Infringement first commit

app.py

@@ -0,0 +1,132 @@
+import streamlit as st
+import concurrent.futures
+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, search, logger, log_area):
+    data = {}
+    
+    if search == 'all':
+        similar = extract_similar_products(main_product)[:1]
+
+        with concurrent.futures.ThreadPoolExecutor() as executor:
+            futures = []
+
+            search_functions = [search_google, search_duckduckgo, search_github, search_wikipedia]
+            
+            for search_func in search_functions:
+                futures.append(executor.submit(partial(filtering, search_func(similar), main_product, similar)))
+
+            for future in concurrent.futures.as_completed(futures):
+                data[similar] = future.result()
+
+    else:
+        similar = extract_similar_products(main_product)[:1]
+
+        for product in similar:
+
+            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)
+    log_area.text(logger.getvalue())
+
+    cosine_sim_scores = []
+
+    logger.write("\n\nCreating Similar product Embeddings ---------->\n")
+    log_area.text(logger.getvalue())
+
+    print("main",main_embedding)
+
+    for product in data:
+        for link in data[product][:2]:
+
+            similar_result, similar_embedding = get_embeddings(link)
+            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'])
+
+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, search_method, logger, log_output)
+
+    st.success('Processing complete!')
+
+    st.subheader("Cosine Similarity Scores")
+
+    #  = score(main_product, main_url, search, logger, log_output)
+    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} Cosine Similarity Score: {value:.2f}")