Spaces:
Running
Running
| import streamlit as st | |
| import yaml, os, json, random, time, re, shutil, io | |
| import matplotlib.pyplot as plt | |
| import plotly.graph_objs as go | |
| import numpy as np | |
| from itertools import chain | |
| from PIL import Image | |
| from io import BytesIO | |
| import base64 | |
| import pandas as pd | |
| from typing import Union | |
| from google.oauth2 import service_account | |
| from streamlit_extras.let_it_rain import rain | |
| from streamlit_image_select import image_select | |
| 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 | |
| PROMPTS_THAT_NEED_DOMAIN_KNOWLEDGE = ["Version 1","Version 1 PaLM 2"] | |
| LLM_VERSIONS = ["GPT 4", "GPT 3.5", "Azure GPT 4", "Azure GPT 3.5", "PaLM 2"] | |
| COLORS_EXPENSE_REPORT = { | |
| 'GPT_4': '#8fff66', # Bright Green | |
| 'GPT_3_5': '#006400', # Dark Green | |
| 'PALM2': '#66a8ff' # blue | |
| } | |
| 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.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 | |
| 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 | |
| maxUploadSize = 5000 | |
| """ | |
| 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) | |
| 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 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 get_prompt_versions(LLM_version): | |
| yaml_files = [f for f in os.listdir(os.path.join(st.session_state.dir_home, 'custom_prompts')) if f.endswith('.yaml')] | |
| if LLM_version in ["GPT 4", "GPT 3.5", "Azure GPT 4", "Azure GPT 3.5"]: | |
| versions = ["Version 1", "Version 1 No Domain Knowledge", "Version 2"] | |
| return (versions + yaml_files, "Version 2") | |
| elif LLM_version in ["PaLM 2",]: | |
| versions = ["Version 1 PaLM 2", "Version 1 PaLM 2 No Domain Knowledge", "Version 2 PaLM 2"] | |
| return (versions + yaml_files, "Version 2 PaLM 2") | |
| else: | |
| # Handle other cases or raise an error | |
| return (yaml_files, None) | |
| 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_space_saver(): | |
| st.subheader("Space Saving Options") | |
| col_ss_1, col_ss_2 = st.columns([2,2]) | |
| with col_ss_1: | |
| st.write("Several folders are created and populated with data during the VoucherVision transcription process.") | |
| st.write("Below are several options that will allow you to automatically delete temporary files that you may not need for everyday operations.") | |
| st.write("VoucherVision creates the following folders. Folders marked with a :star: are required if you want to use VoucherVisionEditor for quality control.") | |
| st.write("`../[Run Name]/Archival_Components`") | |
| st.write("`../[Run Name]/Config_File`") | |
| st.write("`../[Run Name]/Cropped_Images` :star:") | |
| st.write("`../[Run Name]/Logs`") | |
| st.write("`../[Run Name]/Original_Images` :star:") | |
| st.write("`../[Run Name]/Transcription` :star:") | |
| with col_ss_2: | |
| st.session_state.config['leafmachine']['project']['delete_temps_keep_VVE'] = st.checkbox("Delete Temporary Files (KEEP files required for VoucherVisionEditor)", st.session_state.config['leafmachine']['project'].get('delete_temps_keep_VVE', False)) | |
| st.session_state.config['leafmachine']['project']['delete_all_temps'] = st.checkbox("Keep only the final transcription file", st.session_state.config['leafmachine']['project'].get('delete_all_temps', False),help="*WARNING:* This limits your ability to do quality assurance. This will delete all folders created by VoucherVision, leaving only the `transcription.xlsx` file.") | |
| def save_uploaded_file(directory, img_file, image=None): | |
| if not os.path.exists(directory): | |
| os.makedirs(directory) | |
| # Assuming the uploaded file is an image | |
| try: | |
| with Image.open(img_file) as image: | |
| full_path = os.path.join(directory, img_file.name) | |
| image.save(full_path, "JPEG") | |
| # Return the full path of the saved image | |
| return full_path | |
| except: | |
| full_path = os.path.join(directory, img_file.name) | |
| image.save(img_file, "JPEG") | |
| return directory | |
| def delete_directory(dir_path): | |
| try: | |
| shutil.rmtree(dir_path) | |
| st.session_state['input_list'] = [] | |
| st.session_state['input_list_small'] = [] | |
| st.success(f"Deleted previously uploaded images, making room for new images: {dir_path}") | |
| except OSError as e: | |
| st.error(f"Error: {dir_path} : {e.strerror}") | |
| # def create_private_file(): | |
| # st.session_state.proceed_to_main = False | |
| # st.title("VoucherVision") | |
| # col_private, _ = st.columns([12, 2]) | |
| # openai_api_key = None | |
| # azure_openai_api_version = None | |
| # azure_openai_api_key = None | |
| # azure_openai_api_base = None | |
| # azure_openai_organization = None | |
| # azure_openai_api_type = None | |
| # google_vision = None | |
| # google_palm = None | |
| # # Fetch the environment variables or set to empty if not found | |
| # env_variables = { | |
