subheader("Space Saving Options") + col_ss_1, col_ss_2 = st.columns([2,2]) + with col_ss_1: + st.write("Several folders are created and populated with data during the VoucherVision transcription process.") + st.write("Below are several options that will allow you to automatically delete temporary files that you may not need for everyday operations.") + st.write("VoucherVision creates the following folders. Folders marked with a :star: are required if you want to use VoucherVisionEditor for quality control.") + st.write("`../[Run Name]/Archival_Components`") + st.write("`../[Run Name]/Config_File`") + st.write("`../[Run Name]/Cropped_Images` :star:") + st.write("`../[Run Name]/Logs`") + st.write("`../[Run Name]/Original_Images` :star:") + st.write("`../[Run Name]/Transcription` :star:") + with col_ss_2: + 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.") -######################################################################################################## -### Main Settings #### -######################################################################################################## -def content_tab_settings(): - st.write("---") - st.header("Configuration Settings") - col_project_1, col_project_2, col_project_3 = st.columns([2,2,2]) - st.write("---") - st.header('Input Images') - st.write('Upload a batch of images using the uploader below. These images will be store temporarily on this server. Each time you upload new images the ***previously uploaded images will be deleted***. You can also clear these cached images by pressing the "Clear Staged Images" button.') - col_local_1, col_local_2 = st.columns([2,6]) +################################################################################################################################################# +# render_expense_report_summary ################################################################################################################# +################################################################################################################################################# +def render_expense_report_summary(): + expense_summary = st.session_state.expense_summary + expense_report = st.session_state.expense_report + st.header('Expense Report Summary') - st.write("---") - st.header('LeafMachine2 Label Collage') - col_cropped_1, col_cropped_2 = st.columns([4,4]) + if expense_summary: + st.metric(label="Total Cost", value=f"${round(expense_summary['total_cost_sum'], 4):,}") + col1, col2 = st.columns(2) - st.write("---") - st.header('OCR Overlay Image') - col_ocr_1, col_ocr_2 = st.columns([4,4]) + # Run count and total costs + with col1: + st.metric(label="Run Count", value=expense_summary['run_count']) + st.metric(label="Tokens In", value=f"{expense_summary['tokens_in_sum']:,}") - ### Project - with col_project_1: - st.subheader('Run name') - st.session_state.config['leafmachine']['project']['run_name'] = st.text_input("Run name", st.session_state.config['leafmachine']['project'].get('run_name', ''), - label_visibility='collapsed') - st.write("Run name will be the name of the final zipped folder.") + # Token information + with col2: + st.metric(label="Total Images", value=expense_summary['n_images_sum']) + st.metric(label="Tokens Out", value=f"{expense_summary['tokens_out_sum']:,}") - ### LLM Version - with col_project_2: - # Determine the available versions based on the API keys present - available_versions = [] - for api_name, versions in st.session_state['LLM_VERSIONS'].items(): - key_state = st.session_state['api_name_to_key_state'][api_name] - if st.session_state.get(key_state, False): - available_versions.extend(versions) - - # Show available LLM versions in a select box if there are any - st.subheader('LLM Version') - if available_versions: - # Get current selection from session_state, defaulting to the first available version - current_selection = st.session_state.config['leafmachine'].get('LLM_version', available_versions[0]) - # Update the selection with a selectbox - st.session_state.config['leafmachine']['LLM_version'] = st.selectbox( - "LLM version", available_versions, - index=available_versions.index(current_selection), - label_visibility='collapsed' - ) - st.markdown("""***Note:*** GPT-4 is significantly more expensive than GPT-3.5""") - else: - st.error("No LLM versions are available due to missing API keys.") - - ### Prompt Version - with col_project_3: - st.subheader('Prompt Version') - versions, default_version = get_prompt_versions(st.session_state.config['leafmachine']['LLM_version']) - if versions: - selected_version = st.session_state.config['leafmachine']['project'].get('prompt_version', default_version) - if selected_version not in versions: - selected_version = default_version - st.session_state.config['leafmachine']['project']['prompt_version'] = st.selectbox("Prompt Version", versions, index=versions.index(selected_version),label_visibility='collapsed') - st.markdown("Several prompts