import streamlit as st st.set_page_config(layout="wide") import numpy as np from abc import ABC, abstractmethod from typing import List, Dict, Any, Tuple from collections import defaultdict from tqdm import tqdm import pandas as pd from datetime import datetime, date from datasets import load_dataset, load_from_disk from collections import Counter import yaml, json, requests, sys, os, time import concurrent.futures from langchain import hub from langchain_openai import ChatOpenAI as openai_llm from langchain_core.runnables import RunnableConfig from langchain_community.callbacks import StreamlitCallbackHandler from langchain.agents import create_react_agent, Tool, AgentExecutor from langchain_community.utilities import DuckDuckGoSearchAPIWrapper ts = time.time() anthropic_key = st.secrets["anthropic_key"] openai_key = st.secrets["openai_key"] from nltk.corpus import stopwords import nltk from openai import OpenAI import anthropic import cohere import faiss import spacy from string import punctuation import pytextrank nlp = spacy.load("en_core_web_sm") nlp.add_pipe("textrank") try: stopwords.words('english') except: nltk.download('stopwords') stopwords.words('english') from bokeh.plotting import figure from bokeh.models import ColumnDataSource from bokeh.io import output_notebook from bokeh.palettes import Spectral5 from bokeh.transform import linear_cmap st.image('local_files/pathfinder_logo.png') st.expander("About", expanded=False).write( """ Pathfinder v2.0 is a framework for searching and visualizing astronomy papers on the [arXiv](https://arxiv.org/) and [ADS](https://ui.adsabs.harvard.edu/) using the context sensitivity from modern large language models (LLMs) to better parse patterns in paper contexts. This tool was built during the [JSALT workshop](https://www.clsp.jhu.edu/2024-jelinek-summer-workshop-on-speech-and-language-technology/) to do awesome things. **👈 Select a tool from the sidebar** to see some examples of what this framework can do! ### Tool summary: - Please wait while the initial data loads and compiles, this takes about a minute initially. - `Paper search` looks for relevant papers given an arxiv id or a question. This is not meant to be a replacement to existing tools like the [ADS](https://ui.adsabs.harvard.edu/), [arxivsorter](https://www.arxivsorter.org/), semantic search or google scholar, but rather a supplement to find papers that otherwise might be missed during a literature survey. It is trained on astro-ph (astrophysics of galaxies) papers up to last-year-ish mined from arxiv and supplemented with ADS metadata, if you are interested in extending it please reach out! Also add: more pages, actual generation, diff. toggles for retrieval/gen, feedback form, socials, literature, contact us, copyright, collaboration, etc. The image below shows a representation of all the astro-ph.GA papers that can be explored in more detail using the `Arxiv embedding` page. The papers tend to cluster together by similarity, and result in an atlas that shows well studied (forests) and currently uncharted areas (water). """ ) if 'arxiv_corpus' not in st.session_state: with st.spinner('loading data...'): try: arxiv_corpus = load_from_disk('data/') except: st.write('downloading data') arxiv_corpus = load_dataset('kiyer/pathfinder_arxiv_data',split='train') arxiv_corpus.save_to_disk('data/') arxiv_corpus.add_faiss_index('embed') st.session_state.arxiv_corpus = arxiv_corpus st.toast('loaded arxiv corpus') else: arxiv_corpus = st.session_state.arxiv_corpus if 'ids' not in st.session_state: st.session_state.ids = arxiv_corpus['ads_id'] st.session_state.titles = arxiv_corpus['title'] st.session_state.abstracts = arxiv_corpus['abstract'] st.session_state.cites = arxiv_corpus['cites'] st.session_state.years = arxiv_corpus['date'] st.session_state.kws = arxiv_corpus['keywords'] st.toast('done caching. time taken: %.2f sec' %(time.time()-ts)) #--------------------------------------------------------------- # A hack to "clear" the previous result when submitting a new prompt. This avoids # the "previous run's text is grayed-out but visible during rerun" Streamlit behavior. class DirtyState: NOT_DIRTY = "NOT_DIRTY" DIRTY = "DIRTY" UNHANDLED_SUBMIT = "UNHANDLED_SUBMIT" def get_dirty_state() -> str: return st.session_state.get("dirty_state", DirtyState.NOT_DIRTY) def set_dirty_state(state: str) -> None: st.session_state["dirty_state"] = state def with_clear_container(submit_clicked: bool) -> bool: if get_dirty_state() == DirtyState.DIRTY: if submit_clicked: set_dirty_state(DirtyState.UNHANDLED_SUBMIT) st.experimental_rerun() else: set_dirty_state(DirtyState.NOT_DIRTY) if submit_clicked or get_dirty_state() == DirtyState.UNHANDLED_SUBMIT: set_dirty_state(DirtyState.DIRTY) return True return False #---------------------------------------------------------------- class Filter(): def filter(self, query: str, arxiv_id: str) -> List[str]: pass class CitationFilter(Filter): # can do it with all metadata def __init__(self, corpus): self.corpus = corpus ids = ids cites = cites self.citation_counts = {ids[i]: cites[i] for i in range(len(ids))} def citation_weight(self, x, shift, scale): return 1 / (1 + np.exp(-1 * (x - shift) / scale)) # sigmoid function def filter(self, doc_scores, weight = 0.1): # additive weighting citation_count = np.array([self.citation_counts[doc[0]] for doc in doc_scores]) cmean, cstd = np.median(citation_count), np.std(citation_count) citation_score = self.citation_weight(citation_count, cmean, cstd) for i, doc in enumerate(doc_scores): doc_scores[i][2] += weight * citation_score[i] class DateFilter(Filter): # include time weighting eventually def __init__(self, document_dates): self.document_dates = document_dates def parse_date(self, arxiv_id: str) -> datetime: # only for documents if arxiv_id.startswith('astro-ph'): arxiv_id = arxiv_id.split('astro-ph')[1].split('_arXiv')[0] try: year = int("20" + arxiv_id[:2]) month = int(arxiv_id[2:4]) except: year = 2023 month = 1 return date(year, month, 1) def weight(self, time, shift, scale): return 1 / (1 + np.exp((time - shift) / scale)) def evaluate_filter(self, year, filter_string): try: # Use ast.literal_eval to safely evaluate the expression result = eval(filter_string, {"__builtins__": None}, {"year": year}) return result except Exception as e: print(f"Error evaluating filter: {e}") return False def filter(self, docs, boolean_date = None, min_date = None, max_date = None, time_score = 0): filtered = [] if boolean_date is not None: boolean_date = boolean_date.replace("AND", "and").replace("OR", "or") for doc in docs: if self.evaluate_filter(self.document_dates[doc[0]].year, boolean_date): filtered.append(doc) else: if min_date == None: min_date = date(1990, 1, 1) if max_date == None: max_date = date(2024, 7, 3) for doc in docs: if self.document_dates[doc[0]] >= min_date and self.document_dates[doc[0]] <= max_date: filtered.append(doc) if time_score is not None: # apply time weighting for i, item in enumerate(filtered): time_diff = (max_date - self.document_dates[filtered[i][0]]).days / 365 filtered[i][2] += time_score * 0.1 * self.weight(time_diff, 5, 5) return filtered class KeywordFilter(Filter): def __init__(self, corpus, remove_capitals: bool = True, metadata = None, ne_only = True, verbose = False): self.index_path = 'keyword_index.json' # self.metadata = metadata self.remove_capitals = remove_capitals self.ne_only = ne_only self.stopwords = set(stopwords.words('english')) self.verbose = verbose self.index = None self.kws = st.session_state.kws self.ids = st.session_state.ids self.titles = st.session_state.titles self.load_or_build_index() def preprocess_text(self, text: str) -> str: text = ''.join(char for char in text if char.isalnum() or char.isspace()) if self.remove_capitals: text = text.lower() return ' '.join(word for word in text.split() if word.lower() not in self.stopwords) def build_index(self): # include the title in the index print("Building index...") self.index = {} for i in range(len(self.kws)): paper = self.ids[i] title = self.titles[i] title_keywords = set() for keyword in