| # 'OPENAI_API_KEY': os.getenv('OPENAI_API_KEY'), | |
| # 'AZURE_API_VERSION': os.getenv('AZURE_API_VERSION'), | |
| # 'AZURE_API_KEY': os.getenv('AZURE_API_KEY'), | |
| # 'AZURE_API_BASE': os.getenv('AZURE_API_BASE'), | |
| # 'AZURE_ORGANIZATION': os.getenv('AZURE_ORGANIZATION'), | |
| # 'AZURE_API_TYPE': os.getenv('AZURE_API_TYPE'), | |
| # 'AZURE_DEPLOYMENT_NAME': os.getenv('AZURE_DEPLOYMENT_NAME'), | |
| # 'GOOGLE_APPLICATION_CREDENTIALS': os.getenv('GOOGLE_APPLICATION_CREDENTIALS'), | |
| # 'PALM_API_KEY': os.getenv('PALM_API_KEY') | |
| # } | |
| # # Check if all environment variables are set | |
| # all_env_set = all(value is not None for value in env_variables.values()) | |
| # with col_private: | |
| # # Your existing UI code for showing the forms goes here | |
| # 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.") | |
| # if os.getenv('GOOGLE_APPLICATION_CREDENTIALS') is None: | |
| # 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 = '', | |
| # placeholder = 'e.g. copy contents of file 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') | |
| # st.secrets["db_username"] | |
| # with c_button_ocr: | |
| # st.empty() | |
| # with st.container(): | |
| # with c_button_ocr: | |
| # st.write("##") | |
| # st.button("Test OCR", on_click=test_API, args=['google_vision',c_in_ocr,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 os.getenv('OPENAI_API_KEY') is None: | |
| # 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", os.environ.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() | |
| # with st.container(): | |
| # with c_button_openai: | |
| # st.write("##") | |
| # st.button("Test OpenAI", on_click=test_API, args=['openai',c_in_openai,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 os.getenv('AZURE_API_KEY') is None: | |
| # 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", os.environ.get('AZURE_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", os.environ.get('AZURE_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", os.environ.get('AZURE_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", os.environ.get('AZURE_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", os.environ.get('AZURE_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() | |
| # with st.container(): | |
| # with c_button_azure: | |
| # st.write("##") | |
| # st.button("Test Azure OpenAI", on_click=test_API, args=['azure_openai',c_in_azure,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 os.getenv('PALM_API_KEY') is None: | |
| # 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", os.environ.get('PALM_API_KEY', ''), | |
| # help='The MakerSuite API key e.g. a 32-character string', | |
| # placeholder='e.g. SATgthsykuE64FgrrrrEervr3S4455t_geyDeGq', | |
| # type='password') | |
| # with st.container(): | |
| # with c_button_palm: | |
| # st.write("##") | |
| # st.button("Test PaLM 2", on_click=test_API, args=['palm',c_in_palm,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=set_API_keys, args=[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]) | |
| # # # UI form for entering environment variables if not all are set | |
| # # with st.form("env_variables"): | |
| # # for var, value in env_variables.items(): | |
| # # env_variables[var] = st.text_input(f"Enter {var}", value or "") | |
| # # submitted = st.form_submit_button("Submit") | |
| # # if submitted: | |
| # # # Assuming the environment variables should be set for the session | |
| # # for var, value in env_variables.items(): | |
| # # os.environ[var] = value | |
| # # st.success("Environment variables updated. Please restart your app.") | |
| # if st.button('Proceed to VoucherVision'): | |
| # st.session_state.proceed_to_private = False | |
| # st.session_state.proceed_to_main = True | |
| # def set_API_keys(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): | |
| # # Set the environment variable if the key is not None or an empty string | |
| # if openai_api_key: | |
| # os.environ['OPENAI_API_KEY'] = openai_api_key | |
| # if azure_openai_api_version: | |
| # os.environ['AZURE_API_VERSION'] = azure_openai_api_version | |
| # if azure_openai_api_key: | |
| # os.environ['AZURE_API_KEY'] = azure_openai_api_key | |
| # if azure_openai_api_base: | |
| # os.environ['AZURE_API_BASE'] = azure_openai_api_base | |
| # if azure_openai_organization: | |
| # os.environ['AZURE_ORGANIZATION'] = azure_openai_organization | |
| # if azure_openai_api_type: | |
| # os.environ['AZURE_API_TYPE'] = azure_openai_api_type | |
| # if google_vision: | |
| # os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = google_vision | |
| # if google_palm: | |
| # os.environ['GOOGLE_PALM_API'] = google_palm | |
| # st.success("API keys set successfully!") | |
| # def test_API(api, message_loc,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(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) | |
| # set_API_keys(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 image_to_base64(img): | |
