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d90fc5a 6e20b89 d90fc5a 6e20b89 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 | import streamlit as st
import pandas as pd
import requests
from PIL import Image, ImageStat
from io import BytesIO
import numpy as np
import time
import base64
st.set_page_config(page_title="Amazon Image Optimizer", layout="wide")
st.title("Amazon Image Optimizer")
st.markdown("""
This app helps reorganize your product images to comply with Amazon's listing requirements.
Upload your CSV file containing SKU and image URLs, and the app will:
1. Detect which image has a white background
2. Ensure the white background image is placed in the image1 column
3. Allow you to download the reorganized CSV
""")
def is_white_background(image_url, threshold=240):
"""Determine if an image has a predominantly white background"""
try:
response = requests.get(image_url, timeout=5)
img = Image.open(BytesIO(response.content)).convert('RGB')
# Get the edges of the image (10% from each border)
width, height = img.size
border_width = int(width * 0.1)
border_height = int(height * 0.1)
# Create masks for the edges
left_edge = img.crop((0, 0, border_width, height))
right_edge = img.crop((width - border_width, 0, width, height))
top_edge = img.crop((0, 0, width, border_height))
bottom_edge = img.crop((0, height - border_height, width, height))
# Calculate average RGB values for edges
edges = [left_edge, right_edge, top_edge, bottom_edge]
edge_stats = [ImageStat.Stat(edge) for edge in edges]
edge_means = [stat.mean for stat in edge_stats]
# Check if edges are predominantly white
is_white = all(all(channel > threshold for channel in mean) for mean in edge_means)
# Also check overall brightness
overall_stat = ImageStat.Stat(img)
overall_brightness = sum(overall_stat.mean) / 3
return is_white and overall_brightness > threshold
except Exception as e:
st.error(f"Error processing image: {e}")
return False
def process_csv(df):
"""Process the dataframe to reorder images putting white background first"""
progress_bar = st.progress(0)
status_text = st.empty()
# Create a copy of the original dataframe to preserve all columns
result_df = df.copy()
# Get the image column names
image_columns = [col for col in df.columns if col.startswith('image') and col != 'image1']
for i, row in df.iterrows():
sku = row['sku']
status_text.text(f"Processing SKU: {sku} ({i+1}/{len(df)})")
white_bg_found = False
img_urls = []
white_bg_url = None
# Check if image1 already has white background
if pd.notna(row['image1']) and is_white_background(row['image1']):
white_bg_found = True
white_bg_url = row['image1']
# Collect all other image URLs
for col in image_columns:
if pd.notna(row[col]):
if not white_bg_found and is_white_background(row[col]):
white_bg_found = True
white_bg_url = row[col]
else:
img_urls.append(row[col])
# Reorganize images if white background found
if white_bg_found:
# Store the white background image in image1
result_df.at[i, 'image1'] = white_bg_url
# Fill in remaining image slots
remaining_img_count = 0
for idx, url in enumerate(img_urls, start=0):
col_name = f'image{idx+2}'
if col_name in result_df.columns:
result_df.at[i, col_name] = url
remaining_img_count += 1
progress_bar.progress((i + 1) / len(df))
status_text.text("Processing complete!")
return result_df
def get_download_link(df, filename):
"""Generate a download link for the dataframe"""
# Prevent SKUs from being converted to scientific notation
csv = df.to_csv(index=False, float_format='%.0f')
b64 = base64.b64encode(csv.encode()).decode()
href = f'data:file/csv;base64,{b64}'
return href
# File uploader
uploaded_file = st.file_uploader("Upload your product CSV file", type=["csv"])
if uploaded_file is not None:
# Load and display the uploaded file
# Treat SKUs as strings to prevent scientific notation issues
df = pd.read_csv(uploaded_file, dtype={'sku': str})
st.subheader("Original Data Preview")
st.dataframe(df.head())
# Check if required columns exist
required_columns = ['sku', 'image1']
missing_columns = [col for col in required_columns if col not in df.columns]
if missing_columns:
st.error(f"Missing required columns: {', '.join(missing_columns)}")
else:
if st.button("Process Images"):
with st.spinner("Processing images..."):
result_df = process_csv(df)
st.subheader("Results")
st.dataframe(result_df.head())
# Create download link
dl_link = get_download_link(result_df, "amazon_optimized_images.csv")
st.markdown(f'<a href="{dl_link}" download="amazon_optimized_images.csv">Download Processed CSV</a>', unsafe_allow_html=True)
# Summary
total_rows = len(df)
white_bg_found = sum(1 for _, row in result_df.iterrows() if pd.notna(row['image1']))
st.subheader("Summary")
st.write(f"Total products processed: {total_rows}")
st.write(f"Products with white background images: {white_bg_found}")
st.write(f"Products missing white background images: {total_rows - white_bg_found}")
# Add instructions and tips
st.sidebar.header("Instructions")
st.sidebar.markdown("""
### CSV Format Requirements
Your CSV file should include:
- A 'sku' column with product identifiers
- Image columns named 'image1', 'image2', etc. containing image URLs
### How It Works
The app analyzes the edges of each image to detect white backgrounds.
It then reorganizes the URLs to ensure white background images are in the image1 position.
### Tips
- Ensure your image URLs are publicly accessible
- The process may take some time for large datasets
- For best results, make sure product images have clear contrasts between product and background
""")
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