are provided. Visit the 'Prompt Builder' tab to upload your own prompt. If you would like to make your prompt available to others or have the prompt in the dropdown by default, [please submit the yaml through this form.](https://forms.gle/d1sHV5Y7Y5NxMQzM9)") + # Calculate cost proportion per image for each API version + st.subheader('Average Cost per Image by API Version') + cost_labels = [] + cost_values = [] + total_images = 0 + cost_per_image_dict = {} + # Iterate through the expense report to accumulate costs and image counts + for index, row in expense_report.iterrows(): + api_version = row['api_version'] + total_cost = row['total_cost'] + n_images = row['n_images'] + total_images += n_images # Keep track of total images processed + if api_version not in cost_per_image_dict: + cost_per_image_dict[api_version] = {'total_cost': 0, 'n_images': 0} + cost_per_image_dict[api_version]['total_cost'] += total_cost + cost_per_image_dict[api_version]['n_images'] += n_images - if st.button("Build Custom LLM Prompt",help="It may take a moment for the page to refresh."): - st.session_state.proceed_to_build_llm_prompt = True - st.rerun() + api_versions = list(cost_per_image_dict.keys()) + colors = [ModelMaps.COLORS_EXPENSE_REPORT[version] if version in ModelMaps.COLORS_EXPENSE_REPORT else '#DDDDDD' for version in api_versions] + + # Calculate the cost per image for each API version + for version, cost_data in cost_per_image_dict.items(): + total_cost = cost_data['total_cost'] + n_images = cost_data['n_images'] + # Calculate the cost per image for this version + cost_per_image = total_cost / n_images if n_images > 0 else 0 + cost_labels.append(version) + cost_values.append(cost_per_image) + # Generate the pie chart + cost_pie_chart = go.Figure(data=[go.Pie(labels=cost_labels, values=cost_values, hole=.3)]) + # Update traces for custom text in hoverinfo, displaying cost with a dollar sign and two decimal places + cost_pie_chart.update_traces( + marker=dict(colors=colors), + text=[f"${value:.4f}" for value in cost_values], # Formats the cost as a string with a dollar sign and two decimals + textinfo='percent+label', + hoverinfo='label+percent+text' # Adds custom text (formatted cost) to the hover information + ) + st.plotly_chart(cost_pie_chart, use_container_width=True) - ### Input Images Local - with col_local_1: - st.session_state['dir_uploaded_images'] = os.path.join(st.session_state.dir_home,'uploads') - st.session_state['dir_uploaded_images_small'] = os.path.join(st.session_state.dir_home,'uploads_small') - uploaded_files = st.file_uploader("Upload Images", type=['jpg', 'jpeg'], accept_multiple_files=True, key=st.session_state['uploader_idk']) - if uploaded_files: - # Clear input image gallery and input list - clear_image_gallery() - # Process the new iamges - for uploaded_file in uploaded_files: - file_path = save_uploaded_file(st.session_state['dir_uploaded_images'], uploaded_file) - st.session_state['input_list'].append(file_path) - img = Image.open(file_path) - img.thumbnail((GALLERY_IMAGE_SIZE, GALLERY_IMAGE_SIZE), Image.Resampling.LANCZOS) - file_path_small = save_uploaded_file(st.session_state['dir_uploaded_images_small'], uploaded_file, img) - st.session_state['input_list_small'].append(file_path_small) - print(uploaded_file.name) + st.subheader('Proportion of Total Cost by API Version') + cost_labels = [] + cost_proportions = [] + total_cost_by_version = {} + # Sum the total cost for each API version + for index, row in expense_report.iterrows(): + api_version = row['api_version'] + total_cost = row['total_cost'] + if api_version not in total_cost_by_version: + total_cost_by_version[api_version] = 0 + total_cost_by_version[api_version] += total_cost + # Calculate the combined total cost for all versions + combined_total_cost = sum(total_cost_by_version.values()) + # Calculate the proportion of total cost for each API version + for version, total_cost in total_cost_by_version.items(): + proportion = (total_cost / combined_total_cost) * 100 if combined_total_cost > 0 else 0 + cost_labels.append(version) + cost_proportions.append(proportion) + # Generate the pie chart + cost_pie_chart = go.Figure(data=[go.Pie(labels=cost_labels, values=cost_proportions, hole=.3)]) + # Update traces for custom text in hoverinfo + cost_pie_chart.update_traces( + marker=dict(colors=colors), + text=[f"${cost:.4f}" for cost in total_cost_by_version.values()], # This will format the cost to 2 decimal places + textinfo='percent+label', + hoverinfo='label+percent+text' # This tells Plotly to show the label, percent, and custom text (cost) on hover + ) + st.plotly_chart(cost_pie_chart, use_container_width=True) - # Set the local images to the uploaded images - st.session_state.config['leafmachine']['project']['dir_images_local'] = st.session_state['dir_uploaded_images'] + # API version usage percentages pie chart + st.subheader('Runs by API Version') + api_versions = list(expense_summary['api_version_percentages'].keys()) + percentages = [expense_summary['api_version_percentages'][version] for version in api_versions] + pie_chart = go.Figure(data=[go.Pie(labels=api_versions, values=percentages, hole=.3)]) + pie_chart.update_layout(margin=dict(t=0, b=0, l=0, r=0)) + pie_chart.update_traces(marker=dict(colors=colors),) + st.plotly_chart(pie_chart, use_container_width=True) - n_images = len([f for f in os.listdir(st.session_state.config['leafmachine']['project']['dir_images_local']) if os.path.isfile(os.path.join(st.session_state.config['leafmachine']['project']['dir_images_local'], f))]) - st.session_state['processing_add_on'] = f" {n_images} Images" - uploaded_files = None - st.session_state['uploader_idk'] += 1 - st.info(f"Processing **{n_images}** images from {st.session_state.config['leafmachine']['project']['dir_images_local']}") + else: + st.error('No expense report data available.') - st.button("Use Test Image",help="This will clear any uploaded images and load the 1 provided test image.",on_click=use_test_image) - # Show uploaded images gallery (thumbnails only) - with col_local_2: - if st.session_state['input_list_small']: - st.subheader('Image Gallery') - if len(st.session_state['input_list_small']) > MAX_GALLERY_IMAGES: - # Only take the first 100 images from the list - images_to_display = st.session_state['input_list_small'][:MAX_GALLERY_IMAGES] - else: - # If there are less than 100 images, take them all - images_to_display = st.session_state['input_list_small'] - st.image(images_to_display) +def content_less_used(): + st.write('---') + st.write(':octagonal_sign: ***NOTE:*** Settings below are not relevant for most projects. Some settings below may not be reflected in saved settings files and would need to be set each time.') - 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)") - st.session_state.config['leafmachine']['use_RGB_label_images'] = st.checkbox("Use LeafMachine2 label collage for transcriptions", st.session_state.config['leafmachine'].get('use_RGB_label_images', False)) +################################################################################################################################################# +# Sidebar ####################################################################################################################################### +################################################################################################################################################# +def sidebar_content(): + if not os.path.exists(os.path.join(st.session_state.dir_home,'expense_report')): + validate_dir(os.path.join(st.session_state.dir_home,'expense_report')) + expense_report_path = os.path.join(st.session_state.dir_home, 'expense_report', 'expense_report.csv') - st.session_state.config['leafmachine']['cropped_components']['save_cropped_annotations'] = st.multiselect("Components to crop", - ['ruler', 'barcode','label', 'colorcard','map','envelope','photo','attached_item','weights', - 'leaf_whole', 'leaf_partial', 'leaflet', 'seed_fruit_one', 'seed_fruit_many', 'flower_one', 'flower_many', 'bud','specimen','roots','wood'],default=default_crops) - - with col_cropped_2: - ba = os.path.join(st.session_state.dir_home,'demo', 'ba','ba2.png') - image = Image.open(ba) - st.image(image, caption='LeafMachine2 Collage', output_format = "PNG") - - with col_ocr_1: - st.write('This will plot bounding boxes around all text that Google Vision was able to detect. If there are no boxes around text, then the OCR failed, so that missing text will not be seen by the LLM when it is creating the JSON object. The created image will be viewable in the VoucherVisionEditor.') - st.session_state.config['leafmachine']['do_create_OCR_helper_image'] = st.checkbox("Create image showing an overlay of the OCR detections", st.session_state.config['leafmachine'].get('do_create_OCR_helper_image', False)) - - with col_ocr_2: - ocr = os.path.join(st.session_state.dir_home,'demo', 'ba','ocr.png') - image_ocr = Image.open(ocr) - st.image(image_ocr, caption='OCR Overlay Images', output_format = "PNG") + if os.path.exists(expense_report_path): + # File exists, proceed with summarization + st.session_state.expense_summary, st.session_state.expense_report = summarize_expense_report(expense_report_path) + render_expense_report_summary() + else: + # File does not exist, handle this case appropriately + # For example, you could set the session state variables to None or an empty value + st.session_state.expense_summary, st.session_state.expense_report = None, None + st.header('Expense Report Summary') + st.write('Available after first run...') + + + +################################################################################################################################################# +# Routing Function ############################################################################################################################## +################################################################################################################################################# +# Function