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import os
import similarity_search
import gradio as gr
# Path to the directory containing similar images
# similar_images_dir = ".\gallery"
# def read_image(image_file):
# img = cv2.imread(
# image_file, cv2.IMREAD_COLOR | cv2.IMREAD_IGNORE_ORIENTATION
# )
# img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# if img is None:
# raise ValueError('Failed to read {}'.format(image_file))
# return img
def get_similar_images(image):
similar_image_ids = similarity_search.find(image)
return similar_image_ids
# def get_image_paths(prod_ids):
# csv_path = './gallery.csv' # Replace with the actual path
# data = pd.read_csv(csv_path)
# image_paths = []
# for i, prod_id in enumerate(prod_ids):
# row = data[data['seller_img_id'] == prod_id]
# if not row.empty:
# image_path = './' + row.iloc[0]['img_path']
# print(image_path)
# image_paths.append(image_path)
# return image_paths
def predict(image):
similar_image_ids = get_similar_images(image)
return {"similar_image_ids" : similar_image_ids}
# Create title, description and article strings
title = "Visual Product Recognition"
description = "A model to find the similar images in the e-commerce platform"
article = "Created by Thenujan Nagaratnam for Data Science Project at UoM"
# Create examples list from "examples/" directory
example_list = [["examples/" + example] for example in os.listdir("examples")]
# Create the Gradio demo
demo = gr.Interface(fn=predict, # mapping function from input to output
inputs=gr.Image(type="pil"), # what are the inputs?
outputs=gr.JSON(label="Predictions"),
examples=example_list,
title=title,
description=description,
article=article)
# Launch the demo!
demo.launch() |