# import feedparser # import urllib.parse # import yaml # import gradio as gr # from smolagents import CodeAgent, HfApiModel, tool # from tools.final_answer import FinalAnswerTool # @tool # def fetch_latest_arxiv_papers(keywords: list, num_results: int = 3) -> 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 3). # Returns: # A list of dictionaries containing: # - "title": The title of the research paper. # - "authors": The authors of the paper. # - "year": The publication year. # - "abstract": A summary of the research paper. # - "link": A direct link to the paper on arXiv. # """ # try: # print(f"DEBUG: Searching arXiv papers with keywords: {keywords}") # Debug input # #Properly format query with +AND+ for multiple keywords # query = "+AND+".join([f"all:{kw}" for kw in keywords]) # query_encoded = urllib.parse.quote(query) # Encode spaces and special characters # url = f"http://export.arxiv.org/api/query?search_query={query_encoded}&start=0&max_results={num_results}&sortBy=submittedDate&sortOrder=descending" # print(f"DEBUG: Query URL - {url}") # Debug URL # 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)}"] #"""------Applied BM25 search for paper retrival------""" # from rank_bm25 import BM25Okapi # import nltk # import os # import shutil # nltk_data_path = os.path.join(nltk.data.path[0], "tokenizers", "punkt") # if os.path.exists(nltk_data_path): # shutil.rmtree(nltk_data_path) # Remove corrupted version # print("Removed old NLTK 'punkt' data. Reinstalling...") # # Step 2: Download the correct 'punkt' tokenizer # nltk.download("punkt_tab") # print("Successfully installed 'punkt'!") # @tool # Register the function properly as a SmolAgents tool # def fetch_latest_arxiv_papers(keywords: list, num_results: int = 5) -> list: # """Fetches and ranks arXiv papers using BM25 keyword relevance. # Args: # keywords: List of keywords for search. # num_results: Number of results to return. # Returns: # List of the most relevant papers based on BM25 ranking. # """ # try: # print(f"DEBUG: Searching arXiv papers with keywords: {keywords}") # # Use a general keyword search (without `ti:` and `abs:`) # query = "+AND+".join([f"all:{kw}" for kw in keywords]) # query_encoded = urllib.parse.quote(query) # url = f"http://export.arxiv.org/api/query?search_query={query_encoded}&start=0&max_results=50&sortBy=submittedDate&sortOrder=descending" # print(f"DEBUG: Query URL - {url}") # feed = feedparser.parse(url) # papers = [] # # Extract papers from arXiv # for entry in feed.entries: # papers.append({ # "title": entry.title, # "authors": ", ".join(author.name for author in entry.authors), # "year": entry.published[:4], # "abstract": entry.summary, # "link": entry.link # }) # if not papers: # return [{"error": "No results found. Try different keywords."}] # # Apply BM25 ranking # tokenized_corpus = [nltk.word_tokenize(paper["title"].lower() + " " + paper["abstract"].lower()) for paper in papers] # bm25 = BM25Okapi(tokenized_corpus) # tokenized_query = nltk.word_tokenize(" ".join(keywords).lower()) # scores = bm25.get_scores(tokenized_query) # # Sort papers based on BM25 score # ranked_papers = sorted(zip(papers, scores), key=lambda x: x[1], reverse=True) # # Return the most relevant ones # return [paper[0] for paper in ranked_papers[:num_results]] # except Exception as e: # print(f"ERROR: {str(e)}") # return [{"error": f"Error fetching research papers: {str(e)}"}] """------Applied TF-IDF for better semantic search------""" import feedparser import urllib.parse import yaml from tools.final_answer import FinalAnswerTool import numpy as np from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.metrics.pairwise import cosine_similarity import gradio as gr from smolagents import CodeAgent,DuckDuckGoSearchTool, HfApiModel,load_tool,tool import nltk import datetime import requests import pytz from tools.final_answer import FinalAnswerTool from Gradio_UI import GradioUI nltk.download("stopwords") from nltk.corpus import stopwords @tool # ✅ Register the function properly as a SmolAgents tool def fetch_latest_arxiv_papers(keywords: list, num_results: int = 5) -> list: """Fetches and ranks arXiv papers using TF-IDF and Cosine Similarity. Args: keywords: List of keywords for search. num_results: Number of results to return. Returns: List