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
# )

# # # 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()

import os
import datetime
import requests
import pytz
import yaml
from smolagents import CodeAgent, HfApiModel, load_tool, tool
from tools.final_answer import FinalAnswerTool
from Gradio_UI import GradioUI

# Step 1: Set Hugging Face API Token
os.environ["HUGGINGFACEHUB_API_TOKEN"] = "your_huggingface_api_token"

# Step 2: Define ScholarAgent's Paper Search Functionality
@tool
def fetch_arxiv_papers(query: str) -> str:
    """Fetches the top 3 most recent research papers from ArXiv based on a keyword search.

    Args:
        query: A string containing keywords or a full sentence describing the research topic.
    
    Returns:
        A formatted string with the top 3 recent papers, including title, authors, and ArXiv links.
    """
    base_url = "http://export.arxiv.org/api/query"
    params = {
        "search_query": query,
        "start": 0,
        "max_results": 3,
        "sortBy": "submittedDate",
        "sortOrder": "descending",
    }

    try:
        response = requests.get(base_url, params=params)
        if response.status_code == 200:
            papers = response.text.split("<entry>")
            results = []
            for paper in papers[1:4]:  # Extract top 3 papers
                title = paper.split("<title>")[1].split("</title>")[0].strip()
                authors = paper.split("<author><name>")[1].split("</name>")[0].strip()
                link = paper.split("<id>")[1].split("</id>")[0].strip()
                results.append(f"- **{title}**\n  - 📖 Authors: {authors}\n  - 🔗 [Read here]({link})\n")
            return "\n".join(results) if results else "No relevant papers found."
        else:
            return "Error: Unable to retrieve papers from ArXiv."
    except Exception as e:
        return f"API Error: {str(e)}"

# Step 3: Add a Timezone Utility Tool
@tool
def get_current_time_in_timezone(timezone: str) -> str:
    """Fetches the current local time in a specified timezone.

    Args:
        timezone: A string representing a valid timezone (e.g., 'America/New_York').

    Returns:
        A formatted string with the current time.
    """
    try:
        tz = pytz.timezone(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)}"

# Step 4: Define Final Answer Tool (Required)
final_answer = FinalAnswerTool()

# Step 5: Configure Hugging Face Model with API Token
model = HfApiModel(
    max_tokens=2096,
    temperature=0.5,
    model_id='Qwen/Qwen2.5-Coder-32B-Instruct',  # Default model
    custom_role_conversions=None,
    
)

# Step 6: Load Additional Tools
image_generation_tool = load_tool("agents-course/text-to-image", trust_remote_code=True)

# Step 7: Load Prompt Templates
with open("prompts.yaml", 'r') as stream:
    prompt_templates = yaml.safe_load(stream)

# Step 8: Define ScholarAgent (AI Agent)
agent = CodeAgent(
    model=model,
    tools=[final_answer, fetch_arxiv_papers, get_current_time_in_timezone],  # ScholarAgent tools
    max_steps=6,
    verbosity_level=1,
    grammar=None,
    planning_interval=None,
    name="ScholarAgent",
    description="An AI-powered research assistant that fetches top research papers from ArXiv.",
    prompt_templates=prompt_templates
)

# Step 9: Launch Gradio UI with CodeAgent
GradioUI(agent).launch()