app.py

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"![Related Image]({images[0]})\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}")