b.py

import streamlit as st
import os
from PIL import Image
from collections import Counter
import pandas as pd
# Function to list files with given extensions
def list_files(folder_path, extensions):
    files = [f for f in os.listdir(folder_path) if os.path.isfile(os.path.join(folder_path, f))]
    return [f for f in files if f.split('.')[-1] in extensions]

# Function to get tag frequencies from text files
def get_tag_frequencies(text_files):
    tag_counter = Counter()
    for text_file in text_files:
        with open(text_file, 'r') as file:
            tags = file.read().split()
            tag_counter.update(tags)
    return tag_counter

# Set up Streamlit app
st.title("Display Images and Corresponding Text Files")

# Define the folder path
folder_path = "/home/caimera-prod/kohya_new_dataset"

# List of allowed image and text extensions
image_extensions = ['jpg', 'jpeg', 'png']
text_extensions = ['txt']

# Get the list of image and text files
files = list_files(folder_path, image_extensions + text_extensions)

# Filter files into images and texts
images = [f for f in files if f.split('.')[-1] in image_extensions]
texts = [f for f in files if f.split('.')[-1] in text_extensions]

# Create a dictionary to map image files to their corresponding text files
file_map = {}
for image in images:
    base_name = os.path.splitext(image)[0]
    corresponding_text = base_name + '.txt'
    if corresponding_text in texts:
        file_map[image] = corresponding_text

# Get tag frequencies
text_files_paths = [os.path.join(folder_path, text) for text in texts]
tag_frequencies = get_tag_frequencies(text_files_paths)

# Prepare tag frequencies for display
tag_frequencies_data = [{'Tag': tag, 'Frequency': freq} for tag, freq in tag_frequencies.items()]
tag_frequencies_df = pd.DataFrame(tag_frequencies_data)

# Display tag frequencies in a table
st.subheader("Tag Frequencies")
st.table(tag_frequencies_df)

# Display images and text files side by side
for image_file, text_file in file_map.items():
    col1, col2 = st.columns(2)

    with col1:
        st.image(os.path.join(folder_path, image_file), caption=image_file, use_column_width=True)

    with col2:
        with open(os.path.join(folder_path, text_file), 'r') as file:
            st.text_area(text_file, file.read(), height=300)