Update app.py

app.py

@@ -22,66 +22,21 @@ def call_llm(messages, model="deepseek/deepseek-chat-v3-0324:free", max_tokens=2
         "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)"
-    )
+    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}")
 
-    return call_llm([{"role": "user", "content": prompt}], max_tokens=3000)
+    if response.status_code != 200:
+        err_msg = result.get("error", {}).get("message", "Unknown API error")
+        raise RuntimeError(f"OpenRouter API returned error: {err_msg}")
 
+    if "choices" not in result or not result["choices"]:
+        raise RuntimeError(f"Invalid response structure from OpenRouter: {result}")
+
+    return result["choices"][0]["message"]["content"]
 # --- Streamlit UI ---
 st.set_page_config("Autonomous Research Agent", layout="wide")
 st.title("🤖 Autonomous AI Research Assistant")