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app.py
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app.py
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import argparse
import logging
from contextlib import redirect_stdout
from io import StringIO
from pprint import pformat
from flask import Flask, Response, jsonify, request, url_for
from flask_cors import CORS
from chat_arxiv import ArxivParams, chat_arxiv_main
from chat_paper import PaperParams, chat_paper_main
from chat_response import ResponseParams, chat_response_main
from chat_reviewer import ReviewerParams, chat_reviewer_main
app = Flask(__name__)
CORS(app)
@app.route("/", methods=["GET"])
@app.route("/index", methods=["GET"])
def home():
arxiv_url = url_for("arxiv", _external=True, query="GPT-4", key_word="GPT robot", page_num=1, max_results=1, days=1,
sort="web", save_image=False, file_format="md", language="zh")
paper_url = url_for("paper", _external=True, pdf_path="", query="all: ChatGPT robot",
key_word="reinforcement learning", filter_keys="ChatGPT robot", max_results=1, sort="Relevance",
save_image=False, file_format="md", language="zh")
response_url = url_for("response", _external=True, comment_path="review_comments.txt", file_format="txt",
language="en")
reviewer_url = url_for("reviewer", _external=True, paper_path="", file_format="txt",
research_fields="computer science, artificial intelligence and reinforcement learning",
language="en")
return f'''
<h1>ChatPaper，Flask版本的优势</h1>
<p>GitHub 项目地址：<a href="https://github.com/kaixindelele/ChatPaper" target="_blank">https://github.com/kaixindelele/ChatPaper</a></p>
<p>将原始的 Python 脚本改为使用 Flask 构建的 Web 服务具有以下优点：</p>
<ul>
<li><strong>易用性</strong>：通过简单的 HTTP 请求，用户可以轻松访问和使用各个功能，无需在本地安装 Python 或其他依赖。</li>
<li><strong>跨平台兼容性</strong>：作为 Web 服务，Flask 应用程序可以在任何支持 HTTP 请求的设备和平台上运行，从而提高了应用程序的可访问性。</li>
<li><strong>可扩展性</strong>：使用 Flask 可以更轻松地扩展应用程序，以包含其他功能、中间件和 API 端点。</li>
<li><strong>易于集成</strong>：Flask 应用程序可以与其他 Web 服务和前端框架（如 React、Vue.js 等）轻松集成，从而提供更丰富的用户体验。</li>
</ul>
<h1>功能描述和调用方法</h1>
<h2>arxiv</h2>
<p>搜索 Arxiv 上的论文。参数：query, key_word, page_num, max_results, days, sort, save_image, file_format, language</p>
<p>示例：<a href="{arxiv_url}" target="_blank">{arxiv_url}</a></p>
<h2>paper</h2>
<p>搜索并分析论文。参数：pdf_path, query, key_word, filter_keys, max_results, sort, save_image, file_format, language</p>
<p>示例：<a href="{paper_url}" target="_blank">{paper_url}</a></p>
<h2>response</h2>
<p>处理论文审稿评论。参数：comment_path, file_format, language</p>
<p>示例：<a href="{response_url}" target="_blank">{response_url}</a></p>
<h2>reviewer</h2>
<p>查找论文审稿人。参数：paper_path, file_format, research_fields, language</p>
<p>示例：<a href="{reviewer_url}" target="_blank">{reviewer_url}</a></p>
'''
def process_request(main_function, params_class, default_values):
args = request.args.to_dict()
for key, value in args.items():
if key in default_values:
args[key] = type(default_values[key])(value)
params = params_class(**{**default_values, **args})
output = StringIO()
with redirect_stdout(output):
main_function(args=params)
output_str = output.getvalue()
output_lines = [line.strip() for line in output_str.split("\n") if line.strip()]
formatted_output_str = "\n".join(output_lines)
return pformat(formatted_output_str)
@app.route("/arxiv", methods=["GET"])
def arxiv():
default_values = {
"query": "GPT-4",
"key_word": "GPT robot",
"page_num": 1,
"max_results": 1,
"days": 1,
"sort": "web",
"save_image": False,
"file_format": "md",
"language": "zh"
}
return process_request(chat_arxiv_main, ArxivParams, default_values)
@app.route("/paper", methods=["GET"])
def paper():
default_values = {
"pdf_path": "",
"query": "all: ChatGPT robot",
"key_word": "reinforcement learning",
"filter_keys": "ChatGPT robot",
"max_results": 1,
"sort": "Relevance",
"save_image": False,
"file_format": "md",
"language": "zh"
}
return process_request(chat_paper_main, PaperParams, default_values)
@app.route("/response", methods=["GET"])
def response():
default_values = {
"comment_path": "review_comments.txt",
"file_format": "txt",
"language": "en"
}
return process_request(chat_response_main, ResponseParams, default_values)
@app.route("/reviewer", methods=["GET"])
def reviewer():
default_values = {
"paper_path": "",
"file_format": "txt",
"research_fields": "computer science, artificial intelligence and reinforcement learning",
"language": "en"
}
return process_request(chat_reviewer_main, ReviewerParams, default_values)
def get_log_level(args):
if args.verbose == 2:
return logging.INFO
elif args.verbose > 2:
return logging.DEBUG
else:
return logging.WARN
if __name__ == "__main__":
# Initialize the main argument parser
parser = argparse.ArgumentParser(description='Description of main script')
parser.add_argument("--debug", "-d", help="deploy debug mode",
action="store_true", default=False)
parser.add_argument("--verbose", "-v", action="count", default=1)
args = parser.parse_args()
logging.basicConfig(level=get_log_level(
args), format="%(asctime)s %(message)s", datefmt="%m/%d/%Y %I:%M:%S %p")
if args.debug:
app.run(debug=True, threaded=True, host='0.0.0.0', port=5000)
else:
app.run(debug=False, threaded=True, host='0.0.0.0', port=5000)