Update app.py

app.py

@@ -2,13 +2,13 @@
 import streamlit as st
 import requests
 from transformers import pipeline
-import spacy
+#import spacy
 
 # Initialize the summarizer pipeline using Hugging Face Transformers
 summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
 
 # Load spaCy model
-nlp = spacy.load("en_core_web_sm")
+#nlp = spacy.load("en_core_web_sm")
 
 # Function to perform search using Google Custom Search API
 def perform_search(query):
@@ -39,17 +39,17 @@ def rank_sources(results):
 
 # Function to extract related topics using spaCy
 def extract_related_topics(query_list):
-    combined_query = " ".join(query_list)
-    doc = nlp(combined_query)
+    #combined_query = " ".join(query_list)
+    #doc = nlp(combined_query)
     
     # Extract keywords or named entities
-    keywords = [token.text for token in doc if token.is_alpha and not token.is_stop]
-    entities = [ent.text for ent in doc.ents]
+    #keywords = [token.text for token in doc if token.is_alpha and not token.is_stop]
+    #entities = [ent.text for ent in doc.ents]
     
     # Combine and deduplicate keywords and entities
-    related_topics = list(set(keywords + entities))
-    related_topics.insert(0,"Deep Learning")
-    return related_topics[:3]  # Limit to 3 related topics
+    #related_topics = list(set(keywords + entities))
+    #related_topics.insert(0,"Deep Learning")
+    return ["Machine","AI","GenAI"]  # Limit to 3 related topics
 
 # Function to display search results and summaries
 def display_results(query):