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Update app.py

ed3c2dc almost 3 years ago

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import streamlit as st
import numpy as np
import similarSearch
import pandas as pd
import cv2
import os
from PIL import Image

df = pd.read_csv('./gallery.csv')

def read_image(uploaded_file):
    with open(uploaded_file.name, "wb") as f:
        f.write(uploaded_file.getbuffer())
    img = cv2.imread(uploaded_file.name, cv2.IMREAD_COLOR | cv2.IMREAD_IGNORE_ORIENTATION)
    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    if img is None:
        raise ValueError('Failed to read {}'.format(uploaded_file.name))
    os.remove(uploaded_file.name)  # Remove the temporary file
    return img

def image_open(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

    
# Function to display images from their paths
def display_images(image_ids):
    images = []
    captions = []
    for img_id in image_ids:
        image_path = df.loc[df['seller_img_id'] == img_id, 'img_path'].values[0]
        print(image_path)
        image = image_open(image_path)
        images.append(image)
        captions.append(img_id)
    return images, captions
    
# Function to find similar image IDs for a newly updated image
def find_similar_images(image):
    # Placeholder for your code to find similar image IDs based on the new_image
    similar_ids = similarSearch.predict(image)  # Replace with the actual logic for finding similar IDs
    images, captions = display_images(similar_ids)
    length = len(images)
    return similar_ids,length
    # return similar_ids

def main():
    st.set_page_config(
    page_title='See it, search it',
    page_icon=':mag:',
    layout='wide',
    initial_sidebar_state='expanded'
    )
    st.title('See it, search it')
    st.markdown(
    """
    <style>
    .stTitle {
        text-align: center;
        font-size: 36px;
        margin-bottom: 30px;
    }
    .stRadio > div {
        display: flex;
        justify-content: center;
        align-items: center;
    }
    .stImageContainer {
        display: flex;
        justify-content: center;
        align-items: center;
        margin-bottom: 30px;
    }
    .stImage {
        max-width: 100%;
        height: auto;
    }
    </style>
    """,
    unsafe_allow_html=True
    )

    

    image_columns = st.columns(5)

    # Define the desired width and height for the resized images
    image_width = 150
    image_height = 150

    # Calculate the number of columns based on the available width
    num_columns = 5

    # Calculate the number of rows required
    images_per_page = 50

    # Check if the image is uploaded by the user
    uploaded_image = st.file_uploader("Upload Image", type=['png', 'jpg', 'jpeg'])

    if uploaded_image is not None:
        # Convert the uploaded image to an array using the read_image function
        image = read_image(uploaded_image)

        image = Image.fromarray(image)
        image = image.resize((224, 224))

        # Display the resized image
        st.image(image, caption="Query Image", width=224)

        # Find similar images
        similar_images, length = find_similar_images(image)

        # Calculate the total number of pages required
        total_pages = (length + images_per_page - 1) // images_per_page

        page_number = st.sidebar.number_input("Select Page", min_value=1, max_value=total_pages)

        # Calculate the range of images to display for the selected page
        start_index = (page_number - 1) * images_per_page
        end_index = min(start_index + images_per_page, length)

        image_columns = st.columns(num_columns)

        index = 0 

        for img_id in similar_images[start_index:end_index]:
            row = df[df['seller_img_id'] == img_id]
            if not row.empty:
                img_path = row['img_path'].values[0]
                product_id = row['product_id'].values[0]
        
                # Construct the full path to the image
                full_img_path = img_path

                # Open and resize the image to the desired dimensions
                image = image_open(full_img_path)
                resized_image = cv2.resize(image, (image_width, image_height))

