arxiv_agent.py

import os
import pickle
import json
import time
import datetime
from xml.etree import ElementTree

import requests
import warnings
warnings.filterwarnings("ignore")
os.environ['KMP_DUPLICATE_LIB_OK']='True'
from utils import *
import thread6
MAX_DAILY_PAPER = 200
DAY_TIME = 60 * 60 * 24

def feedback_thought(input_ls): # preload
    agent, query, ansA, ansB, feedbackA, feedbackB = input_ls
    filename_thought = agent.thought_path
    filename = agent.feedback_path

    date = agent.today

    json_data = agent.feedback
    json_data_thought = agent.thought

    if date in json_data:
        if query not in json_data[date]:
            json_data[date][query] = {}
    else:
        json_data[date] = {}        
        json_data[date][query] = {}

    if date not in json_data_thought:
        json_data_thought[date] = []        
    
    
    json_data[date][query]["answerA"] = (ansA)
    json_data[date][query]["feedbackA"] = feedbackA
    json_data[date][query]["answerB"] = (ansB)
    json_data[date][query]["feedbackB"] = feedbackB
    with open(filename,"w") as f:
        json.dump(json_data,f)

    preferred_ans = ""
    if feedbackA == 1:
        new_knowledge = response_verify([query], [ansA], verify=False)
        preferred_ans = ansA
        # json_data_thought[date].append(query + ansA)
    else:
        new_knowledge = response_verify([query], [ansB], verify=False)
        preferred_ans = ansB
        # json_data_thought[date].append(query + ansB)

    if ('idk' not in new_knowledge[0]):

        new_knowledge_embedding = get_bert_embedding(new_knowledge)
        thought_embedding_all = []
        for k in agent.thought_embedding.keys():
            thought_embedding_all.extend(agent.thought_embedding[k])

        similarity = calculate_similarity(thought_embedding_all, new_knowledge_embedding[0])

        similarity_values = [s.item() for s in similarity]  # Convert each tensor to a scalar
        if all(s < 0.85 for s in similarity_values):
            # self.update_feedback(an, answer_l_org, query)
            tem_thought = query + preferred_ans
            json_data_thought[date].append(tem_thought)
            if date not in agent.thought_embedding:
                agent.thought_embedding = {}
                agent.thought_embedding[date] = [get_bert_embedding([tem_thought])[0]]
            else:
                agent.thought_embedding[date].append(get_bert_embedding([tem_thought])[0])

            with open(filename_thought,"w") as f:
                json.dump(json_data_thought,f)

            with open(agent.thought_embedding_path, "wb") as f:
                pickle.dump(agent.thought_embedding, f)

    # return "Give feedback successfully!"

def dailyDownload(agent_ls):

    agent = agent_ls[0]
    while True:
        time.sleep(DAY_TIME)
        data_collector = []
        keywords = dict()
        keywords["Machine Learning"] = "Machine Learning"

        for topic,keyword in keywords.items():

            data, agent.newest_day = get_daily_papers(topic, query = keyword, max_results = MAX_DAILY_PAPER)
            data_collector.append(data)

        json_file = agent.dataset_path

        update_file=update_json_file(json_file, data_collector)

        time_chunks_embed={}

        for data in data_collector:
            for date in data.keys():
                papers = data[date]['abstract']
                papers_embedding=get_bert_embedding(papers)
                time_chunks_embed[date.strftime("%m/%d/%Y")] = papers_embedding
        update_paper_file=update_pickle_file(agent.embedding_path,time_chunks_embed)
        agent.paper = update_file
        agent.paper_embedding = update_paper_file
        print("Today is " + agent.newest_day.strftime("%m/%d/%Y"))





class ArxivAgent:
    def __init__(self):
        
        self.dataset_path = "./dataset/paper.json"
        self.thought_path = "./dataset/thought.json"

        self.embedding_path = "./dataset/paper_embedding.pkl"
        self.thought_embedding_path = './dataset/thought_embedding.pkl'

        self.feedback_path = 'dataset/feedback.json'
        self.today = datetime.datetime.now().strftime("%m/%d/%Y")

        self.newest_day = ""
        self.load_thought()
        self.load_feedback()
        self.download()
        try:
            thread6.run_threaded(dailyDownload, [self])
            # thread6.start_new_thread( print_time, ["Thread-2", 4] )
        except:
            print("Error: unable to start thread")
        # self.paper = self.download()
        # self.paper_by_date = self.paper
    def edit_profile(self, profile, author_name):
        profile = profile
        filename = 'dataset/profile.json'
        with open(filename, 'r', encoding='utf-8') as file:
            data = json.load(file)
        data[author_name]=profile
        with open(filename, "w") as f:
            json.dump(data, f)
        return "Successfully edit profile!"
    
