Upload app3.py

app3.py

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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!")