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import streamlit as st
import requests
from transformers import pipeline
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")
# Function to perform search using Google Custom Search API
def perform_search(query):
api_key = 'AIzaSyAgKac39wfstboizc1StYGjqlT2rdQqVQ4'
cx = "7394b4ca2ca1040ef"
search_url = f"https://www.googleapis.com/customsearch/v1?q={query}&key={api_key}&cx={cx}"
response = requests.get(search_url)
return response.json()
# Function to summarize the overall combined content (make it longer)
def summarize_overall_content(content):
if len(content) > 3000: # Summarize up to 3000 characters for a larger summary
content = content[:3000]
summary = summarizer(content, max_length=300, min_length=100, do_sample=False) # Larger overall summary
return summary[0]['summary_text']
# Function to summarize individual search results (keep shorter)
def summarize_individual_content(content):
if len(content) > 1000: # Summarize first 1000 characters for brevity
content = content[:1000]
summary = summarizer(content, max_length=50, min_length=30, do_sample=False) # Shorter summary
return summary[0]['summary_text']
# Function to rank search results based on custom criteria
def rank_sources(results):
# For now, assume sources are ranked by default order from API
return results
# Function to extract related topics using spaCy
def extract_related_topics(query_list):
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]
# 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
# Function to display search results and summaries
def display_results(query):
st.write(f"Searching for: {query}")
# Perform search and get results
search_results = perform_search(query)
# Extract relevant items from search results
if 'items' in search_results:
ranked_results = rank_sources(search_results['items'])
# Overall summary (bigger)
st.write("### Overall Summary:")
combined_content = " ".join([item['snippet'] for item in ranked_results])
overall_summary = summarize_overall_content(combined_content) # Use larger summary function
st.write(overall_summary)
# Individual results (shorter)
st.write("### Individual Results:")
for item in ranked_results:
st.write(f"**[{item['title']}]({item['link']})**")
st.write(summarize_individual_content(item['snippet'])) # Use shorter summary function
st.write("---")
else:
st.write("No results found.")
# Main Streamlit App UI
st.title("AI-Powered Information Retrieval and Summarization")
# Initialize query list to store search queries
if 'querylist' not in st.session_state:
st.session_state.querylist = []
# Search input by user
query = st.text_input("Enter your search query:")
# If query is provided, display results and update query list
if query:
st.session_state.querylist.append(query)
display_results(query)
# Generate related topics based on query list
related_topics = extract_related_topics(st.session_state.querylist)
st.write("### Related Topics:")
for topic in related_topics:
st.write(f"- **[{topic}]({requests.utils.requote_uri(f'https://www.google.com/search?q={topic}')})**")
# Trending Topics Section with clickable links
st.sidebar.title("Trending Topics")
trending_topics = ["AI", "Machine Learning", "Sustainability", "Technology Trends"]
for idx, topic in enumerate(trending_topics):
if st.sidebar.button(topic, key=f'topic_button_{idx}'):
query = topic # Automatically search for this topic when clicked
# Feedback Section (Visible after results)
if query or any(st.sidebar.button(topic) for topic in trending_topics):
st.write("### Feedback")
feedback = st.radio("Was this summary helpful?", ["Yes", "No"])
if feedback == "Yes":
st.write("Thank you for your feedback!")
else:
st.write("We will try to improve!")
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