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import os
import streamlit as st
import requests
import feedparser
from duckduckgo_search import DDGS
from dotenv import load_dotenv
load_dotenv()
OPENROUTER_API_KEY = os.getenv("OPENROUTER_API_KEY") # secure access
# --- LLM Wrapper ---
def call_llm(messages, model="deepseek/deepseek-chat-v3-0324:free", max_tokens=2048, temperature=0.7):
url = "https://openrouter.ai/api/v1/chat/completions"
headers = {
"Authorization": f"Bearer {OPENROUTER_API_KEY}",
"Content-Type": "application/json",
"X-Title": "Autonomous Research Agent"
}
data = {
"model": model,
"messages": messages,
"max_tokens": max_tokens,
"temperature": temperature
}
response = requests.post(url, headers=headers, json=data)
result = response.json()
if "choices" not in result:
raise RuntimeError(f"LLM returned invalid response: {result}")
return result["choices"][0]["message"]["content"]
# --- Research Source Functions ---
def get_arxiv_papers(query, max_results=3):
from urllib.parse import quote_plus
url = f"http://export.arxiv.org/api/query?search_query=all:{quote_plus(query)}&start=0&max_results={max_results}"
feed = feedparser.parse(url)
papers = []
for entry in feed.entries:
pdf = next((link.href for link in entry.links if link.type == "application/pdf"), "")
papers.append({"title": entry.title, "summary": entry.summary[:300], "url": pdf})
return papers
def get_semantic_scholar_papers(query, max_results=3):
url = "https://api.semanticscholar.org/graph/v1/paper/search"
params = {"query": query, "limit": max_results, "fields": "title,abstract,url"}
response = requests.get(url, params=params)
results = response.json().get("data", [])
return [{"title": p["title"], "summary": p.get("abstract", "N/A")[:300], "url": p.get("url", "")} for p in results]
def search_duckduckgo_snippets(query, max_results=3):
with DDGS() as ddgs:
return [
{"title": r["title"], "snippet": r["body"], "url": r["href"]}
for r in ddgs.text(query, max_results=max_results)
]
def get_image_urls(query, max_images=1):
with DDGS() as ddgs:
return [img["image"] for img in ddgs.images(query, max_results=max_images)]
# --- Research Agent ---
def autonomous_research_agent(topic):
arxiv = get_arxiv_papers(topic)
scholar = get_semantic_scholar_papers(topic)
web = search_duckduckgo_snippets(topic)
images = get_image_urls(topic)
prompt = f"Topic: {topic}\n\n"
if images:
prompt += f"\n\n"
prompt += "## ArXiv:\n" + "\n".join(f"- [{p['title']}]({p['url']})\n> {p['summary']}..." for p in arxiv) + "\n\n"
prompt += "## Semantic Scholar:\n" + "\n".join(f"- [{p['title']}]({p['url']})\n> {p['summary']}..." for p in scholar) + "\n\n"
prompt += "## Web:\n" + "\n".join(f"- [{w['title']}]({w['url']})\n> {w['snippet']}" for w in web) + "\n\n"
prompt += (
"Now synthesize all this into:\n"
"1. Research gap\n"
"2. Proposed research direction\n"
"3. A full academic narrative (markdown format, formal tone)"
)
return call_llm([{"role": "user", "content": prompt}], max_tokens=3000)
# --- Streamlit UI ---
st.set_page_config("Autonomous Research Agent", layout="wide")
st.title("🤖 Autonomous AI Research Assistant")
if "chat_history" not in st.session_state:
st.session_state.chat_history = []
topic = st.text_input("Enter a research topic:")
if st.button("Run Agent"):
with st.spinner("Researching..."):
try:
output = autonomous_research_agent(topic)
st.session_state.chat_history.append({"role": "user", "content": topic})
st.session_state.chat_history.append({"role": "assistant", "content": output})
st.markdown(output)
except Exception as e:
st.error(f"Error: {e}")
# --- Follow-up Chat ---
st.divider()
st.subheader("💬 Ask Follow-up Questions")
followup = st.text_input("Ask something based on the previous research:")
if st.button("Send"):
if followup:
chat = st.session_state.chat_history + [{"role": "user", "content": followup}]
with st.spinner("Thinking..."):
try:
response = call_llm(chat, max_tokens=1500)
st.session_state.chat_history.append({"role": "user", "content": followup})
st.session_state.chat_history.append({"role": "assistant", "content": response})
st.markdown(response)
except Exception as e:
st.error(f"Follow-up failed: {e}") |