of the most relevant papers based on TF-IDF ranking. """ try: print(f"DEBUG: Searching arXiv papers with keywords: {keywords}") # Use a general keyword search query = "+AND+".join([f"all:{kw}" for kw in keywords]) query_encoded = urllib.parse.quote(query) url = f"http://export.arxiv.org/api/query?search_query={query_encoded}&start=0&max_results=50&sortBy=submittedDate&sortOrder=descending" print(f"DEBUG: Query URL - {url}") feed = feedparser.parse(url) papers = [] # Extract papers from arXiv for entry in feed.entries: papers.append({ "title": entry.title, "authors": ", ".join(author.name for author in entry.authors), "year": entry.published[:4], "abstract": entry.summary, "link": entry.link }) if not papers: return [{"error": "No results found. Try different keywords."}] # Prepare TF-IDF Vectorization corpus = [paper["title"] + " " + paper["abstract"] for paper in papers] vectorizer = TfidfVectorizer(stop_words=stopwords.words('english')) # Remove stopwords tfidf_matrix = vectorizer.fit_transform(corpus) # Transform Query into TF-IDF Vector query_str = " ".join(keywords) query_vec = vectorizer.transform([query_str]) #Compute Cosine Similarity similarity_scores = cosine_similarity(query_vec, tfidf_matrix).flatten() #Sort papers based on similarity score ranked_papers = sorted(zip(papers, similarity_scores), key=lambda x: x[1], reverse=True) # Return the most relevant papers return [paper[0] for paper in ranked_papers[:num_results]] except Exception as e: print(f"ERROR: {str(e)}") return [{"error": f"Error fetching research papers: {str(e)}"}] @tool def get_current_time_in_timezone(timezone: str) -> str: """A tool that fetches the current local time in a specified timezone. Args: timezone: A string representing a valid timezone (e.g., 'America/New_York'). """ try: # Create timezone object tz = pytz.timezone(timezone) # Get current time in that timezone local_time = datetime.datetime.now(tz).strftime("%Y-%m-%d %H:%M:%S") return f"The current local time in {timezone} is: {local_time}" except Exception as e: return f"Error fetching time for timezone '{timezone}': {str(e)}" final_answer = FinalAnswerTool() # AI Model model = HfApiModel( max_tokens=2096, temperature=0.5, model_id='Qwen/Qwen2.5-Coder-32B-Instruct', custom_role_conversions=None, ) # Import tool from Hub image_generation_tool = load_tool("agents-course/text-to-image", trust_remote_code=True) # Load prompt templates with open("prompts.yaml", 'r') as stream: prompt_templates = yaml.safe_load(stream) # Create the AI Agent agent = CodeAgent( model=model, tools=[final_answer,fetch_latest_arxiv_papers], # Add your tools here 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 ) # # Define Gradio Search Function # 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): # #Format output with better readability and clarity # formatted_results = "\n\n".join([ # f"---\n\n" # f"📌 **Title:**\n{paper['title']}\n\n" # f"👨‍🔬 **Authors:**\n{paper['authors']}\n\n" # f"📅 **Year:** {paper['year']}\n\n" # f"📖 **Abstract:**\n{paper['abstract'][:500]}... *(truncated for readability)*\n\n" # f"[🔗 Read Full Paper]({paper['link']})\n\n" # for paper in results # ]) # return formatted_results # print("DEBUG: No results found.") # return "No results found. Try different keywords." #Search Papers 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 # Check if the API returned an error if isinstance(results, list) and len(results) > 0 and "error" in results[0]: return results[0]["error"] # Return the error message directly # Format results only if valid papers exist if isinstance(results, list) and results and isinstance(results[0], dict): formatted_results = "\n\n".join([ f"---\n\n" f"📌 **Title:** {paper['title']}\n\n" f"👨‍🔬 **Authors:** {paper['authors']}\n\n" f"📅 **Year:** {paper['year']}\n\n" f"📖 **Abstract:** {paper['abstract'][:500]}... *(truncated for readability)*\n\n" f"[🔗 Read Full Paper]({paper['link']})\n\n" for paper in results ]) return formatted_results print("DEBUG: No results found.") return "No results found. Try different keywords." # Launch Gradio UI with CodeAgent GradioUI(agent).launch() # # Create Gradio UI # with gr.Blocks() as demo: # gr.Markdown("# ScholarAgent") # 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...") # # Launch Gradio App # demo.launch()