File size: 1,914 Bytes
f37c37a d166583 f37c37a d166583 e1aeb67 d166583 e1aeb67 d166583 e1aeb67 d166583 e1aeb67 d166583 e1aeb67 d166583 e7eada4 e1aeb67 d166583 |
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 |
import gradio as gr
import sys
import logging
from inference import main, load_index, load_metadata
from PIL import Image
def run_pipeline(prompt, image):
"""
Gradio interface function to run the inference pipeline
"""
try:
logging.info("Loading required data...")
index = load_index()
metadata_df = load_metadata()
logging.info("Starting inference pipeline...")
results = main(prompt, image, index, metadata_df)
# Return the generated image and similar products
image_path = results['generated_image_path']
similar_products = results['similar_products']
image_output = Image.open(image_path)
# Format product URLs as a numbered list with similarity scores
product_urls = []
for i, product in enumerate(similar_products, 1):
similarity = 1 / (1 + product['distance'])
product_urls.append(f"{i}. Similarity: {similarity:.2f}\nProduct: {product['product_url']}\n")
formatted_urls = "\n".join(product_urls)
return image_output, formatted_urls
except Exception as e:
logging.error(f"Error in pipeline: {str(e)}")
return None, None
# Create Gradio interface
iface = gr.Interface(
fn=run_pipeline,
inputs=[
gr.Textbox(label="Enter your prompt", placeholder="e.g., modern living room with minimalist furniture"),
gr.Image(label="Upload control image", type="filepath")
],
outputs=[
gr.Image(label="Generated Image"),
gr.Textbox(label="Similar IKEA Products", lines=15)
],
title="Interior Design Image Generator",
description="Upload an image and provide a prompt to generate interior design variations and find similar IKEA products.",
theme="default",
flagging_mode="never"
)
if __name__ == "__main__":
iface.launch(share=True) |