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| import streamlit as st | |
| import yaml, os, json, random, time, re, torch, random, warnings, uuid | |
| import seaborn as sns | |
| import matplotlib.pyplot as plt | |
| import plotly.graph_objs as go | |
| import numpy as np | |
| from itertools import chain | |
| from PIL import Image | |
| import pandas as pd | |
| from typing import Union | |
| 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, 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 | |
| class ProgressReport: | |
| def __init__(self, overall_bar, batch_bar, text_overall, text_batch): | |
| self.overall_bar = overall_bar | |
| self.batch_bar = batch_bar | |
| self.text_overall = text_overall | |
| self.text_batch = text_batch | |
| self.current_overall_step = 0 | |
| self.total_overall_steps = 20 # number of major steps in machine function | |
| self.current_batch = 0 | |
| self.total_batches = 20 | |
| def update_overall(self, step_name=""): | |
| self.current_overall_step += 1 | |
| self.overall_bar.progress(self.current_overall_step / self.total_overall_steps) | |
| self.text_overall.text(step_name) | |
| def update_batch(self, step_name=""): | |
| self.current_batch += 1 | |
| self.batch_bar.progress(self.current_batch / self.total_batches) | |
| self.text_batch.text(step_name) | |
| def set_n_batches(self, n_batches): | |
| self.total_batches = n_batches | |
| def set_n_overall(self, total_overall_steps): | |
| self.current_overall_step = 0 | |
| self.overall_bar.progress(0) | |
| self.total_overall_steps = total_overall_steps | |
| def reset_batch(self, step_name): | |
| self.current_batch = 0 | |
| self.batch_bar.progress(0) | |
| self.text_batch.text(step_name) | |
| def reset_overall(self, step_name): | |
| 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()) | |
| def display_scrollable_results(JSON_results, test_results, OPT2, OPT3): | |
| """ | |
| Display the results from JSON_results in a scrollable container. | |
| """ | |
| # Initialize the container | |
| con_results = st.empty() | |
| with con_results.container(): | |
| # Start the custom container for all the results | |
| results_html = """<div class='scrollable-results-container'>""" | |
| for idx, (test_name, _) 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" | |
| opt3_readable = f"{OPT3[int(ind_opt3.split('-')[1])]}" | |
| if JSON_results[idx] is None: | |
| results_html += f"<p>None</p>" | |
| else: | |
| formatted_json = json.dumps(JSON_results[idx], indent=4, sort_keys=False) | |
| results_html += f"<pre>[{opt2_readable}] + [{opt3_readable}]<br/>{formatted_json}</pre>" | |
| # End the custom container | |
| results_html += """</div>""" | |
| # The CSS to make this container scrollable | |
| css = """ | |
| <style> | |
| .scrollable-results-container { | |
| overflow-y: auto; | |
| height: 600px; | |
| width: 100%; | |
| white-space: pre-wrap; # To wrap the content | |
| font-family: monospace; # To give the JSON a code-like appearance | |
| } | |
| </style> | |
| """ | |
| # Apply the CSS and then the results | |
| st.markdown(css, unsafe_allow_html=True) | |
| st.markdown(results_html, unsafe_allow_html=True) | |
| def refresh(): | |
| st.write('') | |
| def display_test_results(test_results, JSON_results, llm_version): | |
| if llm_version == 'gpt': | |
| OPT1, OPT2, OPT3 = TestOptionsGPT.get_options() | |
| elif llm_version == 'palm': | |
| OPT1, OPT2, OPT3 = TestOptionsPalm.get_options() | |
| else: | |
| raise | |
| widths = [1] * (len(OPT1) + 2) + [2] | |
| columns = st.columns(widths) | |
| with columns[0]: | |
| st.write("LeafMachine2") | |
| with columns[1]: | |
| st.write("Prompt") | |
| with columns[len(OPT1) + 2]: | |
| st.write("Scroll to See Last Transcription in Each Test") | |
| already_written = set() | |
| for test_name, result in sorted(test_results.items()): | |
| _, ind_opt1, _, _ = test_name.split('__') | |
| option_value = OPT1[int(ind_opt1.split('-')[1])] | |
| if option_value not in already_written: | |
| with columns[int(ind_opt1.split('-')[1]) + 2]: | |
| st.write(option_value) | |
| already_written.add(option_value) | |
| printed_options = set() | |
| with columns[-1]: | |
| display_scrollable_results(JSON_results, test_results, OPT2, OPT3) | |
| # Close the custom container | |
| st.write('</div>', 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" | |
| opt3_readable = f"{OPT3[int(ind_opt3.split('-')[1])]}" | |
| if (opt2_readable, opt3_readable) not in printed_options: | |
| with columns[0]: | |
| st.info(f"{opt2_readable}") | |
| st.write('---') | |
| with columns[1]: | |
| st.info(f"{opt3_readable}") | |