| buffered = BytesIO() | |
| img.save(buffered, format="JPEG") | |
| return base64.b64encode(buffered.getvalue()).decode() | |
| # def display_image_gallery(): | |
| # """ | |
| # Display an image gallery from st.session_state['input_list'] in a scrollable container. | |
| # Each image will have a maximum width of 500 pixels. | |
| # """ | |
| # # Initialize the container | |
| # con_image = st.empty() | |
| # with con_image.container(): | |
| # # Loop through each image in the input list | |
| # for image_path in st.session_state['input_list']: | |
| # img = Image.open(image_path) | |
| # img.thumbnail((120, 120), Image.Resampling.LANCZOS) | |
| # # Convert the image to base64 | |
| # base64_image = image_to_base64(img) | |
| # # Embed the image with the determined width in the custom div | |
| # img_html = f""" | |
| # <div style='display: flex; flex-wrap: wrap; overflow-y: auto; max-height: 400px;'> | |
| # <img src='data:image/jpeg;base64,{base64_image}' alt='Image' style='max-width: 100%; height: auto;'> | |
| # </div> | |
| # """ | |
| # # Apply the image with HTML | |
| # st.markdown(img_html, unsafe_allow_html=True) | |
| # # The CSS to make the images display inline and be responsive | |
| # css = """ | |
| # <style> | |
| # @media (max-width: 120px) { | |
| # .scrollable-image-container img { | |
| # max-width: 100%; | |
| # height: 400px; | |
| # height: auto; | |
| # } | |
| # } | |
| # </style> | |
| # """ | |
| # # Apply the CSS | |
| # st.markdown(css, unsafe_allow_html=True) | |
| def display_image_gallery(): | |
| # Initialize the container | |
| con_image = st.empty() | |
| # Start the div for the image grid | |
| img_grid_html = """ | |
| <div style='display: flex; flex-wrap: wrap; align-items: flex-start; overflow-y: auto; max-height: 400px; gap: 10px;'> | |
| """ | |
| # Loop through each image in the input list | |
| # with con_image.container(): | |
| for image_path in st.session_state['input_list']: | |
| # Open the image and create a thumbnail | |
| img = Image.open(image_path) | |
| img.thumbnail((120, 120), Image.Resampling.LANCZOS) | |
| # Convert the image to base64 | |
| base64_image = image_to_base64(img) | |
| # Append the image to the grid HTML | |
| # img_html = f""" | |
| # <div style='display: flex; flex-wrap: wrap; overflow-y: auto; max-height: 400px;'> | |
| # <img src='data:image/jpeg;base64,{base64_image}' alt='Image' style='max-width: 100%; height: auto;'> | |
| # </div> | |
| # """ | |
| img_html = f""" | |
| <img src='data:image/jpeg;base64,{base64_image}' alt='Image' style='max-width: 100%; height: auto;'> | |
| """ | |
| img_grid_html += img_html | |
| # st.markdown(img_html, unsafe_allow_html=True) | |
| # Close the div for the image grid | |
| img_grid_html += "</div>" | |
| # Display the image grid in the container | |
| with con_image.container(): | |
| st.markdown(img_grid_html, unsafe_allow_html=True) | |
| # The CSS to make the images display inline and be responsive | |
| css = """ | |
| <style> | |
| .scrollable-image-container img { | |
| max-width: 100%; | |
| height: auto; | |
| } | |
| </style> | |
| """ | |
| # Apply the CSS | |
| st.markdown(css, unsafe_allow_html=True) | |
| 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['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', 'gpt') | |
| # Placeholder: | |
| st.session_state['assigned_columns'] = list(chain.from_iterable(st.session_state['mapping'].values())) | |
| def save_prompt_yaml(filename): | |
| yaml_content = { | |
| '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'] | |
| } | |
| 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(yaml_content, file) | |
| st.success(f"Prompt saved as '{filename}.yaml'.") | |
| def check_unique_mapping_assignments(): | |
| 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 | |
| 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 create_download_button(zip_filepath): | |
| with open(zip_filepath, 'rb') as f: | |
| bytes_io = BytesIO(f.read()) | |
| st.download_button( | |
| label="Download Results", | |
| data=bytes_io, | |
| file_name=os.path.basename(zip_filepath), | |
| mime='application/zip' | |
| ) | |
| 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['instructions'] = st.session_state['default_instructions'] | |
| st.session_state['json_formatting_instructions'] = st.session_state['default_json_formatting_instructions'] | |
| st.session_state['rules'] = {} | |
| st.session_state['LLM'] = 'gpt' | |
| st.session_state['assigned_columns'] = [] | |
| st.session_state['prompt_info'] = { | |
| '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(): | |
| st.session_state['assigned_columns'] = [] | |
| st.session_state['default_instructions'] = """1. Refactor the unstructured OCR text into a dictionary based on the JSON structure outlined below. | |
