app.py

import streamlit as st
import yaml, os, json, random, time, shutil
import plotly.graph_objs as go
from itertools import chain
from PIL import Image
from io import BytesIO
from streamlit_extras.let_it_rain import rain

from vouchervision.LeafMachine2_Config_Builder import write_config_file
from vouchervision.VoucherVision_Config_Builder import build_VV_config , TestOptionsGPT, TestOptionsPalm, check_if_usable
from vouchervision.vouchervision_main import voucher_vision
from vouchervision.general_utils import summarize_expense_report, validate_dir
from vouchervision.utils import upload_to_drive, image_to_base64, setup_streamlit_config, save_uploaded_file, check_prompt_yaml_filename



########################################################################################################
### Constants                                                                                       ####
########################################################################################################
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
    }
MAX_GALLERY_IMAGES = 50
GALLERY_IMAGE_SIZE = 128
N_OVERALL_STEPS = 6



########################################################################################################
### Progress bar                                                                                    ####
########################################################################################################
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
    


########################################################################################################
###  Streamlit helper functions                                                                     ####
########################################################################################################
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 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 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}")



# Function to load a YAML file and update session_state
def load_prompt_yaml(filename):
    st.session_state['user_clicked_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_description'] = st.session_state['prompt_info'].get('prompt_description', st.session_state['default_prompt_description']) 
        st.session_state['LLM'] = st.session_state['prompt_info'].get('LLM', 'gpt')
        st.session_state['instructions'] = st.session_state['prompt_info'].get('instructions', st.session_state['default_instructions']) 
        st.session_state['json_formatting_instructions'] = st.session_state['prompt_info'].get('json_formatting_instructions', st.session_state['default_json_formatting_instructions'] )
        st.session_state['rules'] = st.session_state['prompt_info'].get('rules', {})
        st.session_state['mapping'] = st.session_state['prompt_info'].get('mapping', {})

        st.session_state['prompt_info'] = {
            'prompt_author': st.session_state['prompt_author'],
            'prompt_author_institution': st.session_state['prompt_author_institution'],
            'prompt_description': st.session_state['prompt_description'],
            'LLM': st.session_state['LLM'],
            'instructions': st.session_state['instructions'],
            'json_formatting_instructions': st.session_state['json_formatting_instructions'],
            'rules': st.session_state['rules'],
            'mapping': st.session_state['mapping'],
        }

        # Placeholder:
        st.session_state['assigned_columns'] = list(chain.from_iterable(st.session_state['mapping'].values())) 



def save_prompt_yaml(filename, col_right_save):
    yaml_content = {
        'prompt_author': st.session_state['prompt_author'],
        'prompt_author_institution': st.session_state['prompt_author_institution'],
        'prompt_description': st.session_state['prompt_description'],
        'LLM': st.session_state['LLM'],
        'instructions': st.session_state['instructions'],
        'json_formatting_instructions': st.session_state['json_formatting_instructions'],
        'rules': st.session_state['rules'],
        'mapping': st.session_state['mapping'],
    }
    
    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'.")

    upload_to_drive(filepath, filename)

    with col_right_save:
        create_download_button_yaml(filepath, filename)
    


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 create_download_button(zip_filepath):
    with open(zip_filepath, 'rb') as f:
        bytes_io = BytesIO(f.read())
    st.download_button(
        label=f"Download Results for{st.session_state['processing_add_on']}",type='primary',
        data=bytes_io,
        file_name=os.path.basename(zip_filepath),
        mime='application/zip'
    )



def 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_description'] = st.session_state['default_prompt_description']
        st.session_state['instructions'] = st.session_state['default_instructions']
        st.session_state['json_formatting_instructions'] = st.session_state['default_json_formatting_instructions'] 
        st.session_state['rules'] = {}
        st.session_state['LLM'] = 'gpt'
        
        st.session_state['assigned_columns'] = []

        st.session_state['prompt_info'] = {
            'prompt_author': st.session_state['prompt_author'],
            'prompt_author_institution': st.session_state['prompt_author_institution'],
            'prompt_description': st.session_state['prompt_description'],
            'LLM': st.session_state['LLM'],
            'instructions': st.session_state['instructions'],
            'json_formatting_instructions': st.session_state['json_formatting_instructions'],
            'rules': st.session_state['rules'],
            'mapping': st.session_state['mapping'],
        }



def refresh():
    st.write('')



def upload_local_prompt_to_server(dir_prompt):
    uploaded_file = st.file_uploader("Upload a custom prompt file", type=['yaml'])
    if uploaded_file is not None:
        # Check the file extension
        file_name = uploaded_file.name
        if file_name.endswith('.yaml'):
            file_path = os.path.join(dir_prompt, file_name)
            
