Spaces:
Paused
Paused
| import io | |
| import os | |
| import re | |
| import tarfile | |
| import anthropic | |
| import gradio as gr | |
| import requests | |
| import arxiv | |
| def replace_texttt(text): | |
| return re.sub(r"\\texttt\{(.*?)\}", r"*\1*", text) | |
| def get_paper_info(paper_id): | |
| # Create a search query with the arXiv ID | |
| search = arxiv.Search(id_list=[paper_id]) | |
| # Fetch the paper using its arXiv ID | |
| paper = next(search.results(), None) | |
| if paper is not None: | |
| # Return the paper's title and abstract | |
| return paper.title, paper.summary | |
| else: | |
| return None, None | |
| def download_arxiv_source(paper_id): | |
| url = f"https://arxiv.org/e-print/{paper_id}" | |
| # Get the tar file | |
| response = requests.get(url) | |
| response.raise_for_status() | |
| # Open the tar file | |
| tar = tarfile.open(fileobj=io.BytesIO(response.content), mode="r") | |
| # Load all .tex files into memory, including their subdirectories | |
| tex_files = { | |
| member.name: tar.extractfile(member).read().decode("utf-8") | |
| for member in tar.getmembers() | |
| if member.name.endswith(".tex") | |
| } | |
| # Load all .tex files into memory, including their subdirectories | |
| tex_files = { | |
| member.name: tar.extractfile(member).read().decode("utf-8") | |
| for member in tar.getmembers() | |
| if member.isfile() and member.name.endswith(".tex") | |
| } | |
| # Pattern to match \input{filename} and \include{filename} | |
| pattern = re.compile(r"\\(input|include){(.*?)}") | |
| # Function to replace \input{filename} and \include{filename} with file contents | |
| def replace_includes(text): | |
| output = [] | |
| for line in text.split("\n"): | |
| match = re.search(pattern, line) | |
| if match: | |
| command, filename = match.groups() | |
| # LaTeX automatically adds .tex extension for \input and \include commands | |
| if not filename.endswith(".tex"): | |
| filename += ".tex" | |
| if filename in tex_files: | |
| output.append(replace_includes(tex_files[filename])) | |
| else: | |
| output.append(f"% {line} % FILE NOT FOUND") | |
| else: | |
| output.append(line) | |
| return "\n".join(output) | |
| if "main.tex" in tex_files: | |
| # Start with the contents of main.tex | |
| main_tex = replace_includes(tex_files["main.tex"]) | |
| else: | |
| # No main.tex, concatenate all .tex files | |
| main_tex = "\n".join(replace_includes(text) for text in tex_files.values()) | |
| return main_tex | |
| class ContextualQA: | |
| def __init__(self, client, model="claude-v1.3-100k"): | |
| self.client = client | |
| self.model = model | |
| self.context = "" | |
| self.questions = [] | |
| self.responses = [] | |
| def load_text(self, text): | |
| self.context = text | |
| def ask_question(self, question): | |
| leading_prompt = "Here is the content of a paper:" | |
| trailing_prompt = "Now, answer the following question below. You can optionally use Markdown to format your answer." | |
| prompt = f"{anthropic.HUMAN_PROMPT} {leading_prompt}\n\n{self.context}\n\n{trailing_prompt}\n\n{anthropic.HUMAN_PROMPT} {question}\n\n{anthropic.AI_PROMPT}" | |
| response = self.client.completion_stream( | |
| prompt=prompt, | |
| stop_sequences=[anthropic.HUMAN_PROMPT], | |
| max_tokens_to_sample=6000, | |
| model=self.model, | |
| stream=False, | |
| ) | |
| responses = [data for data in response] | |
| self.questions.append(question) | |
| self.responses.append(responses) | |
| return responses | |
| def clear_context(self): | |
| self.context = "" | |
| self.questions = [] | |
| self.responses = [] | |
| def __getstate__(self): | |
| state = self.__dict__.copy() | |
| del state["client"] | |
