Spaces:
Sleeping
Sleeping
File size: 7,433 Bytes
0fa8876 a687c6a 9acac5c a687c6a 9acac5c 0fa8876 9acac5c 0fa8876 a687c6a 0fa8876 a687c6a 9acac5c 0fa8876 9acac5c 0fa8876 9acac5c 0fa8876 9acac5c a687c6a 9acac5c a687c6a 9acac5c 0fa8876 9acac5c 0fa8876 9acac5c 0fa8876 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 |
import gradio as gr
import pandas as pd
import numpy as np
from typing import List, Dict
import os
# Create static directory if it doesn't exist
if not os.path.exists('static'):
os.makedirs('static')
# Move thumbs directory into static if it exists
if os.path.exists('thumbs'):
import shutil
if not os.path.exists('static/thumbs'):
shutil.move('thumbs', 'static/')
else:
# If static/thumbs already exists, merge the contents
for file in os.listdir('thumbs'):
shutil.move(os.path.join('thumbs', file), 'static/thumbs/')
os.rmdir('thumbs')
class CSVViewer:
def __init__(self, csv_path: str):
self.df = pd.read_csv(csv_path)
self.current_page = 0
self.rows_per_page = 1
self.total_pages = len(self.df)
def get_page_data(self, page_num: int) -> Dict:
if page_num < 0 or page_num >= self.total_pages:
return None
row = self.df.iloc[page_num]
# Get product information
product_info = {
'Web link': str(row['Web link']),
'Product Code': str(row['Product Code']),
'Product Name': str(row['Product Name'])
}
# Separate original and AI-predicted columns
original_data = {}
ai_data = {}
for col in self.df.columns:
if 'Score' in col or 'Distance' in col or col == '':
continue # Skip score and distance columns
if col in ['Web link', 'Product Code', 'Product Name']:
continue # Skip these columns as they're shown at the top
if col in ["Length", "Style", "Sleeve Length", "Neckline", "Pattern", "Fabric", "Features", "Closure", "Body Fit", "Occasion", "Season"]:
original_data[col] = str(row[col])
if col.endswith('_AI'):
ai_data[col] = str(row[col])
return {
'product_info': product_info,
'original_data': original_data,
'ai_data': ai_data,
'page_num': page_num + 1,
'total_pages': self.total_pages
}
def create_page_view(page_num: int):
viewer = CSVViewer('attr.csv')
page_data = viewer.get_page_data(page_num)
if not page_data:
return "Invalid page number"
# Get product image
product_code = page_data['product_info']['Product Code']
# Find matching image file
image_files = [f for f in os.listdir('static/thumbs') if f.startswith(product_code)]
if image_files:
# Use Gradio's static file serving
image_path = f"/static/thumbs/{image_files[0]}"
print(f"Found image: {image_path}")
else:
image_path = None
# Create HTML content
html = f"<h3>Page {page_data['page_num']} of {page_data['total_pages']}</h3>"
html += "<div style='margin-bottom: 20px; display: flex; gap: 20px;'>"
# Product info section
html += "<div>"
if image_path:
html += f"<img src='{image_path}' alt='Product Image' style='max-width: 100%; border-radius: 5px;'>"
for key, value in page_data['product_info'].items():
html += f"<p><strong>{key}:</strong> {value}</p>"
html += "</div></div>"
html += "<div style='display: flex; gap: 20px;'>"
# Comparison section
html += "<div style='flex: 1; border: 1px solid #ddd; padding: 15px; border-radius: 5px;'>"
html += "<h4>Comparison</h4>"
html += "<table style='width: 100%;'>"
html += "<tr style='background-color: #f5f5f5;'><th>Attribute</th><th>Original</th><th>AI Prediction</th><th>Status</th></tr>"
# Add comparison rows
for key, orig_value in page_data['original_data'].items():
# Find matching AI prediction
ai_key = key + '_AI'
ai_value = page_data['ai_data'].get(ai_key, '')
# Skip if AI value is empty
if not ai_value.strip():
continue
# Clean up values by removing brackets and quotes
orig_value = str(orig_value).strip('[]').strip('"').strip()
ai_value = str(ai_value).strip('[]').strip('"').strip()
# Handle array values
try:
# Try to parse as arrays
orig_items = [item.strip() for item in orig_value.split(',') if item.strip()]
ai_items = [item.strip() for item in ai_value.split(',') if item.strip()]
# Check if all original items are in AI items
if all(item.lower() in [x.lower() for x in ai_items] for item in orig_items):
if len(ai_items) > len(orig_items):
status = "✓ All and more"
status_color = "#4CAF50"
row_color = "#e8f5e9"
else:
status = "✓ Match"
status_color = "#4CAF50"
row_color = "#e8f5e9"
else:
status = "✗ Mismatch"
status_color = "#f44336"
row_color = "#ffebee"
# Format the values nicely
orig_display = ', '.join(orig_items)
ai_display = ', '.join(ai_items)
except:
# Handle non-array values
if orig_value.lower() == ai_value.lower():
status = "✓ Match"
status_color = "#4CAF50"
row_color = "#e8f5e9"
orig_display = orig_value
ai_display = ai_value
else:
status = "✗ Mismatch"
status_color = "#f44336"
row_color = "#ffebee"
orig_display = orig_value
ai_display = ai_value
# Add row with comparison
html += f"<tr style='background-color: {row_color};'>"
html += f"<td style='width: 150px;'>{key}</td>"
html += f"<td>{orig_display}</td>"
html += f"<td>{ai_display}</td>"
html += f"<td style='color: {status_color}; font-weight: bold;'>{status}</td>"
html += "</tr>"
html += "</table></div>"
# Original data section
html += "<div style='flex: 1; border: 1px solid #ddd; padding: 15px; border-radius: 5px;'>"
html += "<h4>Original Data</h4>"
html += "<table style='width: 100%;'>"
for key, value in page_data['original_data'].items():
html += f"<tr><td style='width: 150px;'>{key}</td><td>{value}</td></tr>"
html += "</table></div>"
# AI data section
html += "<div style='flex: 1; border: 1px solid #ddd; padding: 15px; border-radius: 5px;'>"
html += "<h4>AI Predicted Data</h4>"
html += "<table style='width: 100%;'>"
for key, value in page_data['ai_data'].items():
html += f"<tr><td style='width: 150px;'>{key}</td><td>{value}</td></tr>"
html += "</table></div>"
html += "</div>"
return html
# Create Gradio interface
demo = gr.Blocks()
with demo:
gr.Markdown("# CSV Data Viewer")
gr.Markdown("Navigate through CSV data pages to compare original and AI-predicted values")
page_num = gr.Slider(0, 100, step=1, label="Page Number")
html_output = gr.HTML()
page_num.change(create_page_view, [page_num], [html_output])
# Launch Gradio interface
demo.launch(server_name="0.0.0.0", server_port=7860, share=True)
|