            # Save the file
            with open(file_path, 'wb') as f:
                f.write(uploaded_file.getbuffer())
            st.success(f"Saved file {file_name} in {dir_prompt}")
        else:
            st.error("Please upload a .yaml file that you previously created using this Prompt Builder tool.")



def create_download_button_yaml(file_path, selected_yaml_file):
    file_label = f"Download {selected_yaml_file}"
    with open(file_path, 'rb') as f:
        st.download_button(
            label=file_label,
            data=f,
            file_name=os.path.basename(file_path),
            mime='application/x-yaml'
        )



def clear_image_gallery():
    delete_directory(st.session_state['dir_uploaded_images'])
    delete_directory(st.session_state['dir_uploaded_images_small'])
    validate_dir(st.session_state['dir_uploaded_images'])
    validate_dir(st.session_state['dir_uploaded_images_small'])



def use_test_image():
    st.info(f"Processing images from {os.path.join(st.session_state.dir_home,'demo','demo_images')}")
    st.session_state.config['leafmachine']['project']['dir_images_local'] = os.path.join(st.session_state.dir_home,'demo','demo_images')
    n_images = len([f for f in os.listdir(st.session_state.config['leafmachine']['project']['dir_images_local']) if os.path.isfile(os.path.join(st.session_state.config['leafmachine']['project']['dir_images_local'], f))])
    st.session_state['processing_add_on'] = f" {n_images} Images"




########################################################################################################
###  Streamlit sections                                                                             ####
########################################################################################################
def create_space_saver():
    st.subheader("Space Saving Options")
    col_ss_1, col_ss_2 = st.columns([2,2])
    with col_ss_1:
        st.write("Several folders are created and populated with data during the VoucherVision transcription process.")
        st.write("Below are several options that will allow you to automatically delete temporary files that you may not need for everyday operations.")
        st.write("VoucherVision creates the following folders. Folders marked with a :star: are required if you want to use VoucherVisionEditor for quality control.")
        st.write("`../[Run Name]/Archival_Components`")
        st.write("`../[Run Name]/Config_File`")
        st.write("`../[Run Name]/Cropped_Images` :star:")
        st.write("`../[Run Name]/Logs`")
        st.write("`../[Run Name]/Original_Images` :star:")
        st.write("`../[Run Name]/Transcription` :star:")
    with col_ss_2:
        st.session_state.config['leafmachine']['project']['delete_temps_keep_VVE'] = st.checkbox("Delete Temporary Files (KEEP files required for VoucherVisionEditor)", st.session_state.config['leafmachine']['project'].get('delete_temps_keep_VVE', False))
        st.session_state.config['leafmachine']['project']['delete_all_temps'] = st.checkbox("Keep only the final transcription file", st.session_state.config['leafmachine']['project'].get('delete_all_temps', False),help="*WARNING:* This limits your ability to do quality assurance. This will delete all folders created by VoucherVision, leaving only the `transcription.xlsx` file.")



def show_available_APIs():
    st.session_state['has_key_openai'] = (os.getenv('OPENAI_API_KEY') is not None) and (os.getenv('OPENAI_API_KEY') != '')
    st.session_state['has_key_google_OCR'] = (os.getenv('GOOGLE_APPLICATION_CREDENTIALS') is not None) and (os.getenv('GOOGLE_APPLICATION_CREDENTIALS') != '')
    st.session_state['has_key_palm2'] = (os.getenv('PALM_API_KEY') is not None) and (os.getenv('PALM_API_KEY') != '')
    st.session_state['has_key_azure'] = (os.getenv('AZURE_API_KEY') is not None) and (os.getenv('AZURE_API_KEY') != '')

    emoji_good = ":heavy_check_mark:"
    emoji_bad = ":x:"

    table = {
        'Google Vision OCR API (required!)': emoji_good if st.session_state['has_key_google_OCR'] else emoji_bad,
        'OpenAI API': emoji_good if st.session_state['has_key_openai'] else emoji_bad,
        'PaLM 2 API': emoji_good if st.session_state['has_key_palm2'] else emoji_bad,
        'OpenAI API (Azure)': emoji_good if st.session_state['has_key_azure'] else emoji_bad,
    }
    for api_name, status in table.items():
        st.markdown(f"* {status} {api_name}")

def display_image_gallery():
    # Initialize the container
    con_image = st.empty()
    
    # Start the div for the image grid
    img_grid_html = """
    <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 show_header_welcome():
    st.session_state.logo_path = os.path.join(st.session_state.dir_home, 'img','logo.png')
    st.session_state.logo = Image.open(st.session_state.logo_path)
    st.image(st.session_state.logo, width=250)