| 2. You should map the unstructured OCR text to the appropriate JSON key and then populate the field based on its rules. | |
| 3. Some JSON key fields are permitted to remain empty if the corresponding information is not found in the unstructured OCR text. | |
| 4. Ignore any information in the OCR text that doesn't fit into the defined JSON structure. | |
| 5. Duplicate dictionary fields are not allowed. | |
| 6. Ensure that all JSON keys are in lowercase. | |
| 7. Ensure that new JSON field values follow sentence case capitalization. | |
| 8. Ensure all key-value pairs in the JSON dictionary strictly adhere to the format and data types specified in the template. | |
| 9. Ensure the output JSON string is valid JSON format. It should not have trailing commas or unquoted keys. | |
| 10. Only return a JSON dictionary represented as a string. You should not explain your answer.""" | |
| st.session_state['default_json_formatting_instructions'] = """The next section of instructions outlines how to format the JSON dictionary. The keys are the same as those of the final formatted JSON object. | |
| For each key there is a format requirement that specifies how to transcribe the information for that key. | |
| The possible formatting options are: | |
| 1. "verbatim transcription" - field is populated with verbatim text from the unformatted OCR. | |
| 2. "spell check transcription" - field is populated with spelling corrected text from the unformatted OCR. | |
| 3. "boolean yes no" - field is populated with only yes or no. | |
| 4. "boolean 1 0" - field is populated with only 1 or 0. | |
| 5. "integer" - field is populated with only an integer. | |
| 6. "[list]" - field is populated from one of the values in the list. | |
| 7. "yyyy-mm-dd" - field is populated with a date in the format year-month-day. | |
| The desired null value is also given. Populate the field with the null value of the information for that key is not present in the unformatted OCR text.""" | |
| # Start building the Streamlit app | |
| col_prompt_main_left, ___, col_prompt_main_right = st.columns([6,1,3]) | |
| with col_prompt_main_left: | |
| st.title("Custom LLM Prompt Builder") | |
| st.subheader('About') | |
| st.write("This form allows you to craft a prompt for your specific task.") | |
| st.subheader('How it works') | |
| st.write("1. Edit this page until you are happy with your instructions. We recommend looking at the basic structure, writing down your prompt inforamtion in a Word document so that it does not randomly disappear, and then copying and pasting that info into this form once your whole prompt structure is defined.") | |
| st.write("2. After you enter all of your prompt instructions, click 'Save' and give your file a name.") | |
| st.write("3. This file will be saved as a yaml configuration file in the `..VoucherVision/custom_prompts` folder.") | |
| st.write("4. When you go back the main VoucherVision page you will now see your custom prompt available in the 'Prompt Version' dropdown menu.") | |
| st.write("5. Select your custom prompt. Note, your prompt will only be available for the LLM that you set when filling out the form below.") | |
| 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]) | |
| # Define the options for the dropdown | |
| llm_options = ['gpt', 'palm'] | |
| # Create the dropdown and set the value to session_state['LLM'] | |
| st.session_state['LLM'] = st.selectbox('Set LLM:', llm_options, index=llm_options.index(st.session_state.get('LLM', 'gpt'))) | |
| # Instructions Section | |
| st.header("Instructions") | |
| st.write("These are the general instructions that guide the LLM through the transcription task. We recommend using the default instructions unless you have a specific reason to change them.") | |
| st.session_state['instructions'] = st.text_area("Enter instructions:", value=st.session_state['default_instructions'].strip(), height=350, disabled=True) | |
| st.write('---') | |
| # Column Instructions Section | |
| st.header("JSON Formatting Instructions") | |
| st.write("The following section tells the LLM how we want to structure the JSON dictionary. We do not recommend changing this section because it would likely result in unstable and inconsistent behavior.") | |
| st.session_state['json_formatting_instructions'] = st.text_area("Enter column instructions:", value=st.session_state['default_json_formatting_instructions'], height=350, disabled=True) | |
| st.write('---') | |
| col_left, col_right = st.columns([6,4]) | |
| with col_left: | |
| st.subheader('Add/Edit Columns') | |
| # Initialize rules in session state if not already present | |
| if 'rules' not in st.session_state or not st.session_state['rules']: | |
| st.session_state['rules']['Dictionary'] = { | |
| "catalog_number": { | |
| "format": "verbatim transcription", | |
| "null_value": "", | |
| "description": "The barcode identifier, typically a number with at least 6 digits, but fewer than 30 digits." | |
| } | |
| } | |
