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52e9d16 ad29580 52e9d16 ad29580 52e9d16 ad29580 52e9d16 ad29580 52e9d16 ad29580 52e9d16 ad29580 52e9d16 ad29580 52e9d16 ad29580 52e9d16 ad29580 52e9d16 ad29580 52e9d16 ad29580 52e9d16 ad29580 52e9d16 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 | 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}) |