                # Determine the column to place the image in
                col_index = index % num_columns
                with image_columns[col_index]:
                    st.image(resized_image, caption=f'Product ID: {product_id}\nImage ID: {img_id}', width=image_width)
                index += 1
                    
            else:
                st.write(f"Could not find details for image with ID: {img_id}")
        


if __name__ == '__main__':
    main()

	import streamlit as st
	import numpy as np
	import similarSearch
	import pandas as pd
	import cv2
	import os
	from PIL import Image

	df = pd.read_csv('./gallery.csv')

	def read_image(uploaded_file):
	with open(uploaded_file.name, "wb") as f:
	f.write(uploaded_file.getbuffer())
	img = cv2.imread(uploaded_file.name, cv2.IMREAD_COLOR \| cv2.IMREAD_IGNORE_ORIENTATION)
	img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
	if img is None:
	raise ValueError('Failed to read {}'.format(uploaded_file.name))
	os.remove(uploaded_file.name) # Remove the temporary file
	return img

	def image_open(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


	# Function to display images from their paths
	def display_images(image_ids):
	images = []
	captions = []
	for img_id in image_ids:
	image_path = df.loc[df['seller_img_id'] == img_id, 'img_path'].values[0]
	print(image_path)
	image = image_open(image_path)
	images.append(image)
	captions.append(img_id)
	return images, captions

	# Function to find similar image IDs for a newly updated image
	def find_similar_images(image):
	# Placeholder for your code to find similar image IDs based on the new_image
	similar_ids = similarSearch.predict(image) # Replace with the actual logic for finding similar IDs
	images, captions = display_images(similar_ids)
	length = len(images)
	return similar_ids,length
	# return similar_ids

	def main():
	st.set_page_config(
	page_title='See it, search it',
	page_icon=':mag:',
	layout='wide',
	initial_sidebar_state='expanded'
	)
	st.title('See it, search it')
	st.markdown(
	"""
	<style>
	.stTitle {
	text-align: center;
	font-size: 36px;
	margin-bottom: 30px;
	}
	.stRadio > div {
	display: flex;
	justify-content: center;
	align-items: center;
	}
	.stImageContainer {
	display: flex;
	justify-content: center;
	align-items: center;
	margin-bottom: 30px;
	}
	.stImage {
	max-width: 100%;
	height: auto;
	}
	</style>
	""",
	unsafe_allow_html=True
	)



	image_columns = st.columns(5)

	# Define the desired width and height for the resized images
	image_width = 150
	image_height = 150

	# Calculate the number of columns based on the available width
	num_columns = 5

	# Calculate the number of rows required
	images_per_page = 50

	# Check if the image is uploaded by the user
	uploaded_image = st.file_uploader("Upload Image", type=['png', 'jpg', 'jpeg'])

	if uploaded_image is not None:
	# Convert the uploaded image to an array using the read_image function
	image = read_image(uploaded_image)

	image = Image.fromarray(image)
	image = image.resize((224, 224))

	# Display the resized image
	st.image(image, caption="Query Image", width=224)

	# Find similar images
	similar_images, length = find_similar_images(image)

	# Calculate the total number of pages required
	total_pages = (length + images_per_page - 1) // images_per_page

	page_number = st.sidebar.number_input("Select Page", min_value=1, max_value=total_pages)

	# Calculate the range of images to display for the selected page
	start_index = (page_number - 1) * images_per_page
	end_index = min(start_index + images_per_page, length)

	image_columns = st.columns(num_columns)

	index = 0

	for img_id in similar_images[start_index:end_index]:
	row = df[df['seller_img_id'] == img_id]
	if not row.empty:
	img_path = row['img_path'].values[0]
	product_id = row['product_id'].values[0]

	# Construct the full path to the image
	full_img_path = img_path

	# Open and resize the image to the desired dimensions
	image = image_open(full_img_path)
	resized_image = cv2.resize(image, (image_width, image_height))

	# Determine the column to place the image in
	col_index = index % num_columns
	with image_columns[col_index]:
	st.image(resized_image, caption=f'Product ID: {product_id}\nImage ID: {img_id}', width=image_width)
	index += 1

	else:
	st.write(f"Could not find details for image with ID: {img_id}")



	if __name__ == '__main__':
	main()