| st.session_state['rules']['SpeciesName'] = { | |
| "taxonomy": ["Genus_species"] | |
| } | |
| # Layout for adding a new column name | |
| # col_text, col_textbtn = st.columns([8, 2]) | |
| # with col_text: | |
| new_column_name = st.text_input("Enter a new column name:") | |
| # with col_textbtn: | |
| # st.write('##') | |
| if st.button("Add New Column") and new_column_name: | |
| if new_column_name not in st.session_state['rules']['Dictionary']: | |
| st.session_state['rules']['Dictionary'][new_column_name] = {"format": "", "null_value": "", "description": ""} | |
| st.success(f"New column '{new_column_name}' added. Now you can edit its properties.") | |
| else: | |
| st.error("Column name already exists. Please enter a unique column name.") | |
| # Get columns excluding the protected "catalog_number" | |
| st.write('#') | |
| editable_columns = [col for col in st.session_state['rules']['Dictionary'] if col != "catalog_number"] | |
| column_name = st.selectbox("Select a column to edit:", [""] + editable_columns) | |
| # Handle rules editing | |
| current_rule = st.session_state['rules']['Dictionary'].get(column_name, { | |
| "format": "", | |
| "null_value": "", | |
| "description": "" | |
| }) | |
| if 'selected_column' not in st.session_state: | |
| st.session_state['selected_column'] = column_name | |
| # Form for input fields | |
| with st.form(key='rule_form'): | |
| format_options = ["verbatim transcription", "spell check transcription", "boolean yes no", "boolean 1 0", "integer", "[list]", "yyyy-mm-dd"] | |
| current_rule["format"] = st.selectbox("Format:", format_options, index=format_options.index(current_rule["format"]) if current_rule["format"] else 0) | |
| current_rule["null_value"] = st.text_input("Null value:", value=current_rule["null_value"]) | |
| current_rule["description"] = st.text_area("Description:", value=current_rule["description"]) | |
| commit_button = st.form_submit_button("Commit Column") | |
| default_rule = { | |
| "format": format_options[0], # default format | |
| "null_value": "", # default null value | |
| "description": "", # default description | |
| } | |
| if st.session_state['selected_column'] != column_name: | |
| # Column has changed. Update the session_state selected column. | |
| st.session_state['selected_column'] = column_name | |
| # Reset the current rule to the default for this new column, or a blank rule if not set. | |
| current_rule = st.session_state['rules']['Dictionary'].get(column_name, default_rule.copy()) | |
| # Handle commit action | |
| if commit_button and column_name: | |
| # Commit the rules to the session state. | |
| st.session_state['rules']['Dictionary'][column_name] = current_rule.copy() | |
| st.success(f"Column '{column_name}' added/updated in rules.") | |
| # Force the form to reset by clearing the fields from the session state | |
| st.session_state.pop('selected_column', None) # Clear the selected column to force reset | |
| # st.session_state['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:", [""] + editable_columns, key='delete_column') | |
| # with del_colbtn: | |
| # st.write('##') | |
| if st.button("Delete Column") and delete_column_name: | |
| del st.session_state['rules'][delete_column_name] | |
| st.success(f"Column '{delete_column_name}' removed from rules.") | |
| with col_right: | |
| # Display the current state of the JSON rules | |
| st.subheader('Formatted Columns') | |
| st.json(st.session_state['rules']['Dictionary']) | |
| # st.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']['Dictionary'].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'] = { | |
| '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'] | |
| } | |
| st.json(st.session_state['prompt_info']) | |
| def save_yaml(content, filename="rules_config.yaml"): | |
| with open(filename, 'w') as file: | |
| yaml.dump(content, file) | |
| 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 content_header(): | |
| col_run_1, col_run_2, col_run_3 = st.columns([4,2,2]) | |
| col_test = st.container() | |
| st.write("") | |
| st.write("") | |
| st.write("") | |
| st.write("") | |
| st.subheader("Overall Progress") | |
| col_run_info_1 = st.columns([1])[0] | |
| st.write("") | |
| st.write("") | |
| st.write("") | |
| st.write("") | |
| st.header("Configuration Settings") | |
| 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) | |
| st.info("***Note:*** There is a known bug with tabs in Streamlit. If you update an input field it may take you back to the 'Project Settings' tab. Changes that you made are saved, it's just an annoying glitch. We are aware of this issue and will fix it as soon as we can.") | |
| st.write("If you use VoucherVision frequently, you can change the default values that are auto-populated in the form below. In a text editor or IDE, edit the first few rows in the file `../VoucherVision/vouchervision/VoucherVision_Config_Builder.py`") | |
