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import streamlit as st
import concurrent.futures
from concurrent.futures import ThreadPoolExecutor,as_completed
from functools import partial
import numpy as np
from io import StringIO
import sys
import time
# File Imports
from embedding import get_embeddings # Ensure this file/module is available
from preprocess import filtering # Ensure this file/module is available
from search import *
# Cosine Similarity Function
def cosine_similarity(vec1, vec2):
vec1 = np.array(vec1)
vec2 = np.array(vec2)
dot_product = np.dot(vec1, vec2)
magnitude_vec1 = np.linalg.norm(vec1)
magnitude_vec2 = np.linalg.norm(vec2)
if magnitude_vec1 == 0 or magnitude_vec2 == 0:
return 0.0
cosine_sim = dot_product / (magnitude_vec1 * magnitude_vec2)
return cosine_sim
# Logger class to capture output
class StreamCapture:
def __init__(self):
self.output = StringIO()
self._stdout = sys.stdout
def __enter__(self):
sys.stdout = self.output
return self.output
def __exit__(self, exc_type, exc_val, exc_tb):
sys.stdout = self._stdout
# Main Function
def score(main_product, main_url, product_count, link_count, search, logger, log_area):
data = {}
similar_products = extract_similar_products(main_product)[:product_count]
if search == 'all':
def process_product(product, search_function, main_product):
search_result = search_function(product)
return filtering(search_result, main_product, product)
search_functions = {
'google': search_google,
'duckduckgo': search_duckduckgo,
'github': search_github,
'wikipedia': search_wikipedia
}
with ThreadPoolExecutor() as executor:
future_to_product_search = {
executor.submit(process_product, product, search_function, main_product): (product, search_name)
for product in similar_products
for search_name, search_function in search_functions.items()
}
for future in as_completed(future_to_product_search):
product, search_name = future_to_product_search[future]
try:
if product not in data:
data[product] = {}
data[product] = future.result()
except Exception as e:
print(f"Error processing product {product} with {search_name}: {e}")
else:
for product in similar_products:
if search == 'google':
data[product] = filtering(search_google(product), main_product, product)
elif search == 'duckduckgo':
data[product] = filtering(search_duckduckgo(product), main_product, product)
elif search == 'archive':
data[product] = filtering(search_archive(product), main_product, product)
elif search == 'github':
data[product] = filtering(search_github(product), main_product, product)
elif search == 'wikipedia':
data[product] = filtering(search_wikipedia(product), main_product, product)
logger.write("\n\nFiltered Links ------------------>\n")
logger.write(str(data) + "\n")
log_area.text(logger.getvalue())
logger.write("\n\nCreating Main product Embeddings ---------->\n")
main_result, main_embedding = get_embeddings(main_url,tag_option)
log_area.text(logger.getvalue())
print("main",main_embedding)
cosine_sim_scores = []
logger.write("\n\nCreating Similar product Embeddings ---------->\n")
log_area.text(logger.getvalue())
for product in data:
for link in data[product][:link_count]:
similar_result, similar_embedding = get_embeddings(link,tag_option)
log_area.text(logger.getvalue())
print(similar_embedding)
for i in range(len(main_embedding)):
score = cosine_similarity(main_embedding[i], similar_embedding[i])
cosine_sim_scores.append((product, link, i, score))
log_area.text(logger.getvalue())
logger.write("--------------- DONE -----------------\n")
log_area.text(logger.getvalue())
return cosine_sim_scores, main_result
# Streamlit Interface
st.title("Product Infringement Checker")
# Inputs
main_product = st.text_input('Enter Main Product Name', 'Philips led 7w bulb')
main_url = st.text_input('Enter Main Product Manual URL', 'https://www.assets.signify.com/is/content/PhilipsConsumer/PDFDownloads/Colombia/technical-sheets/ODLI20180227_001-UPD-es_CO-Ficha_Tecnica_LED_MR16_Master_7W_Dim_12V_CRI90.pdf')
search_method = st.selectbox('Choose Search Engine', ['duckduckgo', 'google', 'archive', 'github', 'wikipedia', 'all'])
col1, col2 = st.columns(2)
with col1:
product_count = st.number_input("Number of Simliar Products",min_value=1, step=1, format="%i")
with col2:
link_count = st.number_input("Number of Links per product",min_value=1, step=1, format="%i")
tag_option = st.selectbox('Choose Similarity Method', ["Single","Tag Wise"])
if st.button('Check for Infringement'):
log_output = st.empty() # Placeholder for log output
with st.spinner('Processing...'):
with StreamCapture() as logger:
cosine_sim_scores, main_result = score(main_product, main_url,product_count, link_count, search_method, logger, log_output)
st.success('Processing complete!')
st.subheader("Cosine Similarity Scores")
if tag_option=="Single":
tags=["Details"]
else:
tags = ['Introduction', 'Specifications', 'Product Overview', 'Safety Information', 'Installation Instructions', 'Setup and Configuration', 'Operation Instructions', 'Maintenance and Care', 'Troubleshooting', 'Warranty Information', 'Legal Information']
for product, link, index, value in cosine_sim_scores:
if not index:
st.write(f"Product: {product}, Link: {link}")
st.write(f"{tags[index]:<20} - Similarity: {value:.2f}") |