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import streamlit as st
import os
from dotenv import load_dotenv
from agents import ResearchAgents
from data_loader import DataLoader
load_dotenv()
# Move set_page_config() to be the first Streamlit command.
st.set_page_config(
page_title="Autogen Agent",
page_icon="⚡",
initial_sidebar_state="expanded"
)
# --- Added Custom CSS for a Professional Look ---
custom_css = """
<style>
body {
background-color: #f5f5f5;
font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
}
.css-18e3th9, .css-1d391kg { /* Streamlit title and header adjustments */
color: #333333;
}
.stButton>button {
background-color: #4a90e2;
color: #ffffff;
border-radius: 5px;
border: none;
}
.sidebar .sidebar-content {
background-color: #ffffff;
}
.stMarkdown, .css-1d391kg {
color: #555555;
}
</style>
"""
st.markdown(custom_css, unsafe_allow_html=True)
# Streamlit UI Title
st.title("📚 Virtual Research Assistant")
num_results = 5
source_choice = st.sidebar.multiselect("Select Data Sources", options=["ArXiv", "Google Scholar"], default=["ArXiv"])
# Sidebar with features and footer
with st.sidebar:
st.divider()
st.markdown("<h3 style='text-align: center; color: #333;'>Key Features</h3>", unsafe_allow_html=True)
st.markdown("""
<ul style='list-style: none; padding: 0;'>
<li style='margin-bottom: 8px;'>🔍 <strong>Multi-Source Research Retrieval</strong></li>
<li style='margin-bottom: 8px;'>🤖 <strong>Integrated Chatbot Interaction</strong></li>
<li style='margin-bottom: 8px;'>✨ <strong>Advanced Summarization</strong></li>
<li style='margin-bottom: 8px;'>🔄 <strong>Automatic Query Expansion & Refinement</strong></li>
<li style='margin-bottom: 8px;'>📊 <strong>Visual Data Presentation</strong></li>
</ul>
""", unsafe_allow_html=True)
st.divider()
st.markdown("<p style='text-align: center;'><em>Built with Groq | Autogen</em></p>", unsafe_allow_html=True)
# Retrieve the API key from environment variables
groq_api_key = os.getenv("GROQ_API_KEY")
# Check if API key is set, else stop execution
if not groq_api_key:
st.error("GROQ_API_KEY is missing. Please set it in your environment variables.")
st.stop()
# Initialize AI Agents for summarization and analysis
agents = ResearchAgents(groq_api_key)
# Initialize DataLoader for fetching research papers
data_loader = DataLoader()
# Use chat_input instead of text_input for entering the research topic.
query = st.chat_input("Enter a research topic:")
# Trigger the search automatically if a query is provided.
if query:
with st.spinner("Fetching research papers..."): # Show a loading spinner
all_papers = []
# Fetch from selected sources based on sidebar choices
if "ArXiv" in source_choice:
arxiv_papers = data_loader.fetch_arxiv_papers(query, limit=num_results)
all_papers.extend(arxiv_papers)
if "Google Scholar" in source_choice:
google_scholar_papers = data_loader.fetch_google_scholar_papers(query)
all_papers.extend(google_scholar_papers)
# If no papers are found, display an error message
if not all_papers:
st.error("Failed to fetch papers. Try again!")
else:
processed_papers = []
# Process each paper: generate summary and analyze advantages/disadvantages
for paper in all_papers:
summary = agents.summarize_paper(paper['summary']) # Generate summary
adv_dis = agents.analyze_advantages_disadvantages(summary) # Analyze pros/cons
processed_papers.append({
"title": paper["title"],
"link": paper["link"],
"summary": summary,
"advantages_disadvantages": adv_dis,
})
# Display the processed research papers
st.subheader("Top Research Papers:")
for i, paper in enumerate(processed_papers, 1):
st.markdown(f"### {i}. {paper['title']}") # Paper title
st.markdown(f"🔗 [Read Paper]({paper['link']})") # Paper link
st.write(f"**Summary:** {paper['summary']}") # Paper summary
st.write(f"{paper['advantages_disadvantages']}") # Pros/cons analysis
st.markdown("---") # Separator between papers
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