########################################################################################################
###  Sidebar for Expense Report                                                                     ####
########################################################################################################
def render_expense_report_summary():
    cost_labels = []
    cost_values = []
    total_images = 0
    cost_per_image_dict = {}
    st.header('Expense Report Summary')

    if st.session_state.expense_summary:
        st.metric(label="Total Cost", value=f"${round(st.session_state.expense_summary['total_cost_sum'], 4):,}")
        col1, col2 = st.columns(2)

        # Run count and total costs
        with col1:
            st.metric(label="Run Count", value=st.session_state.expense_summary['run_count'])
            st.metric(label="Tokens In", value=f"{st.session_state.expense_summary['tokens_in_sum']:,}")

        # Token information
        with col2:
            st.metric(label="Total Images", value=st.session_state.expense_summary['n_images_sum'])
            st.metric(label="Tokens Out", value=f"{st.session_state.expense_summary['tokens_out_sum']:,}")

        # Calculate cost proportion per image for each API version
        st.subheader('Average Cost per Image by API Version')
        
        # Iterate through the expense report to accumulate costs and image counts
        for index, row in st.session_state.expense_report.iterrows():
            api_version = row['api_version']
            total_cost = row['total_cost']
            n_images = row['n_images']
            total_images += n_images  # Keep track of total images processed
            if api_version not in cost_per_image_dict:
                cost_per_image_dict[api_version] = {'total_cost': 0, 'n_images': 0}
            cost_per_image_dict[api_version]['total_cost'] += total_cost
            cost_per_image_dict[api_version]['n_images'] += n_images

        api_versions = list(cost_per_image_dict.keys())
        colors = [COLORS_EXPENSE_REPORT[version] if version in COLORS_EXPENSE_REPORT else '#DDDDDD' for version in api_versions]
        
        # Calculate the cost per image for each API version
        for version, cost_data in cost_per_image_dict.items():
            total_cost = cost_data['total_cost']
            n_images = cost_data['n_images']

            # Calculate the cost per image for this version
            cost_per_image = total_cost / n_images if n_images > 0 else 0
            cost_labels.append(version)
            cost_values.append(cost_per_image)

        # Generate the pie chart
        cost_pie_chart = go.Figure(data=[go.Pie(labels=cost_labels, values=cost_values, hole=.3)])

        # Update traces for custom text in hoverinfo, displaying cost with a dollar sign and two decimal places
        cost_pie_chart.update_traces(
            marker=dict(colors=colors),
            text=[f"${value:.2f}" for value in cost_values],  
            textinfo='percent+label',
            hoverinfo='label+percent+text' 
        )
        st.plotly_chart(cost_pie_chart, use_container_width=True)

        st.subheader('Proportion of Total Cost by API Version')
        cost_labels = []
        cost_proportions = []
        total_cost_by_version = {}

        # Sum the total cost for each API version
        for index, row in st.session_state.expense_report.iterrows():
            api_version = row['api_version']
            total_cost = row['total_cost']
            if api_version not in total_cost_by_version:
                total_cost_by_version[api_version] = 0
            total_cost_by_version[api_version] += total_cost
            
        # Calculate the combined total cost for all versions
        combined_total_cost = sum(total_cost_by_version.values())

        # Calculate the proportion of total cost for each API version
        for version, total_cost in total_cost_by_version.items():
            proportion = (total_cost / combined_total_cost) * 100 if combined_total_cost > 0 else 0
            cost_labels.append(version)
            cost_proportions.append(proportion)

        # Generate the pie chart
        cost_pie_chart = go.Figure(data=[go.Pie(labels=cost_labels, values=cost_proportions, hole=.3)])

        # Update traces for custom text in hoverinfo
        cost_pie_chart.update_traces(
            marker=dict(colors=colors),
            text=[f"${cost:.2f}" for cost in total_cost_by_version.values()],  
            textinfo='percent+label',
            hoverinfo='label+percent+text'  
        )
        st.plotly_chart(cost_pie_chart, use_container_width=True)

        # API version usage percentages pie chart
        st.subheader('Runs by API Version')
        api_versions = list(st.session_state.expense_summary['api_version_percentages'].keys())
        percentages = [st.session_state.expense_summary['api_version_percentages'][version] for version in api_versions]
        pie_chart = go.Figure(data=[go.Pie(labels=api_versions, values=percentages, hole=.3)])
        pie_chart.update_layout(margin=dict(t=0, b=0, l=0, r=0))
        pie_chart.update_traces(marker=dict(colors=colors),)
        st.plotly_chart(pie_chart, use_container_width=True)

    else:
        st.error('No expense report data available.')



def sidebar_content():
    if not os.path.exists(os.path.join(st.session_state.dir_home,'expense_report')):
        validate_dir(os.path.join(st.session_state.dir_home,'expense_report'))
    expense_report_path = os.path.join(st.session_state.dir_home, 'expense_report', 'expense_report.csv')

    if os.path.exists(expense_report_path):
        # File exists, proceed with summarization
        st.session_state.expense_summary, st.session_state.expense_report = summarize_expense_report(expense_report_path)
        render_expense_report_summary()  
    else:
        st.session_state.expense_summary, st.session_state.expense_report = None, None
        st.header('Expense Report Summary')
        st.write('Available after first run...')



