app.py

import numpy as np
import os
import re
from io import BytesIO
import datetime
import time
import openai, tenacity
import argparse
import configparser
import json
import fitz
import PyPDF2
import gradio
import sys
from pathlib import Path
utils_dir = Path(__file__).parent / 'utils'
sys.path.append(str(utils_dir))
from openai_utils import *
import base64
from pdf2image import convert_from_bytes
import requests
PRIVATE_API_KEY = os.getenv('PRIVATE_API_KEY')
PRIVATE_API_BASE = os.getenv('PRIVATE_API_BASE')


def insert_sentence(text, sentence, interval):
    lines = text.split('\n')
    new_lines = []

    for line in lines:
        words = line.split()
        separator = ' '

        new_words = []
        count = 0

        for word in words:
            new_words.append(word)
            count += 1

            if count % interval == 0:
                new_words.append(sentence)

        new_lines.append(separator.join(new_words))

    return '\n'.join(new_lines)
    
def search_paper(query):
    SEMANTIC_SCHOLAR_API_URL = "https://api.semanticscholar.org/graph/v1/paper/"
    url = f"{SEMANTIC_SCHOLAR_API_URL}search?query={query}&limit=3&fields=url,title,abstract&fieldsOfStudy=Computer Science"

    response = requests.get(url)
    while response.status_code != 200:
        time.sleep(1)
        # print(response)
        response = requests.get(url)

    return response.json()    

def split_text_into_chunks(text, chunk_size=300):
    words = text.split()  
    chunks = [" ".join(words[i:i + chunk_size]) for i in range(0, len(words), chunk_size)]
    return chunks

def download_pdf(paper):
    pdf_url = paper["openAccessPdf"]["url"]
    try:
        response = requests.get(pdf_url)
        response.raise_for_status()  


        file_object = BytesIO(response.content)
        extract_text = extract_chapter(file_object)
        chunks = split_text_into_chunks(extract_text)
        return chunks
    except:
        return []
    

def recommendation(s2_id, limit=500):
    SEMANTIC_SCHOLAR_API_URL = "https://api.semanticscholar.org/recommendations/v1/papers/forpaper/"
    url = f"{SEMANTIC_SCHOLAR_API_URL}{s2_id}?limit={limit}&fields=url,title,abstract,publicationDate,isOpenAccess,openAccessPdf"

    # print(url)
    response = requests.get(url)
    while response.status_code != 200:
        time.sleep(1)
        # print(response)
        response = requests.get(url)

    return response.json()


def extract_chapter(file_object):
    pdf_reader = PyPDF2.PdfReader(file_object)
    

    num_pages = len(pdf_reader.pages)
    extraction_started = False
    extracted_text = ""
    for page_number in range(num_pages):
        page = pdf_reader.pages[page_number]
        page_text = page.extract_text()
        extraction_started = True
        page_number_start = page_number
        if extraction_started:
            extracted_text += page_text
            if page_number_start + 1 < page_number:
                break
    return extracted_text
        


class Reviewer:

    def __init__(self, api, api_base, paper_pdf, aspect, model_name, enable_rag):
        self.api = api
        self.api_base = api_base
        self.aspect = aspect
        self.paper_pdf = paper_pdf
        self.model_name = model_name
        self.enable_rag = enable_rag
        # self.max_token_num = 50000
        # self.encoding = tiktoken.get_encoding("gpt2")


    def review_by_chatgpt(self, paper_list):
        text, title, abstract = self.extract_from_paper(self.paper_pdf) 
        content = f"Paper to review: \nTitle: {title}\n" + text