    def get_profile(self, author_name):
        if author_name == "": return None
        # import pdb
        # pdb.set_trace()
        profile = self.get_arxiv_data_by_author(author_name)
        return profile
    def select_date(self, method, profile_input):
   
        today = self.newest_day
        chunk_embedding_date={}

        
        paper_by_date = {}
        if method == "day":
            offset_day = today 
            str_day = offset_day.strftime("%m/%d/%Y")
            if str_day in self.paper:
                paper_by_date[str_day] = self.paper[str_day]
                chunk_embedding_date[str_day]=self.paper_embedding[str_day]

        elif method == "week":
            for i in range(7):
                offset_day = today - datetime.timedelta(days=i)
                str_day = offset_day.strftime("%m/%d/%Y")
                
                if str_day in self.paper:
                    # print(str_day)
                    paper_by_date[str_day] = self.paper[str_day]
                    chunk_embedding_date[str_day] = self.paper_embedding[str_day]
        else:
            # import pdb
            # pdb.set_trace()
            paper_by_date = self.paper
            chunk_embedding_date=self.paper_embedding

        dataset = paper_by_date
        data_chunk_embedding=chunk_embedding_date
        profile = profile_input

        # trend, paper_link = summarize_research_field(profile, "Machine Learning", dataset) # trend
        trend, paper_link = summarize_research_field(profile, "Machine Learning", dataset,data_chunk_embedding) # trend

        # import pdb
        # pdb.set_trace()
        reference = papertitleAndLink(paper_link)
        # print("Trend:", self.trend,"\n")
        idea = generate_ideas(trend) # idea

        key_update = list(self.paper.keys())[-1]
        if key_update not in self.thought:
            self.thought[key_update] = []
        if key_update not in self.thought_embedding:
            self.thought_embedding[key_update] = []

        self.thought[key_update].append(trend[0])
        self.thought_embedding[key_update].append(get_bert_embedding([trend])[0])
        self.thought[key_update].append(idea[0])
        self.thought_embedding[key_update].append(get_bert_embedding([idea])[0])
        # with open(self.dataset_path, "w") as f_:
        #     json.dump(self.paper, f_)

        with open(self.thought_path, "w") as f_:
            json.dump(self.thought, f_)

        with open(self.thought_embedding_path, "wb") as f:
            pickle.dump(self.thought_embedding, f)


        return trend, reference, idea

    def response(self, data, profile_input):
        # dataset = self.paper_by_date

        # dataset = self.paper
        query = [data]
        profile = profile_input

        query_embedding=get_bert_embedding(query)


        retrieve_text,retrieve_text_org=self.generate_pair_retrieve_text(query_embedding)

        context,context_org = [retrieve_text],[retrieve_text_org]

        answer_l = get_response_through_LLM_answer(query, context,profile)
        answer_l_org = get_response_through_LLM_answer(query, context_org, profile)



        return answer_l,answer_l_org

    def generate_pair_retrieve_text(self, query_embedding):
        # Access dataset
        dataset = self.paper
        thought = self.thought

        text_chunk_l = []
        chunks_embedding_text_all = []

        text_org_chunk_l = []
        chunks_org_embedding_text_all = []

        # Include all text chunks and their embeddings
        for k in dataset.keys():
            text_chunk_l.extend(dataset[k]['abstract'])
            chunks_embedding_text_all.extend(self.paper_embedding[k])

            text_org_chunk_l.extend(dataset[k]['abstract'])
            chunks_org_embedding_text_all.extend(self.paper_embedding[k])
            
        for k in thought.keys():
            if k in self.thought_embedding.keys():
                text_chunk_l.extend(thought[k])
                chunks_embedding_text_all.extend(self.thought_embedding[k])