| with col_run_1: | |
| show_header_welcome() | |
| st.subheader('Run VoucherVision') | |
| if check_if_usable(): | |
| if st.button("Start Processing", type='primary'): | |
| # 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. | |
| last_JSON_response, total_cost, st.session_state['zip_filepath'] = voucher_vision(None, st.session_state.dir_home, path_custom_prompts, None, progress_report,path_api_cost=os.path.join(st.session_state.dir_home,'api_cost','api_cost.yaml')) | |
| if total_cost: | |
| st.success(f":money_with_wings: This run cost :heavy_dollar_sign:{total_cost:.4f}") | |
| # Format the JSON string for display. | |
| if last_JSON_response is None: | |
| st.markdown(f"Last JSON object in the batch: NONE") | |
| else: | |
| try: | |
| formatted_json = json.dumps(json.loads(last_JSON_response), indent=4) | |
| except: | |
| formatted_json = json.dumps(last_JSON_response, indent=4) | |
| st.markdown(f"Last JSON object in the batch:\n```\n{formatted_json}\n```") | |
| st.balloons() | |
| if st.session_state['zip_filepath']: | |
| create_download_button(st.session_state['zip_filepath']) | |
| 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.") | |
| st.error(":heavy_exclamation_mark: Required API keys not set. Please set the API keys as 'Secrets' for your Hugging Face Space. Visit the 'Settings' tab at the top of the page.") | |
| with col_run_2: | |
| st.subheader('Run Tests', help="") | |
| st.write('We include a single image for testing. If you want to test all of the available prompts and LLMs on a different set of images, copy your images into `../VoucherVision/demo/demo_images`.') | |
| 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_3: | |
| st.subheader('Check GPU') | |
| if st.button("GPU"): | |
| success, info = test_GPU() | |
| if success: | |
| st.balloons() | |
| for message in info: | |
| st.success(message) | |
| else: | |
| for message in info: | |
| st.error(message) | |
| def content_tab_settings(): | |
| col_project_1, col_project_2, col_project_3 = st.columns([2,2,2]) | |
| st.write("---") | |
| st.header('Input Images') | |
| st.write('Upload a batch of images using the uploader below. These images will be store temporarily on this server. Each time you upload new images the ***previously uploaded images will be deleted***. You can also clear these cached images by pressing the "Clear Staged Images" button.') | |
| col_local_1, col_local_2 = st.columns([2,6]) | |
| # st.write("---") | |
| # st.header('Modules') | |
| # col_m1, col_m2 = st.columns(2) | |
| st.write("---") | |
| st.header('Cropped Components') | |
| col_cropped_1, col_cropped_2 = st.columns([4,4]) | |
| os.path.join(st.session_state.dir_home, ) | |
| ### Project | |
| with col_project_1: | |
| st.subheader('Run name') | |
| st.session_state.config['leafmachine']['project']['run_name'] = st.text_input("Run name", st.session_state.config['leafmachine']['project'].get('run_name', ''), | |
| label_visibility='collapsed') | |
| # st.session_state.config['leafmachine']['project']['dir_output'] = st.text_input("Output directory", st.session_state.config['leafmachine']['project'].get('dir_output', '')) | |
| st.write("Run name will be the name of the final zipped folder.") | |
| ### LLM Version | |
| with col_project_2: | |
| st.session_state.config['leafmachine']['project']['dir_images_local'] = st.session_state['dir_uploaded_images'] #st.text_input("Input images directory", st.session_state.config['leafmachine']['project'].get('dir_images_local', '')) | |
| # st.session_state.config['leafmachine']['project']['continue_run_from_partial_xlsx'] = st.text_input("Continue run from partially completed project XLSX", st.session_state.config['leafmachine']['project'].get('continue_run_from_partial_xlsx', ''), disabled=True) | |
| st.subheader('LLM Version') | |
| st.session_state.config['leafmachine']['LLM_version'] = st.selectbox("LLM version", LLM_VERSIONS, | |
| index=LLM_VERSIONS.index(st.session_state.config['leafmachine'].get('LLM_version', 'Azure GPT 4')), | |
| label_visibility='collapsed') | |
| st.markdown("""***Note:*** GPT-4 is significantly more expensive than GPT-3.5 """) | |
| ### Prompt Version | |
| with col_project_3: | |
| st.subheader('Prompt Version') | |
| versions, default_version = get_prompt_versions(st.session_state.config['leafmachine']['LLM_version']) | |
| if versions: | |
| selected_version = st.session_state.config['leafmachine']['project'].get('prompt_version', default_version) | |
| if selected_version not in versions: | |
| selected_version = default_version | |
| st.session_state.config['leafmachine']['project']['prompt_version'] = st.selectbox("Prompt Version", versions, index=versions.index(selected_version),label_visibility='collapsed') | |
| st.markdown("Several prompts are provided. Visit the 'Prompt Builder' tab to upload your own prompt. If you would like to make your prompt available to others or have the prompt in the dropdown by default, [please submit the yaml through this form.](https://forms.gle/d1sHV5Y7Y5NxMQzM9)") | |