        if self.enable_rag:
            papers = self.retrieve_papers(title, abstract)
            if papers != None:
                retrieval_content = ""
                retrieved_papers = "" 
                cnt = 1
                for paper in papers:
                    retrieval_content += f"Relevant Paper {str(cnt)}:\n"
                    retrieval_content += f"Title: {paper['title']}\n{paper['content']}\n\n"
                    retrieved_papers += f"{str(cnt)}. {paper['title']}\n"
                    cnt += 1
                text = retrieval_content + content
                chat_review_text = self.chat_review(text=text)  
            else:
                text = content
                chat_review_text = self.chat_review(text=text)   
                retrieved_papers = "" 
        else:
            text = content
            chat_review_text = self.chat_review(text=text)   
            retrieved_papers = ""   

        return chat_review_text, retrieved_papers

    def query_gen(self, abstract):
        os.environ["OPENAI_BASE_URL"] = PRIVATE_API_BASE
        os.environ["OPENAI_API_KEY"] = PRIVATE_API_KEY
        client = AsyncOpenAI()

        messages=[
                {"role": "system", "content": f"Generate a TLDR in 5 words of the following text. Do not use any proposed model names or dataset names from the text. Output only the 5 words without punctuation."} ,
                {"role": "user", "content": abstract},
            ]

        responses = asyncio.run(
            generate_from_openai_chat_completion(
                client,
                messages=[messages], 
                engine_name="gpt-4o-mini", # gpt-3.5-turbo
                max_tokens=1000, # 32
                requests_per_minute = 20,
                # response_format={"type":"json_object"},
            )
        )   
        return responses[0]


    def rerank(self, paper_list, title, abstract): 
        os.environ["OPENAI_BASE_URL"] = PRIVATE_API_BASE
        os.environ["OPENAI_API_KEY"] = PRIVATE_API_KEY
        client = AsyncOpenAI()

        rec_content = ""
        rec_paper_cnt = 1

        for rec_paper in paper_list:
            rec_content += f"Paper {rec_paper_cnt}: {rec_paper['title']}\n{rec_paper['abstract']}\n\n"
            rec_paper_cnt += 1

        rec_content += f"Reference Paper: {title}\n"
        rec_content += f"Abstract: {abstract}\n"

        messages=[
                {"role": "system", "content": f"Given the abstracts of {rec_paper_cnt-1} papers and the abstract of a reference paper, rank the papers in order of relevance to the reference paper. Output the top 5 as a list of integers in JSON format: {{'ranking': [1, 10, 4, 2, 8]}}."} ,
                {"role": "user", "content": rec_content},
            ]

        responses = asyncio.run(
            generate_from_openai_chat_completion(
                client,
                messages=[messages], 
                engine_name="gpt-4o-mini", # gpt-3.5-turbo
                max_tokens=1000, # 32
                requests_per_minute = 20,
                response_format={"type":"json_object"},
            )
        )   
        response_data = json.loads(responses[0])
        rec_papers = []
        for rec_num in response_data["ranking"][:5]:
            num = int(rec_num)
            rec_papers.append(paper_list[num-1])

        return rec_papers

    def extract_related_content(self, papers, aspect):
        os.environ["OPENAI_BASE_URL"] = self.api_base
        os.environ["OPENAI_API_KEY"] = self.api
        client = AsyncOpenAI()

        messages = []
        chunk_index_map = [] 
        paper_data_list = []
        paper_chunk_list = []
        for paper_idx, paper in enumerate(papers):
            paper_chunks = download_pdf(paper)
            paper_chunk_list.append(paper_chunks)

            SYSTEM_INPUT = f"Read the following section from a scientific paper. If the section is related to the paper's {aspect}, output 'yes'; otherwise, output 'no'."