        # Include thoughts if not excluded

        neib_all = neiborhood_search(chunks_embedding_text_all, query_embedding, num=10)
        neib_all = neib_all.reshape(-1)
        # import pdb
        # pdb.set_trace()
        # Compile retrieved text
        # import pdb
        # pdb.set_trace()
        retrieve_text = ''.join([text_chunk_l[i] for i in neib_all])

        neib_all = neiborhood_search(chunks_org_embedding_text_all, query_embedding, num=10)
        neib_all = neib_all.reshape(-1)
        # Compile retrieved text
        retrieve_text_org = ''.join([text_org_chunk_l[i] for i in neib_all])

        return retrieve_text,retrieve_text_org

    def download(self):
        # key_word = "Machine Learning"
        data_collector = []
        keywords = dict()
        keywords["Machine Learning"] = "Machine Learning"
    
        for topic,keyword in keywords.items():
    
            data, self.newest_day = get_daily_papers(topic, query = keyword, max_results = MAX_DAILY_PAPER)
            data_collector.append(data)

        json_file = self.dataset_path
        if not os.path.exists(json_file):
            with open(json_file,'w')as a:
                print("create " + json_file)

        update_file=update_json_file(json_file, data_collector)

        if not os.path.exists(self.embedding_path):
            with open(self.embedding_path,'wb')as a:
                print("create " + self.embedding_path)
        time_chunks_embed={}

        for data in data_collector:
            for date in data.keys():
                papers = data[date]['abstract']
                papers_embedding=get_bert_embedding(papers)
                time_chunks_embed[date.strftime("%m/%d/%Y")] = papers_embedding
        update_paper_file=update_pickle_file(self.embedding_path,time_chunks_embed)
        self.paper = update_file
        self.paper_embedding = update_paper_file

    

    def load_feedback(self):
        filename = self.feedback_path

        if os.path.exists(filename):
            with open(filename,"rb") as f:
                content = f.read()
                if not content:
                    m = {}
                else:
                    m = json.loads(content)
        else:
            with open(filename, mode='w', encoding='utf-8') as ff:
                m = {}
        self.feedback = m.copy()

   


    def load_thought(self):
        filename = self.thought_path
        filename_emb = self.thought_embedding_path

        if os.path.exists(filename):
            with open(filename,"rb") as f:
                content = f.read()
                if not content:
                    m = {}
                else:
                    m = json.loads(content)
        else:
            with open(filename, mode='w', encoding='utf-8') as ff:
                m = {}


        if os.path.exists(filename_emb):
            with open(filename_emb,"rb") as f:
                content = f.read()
                if not content:
                    m_emb = {}
                else:
                    m_emb = pickle.loads(content)
        else:
            with open(filename_emb, mode='w', encoding='utf-8') as ff:
                m_emb = {}

        self.thought = m.copy() 
        self.thought_embedding = m_emb.copy() 




        # for date in self.thought.keys():
        #     papers = data[time]['abstract']
        #     papers_embedding=get_bert_embedding(papers)
        #     time_chunks_embed[time.strftime("%m/%d/%Y")] = papers_embedding
        # return 
        # for k in json_data.keys():
    def update_feedback_thought(self, query, ansA, ansB, feedbackA, feedbackB): 
        try:
            thread6.run_threaded(feedback_thought, [self, query, ansA, ansB, feedbackA, feedbackB])
            # thread6.start_new_thread( print_time, ["Thread-2", 4] )
        except:
            print("Error: unable to start thread")

       
    def update_comment(self, comment):
        date = datetime.datetime.now().strftime("%m/%d/%Y")

        filename = 'dataset/comment.json'
        if os.path.exists(filename):
            with open(filename,"r") as f:
                content = f.read()
                if not content:
                    m = {}
                else:
                    m = json.loads(content)
        else:
            with open(filename, mode='w', encoding='utf-8') as ff:
                m = {}
            
                
        json_data = m.copy() 

        if date not in json_data:
            json_data[date] = [comment]
        else: json_data[date].append(comment) 

        with open(filename,"w") as f:
            json.dump(json_data,f)
        return "Thanks for your comment!"
    



