Create app.py

Take user keyword
queries the arXiv API for the latest papers
uses TF-IDF + Cosine Similarity to semantically rank those papers by relevance
Displays the top papers in a Gradio Web Interface.

app.py

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+"""------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()