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| import streamlit as st | |
| import uuid | |
| import os | |
| from dotenv import load_dotenv | |
| from src.graph import app | |
| # Load environment variables (works with .env file or HF Spaces secrets) | |
| load_dotenv() | |
| # --- Streamlit UI Configuration --- | |
| st.set_page_config( | |
| page_title="Autonomous Research Agent", | |
| page_icon="🤖", | |
| layout="wide", | |
| initial_sidebar_state="collapsed" | |
| ) | |
| st.title("🤖 Autonomous Research Agent") | |
| st.markdown(""" | |
| Welcome! This autonomous agent will research any topic for you using AI-powered web search and analysis. | |
| **How it works:** | |
| 1. Enter your research topic below | |
| 2. The agent will search the web, evaluate sources, and summarize findings | |
| 3. Get a comprehensive research report in minutes | |
| *Powered by LangGraph, Groq (Llama 3.3), and Tavily AI* | |
| """) | |
| # Check for API keys | |
| if not os.getenv("GROQ_API_KEY") or not os.getenv("TAVILY_API_KEY"): | |
| st.error("⚠️ API keys not configured. Please add GROQ_API_KEY and TAVILY_API_KEY in Space Settings → Repository Secrets.") | |
| st.stop() | |
| # --- Session State Management --- | |
| if "thread_id" not in st.session_state: | |
| st.session_state.thread_id = str(uuid.uuid4()) | |
| st.session_state.messages = [] | |
| # Display chat history | |
| for message in st.session_state.messages: | |
| with st.chat_message(message["role"]): | |
| st.markdown(message["content"]) | |
| # --- Main Application Logic --- | |
| if prompt := st.chat_input("What topic should I research for you?"): | |
| # Add user's message to session state and display it | |
| st.session_state.messages.append({"role": "user", "content": prompt}) | |
| with st.chat_message("user"): | |
| st.markdown(prompt) | |
| # Prepare to display the agent's response | |
| with st.chat_message("assistant"): | |
| # Use a status container to show the agent's progress | |
| with st.status("🔍 Researching...", expanded=True) as status: | |
| final_report = "" | |
| # LangGraph configuration for the specific session | |
| config = {"configurable": {"thread_id": st.session_state.thread_id}} | |
| initial_state = {"topic": prompt, "summaries": []} | |
| # Stream events from the LangGraph agent | |
| for event in app.stream(initial_state, config=config): | |
| for key, value in event.items(): | |
| if key == "search": | |
| status.write("🔍 Searching for relevant articles...") | |
| elif key == "scrape_and_summarize": | |
| if value.get("scraped_content"): | |
| url = value['scraped_content'].get('url', 'Unknown URL') | |
| is_relevant = value['scraped_content'].get('is_relevant', 'Unknown') | |
| status.write(f"📄 Evaluating: {url} - Relevant: {is_relevant}") | |
| elif key == "summarize": | |
| status.write("📝 Summarizing relevant content...") | |
| elif key == "compile_report": | |
| status.write("📊 Compiling the final report...") | |
| if value.get("report"): | |
| final_report = value["report"] | |
| # Update the status to "complete" when done | |
| status.update(label="✅ Research complete!", state="complete", expanded=False) | |
| # Display the final report | |
| st.markdown(final_report) | |
| # Add the final report to the session state | |
| st.session_state.messages.append({"role": "assistant", "content": final_report}) |