########################################################################################################
###  Config Builder                                                                                 ####
########################################################################################################  
def build_LLM_prompt_config():
    st.session_state['assigned_columns'] = []
    st.session_state['default_prompt_author'] = 'unknown'
    st.session_state['default_prompt_author_institution'] = 'unknown'
    st.session_state['default_prompt_description'] = 'unknown'
    st.session_state['default_instructions'] = """1. Refactor the unstructured OCR text into a dictionary based on the JSON structure outlined below.
2. You should map the unstructured OCR text to the appropriate JSON key and then populate the field based on its rules.
3. Some JSON key fields are permitted to remain empty if the corresponding information is not found in the unstructured OCR text.
4. Ignore any information in the OCR text that doesn't fit into the defined JSON structure.
5. Duplicate dictionary fields are not allowed.
6. Ensure that all JSON keys are in lowercase.
7. Ensure that new JSON field values follow sentence case capitalization.
8. Ensure all key-value pairs in the JSON dictionary strictly adhere to the format and data types specified in the template.
9. Ensure the output JSON string is valid JSON format. It should not have trailing commas or unquoted keys.
10. Only return a JSON dictionary represented as a string. You should not explain your answer."""
    st.session_state['default_json_formatting_instructions'] = """The next section of instructions outlines how to format the JSON dictionary. The keys are the same as those of the final formatted JSON object.
For each key there is a format requirement that specifies how to transcribe the information for that key. 
The possible formatting options are:
1. "verbatim transcription" - field is populated with verbatim text from the unformatted OCR.
2. "spell check transcription" - field is populated with spelling corrected text from the unformatted OCR.
3. "boolean yes no" - field is populated with only yes or no.
4. "boolean 1 0" - field is populated with only 1 or 0.
5. "integer" - field is populated with only an integer.
6. "[list]" - field is populated from one of the values in the list.
7. "yyyy-mm-dd" - field is populated with a date in the format year-month-day.
The desired null value is also given. Populate the field with the null value of the information for that key is not present in the unformatted OCR text."""
    
    # Start building the Streamlit app
    col_prompt_main_left, ___, col_prompt_main_right = st.columns([6,1,3])
    
    with col_prompt_main_left:
        st.title("Custom LLM Prompt Builder")
        st.subheader('About')
        st.write("This form allows you to craft a prompt for your specific task.")
        st.subheader('For Hugging Face Spaces')
        st.write("If you create a prompt with the Hugging Face Spaces implementation of VoucherVision, make sure that you download the prompt immediately after you have 'Saved' the prompt. Default storage space on HF Spaces is not persistant, so if you refresh the page your prompt will probably disappear.")
        st.write("You can submit your prompt using this link and we will add it to our library so it will always be available.")

        st.subheader('How it works')
        st.write("1. Edit this page until you are happy with your instructions. We recommend looking at the basic structure, writing down your prompt inforamtion in a Word document so that it does not randomly disappear, and then copying and pasting that info into this form once your whole prompt structure is defined.")
        st.write("2. After you enter all of your prompt instructions, click 'Save' and give your file a name.")
        st.write("3. This file will be saved as a yaml configuration file in the `..VoucherVision/custom_prompts` folder.")
        st.write("4. When you go back the main VoucherVision page you will now see your custom prompt available in the 'Prompt Version' dropdown menu.")
        st.write("5. Select your custom prompt. Note, your prompt will only be available for the LLM that you set when filling out the form below.")

        dir_prompt = os.path.join(st.session_state.dir_home, 'custom_prompts')
        yaml_files = [f for f in os.listdir(dir_prompt) if f.endswith('.yaml')]
        col_upload_yaml, col_upload_yaml_2 = st.columns([4,4])
        with col_upload_yaml:
            # Upload a prompt from your computer
            upload_local_prompt_to_server(dir_prompt)

        col_select_yaml, col_upload_btn, col_download_btn = st.columns([6,2,2])
        with col_select_yaml:
            # Dropdown for selecting a YAML file
            st.session_state['selected_yaml_file'] = st.selectbox('Select a prompt .YAML file to load:', [''] + yaml_files)

        with col_upload_btn:
            st.write('##')
            # Button to load the selected prompt
            st.button('Load Selected Prompt into Builder', on_click=btn_load_prompt, args=[st.session_state['selected_yaml_file'] , dir_prompt])

        with col_download_btn:
            if st.session_state['selected_yaml_file']:
                # Construct the full path to the file
                download_file_path = os.path.join(dir_prompt, st.session_state['selected_yaml_file'] )
                # Create the download button
                st.write('##')
                create_download_button_yaml(download_file_path, st.session_state['selected_yaml_file'] )

