Updated new app.py

app.py

@@ -1,63 +1,7 @@
-from smolagents import CodeAgent, HfApiModel, load_tool, tool
-import datetime
-import requests
-import pytz
-import yaml
-from tools.final_answer import FinalAnswerTool
-from scholarly import scholarly
 import gradio as gr
 
-@tool
-def fetch_latest_research_papers(keywords: list, num_results: int = 5) -> list:
-    """Fetches the latest research papers from Google Scholar 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:
-        query = " ".join(keywords)
-        search_results = scholarly.search_pubs(query)
-        papers = []
-        for i in range(num_results):
-            paper = next(search_results, None)
-            if paper:
-                papers.append({
-                    "title": paper['bib'].get('title', 'No Title'),
-                    "authors": paper['bib'].get('author', 'Unknown Authors'),
-                    "year": paper['bib'].get('pub_year', 'Unknown Year'),
-                    "abstract": paper['bib'].get('abstract', 'No Abstract Available'),
-                    "link": paper.get('pub_url', 'No Link Available')
-                })
-        return papers
-    except Exception as e:
-        return [f"Error fetching research papers: {str(e)}"]
-
-final_answer = FinalAnswerTool()
-
-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=[final_answer, fetch_latest_research_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 Google Scholar based on user-defined keywords and filters.",
-    prompt_templates=prompt_templates
-)
-
 def search_papers(user_input):
-    keywords = user_input.split("math reasoning,deep learning")  # Split input by commas for multiple keywords
+    keywords = user_input.split(",")  # Split input by commas for multiple keywords
     results = fetch_latest_research_papers(keywords, num_results=5)
     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])