            for chunk_idx, paper_chunk in enumerate(paper_chunks):
                message = [
                    {"role": "system", "content": SYSTEM_INPUT},
                    {"role": "user", "content": paper_chunk},
                ]
                messages.append(message)
                chunk_index_map.append((paper_idx, chunk_idx))  # 标记每个 chunk 归属哪个 paper
        

        responses = asyncio.run(
            generate_from_openai_chat_completion(
                client,
                messages=messages,
                engine_name="gpt-4o-mini",
                max_tokens=1000,
                requests_per_minute=100,
            )
        )

        paper_data_list = [{"title": paper["title"], "content": ""} for paper in papers]

        for (paper_idx, chunk_idx), response in zip(chunk_index_map, responses):
            if response.strip().lower().startswith("yes"):
                paper_data_list[paper_idx]["content"] += paper_chunk_list[paper_idx][chunk_idx] + "\n"

        for idx, paper_data in enumerate(paper_data_list):
            if not paper_data["content"].strip():
                paper_data["content"] = papers[idx]["abstract"]


        if aspect == "Methodology":
            SYSTEM_INPUT = """Concatenate all the content from the methodology sections of a paper.
Remove sentences that are irrelevant to the proposed methodology or models, and keep details about key components and innovations.
Organize the result in JSON format as follows:
{
    "revised_text": str, not dict, not a summary
}
"""
        elif aspect == "Result Analysis":        
            SYSTEM_INPUT = """Concatenate all the content from the result analysis sections of a paper.
Remove sentences that are irrelevant to the result analysis of the experiments, and keep details about the metrics, case study and how the paper presents the results.
Organize the result in JSON format as follows:
{
    "revised_text": str, not dict, not a summary
}
"""
        elif aspect == "Experimental Design":  
            SYSTEM_INPUT = """Concatenate all the content from the experimental design sections of a paper.
Remove sentences that are irrelevant to the experiment setup, and keep details about the datasets, baselines, and main experimental, ablation studies.
Organize the result in JSON format as follows:
{
    "revised_text": str, not dict, not a summary
}
"""
        elif aspect == "Literature Review":  
            SYSTEM_INPUT = """Concatenate all the content from the literature review sections of a paper.
Remove sentences that are irrelevant to the literature review, and keep details about the related works.
Organize the result in JSON format as follows:
{
    "revised_text": str, not dict, not a summary
}
"""
        messages = []
        for paper_data in paper_data_list:
            message=[
                {"role": "system", "content": SYSTEM_INPUT} ,
                {"role": "user", "content": paper_data["content"]},
            ]
            messages.append(message)

        responses = asyncio.run(
            generate_from_openai_chat_completion(
                client,
                messages=messages, 
                engine_name="gpt-4o-mini", # gpt-3.5-turbo
                max_tokens=1000, # 32
                requests_per_minute = 20,
                response_format={"type":"json_object"},
            )
        ) 

        results = []
        for paper_data, response in zip(paper_data_list, responses):
            response = json.loads(response)
            results.append({"title": paper_data["title"], "content": response["revised_text"]})
        return results



    def chat_review(self, text):
        os.environ["OPENAI_BASE_URL"] = self.api_base
        os.environ["OPENAI_API_KEY"] = self.api
        client = AsyncOpenAI()