        # Prompt Author Information
        st.header("Prompt Author Information")
        st.write("We value community contributions! Please provide your name(s) (or pseudonym if you prefer) for credit. If you leave this field blank, it will say 'unknown'.")
        st.session_state['prompt_author'] = st.text_input("Enter names of prompt author(s)", value=st.session_state['prompt_info'].get('prompt_author', st.session_state['default_prompt_author']))

        st.write("Please provide your institution name. If you leave this field blank, it will say 'unknown'.")
        st.session_state['prompt_author_institution'] = st.text_input("Enter name of institution", value=st.session_state['prompt_info'].get('prompt_author_institution', st.session_state['default_prompt_author_institution']))

        st.write("Please provide a description of your prompt and its intended task. Is it designed for a specific collection? Taxa? Database structure?")
        st.session_state['prompt_description'] = st.text_input("Enter description of prompt", value=st.session_state['prompt_info'].get('prompt_description', st.session_state['default_prompt_description']))
        
        # Input for new file name
        st.write('---')
        st.header("Prompt Name")
        st.write('Provide a name for your custom prompt. It can only conatin letters, numbers, and underscores. No spaces, dashes, or special characters.')
        st.session_state['new_prompt_yaml_filename'] = st.text_input("Enter filename to save your prompt as a configuration YAML:", value=None, placeholder='my_prompt_name')

        # Define the options for the LLM Model Type dropdown
        st.write('---')
        st.header("Set LLM Model Type")
        llm_options = ['gpt', 'palm']
        # Create the dropdown and set the value to session_state['LLM']
        st.write("Which LLM is this prompt designed for? This will not restrict its use to a specific LLM, but some prompts will behave in different ways across models.")
        st.write("For example, VoucherVision will automatically add multiple JSON formatting blocks to all PaLM 2 prompts to coax PaLM 2 to return a valid JSON object.")
        st.session_state['LLM'] = st.selectbox('Set LLM', llm_options, index=llm_options.index(st.session_state.get('LLM', 'gpt')))
        
        st.write('---')
        # 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"]
                }

            new_column_name = st.text_input("Enter a new column name:")

            if st.button("Add New Column") and new_column_name:
                if new_column_name not in st.session_state['rules']['Dictionary']:
                    st.session_state['rules']['Dictionary'][new_column_name] = {"format": "", "null_value": "", "description": ""}
                    st.success(f"New column '{new_column_name}' added. Now you can edit its properties.")
                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

            # Layout for removing an existing column
            delete_column_name = st.selectbox("Select a column to delete:", [""] + editable_columns, key='delete_column')
            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.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'])

        st.write('---')
        st.header("Save and Download Custom Prompt")
        st.write('Once you click save, validation checks will verify the formatting and then a download button will appear so that you can ***save a local copy of your custom prompt.***')
        col_left_save, col_right_save, _ = st.columns([2,2,8])
        with col_left_save:
            # Button to save the new YAML file
            if st.button('Save YAML', type='primary'):
                if st.session_state['new_prompt_yaml_filename']:
                    if check_unique_mapping_assignments():
                        if check_prompt_yaml_filename(st.session_state['new_prompt_yaml_filename']):
                            save_prompt_yaml(st.session_state['new_prompt_yaml_filename'], col_right_save)
                        else:
                            st.error("File name can only contain letters, numbers, underscores, and dashes. Cannot contain spaces.")
                    else:
                        st.error("Mapping contains an error. Make sure that each column is assigned to only ***one*** category.")
                else:
                    st.error("Please enter a filename.")
        
        st.write('---')
        st.header("Return to VoucherVision")
        if st.button('Exit'):
            st.session_state.proceed_to_build_llm_prompt = False
            st.session_state.proceed_to_main = True
            st.rerun()

    with col_prompt_main_right:
        if st.session_state['user_clicked_load_prompt_yaml'] is None: # see if user has loaded a yaml to edit
            st.session_state['show_prompt_name_e'] = f"Prompt Status  :arrow_forward:  Building prompt from scratch"
            if st.session_state['new_prompt_yaml_filename']:
                st.session_state['show_prompt_name_w'] = f"New Prompt Name  :arrow_forward:  {st.session_state['new_prompt_yaml_filename']}.yaml"
            else:
                st.session_state['show_prompt_name_w'] = f"New Prompt Name  :arrow_forward:  [PLEASE SET NAME]"
        else:
            st.session_state['show_prompt_name_e'] = f"Prompt Status: Editing  :arrow_forward:  {st.session_state['selected_yaml_file']}"
            if st.session_state['new_prompt_yaml_filename']:
                st.session_state['show_prompt_name_w'] = f"New Prompt Name  :arrow_forward:  {st.session_state['new_prompt_yaml_filename']}.yaml"
            else:
                st.session_state['show_prompt_name_w'] = f"New Prompt Name  :arrow_forward:  [PLEASE SET NAME]"

