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| # # 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 | |
| # ✅ 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)}"}] | |
| 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 | |
| ) | |
| #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." | |
| # Create Gradio UI | |
| with gr.Blocks() as demo: | |
| gr.Markdown("# ScholarAgent") | |
| keyword_input = gr.Textbox(label="Enter keywords(comma-separated) or even full sentences ", placeholder="e.g., deep learning, reinforcement learning or NLP in finance or Deep learning in Medicine") | |
| 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() | |