Wrong Deleting

infridgement_score.py

@@ -1,174 +0,0 @@
-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, link_count)
-        
-        
-        search_functions = {
-            'google': search_google,
-            'duckduckgo': search_duckduckgo,
-            # 'archive': search_archive,
-            '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, link_count)
-            elif search == 'duckduckgo':
-                data[product] = filtering(search_duckduckgo(product), main_product, product, link_count)
-            elif search == 'archive':
-                data[product] = filtering(search_archive(product), main_product, product, link_count)
-            elif search == 'github':
-                data[product] = filtering(search_github(product), main_product, product, link_count)
-            elif search == 'wikipedia':
-                data[product] = filtering(search_wikipedia(product), main_product, product, link_count)
-
-    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:
-
-        if len(data[product])==0:
-            logger.write("\n\nNo Product links Found Increase No of Links or Change Search Source\n")
-            log_area.text(logger.getvalue())
-    
-            cosine_sim_scores.append((product,'No Product links Found Increase Number of Links or Change Search Source',None,None))
-        
-        else:
-            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("Check Infringement")
-
-
-# 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', ['All','duckduckgo', 'google', 'archive', 'github', 'wikipedia'])
-
-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', ["Complete Document Similarity","Feild Wise Document Similarity"])
-
-
-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")
-
-    #  = score(main_product, main_url, search, logger, log_output)
-    if tag_option == 'Complete Document Similarity':
-        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}")
-        if value!=None:
-            st.write(f"{tags[index]:<20} - Similarity: {value:.2f}")