        st.subheader(f'Full Prompt')
        st.write(st.session_state['show_prompt_name_e'])
        st.write(st.session_state['show_prompt_name_w'])
        st.write("---")
        st.session_state['prompt_info'] = {
            'prompt_author': st.session_state['prompt_author'],
            'prompt_author_institution': st.session_state['prompt_author_institution'],
            'prompt_description': st.session_state['prompt_description'],
            'LLM': st.session_state['LLM'],
            'instructions': st.session_state['instructions'],
            'json_formatting_instructions': st.session_state['json_formatting_instructions'],
            'rules': st.session_state['rules'],
            'mapping': st.session_state['mapping'],
        }
        st.json(st.session_state['prompt_info'])

def content_header():
    # Header section, run, quick start, API report
    col_run_1, col_run_2, col_run_3, col_run_4 = st.columns([2,2,2,2])

    # Progress bar
    col_run_info_1 = st.columns([1])[0]

    with col_run_info_1:
        # Progress
        st.subheader("Overall Progress")
        overall_progress_bar = st.progress(0)
        text_overall = st.empty()  # Placeholder for current step name
        
        st.subheader('Transcription Progress')
        batch_progress_bar = st.progress(0)
        text_batch = st.empty()  # Placeholder for current step name

        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 not check_if_usable():
            st.button("Start Processing", type='primary', disabled=True)
            # st.error(":heavy_exclamation_mark: Required API keys not set. Please visit the 'API Keys' tab and set the Google Vision OCR API key and at least one LLM key.")
            st.error(":heavy_exclamation_mark: Required API keys not set. Please set the API keys as 'Secrets' for your Hugging Face Space. Visit the 'Settings' tab at the top of the page.")
        else:
            if st.button(f"Start Processing{st.session_state['processing_add_on']}", type='primary'):
            
                # First, write the config file.
                write_config_file(st.session_state.config, st.session_state.dir_home, filename="VoucherVision.yaml")

                path_custom_prompts = os.path.join(st.session_state.dir_home,'custom_prompts',st.session_state.config['leafmachine']['project']['prompt_version'])

                # Define number of overall steps
                progress_report.set_n_overall(N_OVERALL_STEPS)
                progress_report.update_overall(f"Starting VoucherVision...")

                # Call the machine function.
                last_JSON_response, total_cost, st.session_state['zip_filepath'] = voucher_vision(None, st.session_state.dir_home, path_custom_prompts, None, progress_report,path_api_cost=os.path.join(st.session_state.dir_home,'api_cost','api_cost.yaml'), is_real_run=True)
                
                if total_cost:
                    st.success(f":money_with_wings: This run cost :heavy_dollar_sign:{total_cost:.4f}")
                
                # Format the JSON string for display.
                if last_JSON_response is None:
                    st.markdown(f"Last JSON object in the batch: NONE")
                else:
                    try:
                        formatted_json = json.dumps(json.loads(last_JSON_response), indent=4, sort_keys=False)
                    except:
                        formatted_json = json.dumps(last_JSON_response, indent=4, sort_keys=False)
                    st.markdown(f"Last JSON object in the batch:\n```\n{formatted_json}\n```")
                    st.balloons()
            
            if st.session_state['zip_filepath']:
                create_download_button(st.session_state['zip_filepath'])
        st.button("Refresh", on_click=refresh)

    with col_run_2:
        st.subheader('Quick Start')
        st.write('1. We include a single image for testing. Without uploading your own images, you can select options below and press "Start Processing" to try VoucherVision.')
        st.write('2. Name your run --- If the same name already exist, VV will append the date to the run name.')
        st.write('3. Choose a LLM version --- Only LLMs with valid keys will appear in the dropdown list.')
        st.write('4. Select a prompt version --- Start with "Version 2". Custom Prompts will include ".yaml" in the name. You can build your own Custom Prompt in the Prompt Builder.')
        st.markdown('5. Upload images --- Up to ~100 images can be uploaded in the Hugging Face Spaces implementation. If you want to process more images at once (and have more control in general) then use the [GitHub version](https://github.com/Gene-Weaver/VoucherVision). If you pay for persistent storage for your HF Space, then you may be able to process more too.')
    with col_run_3:
        st.subheader('')
        st.write('6. LeafMachine2 collage --- If selected, LeafMachine2 will isolate all text from the image and create a label collage, which will be sent to the OCR algorithm instead of the full image. This improves OCR detection for small or finely written text.')
        st.write('7. OCR overlay images --- If selected, VoucherVision will overlay the OCR detections onto the input image. This is useful for debugging transcription errors to see if the OCR failed or if the LLM failed.')
        st.write('8. Start processing --- Wait for VoucherVision to finish.')
        st.write('9. Download results --- Click the "Download Results" button to save the VoucherVision output to your computer. ***Output files will disappear if you start a new run or restart the Space.***')
        st.write('10. Editing the LLM transcriptions --- Use the VoucherVisionEditor to revise and correct any mistakes or ommissions.')