| ### Input Images Local | |
| with col_local_1: | |
| st.session_state['dir_uploaded_images'] = os.path.join(st.session_state.dir_home,'uploads') | |
| st.session_state['dir_uploaded_images_small'] = os.path.join(st.session_state.dir_home,'uploads_small') | |
| uploaded_files = st.file_uploader("Upload Images", type=['jpg', 'jpeg'], accept_multiple_files=True) | |
| if uploaded_files: | |
| # Clear input image gallery and input list | |
| delete_directory(st.session_state['dir_uploaded_images']) | |
| st.session_state['dir_uploaded_images'] = os.path.join(st.session_state.dir_home,'uploads') | |
| validate_dir(st.session_state['dir_uploaded_images']) | |
| # Process the new iamges | |
| for uploaded_file in uploaded_files: | |
| file_path = save_uploaded_file(st.session_state['dir_uploaded_images'], uploaded_file) | |
| img = Image.open(file_path) | |
| img.thumbnail((120, 120), Image.Resampling.LANCZOS) | |
| file_path_small = save_uploaded_file(st.session_state['dir_uploaded_images_small'], uploaded_file.name, img) | |
| st.session_state['input_list'].append(file_path) | |
| st.session_state['input_list_small'].append(file_path_small) | |
| print(uploaded_file.name) | |
| with col_local_2: | |
| if st.session_state['input_list_small']: | |
| st.subheader('Image Gallery') | |
| selected_img = image_select("Uploaded Images Ready for Transcription", st.session_state['input_list_small'], use_container_width=False) | |
| # st.image(st.session_state['input_list']) | |
| # display_image_gallery() | |
| # st.button("Clear Staged Images",on_click=delete_directory, args=[st.session_state['dir_uploaded_images']]) | |
| with col_cropped_1: | |
| default_crops = st.session_state.config['leafmachine']['cropped_components'].get('save_cropped_annotations', ['leaf_whole']) | |
| st.write("Prior to transcription, use LeafMachine2 to crop all labels from input images to create label collages for each specimen image. (Requires GPU)") | |
| st.session_state.config['leafmachine']['use_RGB_label_images'] = st.checkbox("Use LeafMachine2 label collage for transcriptions", st.session_state.config['leafmachine'].get('use_RGB_label_images', False)) | |
| st.session_state.config['leafmachine']['cropped_components']['save_cropped_annotations'] = st.multiselect("Components to crop", | |
| ['ruler', 'barcode','label', 'colorcard','map','envelope','photo','attached_item','weights', | |
| 'leaf_whole', 'leaf_partial', 'leaflet', 'seed_fruit_one', 'seed_fruit_many', 'flower_one', 'flower_many', 'bud','specimen','roots','wood'],default=default_crops) | |
| st.subheader('Create OCR Overlay Image') | |
| st.write('This will plot bounding boxes around all text that Google Vision was able to detect. If there are no boxes around text, then the OCR failed, so that missing text will not be seen by the LLM when it is creating the JSON object. The created image will be viewable in the VoucherVisionEditor.') | |
| st.session_state.config['leafmachine']['do_create_OCR_helper_image'] = st.checkbox("Create image showing an overlay of the OCR detections", st.session_state.config['leafmachine'].get('do_create_OCR_helper_image', False)) | |
| with col_cropped_2: | |
| ba = os.path.join(st.session_state.dir_home,'demo', 'ba','ba2.png') | |
| image = Image.open(ba) | |
| st.image(image, caption='LeafMachine2 Collage', output_format = "PNG") | |
| def content_tab_component(): | |
| 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_tab_processing(): | |
| 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=True) | |
| 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('Misc') | |
| 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', '')) | |
| 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', '')) | |
| 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.header('Logging and Image Validation') | |
| col_v1, col_v2 = st.columns(2) | |
| with col_v1: | |
| st.session_state.config['leafmachine']['do']['check_for_illegal_filenames'] = st.checkbox("Check for illegal filenames", st.session_state.config['leafmachine']['do'].get('check_for_illegal_filenames', True)) | |
| 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)) | |
| 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.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 PROMPTS_THAT_NEED_DOMAIN_KNOWLEDGE: | |
| st.markdown(f"- {available_prompts}") | |
| if st.session_state.config['leafmachine']['project']['prompt_version'] in 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 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 = [COLORS_EXPENSE_REPORT[version] if version in COLORS_EXPENSE_REPORT else '#DDDDDD' for version in api_versions] | |
| # Calculate the cost per image for each API version | |
| for version, cost_data in cost_per_image_dict.items(): | |
| total_cost = cost_data['total_cost'] | |
