Update app.py

app.py

@@ -2,19 +2,20 @@ import os
 import streamlit as st
 import requests
 import feedparser
-from duckduckgo_search import DDGS
 from dotenv import load_dotenv
+from duckduckgo_search import DDGS
 
+# Load API key from .env
 load_dotenv()
-OPENROUTER_API_KEY = os.getenv("OPENROUTER_API_KEY")  # secure access
+OPENROUTER_API_KEY = os.getenv("OPENROUTER_API_KEY")
 
-# --- LLM Wrapper ---
+# --- LLM Function ---
 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"
+        "X-Title": "Autonomous Research Assistant"
     }
     data = {
         "model": model,
@@ -22,23 +23,89 @@ def call_llm(messages, model="deepseek/deepseek-chat-v3-0324:free", max_tokens=2
         "max_tokens": max_tokens,
         "temperature": temperature
     }
-    
     try:
         response = requests.post(url, headers=headers, json=data)
         result = response.json()
     except Exception as e:
-        raise RuntimeError(f"Failed to get or parse LLM response: {e}")
-
+        raise RuntimeError(f"Failed to connect or parse response: {e}")
     if response.status_code != 200:
-        err_msg = result.get("error", {}).get("message", "Unknown API error")
-        raise RuntimeError(f"OpenRouter API returned error: {err_msg}")
+        error = result.get("error", {}).get("message", response.text)
+        raise RuntimeError(f"OpenRouter API Error: {error}")
+    if "choices" not in result:
+        raise RuntimeError(f"Invalid response: {result}")
+    return result["choices"][0]["message"]["content"]
 
-    if "choices" not in result or not result["choices"]:
-        raise RuntimeError(f"Invalid response structure from OpenRouter: {result}")
+# --- Source Fetching 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)
+    return [{
+        "title": e.title,
+        "summary": e.summary.strip().replace("\n", " "),
+        "url": next((l.href for l in e.links if l.type == "application/pdf"), "")
+    } for e in feed.entries]
 
-    return result["choices"][0]["message"]["content"]
-    
-# --- Streamlit UI ---
+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)
+    papers = response.json().get("data", [])
+    return [{
+        "title": p.get("title", ""),
+        "summary": p.get("abstract", "No abstract").strip(),
+        "url": p.get("url", "")
+    } for p in papers]
+
+def search_duckduckgo(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_url(query):
+    with DDGS() as ddgs:
+        for r in ddgs.images(query, max_results=1):
+            return r["image"]
+    return None
+
+# --- Autonomous Agent ---
+def autonomous_research_agent(topic):
+    arxiv = get_arxiv_papers(topic)
+    scholar = get_semantic_scholar_papers(topic)
+    web = search_duckduckgo(topic)
+    image = get_image_url(topic)
+
+    prompt = f"# Research Topic: {topic}\n\n"
+
+    if image:
+        prompt += f"![Relevant Image]({image})\n\n"
+
+    prompt += "## ArXiv:\n"
+    for p in arxiv:
+        prompt += f"- [{p['title']}]({p['url']})\n> {p['summary'][:300]}...\n\n"
+
+    prompt += "## Semantic Scholar:\n"
+    for p in scholar:
+        prompt += f"- [{p['title']}]({p['url']})\n> {p['summary'][:300]}...\n\n"
+
+    prompt += "## Web Insights:\n"
+    for w in web:
+        prompt += f"- [{w['title']}]({w['url']})\n> {w['snippet']}\n\n"
+
+    prompt += (
+        "Now, based on the above sources:\n"
+        "1. Identify overlapping research themes\n"
+        "2. Highlight a gap or opportunity\n"
+        "3. Propose a novel research direction\n"
+        "4. Write a full academic-style narrative in markdown (no section labels)\n"
+    )
+
+    return call_llm([{"role": "user", "content": prompt}], max_tokens=3000)
+
+# --- Streamlit App ---
 st.set_page_config("Autonomous Research Agent", layout="wide")
 st.title("🤖 Autonomous AI Research Assistant")
 
@@ -47,27 +114,27 @@ if "chat_history" not in st.session_state:
 
 topic = st.text_input("Enter a research topic:")
 if st.button("Run Agent"):
-    with st.spinner("Researching..."):
+    with st.spinner("Gathering sources & thinking..."):
         try:
-            output = autonomous_research_agent(topic)
+            response = 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)
+            st.session_state.chat_history.append({"role": "assistant", "content": response})
+            st.markdown(response)
         except Exception as e:
-            st.error(f"Error: {e}")
+            st.error(f"Failed: {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"):
+st.subheader("💬 Follow-up Questions")
+followup = st.text_input("Ask something about the research:")
+if st.button("Ask"):
     if followup:
         chat = st.session_state.chat_history + [{"role": "user", "content": followup}]
-        with st.spinner("Thinking..."):
+        with st.spinner("Answering..."):
             try:
-                response = call_llm(chat, max_tokens=1500)
+                answer = 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)
+                st.session_state.chat_history.append({"role": "assistant", "content": answer})
+                st.markdown(answer)
             except Exception as e:
-                st.error(f"Follow-up failed: {e}")
+                st.error(f"Follow-up error: {e}")