    with col_run_4:
        st.subheader('Available LLMs and APIs')
        show_available_APIs()
        st.info('Until the end of 2023, Azure OpenAI models will be available for anyone to use here. Then only PaLM 2 will be available. To use all services, duplicate this Space and provide your own API keys.')




########################################################################################################
###  Main Settings                                                                                  ####
########################################################################################################          
def content_tab_settings():
    st.write("---")
    st.header("Configuration Settings")
    col_project_1, col_project_2, col_project_3 = st.columns([2,2,2])

    st.write("---")
    st.header('Input Images')
    st.write('Upload a batch of images using the uploader below. These images will be store temporarily on this server. Each time you upload new images the ***previously uploaded images will be deleted***. You can also clear these cached images by pressing the "Clear Staged Images" button.')
    col_local_1, col_local_2 = st.columns([2,6])                 

    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])  

    ### Project
    with col_project_1:
        st.subheader('Run name')
        st.session_state.config['leafmachine']['project']['run_name'] = st.text_input("Run name", st.session_state.config['leafmachine']['project'].get('run_name', ''),
                                                                                      label_visibility='collapsed')
        st.write("Run name will be the name of the final zipped folder.")

    ### LLM Version
    with col_project_2:
        # Determine the available versions based on the API keys present
        available_versions = []
        for api_name, versions in st.session_state['LLM_VERSIONS'].items():
            key_state = st.session_state['api_name_to_key_state'][api_name]
            if st.session_state.get(key_state, False):
                available_versions.extend(versions)

        # Show available LLM versions in a select box if there are any
        st.subheader('LLM Version')
        if available_versions:
            # Get current selection from session_state, defaulting to the first available version
            current_selection = st.session_state.config['leafmachine'].get('LLM_version', available_versions[0])
            # Update the selection with a selectbox
            st.session_state.config['leafmachine']['LLM_version'] = st.selectbox(
                "LLM version", available_versions, 
                index=available_versions.index(current_selection),
                label_visibility='collapsed'
            )
            st.markdown("""***Note:*** GPT-4 is significantly more expensive than GPT-3.5""")
        else:
            st.error("No LLM versions are available due to missing API keys.")
    
    ### Prompt Version
    with col_project_3:
        st.subheader('Prompt Version')
        versions, default_version = get_prompt_versions(st.session_state.config['leafmachine']['LLM_version'])

        if versions:
            selected_version = st.session_state.config['leafmachine']['project'].get('prompt_version', default_version)
            if selected_version not in versions:
                selected_version = default_version
            st.session_state.config['leafmachine']['project']['prompt_version'] = st.selectbox("Prompt Version", versions, index=versions.index(selected_version),label_visibility='collapsed')
        st.markdown("Several prompts are provided. Visit the 'Prompt Builder' tab to upload your own prompt. If you would like to make your prompt available to others or have the prompt in the dropdown by default, [please submit the yaml through this form.](https://forms.gle/d1sHV5Y7Y5NxMQzM9)")

        if st.button("Build Custom LLM Prompt",help="It may take a moment for the page to refresh."):
            st.session_state.proceed_to_build_llm_prompt = True
            st.rerun()

    ### 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
            clear_image_gallery()

            # Process the new iamges
            for uploaded_file in uploaded_files:
                file_path = save_uploaded_file(st.session_state['dir_uploaded_images'], uploaded_file)
                st.session_state['input_list'].append(file_path)

                img = Image.open(file_path)
                img.thumbnail((GALLERY_IMAGE_SIZE, GALLERY_IMAGE_SIZE), Image.Resampling.LANCZOS)  
                file_path_small = save_uploaded_file(st.session_state['dir_uploaded_images_small'], uploaded_file, img)
                st.session_state['input_list_small'].append(file_path_small)
                print(uploaded_file.name)

            # Set the local images to the uploaded images
            st.session_state.config['leafmachine']['project']['dir_images_local'] = st.session_state['dir_uploaded_images']

            n_images = len([f for f in os.listdir(st.session_state.config['leafmachine']['project']['dir_images_local']) if os.path.isfile(os.path.join(st.session_state.config['leafmachine']['project']['dir_images_local'], f))])
            st.session_state['processing_add_on'] = f" {n_images} Images"
            uploaded_files = None
            st.info(f"Processing ***{n_images}*** images from {st.session_state.config['leafmachine']['project']['dir_images_local']}")

        st.button("Use Test Image",help="This will clear any uploaded images and load the 1 provided test image.",on_click=use_test_image)