| n_images = cost_data['n_images'] | |
| # Calculate the cost per image for this version | |
| cost_per_image = total_cost / n_images if n_images > 0 else 0 | |
| cost_labels.append(version) | |
| cost_values.append(cost_per_image) | |
| # Generate the pie chart | |
| cost_pie_chart = go.Figure(data=[go.Pie(labels=cost_labels, values=cost_values, hole=.3)]) | |
| # Update traces for custom text in hoverinfo, displaying cost with a dollar sign and two decimal places | |
| cost_pie_chart.update_traces( | |
| marker=dict(colors=colors), | |
| text=[f"${value:.2f}" for value in cost_values], # Formats the cost as a string with a dollar sign and two decimals | |
| textinfo='percent+label', | |
| hoverinfo='label+percent+text' # Adds custom text (formatted cost) to the hover information | |
| ) | |
| st.plotly_chart(cost_pie_chart, use_container_width=True) | |
| st.subheader('Proportion of Total Cost by API Version') | |
| cost_labels = [] | |
| cost_proportions = [] | |
| total_cost_by_version = {} | |
| # Sum the total cost for each API version | |
| for index, row in expense_report.iterrows(): | |
| api_version = row['api_version'] | |
| total_cost = row['total_cost'] | |
| if api_version not in total_cost_by_version: | |
| total_cost_by_version[api_version] = 0 | |
| total_cost_by_version[api_version] += total_cost | |
| # Calculate the combined total cost for all versions | |
| combined_total_cost = sum(total_cost_by_version.values()) | |
| # Calculate the proportion of total cost for each API version | |
| for version, total_cost in total_cost_by_version.items(): | |
| proportion = (total_cost / combined_total_cost) * 100 if combined_total_cost > 0 else 0 | |
| cost_labels.append(version) | |
| cost_proportions.append(proportion) | |
| # Generate the pie chart | |
| cost_pie_chart = go.Figure(data=[go.Pie(labels=cost_labels, values=cost_proportions, hole=.3)]) | |
| # Update traces for custom text in hoverinfo | |
| cost_pie_chart.update_traces( | |
| marker=dict(colors=colors), | |
| text=[f"${cost:.2f}" 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 sidebar_content(): | |
| try: | |
| validate_dir(os.path.join(st.session_state.dir_home,'expense_report')) | |
| st.session_state.expense_summary, st.session_state.expense_report = summarize_expense_report(os.path.join(st.session_state.dir_home,'expense_report','expense_report.csv')) | |
| render_expense_report_summary() | |
| except: | |
| st.header('Expense Report Summary') | |
| st.write('Available after first run...') | |
| st.write('Google PaLM 2 is not tracked since it is currently free.') | |
| def main(): | |
| with st.sidebar: | |
| sidebar_content() | |
| # Main App | |
| content_header() | |
| # tab_settings, tab_prompt, tab_domain, tab_component, tab_processing, tab_private, tab_delete = st.tabs(["Project Settings", "Prompt Builder", "Domain Knowledge","Component Detector", "Processing Options", "API Keys", "Space-Saver"]) | |
| tab_settings, tab_prompt, tab_domain, tab_component, tab_processing, tab_delete = st.tabs(["Project Settings", "Prompt Builder", "Domain Knowledge","Component Detector", "Processing Options", "Space-Saver"]) | |
| with tab_settings: | |
| content_tab_settings() | |
| with tab_prompt: | |
| if st.button("Build Custom LLM Prompt"): | |
| st.session_state.proceed_to_build_llm_prompt = True | |
| st.rerun() | |
| with tab_component: | |
| content_tab_component() | |
| with tab_domain: | |
| content_tab_domain() | |
| with tab_processing: | |
| content_tab_processing() | |
| # with tab_private: | |
| # if st.button("Edit API Keys"): | |
| # st.session_state.proceed_to_private = True | |
| # st.rerun() | |
| with tab_delete: | |
| create_space_saver() | |
| 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() | |
| setup_streamlit_config(st.session_state.dir_home) | |
| if 'proceed_to_main' not in st.session_state: | |
| st.session_state.proceed_to_main = True # 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 'dir_uploaded_images' not in st.session_state: | |
| st.session_state['dir_uploaded_images'] = os.path.join(st.session_state.dir_home,'uploads') | |
| validate_dir(os.path.join(st.session_state.dir_home,'uploads')) | |
| # if '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 | |
| # 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 'zip_filepath' not in st.session_state: | |
| st.session_state['zip_filepath'] = None | |
| if 'input_list' not in st.session_state: | |
| st.session_state['input_list'] = [] | |
| if 'input_list_small' not in st.session_state: | |
| st.session_state['input_list_small'] = [] | |
| # if not st.session_state.private_file: | |
| # create_private_file() | |
| if st.session_state.proceed_to_build_llm_prompt: | |
| build_LLM_prompt_config() | |
| elif st.session_state.proceed_to_private: | |
| # create_private_file() | |
| pass | |
| elif st.session_state.proceed_to_main: | |
| main() |