to parse command line arguments +def parse_command_line_argument(arg_name): + for arg in sys.argv: + if arg.startswith(f'--{arg_name}='): + return arg.split('=')[1] == '1' + return False -######################################################################################################## -### Main #### -######################################################################################################## def main(): with st.sidebar: sidebar_content() # Main App content_header() - tab_settings = st.container() - - with tab_settings: - content_tab_settings() + + if st.session_state['is_hf']: + content_project_settings() + content_input_images_hf() + else: + col1, col2 = st.columns([1,1]) + with col1: + content_project_settings() + with col2: + content_input_images() -######################################################################################################## -### STREAMLIT APP START #### -######################################################################################################## + st.write("---") + col3, col4 = st.columns([1,1]) + with col3: + content_prompt_and_llm_version() + with col4: + content_api_check() + content_llm_cost() + content_collage_overlay() + content_processing_options() + content_less_used() + content_archival_components() + content_space_saver() + # content_tab_domain() + + + +################################################################################################################################################# +# Initializations ############################################################################################################################### +################################################################################################################################################# + st.set_page_config(layout="wide", page_icon='img/icon.ico', page_title='VoucherVision') +# Parse the 'is_hf' argument and set it in session state +if 'is_hf' not in st.session_state: + st.session_state['is_hf'] = parse_command_line_argument('is_hf') - -######################################################################################################## -### STREAMLIT INIT STATES #### -######################################################################################################## +# Default YAML file path if 'config' not in st.session_state: - st.session_state.config, st.session_state.dir_home = build_VV_config() + st.session_state.config, st.session_state.dir_home = build_VV_config(loaded_cfg=None) setup_streamlit_config(st.session_state.dir_home) if 'proceed_to_main' not in st.session_state: - st.session_state.proceed_to_main = True - + st.session_state.proceed_to_main = False # New state variable to control the flow if 'proceed_to_build_llm_prompt' not in st.session_state: - st.session_state.proceed_to_build_llm_prompt = False - + st.session_state.proceed_to_build_llm_prompt = False # New state variable to control the flow if 'proceed_to_private' not in st.session_state: - st.session_state.proceed_to_private = False + st.session_state.proceed_to_private = False # New state variable to control the flow -if 'dir_uploaded_images' not in st.session_state: - st.session_state['dir_uploaded_images'] = os.path.join(st.session_state.dir_home,'uploads') - validate_dir(os.path.join(st.session_state.dir_home,'uploads')) -if 'dir_uploaded_images_small' not in st.session_state: - st.session_state['dir_uploaded_images_small'] = os.path.join(st.session_state.dir_home,'uploads_small') - validate_dir(os.path.join(st.session_state.dir_home,'uploads_small')) +if 'private_file' not in st.session_state: + st.session_state.private_file = does_private_file_exist() + if st.session_state.private_file: + st.session_state.proceed_to_main = True -if 'prompt_info' not in st.session_state: - st.session_state['prompt_info'] = {} -if 'rules' not in st.session_state: - st.session_state['rules'] = {} +if 'processing_add_on' not in st.session_state: + st.session_state['processing_add_on'] = '' -if 'zip_filepath' not in st.session_state: - st.session_state['zip_filepath'] = None -if 'input_list' not in st.session_state: - st.session_state['input_list'] = [] +if 'formatted_json' not in st.session_state: + st.session_state['formatted_json'] = None +if 'formatted_json_WFO' not in st.session_state: + st.session_state['formatted_json_WFO'] = None +if 'formatted_json_GEO' not in st.session_state: + st.session_state['formatted_json_GEO'] = None -if 'input_list_small' not in st.session_state: - st.session_state['input_list_small'] = [] -if 'selected_yaml_file' not in st.session_state: - st.session_state['selected_yaml_file'] = None +if 'lacks_GPU' not in st.session_state: + st.session_state['lacks_GPU'] = not torch.cuda.is_available() -if 'new_prompt_yaml_filename' not in st.session_state: - st.session_state['new_prompt_yaml_filename'] = None -if 