| return state | |
| def __setstate__(self, state): | |
| self.__dict__.update(state) | |
| self.client = None | |
| def load_context(paper_id): | |
| try: | |
| latex_source = download_arxiv_source(paper_id) | |
| except Exception as e: | |
| return None, [(f"Error loading paper with id {paper_id}.", str(e))] | |
| client = anthropic.Client(api_key=os.environ["ANTHROPIC_API_KEY"]) | |
| model = ContextualQA(client, model="claude-v1.3-100k") | |
| model.load_text(latex_source) | |
| # Usage | |
| title, abstract = get_paper_info(paper_id) | |
| # remove special symbols from title and abstract | |
| title = replace_texttt(title) | |
| abstract = replace_texttt(abstract) | |
| return ( | |
| model, | |
| [ | |
| ( | |
| f"Load the paper with id {paper_id}.", | |
| f"\n**Title**: {title}\n\n**Abstract**: {abstract}\n\nPaper loaded, You can now ask questions.", | |
| ) | |
| ], | |
| ) | |
| def answer_fn(model, question, chat_history): | |
| # if question is empty, tell user that they need to ask a question | |
| if question == "": | |
| chat_history.append(("No Question Asked", "Please ask a question.")) | |
| return model, chat_history, "" | |
| client = anthropic.Client(api_key=os.environ["ANTHROPIC_API_KEY"]) | |
| model.client = client | |
| try: | |
| response = model.ask_question(question) | |
| except Exception as e: | |
| chat_history.append(("Error Asking Question", str(e))) | |
| return model, chat_history, "" | |
| chat_history.append((question, response[0]["completion"])) | |
| return model, chat_history, "" | |
| def clear_context(): | |
| return [] | |
| with gr.Blocks(theme=gr.themes.Soft()) as demo: | |
| gr.Markdown( | |
| "# Explore ArXiv Papers in Depth with `claude-v1.3-100k` - Ask Questions and Receive Detailed Answers Instantly" | |
| ) | |
| gr.Markdown( | |
| "Dive into the world of academic papers with our dynamic app, powered by the cutting-edge `claude-v1.3-100k` model. This app allows you to ask detailed questions about any ArXiv paper and receive direct answers from the paper's content. Utilizing a context length of 100k tokens, it provides an efficient and comprehensive exploration of complex research studies, making knowledge acquisition simpler and more interactive. (This text is generated by GPT-4 )" | |
| ) | |
| gr.HTML( | |
| """<center>All the inputs are being sent to Anthropic's Claude endpoints. Please refer to <a href="https://legal.anthropic.com/#privacy">this link</a> for privacy policy.</center>""" | |
| ) | |
| gr.HTML( | |
| """<center><a href="https://huggingface.co/spaces/taesiri/ClaudeReadsArxiv?duplicate=true"><img src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>Duplicate the Space and run securely with your Anthropic API Key </center>""" | |
| ) | |
| with gr.Column(): | |
| with gr.Row(): | |
| paper_id_input = gr.Textbox(label="Enter Paper ID", value="2108.07258") | |
| btn_load = gr.Button("Load Paper") | |
| qa_model = gr.State() | |
| with gr.Column(): | |
| chatbot = gr.Chatbot().style(color_map=("blue", "yellow")) | |
| question_txt = gr.Textbox( | |
| label="Question", lines=1, placeholder="Type your question here..." | |
| ) | |
| btn_answer = gr.Button("Answer Question") | |
| btn_clear = gr.Button("Clear Chat") | |
| btn_load.click(load_context, inputs=[paper_id_input], outputs=[qa_model, chatbot]) | |
| btn_answer.click( | |
| answer_fn, | |
| inputs=[qa_model, question_txt, chatbot], | |
| outputs=[qa_model, chatbot, question_txt], | |
| ) | |
| question_txt.submit( | |
| answer_fn, | |
| inputs=[qa_model, question_txt, chatbot], | |
| outputs=[qa_model, chatbot, question_txt], | |
| ) | |
| btn_clear.click(clear_context, outputs=[chatbot]) | |
| demo.launch() | |