| import os | |
| import pickle | |
| from typing import Optional, Tuple | |
| import gradio as gr | |
| from threading import Lock | |
| from langchain.llms import OpenAI | |
| from langchain.chat_models import ChatOpenAI | |
| from langchain.chains import ChatVectorDBChain, ConversationalRetrievalChain | |
| from template import QA_PROMPT, CONDENSE_QUESTION_PROMPT | |
| from pdf2vectorstore import convert_to_vectorstore | |
| def get_chain(api_key, vectorstore, model_name): | |
| if model_name == "gpt-4": | |
| llm = ChatOpenAI(model_name = model_name, temperature=0, openai_api_key=api_key) | |
| retriever = vectorstore.as_retriever() | |
| retriever.search_kwargs['distance_metric'] = 'cos' | |
| retriever.search_kwargs['fetch_k'] = 100 | |
| retriever.search_kwargs['maximal_marginal_relevance'] = True | |
| retriever.search_kwargs['k'] = 10 | |
| qa_chain = ConversationalRetrievalChain.from_llm( | |
| llm, | |
| retriever, | |
| qa_prompt=QA_PROMPT, | |
| condense_question_prompt=CONDENSE_QUESTION_PROMPT, | |
| ) | |
| return qa_chain | |
| else: | |
| llm = OpenAI(model_name = model_name, temperature=0, openai_api_key=api_key) | |
| qa_chain = ChatVectorDBChain.from_llm( | |
| llm, | |
| vectorstore, | |
| qa_prompt=QA_PROMPT, | |
| condense_question_prompt=CONDENSE_QUESTION_PROMPT, | |
| ) | |
| return qa_chain | |
| def set_openai_api_key(api_key: str, vectorstore, model_name: str): | |
| if api_key: | |
| chain = get_chain(api_key, vectorstore, model_name) | |
| return chain | |
| class ChatWrapper: | |
| def __init__(self): | |
| self.lock = Lock() | |
| self.previous_url = "" | |
| self.vectorstore_state = None | |
| self.chain = None | |
| def __call__( | |
| self, | |
| api_key: str, | |
| arxiv_url: str, | |
| inp: str, | |
| history: Optional[Tuple[str, str]], | |
| model_name: str, | |
| ): | |
| if not arxiv_url or not api_key: | |
| history = history or [] | |
| history.append((inp, "Please provide both arXiv URL and API key to begin")) | |
| return history, history | |
| if arxiv_url != self.previous_url: | |
| history = [] | |
| vectorstore = convert_to_vectorstore(arxiv_url, api_key) | |
| self.previous_url = arxiv_url | |
| self.chain = set_openai_api_key(api_key, vectorstore, model_name) | |
| self.vectorstore_state = vectorstore | |
| if self.chain is None: | |
| self.chain = set_openai_api_key(api_key, self.vectorstore_state, model_name) | |
| self.lock.acquire() | |
| try: | |
| history = history or [] | |
| if self.chain is None: | |
| history.append((inp, "Please paste your OpenAI key to use")) | |
| return history, history | |
| import openai | |
| openai.api_key = api_key | |
| output = self.chain ({"question": inp, "chat_history": history})["answer"] | |
| history.append((inp, output)) | |
| except Exception as e: | |
| raise e | |
| finally: | |
| api_key = "" | |
| self.lock.release() | |
| return history, history | |
| chat = ChatWrapper() | |
| block = gr.Blocks(css=".gradio-container {background-color: #f8f8f8; font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif}") | |
| with block: | |
| gr.HTML(""" | |
| <style> | |
| body { | |
| background-color: #f5f5f5; | |
| font-family: 'Roboto', sans-serif; | |
| padding: 30px; | |
| } | |
| </style> | |
| """) | |
| gr.HTML("<h1 style='text-align: center;'>ArxivGPT</h1>") | |
| gr.HTML("<h3 style='text-align: center;'>Ask questions about research papers</h3>") | |
| with gr.Row(): | |
| with gr.Column(width="auto"): | |
| openai_api_key_textbox = gr.Textbox( | |
| label="OpenAI API Key", | |
| placeholder="Paste your OpenAI API key (sk-...)", | |
| show_label=True, | |
| lines=1, | |
| type="password", | |
| ) | |
| with gr.Column(width="auto"): | |
| arxiv_url_textbox = gr.Textbox( | |
| label="Arxiv URL", | |
| placeholder="Enter the arXiv URL", | |
| show_label=True, | |
| lines=1, | |
| ) | |
| with gr.Column(width="auto"): | |
| model_dropdown = gr.Dropdown( | |
| label="Choose a model", | |
| choices=["gpt-3.5-turbo", "gpt-4"], | |
| ) | |
| chatbot = gr.Chatbot() | |
| with gr.Row(): | |
| message = gr.Textbox( | |
| label="What's your question?", | |
| placeholder="Ask questions about the paper you just linked", | |
| lines=1, | |
| ) | |
| submit = gr.Button(value="Send", variant="secondary").style(full_width=False) | |
| gr.Examples( | |
| examples=[ | |
| "What's this paper about?", | |
| "Please give me a brief summary about this paper", | |
| "Are there any interesting correlations in the given paper?", | |
| "How can this paper be applied in the real world?", | |
| "What are the limitations of this paper?", | |
| ], | |
| inputs=message, | |
| ) | |
| gr.HTML(""" | |
| <div style="text-align:center"> | |
| <p>Developed by <a href='https://www.linkedin.com/in/dekay/'>Github and Huggingface: Volkopat</a></p> | |
| <p>Powered by <a href='https://openai.com/'>OpenAI</a>, <a href='https://arxiv.org/'>arXiv</a> and <a href='https://github.com/hwchase17/langchain'>LangChain 🦜️🔗</a></p> | |
| <p>ArxivGPT is a chatbot that answers questions about research papers. It uses a pretrained GPT-4 model to generate answers.</p> | |
| <p>If you don't get a response for GPT-4, it is likely that you don't have API access, try 3.5</p> | |
| <p>It can take upto a minute for you to start a conversation for every new paper. Please be patient.</p> | |
| </div> | |
| <style> | |
| p { | |
| margin-bottom: 10px; | |
| font-size: 16px; | |
| } | |
| a { | |
| color: #3867d6; | |
| text-decoration: none; | |
| } | |
| a:hover { | |
| text-decoration: underline; | |
| } | |
| </style> | |
| """) | |
| state = gr.State() | |
| submit.click(chat, | |
| inputs=[openai_api_key_textbox, arxiv_url_textbox, message, state, model_dropdown], | |
| outputs=[chatbot, state]) | |
| message.submit(chat, | |
| inputs=[openai_api_key_textbox, arxiv_url_textbox, message, state, model_dropdown], | |
| outputs=[chatbot, state]) | |
| block.launch(width=800) |