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| import gradio as gr | |
| from tools.extract_features import extract_features_image | |
| from tools.search import search_similar_products | |
| from tools.load_database import select_database | |
| # Default values | |
| MARCAS = ["Ninguno", "Zara", "Adidas", "Nike", "Puma", "Levi's", "Forever 21", "H&M", "Gap", "Converse", "Mango"] | |
| PRENDAS = ["bag", "dress", "hat", "jacket", "pants", "shirt", "shoe", "shorts", "skirt", "sunglass"] | |
| def process_image(image, marca, prenda): | |
| """Visual Search Pipeline""" | |
| vgg_search = extract_features_image(image) | |
| database = select_database(marca, prenda) | |
| result = search_similar_products(vgg_search, database) | |
| return result | |
| # Gradio app | |
| examples = [ | |
| ["examples/bag_93.99.png", "Adidas", "bag"], | |
| ["examples/shirt_82.84.png", "Nike", "shirt"], | |
| ["examples/skirt_87.54.png", "Puma", "skirt"] | |
| ] | |
| title = "Visual Search 🔍 | Powered by Xpertium SA" | |
| description = """ | |
| <p style='text-align: center'>Carga tu imagen y selecciona la marca & tipo de prenda.</p> | |
| """ | |
| iface = gr.Interface( | |
| fn=process_image, | |
| inputs=[ | |
| gr.Image(label="imagen"), | |
| gr.Dropdown(label="marca", choices=MARCAS, value=MARCAS[1]), | |
| gr.Dropdown(label="prenda", choices=PRENDAS, value=PRENDAS[1]), | |
| ], | |
| outputs=gr.JSON(label="resultado"), | |
| title=title, | |
| examples=examples, | |
| description=description | |
| ) | |
| iface.launch() | |