Delete b.py

b.py

@@ -1,66 +0,0 @@
-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)