| st.write('---') | |
| printed_options.add((opt2_readable, opt3_readable)) | |
| with columns[int(ind_opt1.split('-')[1]) + 2]: | |
| if result: | |
| st.success(f"Test Passed") | |
| else: | |
| st.error(f"Test Failed") | |
| st.write('---') | |
| # success_count = sum(1 for result in test_results.values() if result) | |
| # failure_count = len(test_results) - success_count | |
| # 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 | |
| # 'test2': True, # Test passed | |
| # 'test3': True, # Test passed | |
| # 'test4': False, # Test failed | |
| # 'test5': False, # Test failed | |
| # 'test6': False, # Test failed | |
| # 'test7': False, # Test failed | |
| # 'test8': False, # Test failed | |
| # 'test9': False, # Test failed | |
| # 'test10': False, # Test failed | |
| # } | |
| success_emojis = ["🥇", "🏆", "🍾", "🙌"] | |
| failure_emojis = ["💔", "😭"] | |
| success_count = sum(1 for result in test_results.values() if result) | |
| failure_count = len(test_results) - success_count | |
| chosen_emoji = random.choice(success_emojis) | |
| for _ in range(success_count): | |
| rain( | |
| emoji=chosen_emoji, | |
| font_size=72, | |
| falling_speed=4, | |
| animation_length=2, | |
| ) | |
| add_emoji_delay() | |
| chosen_emoji = random.choice(failure_emojis) | |
| for _ in range(failure_count): | |
| rain( | |
| emoji=chosen_emoji, | |
| font_size=72, | |
| falling_speed=5, | |
| animation_length=1, | |
| ) | |
| 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')] | |
| 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: | |
| 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]) | |
| 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): | |
| with open(filename, 'r') as file: | |
| 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['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['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): | |
| 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'], | |
| 'json_formatting_instructions': st.session_state['json_formatting_instructions'], | |
| 'rules': st.session_state['rules'], | |
| 'mapping': st.session_state['mapping'], | |
| } | |
| dir_prompt = os.path.join(st.session_state.dir_home, 'custom_prompts') | |
| filepath = os.path.join(dir_prompt, f"{filename}.yaml") | |
| with open(filepath, 'w') as file: | |
| yaml.safe_dump(dict(yaml_content), file, sort_keys=False) | |
| st.success(f"Prompt saved as '{filename}.yaml'.") | |
| 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. | |
| if re.match(pattern, fname): | |
| return True | |
| else: | |
| return False | |
| def btn_load_prompt(selected_yaml_file, dir_prompt): | |
| if selected_yaml_file: | |
| yaml_file_path = os.path.join(dir_prompt, selected_yaml_file) | |
| load_prompt_yaml(yaml_file_path) | |
| elif not selected_yaml_file: | |
| # Directly assigning default values since no file is selected | |
| 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'] = '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'], | |
| '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]) | |
| 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("---") | |
| 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_load_text, col_load_btn = st.columns([8,2]) | |
| with col_load_text: | |
| # Dropdown for selecting a YAML file | |
| selected_yaml_file = st.selectbox('Select a prompt YAML file to load:', [''] + yaml_files) | |
| with col_load_btn: | |
| st.write('##') | |
| # Button to load the selected prompt | |
| st.button('Load Prompt', on_click=btn_load_prompt, args=[selected_yaml_file, dir_prompt]) | |
| # 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'.") | |
| 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=uuid.uuid4()) | |
| else: | |
| st.session_state['prompt_author'] = st.text_input("Enter names of prompt author(s)", value=st.session_state['prompt_author'],key=uuid.uuid4()) | |
| # Institution | |
| st.write("Please provide your institution name. If you leave this field blank, it will say 'unknown'.") | |
| 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=uuid.uuid4()) | |
| else: | |
| st.session_state['prompt_author_institution'] = st.text_input("Enter name of institution", value=st.session_state['prompt_author_institution'],key=uuid.uuid4()) | |
| # 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=uuid.uuid4()) | |
| else: | |
| st.session_state['prompt_name'] = st.text_input("Enter prompt name", value=st.session_state['prompt_name'],key=uuid.uuid4()) | |
| # 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=uuid.uuid4()) | |
| else: | |
| st.session_state['prompt_version'] = st.text_input("Enter prompt version", value=st.session_state['prompt_version'],key=uuid.uuid4()) | |