    def get_arxiv_data_by_author(self, author_name):



        filename = 'dataset/profile.json'
        if os.path.exists(filename):
            with open(filename,"r") as f:
                content = f.read()
                if not content:
                    m = {}
                else:
                    m = json.loads(content)
        else:
            with open(filename, mode='w', encoding='utf-8') as ff:
                m = {}
            
                
        json_data = m.copy() 

        if author_name in json_data: return json_data[author_name]
           
        author_query = author_name.replace(" ", "+")
        url = f"http://export.arxiv.org/api/query?search_query=au:{author_query}&start=0&max_results=300"  # Adjust max_results if needed

        response = requests.get(url)
        papers_list = []

        if response.status_code == 200:
            root = ElementTree.fromstring(response.content)
            entries = root.findall('{http://www.w3.org/2005/Atom}entry')

            total_papers = 0
            data_to_save = []

            papers_by_year = {}

            for entry in entries:

                title = entry.find('{http://www.w3.org/2005/Atom}title').text.strip()
                published = entry.find('{http://www.w3.org/2005/Atom}published').text.strip()
                abstract = entry.find('{http://www.w3.org/2005/Atom}summary').text.strip()
                authors_elements = entry.findall('{http://www.w3.org/2005/Atom}author')
                authors = [author.find('{http://www.w3.org/2005/Atom}name').text for author in authors_elements]
                link = entry.find('{http://www.w3.org/2005/Atom}id').text.strip()  # Get the paper link

                # Check if the specified author is exactly in the authors list
                if author_name in authors:
                    # Remove the specified author from the coauthors list for display
                    coauthors = [author for author in authors if author != author_name]
                    coauthors_str = ", ".join(coauthors)

                    papers_list.append({
                        "date": published,
                        "Title & Abstract": f"{title}; {abstract}",
                        "coauthors": coauthors_str,
                        "link": link  # Add the paper link to the dictionary
                    })
                authors_elements = entry.findall('{http://www.w3.org/2005/Atom}author')
                authors = [author.find('{http://www.w3.org/2005/Atom}name').text for author in authors_elements]

                if author_name in authors:
                    # print(author_name)
                    # print(authors)
                    total_papers += 1
                    published_date = entry.find('{http://www.w3.org/2005/Atom}published').text.strip()
                    date_obj = datetime.datetime.strptime(published_date, '%Y-%m-%dT%H:%M:%SZ')

                    year = date_obj.year
                    if year not in papers_by_year:
                        papers_by_year[year] = []
                    papers_by_year[year].append(entry)

            if total_papers > 40:
                for cycle_start in range(min(papers_by_year), max(papers_by_year) + 1, 5):
                    cycle_end = cycle_start + 4
                    for year in range(cycle_start, cycle_end + 1):
                        if year in papers_by_year:
                            selected_papers = papers_by_year[year][:2]
                            for paper in selected_papers:
                                title = paper.find('{http://www.w3.org/2005/Atom}title').text.strip()
                                abstract = paper.find('{http://www.w3.org/2005/Atom}summary').text.strip()
                                authors_elements = paper.findall('{http://www.w3.org/2005/Atom}author')
                                co_authors = [author.find('{http://www.w3.org/2005/Atom}name').text for author in authors_elements if author.find('{http://www.w3.org/2005/Atom}name').text != author_name]

                                papers_list.append({
                                    "Author": author_name,
                                    "Title & Abstract": f"{title}; {abstract}",
                                    "Date Period": f"{year}",
                                    "Cycle": f"{cycle_start}-{cycle_end}",
                                    "Co_author": ", ".join(co_authors)
                                })
  



            # Trim the list to the 10 most recent papers
            papers_list = papers_list[:10]

            # Prepare the data dictionary with the author's name as a key
            # import pdb
            # pdb.set_trace()
            personal_info = "; ".join([f"{details['Title & Abstract']}" for details in papers_list])
            info = summarize_research_direction(personal_info)
            json_data[author_name] = info
            with open(filename,"w") as f:
                json.dump(json_data,f)
            return json_data[author_name]

            # data = {author_name: {"paper_{}".format(i+1): paper for i, paper in enumerate(papers_list)}}

        else:
            # print("Failed to fetch data from arXiv.")
            return None