'show_prompt_name_e' not in st.session_state: - st.session_state['show_prompt_name_e'] = None +if 'API_key_validation' not in st.session_state: + st.session_state['API_key_validation'] = False +if 'present_annotations' not in st.session_state: + st.session_state['present_annotations'] = None +if 'missing_annotations' not in st.session_state: + st.session_state['missing_annotations'] = None +if 'date_of_check' not in st.session_state: + st.session_state['date_of_check'] = None +if 'API_checked' not in st.session_state: + st.session_state['API_checked'] = False +if 'API_rechecked' not in st.session_state: + st.session_state['API_rechecked'] = False -if 'show_prompt_name_w' not in st.session_state: - st.session_state['show_prompt_name_w'] = None -if 'user_clicked_load_prompt_yaml' not in st.session_state: - st.session_state['user_clicked_load_prompt_yaml'] = None +if 'cost_openai' not in st.session_state: + st.session_state['cost_openai'] = None +if 'cost_azure' not in st.session_state: + st.session_state['cost_azure'] = None +if 'cost_google' not in st.session_state: + st.session_state['cost_google'] = None +if 'cost_mistral' not in st.session_state: + st.session_state['cost_mistral'] = None +if 'cost_local' not in st.session_state: + st.session_state['cost_local'] = None -if 'processing_add_on' not in st.session_state: - st.session_state['processing_add_on'] = ' 1 Image' +if 'settings_filename' not in st.session_state: + st.session_state['settings_filename'] = None +if 'loaded_settings_filename' not in st.session_state: + st.session_state['loaded_settings_filename'] = None -if 'uploader_idk' not in st.session_state: - st.session_state['uploader_idk'] = 1 -if 'LLM_VERSIONS' not in st.session_state: - st.session_state['LLM_VERSIONS'] = { - 'OpenAI API': ["GPT 4", "GPT 3.5"], - 'Azure API': ["Azure GPT 4", "Azure GPT 3.5"], - 'Palm API': ["PaLM 2"] - } -if 'api_name_to_key_state ' not in st.session_state: - st.session_state['api_name_to_key_state'] = { - 'OpenAI API': 'has_key_openai', - 'Google OCR API': 'has_key_google_OCR', - 'Palm API': 'has_key_palm2', - 'Azure API': 'has_key_azure' - } - - # Initialize API key states if not already in session_state -for api_name, key_state in st.session_state['api_name_to_key_state'].items(): - if key_state not in st.session_state: - st.session_state[key_state] = False +# Initialize session_state variables if they don't exist +if 'prompt_info' not in st.session_state: + st.session_state['prompt_info'] = {} +if 'rules' not in st.session_state: + st.session_state['rules'] = {} +if 'required_fields' not in st.session_state: + st.session_state['required_fields'] = ['catalogNumber','order','family','scientificName', + 'scientificNameAuthorship','genus','subgenus','specificEpithet','infraspecificEpithet', + 'verbatimEventDate','eventDate', + 'country','stateProvince','county','municipality','locality','decimalLatitude','decimalLongitude','verbatimCoordinates',] +# These are the fields that are in SLTPvA that are not required by another parsing valication function: +# "identifiedBy": "M.W. Lyon, Jr.", +# "recordedBy": "University of Michigan Herbarium", +# "recordNumber": "", +# "habitat": "wet subdunal woods", +# "occurrenceRemarks": "Indiana : Porter Co.", +# "degreeOfEstablishment": "", +# "minimumElevationInMeters": "", +# "maximumElevationInMeters": "" -######################################################################################################## -### STREAMLIT SESSION GUIDE #### -######################################################################################################## -if st.session_state.proceed_to_build_llm_prompt: + +if 'proceed_to_build_llm_prompt' not in st.session_state: + st.session_state.proceed_to_build_llm_prompt = False +if 'proceed_to_component_detector' not in st.session_state: + st.session_state.proceed_to_component_detector = False +if 'proceed_to_parsing_options' not in st.session_state: + st.session_state.proceed_to_parsing_options = False +if 'proceed_to_api_keys' not in st.session_state: + st.session_state.proceed_to_api_keys = False +if 'proceed_to_space_saver' not in st.session_state: + st.session_state.proceed_to_space_saver = False + +################################################################################################################################################# +# Main ########################################################################################################################################## +################################################################################################################################################# + +if not st.session_state.private_file and not st.session_state['is_hf']: + create_private_file() +elif st.session_state.proceed_to_build_llm_prompt: build_LLM_prompt_config() +elif st.session_state.proceed_to_private and not st.session_state['is_hf']: + create_private_file() elif st.session_state.proceed_to_main: main() \ No newline at end of file