| st.write("Please provide a description of your prompt and its intended task. Is it designed for a specific collection? Taxa? Database structure?") | |
| 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=uuid.uuid4()) | |
| else: | |
| st.session_state['prompt_description'] = st.text_input("Enter description of prompt", value=st.session_state['prompt_description'],key=uuid.uuid4()) | |
| st.write('---') | |
| st.header("Set LLM Model Type") | |
| # 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 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.") | |
| 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=uuid.uuid4()) | |
| else: | |
| st.session_state['instructions'] = st.text_area("Enter guiding instructions", value=st.session_state['instructions'].strip(), height=350,key=uuid.uuid4()) | |
| 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.") | |
| 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=uuid.uuid4()) | |
| else: | |
| st.session_state['json_formatting_instructions'] = st.text_area("Enter general JSON guidelines", value=st.session_state['json_formatting_instructions'],key=uuid.uuid4()) | |
| 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']: | |
| 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 "catalogNumber" | |
| st.write('#') | |
| # 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) | |
| # if 'current_rule' not in st.session_state: | |
| # st.session_state['current_rule'] = current_rule | |
| # 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"]) | |
| 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['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 st.session_state['current_rule']: | |
| # Commit the rules to the session state. | |
| 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('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 | |
| # 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']) | |
| # 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") | |
| st.write("Assign each column name to a single category.") | |
| st.session_state['refresh_mapping'] = False | |
| # 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'].keys()) | |
| categories = ['TAXONOMY', 'GEOGRAPHY', 'LOCALITY', 'COLLECTING', 'MISCELLANEOUS'] | |
| if ('mapping' not in st.session_state) or (st.session_state['mapping'] == {}): | |
| st.session_state['mapping'] = {category: [] for category in categories} | |
| for category in categories: | |
| # Filter out the already assigned columns | |
| available_columns = [col for col in all_column_names if col not in st.session_state['assigned_columns'] or col in st.session_state['mapping'].get(category, [])] | |
| # Ensure the current mapping is a subset of the available options | |
| current_mapping = [col for col in st.session_state['mapping'].get(category, []) if col in available_columns] | |
| # Provide a safe default if the current mapping is empty or contains invalid options | |
| safe_default = current_mapping if all(col in available_columns for col in current_mapping) else [] | |
| # Create a multi-select widget for the category with a safe default | |
| selected_columns = st.multiselect( | |
| f"Select columns for {category}:", | |
| available_columns, | |
| default=safe_default, | |
| key=f"mapping_{category}" | |
| ) | |
| # Update the assigned_columns based on the selections | |
| for col in current_mapping: | |
| if col not in selected_columns and col in st.session_state['assigned_columns']: | |
| st.session_state['assigned_columns'].remove(col) | |
| st.session_state['refresh_mapping'] = True | |
| for col in selected_columns: | |
| if col not in st.session_state['assigned_columns']: | |
| st.session_state['assigned_columns'].append(col) | |
| st.session_state['refresh_mapping'] = True | |
| # Update the mapping in session state when there's a change | |
| st.session_state['mapping'][category] = selected_columns | |
| if st.session_state['refresh_mapping']: | |
| st.session_state['refresh_mapping'] = False | |
| # Button to confirm and save the mapping configuration | |
| if st.button('Confirm Mapping'): | |
| if check_unique_mapping_assignments(): | |
| # Proceed with further actions since the mapping is confirmed and unique | |
| pass | |
| with col_right_mapping: | |
| # Display the current state of the JSON rules | |
| 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() | |
| 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']) | |
| 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_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', '')) | |