        if self.enable_rag:
            messages=[
                    {"role": "system", "content": f"Read the following content from several papers to gain knowledge in the relevant field. Using this knowledge, review a new scientific paper in this field. Based on existing research, identify the limitations of the 'Paper to Review'. Generate the major limitations related to its {self.aspect} in this paper. Do not include any limitation explicitly mentioned in the paper itself and return only the list of limitations. Return only the limitations in the following JSON format: {{\"limitations\": <a list of limitations>"} ,
                    {"role": "user", "content": text},
                ]
        else:
            messages=[
                    {"role": "system", "content": f"Read the following scientific paper and generate major limitations in this paper about its {self.aspect}. Do not include any limitation explicitly mentioned in the paper itself and return only the limitations. Return only the limitations in the following JSON format: {{\"limitations\": <a list of limitations>"} ,
                    {"role": "user", "content": text},
                ]
        try:
            responses = asyncio.run(
                generate_from_openai_chat_completion(
                    client,
                    messages=[messages], 
                    engine_name=self.model_name, # gpt-3.5-turbo
                    max_tokens=1000, # 32
                    requests_per_minute = 20,
                    # response_format={"type":"json_object"},
                )
            )            
            try:
                limitations = json.loads(responses[0])["limitations"]
                result = ""
                limit_cnt = 1
                for limitation in limitations:
                    result += f"{str(limit_cnt)}. {limitation}\n"
                    limit_cnt += 1
            except:
                SYSTEM_INPUT = f"Below is an output from an LLM about several limitations of a scientific paper. Please extract the list of limitations and DO NOT make any modification to the original limitations. Return the limitations in the following JSON format: {{\"limitations\": <a list of limitations>}}. If there is no valid response inthe output, return {{\"limitations\": {{}}}}"
                messages=[
                    {"role": "system", "content": SYSTEM_INPUT},
                    {"role": "user", "content": responses[0]},
                ]
                responses = asyncio.run(
                    generate_from_openai_chat_completion(
                        client,
                        messages=[messages], 
                        engine_name="gpt-4o-mini", # gpt-3.5-turbo
                        max_tokens=1000, # 32
                        requests_per_minute = 20,
                        response_format={"type":"json_object"},
                    )
                )  
                limitations = json.loads(responses[0])["limitations"]
                result = ""
                limit_cnt = 1
                for limitation in limitations:
                    result += f"{str(limit_cnt)}. {limitation}\n"
                    limit_cnt += 1
            # for choice in response.choices:
            #     result += choice.message.content 
            # result = insert_sentence(result, '**Generated by ChatGPT, no copying allowed!**', 50)
        except Exception as e:  
            result = "Error: "+ str(e)
            # usage  = 'xxxxx'
        print("********"*10)
        print(result)
        print("********"*10)      
        return result    


    def retrieve_papers(self, title, abstract):    
        query = title
        search_results = search_paper(query)
        if search_results != [] and search_results["data"][0]["title"].lower() == title.lower():
            search_result = search_results[0]
            retrieval = recommendation(search_result["paperId"])
            recommended_paper_list = []
            for recommended_paper in retrieval["recommendedPapers"]:
                if recommended_paper["abstract"] is None:
                    continue
                if recommended_paper["isOpenAccess"] and recommended_paper["openAccessPdf"]!= None:
                    recommended_paper_list.append(recommended_paper)

                if len(recommended_paper_list) >= 20:
                    break

        else:
            query = self.query_gen(abstract)
            search_results = search_paper(query)
            recommended_paper_list = []
            if search_results["data"] == []:
                return None
            for search_result in search_results["data"]:
                retrieval = recommendation(search_result["paperId"])
                recommended_papers = []
                for recommended_paper in retrieval["recommendedPapers"]:
                    if recommended_paper["abstract"] is None:
                        continue
                    if recommended_paper["isOpenAccess"] and recommended_paper["openAccessPdf"]!= None:
                        recommended_papers.append(recommended_paper)

                    if len(recommended_papers) >= 5:
                        break
                recommended_paper_list.extend(recommended_papers)

        if recommended_paper_list == []:
            return None
        final_papers = self.rerank(recommended_paper_list, title, abstract)
        retrieved_papers = self.extract_related_content(final_papers, self.aspect)

        return retrieved_papers


        

    def extract_from_paper(self, pdf_path):
        os.environ["OPENAI_BASE_URL"] = PRIVATE_API_BASE
        os.environ["OPENAI_API_KEY"] = PRIVATE_API_KEY
        client = AsyncOpenAI()
        
        # with open(pdf_path, 'rb') as f: # TODO
        #     pdf_bytes = f.read()
        #     file_object = BytesIO(pdf_bytes)

        file_object = BytesIO(pdf_path) # TODO
        pdf_reader = PyPDF2.PdfReader(file_object)
        
        doc = fitz.open(stream=pdf_path, filetype="pdf") # TODO
        page = doc.load_page(0)
        pix = page.get_pixmap()
        image_bytes = pix.tobytes("png")

        image_base64 = base64.b64encode(image_bytes).decode('utf-8')
    
