import gradio as gr import os import openai from utils.references import References from utils.gpt_interaction import GPTModel from utils.prompts import SYSTEM openai_key = os.getenv("OPENAI_API_KEY") default_model = os.getenv("DEFAULT_MODEL") if default_model is None: # default_model = "gpt-3.5-turbo-16k" default_model = "gpt-4" openai.api_key = openai_key paper_system_prompt = '''You are an assistant designed to propose choices of research direction. The user will input questions or some keywords of a fields. You need to generate some paper titles and main contributions. Ensure follow the following instructions: Instruction: - Your response should follow the JSON format. - Your response should have the following structure: { "your suggested paper title": { "summary": "an overview introducing what this paper will include", "contributions": { "contribution1": {"statement": "briefly describe this contribution", "reason": "reason why this contribution can make this paper outstanding"}, "contribution2": {"statement": "briefly describe this contribution", "reason": "reason why this contribution can make this paper outstanding"}, ... } } "your suggested paper title": { "summary": "an overview introducing what this paper will include", "contributions": { "contribution1": {"statement": "briefly describe this contribution", "reason": "reason why this contribution can make this paper outstanding"}, "contribution2": {"statement": "briefly describe this contribution", "reason": "reason why this contribution can make this paper outstanding"}, ... } } ... } - Please list three to five suggested title and at least three contributions for each paper. ''' contribution_system_prompt = '''You are an assistant designed to criticize the contributions of a paper. You will be provided Paper's Title, References and Contributions. Ensure follow the following instructions: Instruction: - Your response should follow the JSON format. - Your response should have the following structure: { "title": "the title provided by the user", "comment": "your thoughts on if this title clearly reflects the key ideas of this paper and explain why" "contributions": { "contribution1": {"statement": "briefly describe what the contribution is", "reason": "reason why the user claims it is a contribution", "judge": "your thought about if this is a novel contribution and explain why", "suggestion": "your suggestion on how to modify the research direction to enhance the novelty "}, "contribution2": {"statement": "briefly describe what the contribution is", "reason": "reason why the user claims it is a contribution", "judge": "your thought about if this is a novel contribution and explain why", "suggestion": "your suggestion on how to modify the research direction to enhance the novelty "}, ... } } - You need to carefully check if the claimed contribution has been made in the provided references, which makes the contribution not novel. - You also need to propose your concerns on if any of contributions could be incremental or just a mild modification on an existing work. ''' ANNOUNCEMENT = """

灵感实验室IdeaLab

灵感实验室IdeaLab可以为你选择你下一篇论文的研究方向! 输入你的研究领域或者任何想法, 灵感实验室会自动生成若干个论文标题+论文的主要贡献供你选择.

除此之外, 输入你的论文标题+主要贡献, 它会自动搜索相关文献, 来验证这个想法是不是有人做过了.

""" def criticize_my_idea(title, contributions, max_tokens=4096): ref = References(title=title, description=f"{contributions}") keywords, _ = llm(systems=SYSTEM["keywords"], prompts=title, return_json=True) keywords = {keyword: 10 for keyword in keywords} ref.collect_papers(keywords) ref_prompt = ref.to_prompts(max_tokens=max_tokens) prompt = f"Title: {title}\n References: {ref_prompt}\n Contributions: {contributions}" output, _ = llm(systems=contribution_system_prompt, prompts=prompt, return_json=True) return output, ref_prompt def paste_title(suggestions): if suggestions: title = suggestions['title']['new title'] contributions = suggestions['contributions'] return title, contributions, {}, {}, {} else: return "", "", {}, {}, {} def generate_choices(thoughts): output, _ = llm(systems=paper_system_prompt, prompts=thoughts, return_json=True) return output # def translate_json(json_input): # system_prompt = "You are a translation bot. The user will input a JSON format string. You need to translate it into Chinese and return in the same formmat." # output, _ = llm(systems=system_prompt, prompts=str(json_input), return_json=True) # return output with gr.Blocks() as demo: llm = GPTModel(model=default_model) gr.HTML(ANNOUNCEMENT) with gr.Row(): with gr.Tab("生成论文想法 (Generate Paper Ideas)"): thoughts_input = gr.Textbox(label="Thoughts") with gr.Accordion("Show prompts", open=False): prompts_1 = gr.Textbox(label="Prompts", interactive=False, value=paper_system_prompt) with gr.Row(): button_generate_idea = gr.Button("Make it an idea!", variant="primary") with gr.Tab("验证想法可行性 (Validate Feasibility)"): title_input = gr.Textbox(label="Title") contribution_input = gr.Textbox(label="Contributions", lines=5) with gr.Accordion("Show prompts", open=False): prompts_2 = gr.Textbox(label="Prompts", interactive=False, value=contribution_system_prompt) with gr.Row(): button_submit = gr.Button("Criticize my idea!", variant="primary") with gr.Tab("生成论文 (Generate Paper)"): gr.Markdown("...") with gr.Column(scale=1): contribution_output = gr.JSON(label="Contributions") # cn_output = gr.JSON(label="主要贡献") with gr.Accordion("References", open=False): references_output = gr.JSON(label="References") button_submit.click(fn=criticize_my_idea, inputs=[title_input, contribution_input], outputs=[contribution_output, references_output]) button_generate_idea.click(fn=generate_choices, inputs=thoughts_input, outputs=contribution_output)#.success(translate_json, contribution_output, cn_output) demo.queue(concurrency_count=1, max_size=5, api_open=False) demo.launch(show_error=True)