    # Show uploaded images gallery (thumbnails only)
    with col_local_2:
        if st.session_state['input_list_small']:
            st.subheader('Image Gallery')
            if len(st.session_state['input_list_small']) > MAX_GALLERY_IMAGES:
                # Only take the first 100 images from the list
                images_to_display = st.session_state['input_list_small'][:MAX_GALLERY_IMAGES]
            else:
                # If there are less than 100 images, take them all
                images_to_display = st.session_state['input_list_small']
            st.image(images_to_display)

    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)
        
    with col_cropped_2:
        ba = os.path.join(st.session_state.dir_home,'demo', 'ba','ba2.png')
        image = Image.open(ba)
        st.image(image, caption='LeafMachine2 Collage', output_format = "PNG")
    
    with col_ocr_1:
        st.write('This will plot bounding boxes around all text that Google Vision was able to detect. If there are no boxes around text, then the OCR failed, so that missing text will not be seen by the LLM when it is creating the JSON object. The created image will be viewable in the VoucherVisionEditor.')
        st.session_state.config['leafmachine']['do_create_OCR_helper_image'] = st.checkbox("Create image showing an overlay of the OCR detections", st.session_state.config['leafmachine'].get('do_create_OCR_helper_image', False))
    
    with col_ocr_2:
        ocr = os.path.join(st.session_state.dir_home,'demo', 'ba','ocr.png')
        image_ocr = Image.open(ocr)
        st.image(image_ocr, caption='OCR Overlay Images', output_format = "PNG")



########################################################################################################
###  Main                                                                                           ####
########################################################################################################
def main():
    with st.sidebar:
        sidebar_content()
    # Main App
    content_header()

    tab_settings = st.container()

    with tab_settings:
        content_tab_settings()



########################################################################################################
###  STREAMLIT APP START                                                                            ####
########################################################################################################
st.set_page_config(layout="wide", page_icon='img/icon.ico', page_title='VoucherVision')



########################################################################################################
###  STREAMLIT INIT STATES                                                                          ####
########################################################################################################
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 

if 'proceed_to_build_llm_prompt' not in st.session_state:
    st.session_state.proceed_to_build_llm_prompt = False  

if 'proceed_to_private' not in st.session_state:
    st.session_state.proceed_to_private = False  

if 'dir_uploaded_images' not in st.session_state:
    st.session_state['dir_uploaded_images'] = os.path.join(st.session_state.dir_home,'uploads')
    validate_dir(os.path.join(st.session_state.dir_home,'uploads'))

if 'dir_uploaded_images_small' not in st.session_state:
    st.session_state['dir_uploaded_images_small'] = os.path.join(st.session_state.dir_home,'uploads_small')
    validate_dir(os.path.join(st.session_state.dir_home,'uploads_small'))

if '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 'selected_yaml_file' not in st.session_state:
    st.session_state['selected_yaml_file'] = None

if 'new_prompt_yaml_filename' not in st.session_state:
    st.session_state['new_prompt_yaml_filename'] = None

if 'show_prompt_name_e' not in st.session_state:
    st.session_state['show_prompt_name_e'] = None

if 'show_prompt_name_w' not in st.session_state:
    st.session_state['show_prompt_name_w'] = None

if 'user_clicked_load_prompt_yaml' not in st.session_state:
    st.session_state['user_clicked_load_prompt_yaml'] = None

if 'processing_add_on' not in st.session_state:
    st.session_state['processing_add_on'] = ' 1 Image'

if 'LLM_VERSIONS' not in st.session_state:
    st.session_state['LLM_VERSIONS'] = {
        'OpenAI API': ["GPT 4", "GPT 3.5"],
        'Azure API': ["Azure GPT 4", "Azure GPT 3.5"],
        'Palm API': ["PaLM 2"]
    }
if 'api_name_to_key_state ' not in st.session_state:
    st.session_state['api_name_to_key_state'] = {
        'OpenAI API': 'has_key_openai',
        'Google OCR API': 'has_key_google_OCR',
        'Palm API': 'has_key_palm2',
        'Azure API': 'has_key_azure'
    }
        
 # Initialize API key states if not already in session_state
for api_name, key_state in st.session_state['api_name_to_key_state'].items():
    if key_state not in st.session_state:
        st.session_state[key_state] = False   



########################################################################################################
###  STREAMLIT SESSION GUIDE                                                                        ####
########################################################################################################
if st.session_state.proceed_to_build_llm_prompt:
    build_LLM_prompt_config()
elif st.session_state.proceed_to_main:
    main()