        USER_INPUT = [{"type": "text", "text": "The first page of the paper: "}, {"type": "image_url", "image_url": {"url": f"data:image/png;base64,{image_base64}"}}]
        messages=[
                {"role": "system", "content": "Given the first-page image of a scientific paper in PDF format, extract and return the title and abstract in the following JSON format: {\"title\": \"<extracted title>\", \"abstract\": \"<extracted abstract>\"}."} ,
                {"role": "user", "content": USER_INPUT},
            ]
        responses = asyncio.run(
            generate_from_openai_chat_completion(
                client,
                messages=[messages], 
                engine_name="gpt-4o-mini", # gpt-3.5-turbo
                max_tokens=1000, # 32
                requests_per_minute = 20,
                response_format={"type":"json_object"},
            )
        )   

        response = json.loads(responses[0])
        title = response["title"]
        abstract = response["abstract"]



        num_pages = len(pdf_reader.pages)
        extraction_started = False
        extracted_text = ""
        for page_number in range(num_pages):
            page = pdf_reader.pages[page_number]
            page_text = page.extract_text()

            extraction_started = True
            page_number_start = page_number
            if extraction_started:
                extracted_text += page_text
                if page_number_start + 1 < page_number:
                    break
        return extracted_text, title, abstract

def main(api,api_base, paper_pdf, aspect, model_name, enable_rag):  
    start_time = time.time()
    # print("key: ", PRIVATE_API_KEY, "\nbase: ", PRIVATE_API_BASE)
    comments = ''
    output2 = ''
    retrieved_content = ''
    if not api or not paper_pdf:
        comments =  "It looks like there's a missing API key or PDF input. Make sure you've provided the necessary information or uploaded the required file."
        output2 =  "It looks like there's a missing API key or PDF input. Make sure you've provided the necessary information or uploaded the required file."
    else:
        try:
            reviewer1 = Reviewer(api,api_base, paper_pdf, aspect, model_name, enable_rag)   
            comments, retrieved_content = reviewer1.review_by_chatgpt(paper_list=paper_pdf)
            time_used = time.time() - start_time
            output2 ="Processing Time："+ str(round(time_used, 2)) +"seconds"
        except Exception as e:    
            comments = "Error: "+ str(e)
            output2 = "Error: "+ str(e)
    return retrieved_content, comments, output2





########################################################################################################    

title = "LimitGen"


description = '''<div align='left'>
<strong>We present a demo for our paper: Can LLMs Identify Critical Limitations within Scientific Research? A Systematic Evaluation on AI Research Papers. Upload the PDF of the paper you want to review, and the demo will automatically generate its identified limitations.
</div>
'''

inp = [gradio.Textbox(label="Input your API-key",
                          value="",
                          type='password'),
       gradio.Textbox(label="Input the base URL (ending with /v1). Skip this step if using the original OpenAI API.",
                          value="https://api.openai.com/v1"),

       gradio.File(label="Upload the PDF file of your paper (Make sure the PDF is fully uploaded before clicking Submit)",type="binary"),
       gradio.Radio(choices=["Methodology", "Experimental Design", "Result Analysis", "Literature Review"],
                        value="Methodology",
                        label="Select the aspect"),
       gradio.Textbox(label="Input the model name",
                          value="gpt-4o-mini"), 
       gradio.Checkbox(label="Enable RAG", value=False)
    
]

chat_reviewer_gui = gradio.Interface(fn=main,
                                 inputs=inp,
                                 outputs = [gradio.Textbox(lines=6, label="Retrieved Literature"), gradio.Textbox(lines=15, label="Output"), gradio.Textbox(lines=2, label="Resource Statistics")],
                                 title=title,
                                 description=description)

# Start server
chat_reviewer_gui .launch(quiet=True, show_api=False)