| 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)") | |
| # Set the options for the radio button | |
| options = { | |
| "Use LeafMachine2 label collage for transcriptions": "use_RGB_label_images", | |
| "Use specimen collage for transcriptions": "use_specimen_collage" | |
| } | |
| # Create the radio button with the available options | |
| selected_option = st.radio( | |
| "Select the transcription method:", | |
| options=list(options.keys()), | |
| index=0 if st.session_state.config['leafmachine'].get('use_RGB_label_images', False) else 1 | |
| ) | |
| # Update the session state based on the selected option | |
| st.session_state.config['leafmachine']['use_RGB_label_images'] = (selected_option == "Use LeafMachine2 label collage for transcriptions") | |
| st.session_state.config['leafmachine']['project']['use_specimen_collage'] = (selected_option == "Use specimen collage for transcriptions") | |
| 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") | |
| 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) | |
| 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.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_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 selected_log_level == 'default': | |
| st.session_state.config['leafmachine']['logging']['log_level'] = None | |
| else: | |
| st.session_state.config['leafmachine']['logging']['log_level'] = selected_log_level | |
| 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. | |
| """ | |
| ) | |
| 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) | |
| 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) | |
| 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.") | |
| ################################################################################################################################################# | |
| # 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') | |
| if expense_summary: | |
| st.metric(label="Total Cost", value=f"${round(expense_summary['total_cost_sum'], 4):,}") | |
| col1, col2 = st.columns(2) | |
| # 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']:,}") | |
| # 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']:,}") | |
| # 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 | |
| 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) | |
| 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) | |
| # 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) | |
| else: | |
| st.error('No expense report data available.') | |
| 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.') | |
| ################################################################################################################################################# | |
| # 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') | |
| 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 ############################################################################################################################## | |
| ################################################################################################################################################# | |
| def main(): | |
| with st.sidebar: | |
| sidebar_content() | |
| # Main App | |
| content_header() | |
| col1, col2 = st.columns([1,1]) | |
| with col1: | |
| content_project_settings() | |
| with col2: | |
| content_input_images() | |
| 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') | |
| # Default YAML file path | |
| if 'config' not in st.session_state: | |
| 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 = 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 # New state variable to control the flow | |
| if 'proceed_to_private' not in st.session_state: | |
| st.session_state.proceed_to_private = False # New state variable to control the flow | |
| 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 'processing_add_on' not in st.session_state: | |
| st.session_state['processing_add_on'] = '' | |
| 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 'lacks_GPU' not in st.session_state: | |
| st.session_state['lacks_GPU'] = not torch.cuda.is_available() | |
| 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 '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 '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 | |
| # 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": "" | |
| 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: | |
| create_private_file() | |
| elif st.session_state.proceed_to_build_llm_prompt: | |
| build_LLM_prompt_config() | |
| elif st.session_state.proceed_to_private: | |
| create_private_file() | |
| elif st.session_state.proceed_to_main: | |
| main() |