app.py

# # 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
)



#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()