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from smolagents import CodeAgent, HfApiModel, load_tool, tool
import yaml
import feedparser
import gradio as gr
@tool
def fetch_latest_arxiv_papers(keywords: list, num_results: int = 1) -> list:
"""Fetches the latest research papers from arXiv based on provided keywords.
Args:
keywords: A list of keywords to search for relevant papers.
num_results: The number of papers to fetch (default is 5).
"""
try:
print(f"DEBUG: Searching arXiv papers with keywords: {keywords}") # Debug input
query = "+".join(keywords)
url = f"http://export.arxiv.org/api/query?search_query=all:{query}&start=0&max_results={num_results}&sortBy=submittedDate&sortOrder=descending"
feed = feedparser.parse(url)
papers = []
for entry in feed.entries:
papers.append({
"title": entry.title,
"authors": ", ".join(author.name for author in entry.authors),
"year": entry.published[:4], # Extract year
"abstract": entry.summary,
"link": entry.link
})
return papers
except Exception as e:
print(f"ERROR: {str(e)}") # Debug errors
return [f"Error fetching research papers: {str(e)}"]
model = HfApiModel(
max_tokens=2096,
temperature=0.5,
model_id='Qwen/Qwen2.5-Coder-32B-Instruct',
custom_role_conversions=None,
)
with open("prompts.yaml", 'r') as stream:
prompt_templates = yaml.safe_load(stream)
agent = CodeAgent(
model=model,
tools=[fetch_latest_arxiv_papers],
max_steps=6,
verbosity_level=1,
grammar=None,
planning_interval=None,
name="ScholarAgent",
description="An AI agent that fetches the latest research papers from arXiv based on user-defined keywords and filters.",
prompt_templates=prompt_templates
)
def search_papers(user_input):
keywords = [kw.strip() for kw in user_input.split(",") if kw.strip()] # Ensure valid keywords
print(f"DEBUG: Received input keywords - {keywords}") # Debug user input
if not keywords:
print("DEBUG: No valid keywords provided.")
return "Error: Please enter at least one valid keyword."
results = fetch_latest_arxiv_papers(keywords, num_results=3) # Fetch 3 results
print(f"DEBUG: Results received - {results}") # Debug function output
if isinstance(results, list) and results and isinstance(results[0], dict):
return "\n\n".join([
f"**Title:** {paper['title']}\n**Authors:** {paper['authors']}\n**Year:** {paper['year']}\n**Abstract:** {paper['abstract']}\n[Read More]({paper['link']})"
for paper in results
])
print("DEBUG: No results found.")
return "No results found. Try different keywords."
# Create a simple Gradio interface
with gr.Blocks() as demo:
gr.Markdown("# arXiv Research Paper Fetcher")
keyword_input = gr.Textbox(label="Enter keywords (comma-separated)", placeholder="e.g., deep learning, reinforcement learning")
output_display = gr.Markdown()
search_button = gr.Button("Search")
search_button.click(search_papers, inputs=[keyword_input], outputs=[output_display])
print("DEBUG: Gradio UI is running. Waiting for user input...")
demo.launch()
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