app.py

import requests
from bs4 import BeautifulSoup
import fitz  # pip install PyMuPDF
import os
import openai
import re
import gradio as gr
import gspread
from oauth2client.service_account import ServiceAccountCredentials
import json

def connect_gspread(spread_sheet_key):
    """Google スプレッドシートに接続。"""
    credentials_json = os.getenv('GOOGLE_CREDENTIALS')
    credentials_dict = json.loads(credentials_json)
    scope = ['https://spreadsheets.google.com/feeds', 'https://www.googleapis.com/auth/drive']
    
    credentials = ServiceAccountCredentials.from_json_keyfile_dict(credentials_dict, scope)
    gc = gspread.authorize(credentials)
    SPREADSHEET_KEY = spread_sheet_key
    worksheet = gc.open_by_key(SPREADSHEET_KEY).sheet1
    return worksheet

spread_sheet_key = "1nSh6D_Gqdbhi1CB3wvD4OJUU6bji8-LE6HET7NTEjrM"
worksheet = connect_gspread(spread_sheet_key)

def download_paper(paper_url):
    """論文PDFをダウンロードして保存。"""
    response = requests.get(paper_url)
    temp_pdf_path = "temp_paper.pdf"
    with open(temp_pdf_path, 'wb') as f:
        f.write(response.content)
    return temp_pdf_path

def extract_text_from_pdf(pdf_path):
    """PDFからテキストを抽出。"""
    doc = fitz.open(pdf_path)
    text = ""
    for page in doc:
        text += page.get_text()
    return text

def summarize_text_with_chat(text, max_length=10000):
    """OpenAIのChat APIを使ってテキストを要約。"""
    openai.api_key = os.getenv('OPENAI_API_KEY')
    trimmed_text = text[:max_length]
    response = openai.chat.completions.create(
        model="gpt-3.5-turbo-0125",
        messages=[
            {"role": "system", "content": "次の文書を要約してください。必ず'## タイトル', '## 要約', '## 専門用語解説'を記載してください。"},
            {"role": "user", "content": trimmed_text}
        ],
        temperature=0.7,
        max_tokens=1000
    )
    summary_text = response.choices[0].message.content
    total_token = response.usage.total_tokens
    return summary_text, total_token

def fetch_paper_links(url):
    """指定したURLから論文のリンクを抽出し、重複を排除。"""
    response = requests.get(url)
    soup = BeautifulSoup(response.text, 'html.parser')
    pattern = re.compile(r'^/papers/\d+\.\d+$')
    links = []
    for a in soup.find_all('a', href=True):
        href = a['href']
        if pattern.match(href) and href not in links:
            links.append(href)
    return links

def summarize_paper_and_save_to_sheet(paper_id):
    """論文を要約し、結果をGoogle スプレッドシートに保存。"""
    paper_url = f"https://arxiv.org/pdf/{paper_id}.pdf"
    pdf_path = download_paper(paper_url)
    text = extract_text_from_pdf(pdf_path)
    summary = summarize_text_with_chat(text)
    os.remove(pdf_path)
    worksheet.append_row([paper_id, paper_url, summary])
    return summary

def gradio_interface():
    paper_links = fetch_paper_links("https://huggingface.co/papers")
    paper_ids = set(link.split('/')[-1] for link in paper_links)

    total_tokens_used = 0
    summaries = []

    for paper_id in paper_ids:
        summary_info = ""
        try:
            summary, tokens_used = summarize_paper_and_save_to_sheet(paper_id)
            total_tokens_used += tokens_used
            paper_id_url = f"https://arxiv.org/pdf/{paper_id}.pdf"
            summary_info += f'論文: {paper_id_url}\n{summary}\n'
        except Exception as e:
            summary_info += f"Error processing paper ID {paper_id}: {e}\n"
        
        summaries.append(summary_info)

    summaries_markdown = "\n---\n".join(summaries)  # 要約を水平線で区切る
    return summaries_markdown + f"\n全ての要約で使用されたトータルトークン数: {total_tokens_used}"

iface = gr.Interface(
    fn=gradio_interface,
    inputs=[],
    outputs=gr.Markdown(),
    title="論文要約ツール",
    description="[Daily Papers](https://huggingface.co/papers)に掲載された本日の論文を取得し、日本語で要約します。"
)

if __name__ == "__main__":
    iface.launch()