set(self.kws[i]) | title_keywords: term = ' '.join(word for word in keyword.lower().split() if word.lower() not in self.stopwords) if term not in self.index: self.index[term] = [] self.index[term].append(self.ids[i]) with open(self.index_path, 'w') as f: json.dump(self.index, f) def load_index(self): print("Loading existing index...") with open(self.index_path, 'rb') as f: self.index = json.load(f) print("Index loaded successfully.") def load_or_build_index(self): if os.path.exists(self.index_path): self.load_index() else: self.build_index() def parse_doc(self, doc): local_kws = [] for phrase in doc._.phrases: local_kws.append(phrase.text.lower()) return [self.preprocess_text(word) for word in local_kws] def get_propn(self, doc): result = [] working_str = '' for token in doc: if(token.text in nlp.Defaults.stop_words or token.text in punctuation): if working_str != '': result.append(working_str.strip()) working_str = '' if(token.pos_ == "PROPN"): working_str += token.text + ' ' if working_str != '': result.append(working_str.strip()) return [self.preprocess_text(word) for word in result] def filter(self, query: str, doc_ids = None): doc = nlp(query) query_keywords = self.parse_doc(doc) nouns = self.get_propn(doc) if self.verbose: print('keywords:', query_keywords) if self.verbose: print('proper nouns:', nouns) filtered = set() if len(query_keywords) > 0 and not self.ne_only: for keyword in query_keywords: if keyword != '' and keyword in self.index.keys(): filtered |= set(self.index[keyword]) if len(nouns) > 0: ne_results = set() for noun in nouns: if noun in self.index.keys(): ne_results |= set(self.index[noun]) if self.ne_only: filtered = ne_results # keep only named entity results else: filtered &= ne_results # take the intersection if doc_ids is not None: filtered &= doc_ids # apply filter to results return filtered class EmbeddingRetrievalSystem(): def __init__(self, weight_citation = False, weight_date = False, weight_keywords = False): self.ids = st.session_state.ids self.years = st.session_state.years self.abstract = st.session_state.abstracts self.client = OpenAI(api_key = openai_key) self.embed_model = "text-embedding-3-small" self.dataset = arxiv_corpus self.kws = st.session_state.kws self.weight_citation = weight_citation self.weight_date = weight_date self.weight_keywords = weight_keywords self.id_to_index = {self.ids[i]: i for i in range(len(self.ids))} # self.citation_filter = CitationFilter(self.dataset) # self.date_filter = DateFilter(self.dataset['date']) self.keyword_filter = KeywordFilter(corpus=self.dataset, remove_capitals=True) def parse_date(self, id): # indexval = np.where(self.ids == id)[0][0] indexval = id return self.years[indexval] def make_embedding(self, text): str_embed = self.client.embeddings.create(input = [text], model = self.embed_model).data[0].embedding return str_embed def embed_batch(self, texts: List[str]) -> List[np.ndarray]: embeddings = self.client.embeddings.create(input=texts, model=self.embed_model).data return [np.array(embedding.embedding, dtype=np.float32) for embedding in embeddings] def init_filters(self): self.citation_filter = [] self.date_filter = [] self.keyword_filter = [] def get_query_embedding(self, query): return self.make_embedding(query) def analyze_temporal_query(self, query): return def calc_faiss(self, query_embedding, top_k = 100): # xq = query_embedding.reshape(-1,1).T.astype('float32') # D, I = self.index.search(xq, top_k) # return I[0], D[0] tmp = self.dataset.search('embed',query_embedding, k=top_k) return [tmp.indices, tmp.scores] def rank_and_filter(self, query, query_embedding, query_date, top_k = 10, return_scores=False, time_result=None): topk_indices, similarities = self.calc_faiss(np.array(query_embedding), top_k = 300) if self.weight_keywords: keyword_matches = self.keyword_filter.filter(query) kw_indices = np.zeros_like(similarities) for s in keyword_matches: if self.id_to_index[s] in topk_indices: # print('yes', self.id_to_index[s], topk_indices[np.where(topk_indices == self.id_to_index[s])[0]]) similarities[np.where(topk_indices == self.id_to_index[s])[0]] = similarities[np.where(topk_indices == self.id_to_index[s])[0]] * 10. similarities = similarities / 10. filtered_results = [[topk_indices[i], similarities[i]] for i in range(len(similarities))] top_results = sorted(filtered_results, key=lambda x: x[1], reverse=True)[:top_k] if return_scores: return {doc[0]: doc[1] for doc in top_results} # Only keep the document IDs top_results = [doc[0] for doc in top_results] return top_results def retrieve(self, query, top_k, time_result=None, query_date = None, return_scores = False): query_embedding = self.get_query_embedding(query) # Judge time relevance if time_result is None: if self.weight_date: time_result, time_taken = self.analyze_temporal_query(query, self.anthropic_client) else: time_result = {'has_temporal_aspect': False, 'expected_year_filter': None, 'expected_recency_weight': None} top_results = self.rank_and_filter(query, query_embedding, query_date, top_k, return_scores = return_scores, time_result = time_result) return top_results class HydeRetrievalSystem(EmbeddingRetrievalSystem): def __init__(self, generation_model: str = "claude-3-haiku-20240307", embedding_model: str = "text-embedding-3-small", temperature: float = 0.5, max_doclen: int = 500, generate_n: int = 1, embed_query = True, conclusion = False, **kwargs): # Handle the kwargs for the superclass init -- filters/citation weighting super().__init__(**kwargs) if max_doclen * generate_n > 8191: raise ValueError("Too many tokens. Please reduce max_doclen or generate_n.") self.embedding_model = embedding_model self.generation_model = generation_model # HYPERPARAMETERS self.temperature = temperature # generation temperature self.max_doclen = max_doclen # max tokens for generation self.generate_n = generate_n # how many documents self.embed_query = embed_query # embed the query vector? self.conclusion = conclusion # generate conclusion as well? self.anthropic_key = anthropic_key self.generation_client = anthropic.Anthropic(api_key = self.anthropic_key) def retrieve(self, query: str, top_k: int = 10, return_scores = False, time_result = None) -> List[Tuple[str, str, float]]: if time_result is None: if self.weight_date: time_result, time_taken = analyze_temporal_query(query, self.anthropic_client) else: time_result = {'has_temporal_aspect': False, 'expected_year_filter': None, 'expected_recency_weight': None} docs = self.generate_docs(query) doc_embeddings = self.embed_docs(docs) if self.embed_query: query_emb = self.embed_docs([query])[0] doc_embeddings.append(query_emb) embedding = np.mean(np.array(doc_embeddings), axis = 0) top_results = self.rank_and_filter(query, embedding, query_date=None, top_k = top_k, return_scores = return_scores, time_result = time_result) return top_results def generate_doc(self, query: str): prompt = """You are an expert astronomer. Given a scientific query, generate the abstract""" if self.conclusion: prompt += " and conclusion" prompt += """ of an expert-level research paper that answers the question. Stick to a maximum length of {} tokens and return just the text of the abstract and conclusion. Do not include labels for any section. Use research-specific jargon.""".format(self.max_doclen) message = self.generation_client.messages.create( model = self.generation_model, max_tokens = self.max_doclen, temperature = self.temperature, system = prompt, messages=[{ "role": "user", "content": [{"type": "text", "text": query,}] }] ) return message.content[0].text def generate_docs(self, query: str): docs = [] with concurrent.futures.ThreadPoolExecutor() as executor: future_to_query = {executor.submit(self.generate_doc, query): query for i in range(self.generate_n)} for future in concurrent.futures.as_completed(future_to_query): query = future_to_query[future] try: data = future.result() docs.append(data) except Exception as exc: pass return docs def embed_docs(self, docs: List[str]): return self.embed_batch(docs) class HydeCohereRetrievalSystem(HydeRetrievalSystem): def __init__(self, **kwargs): super().__init__(**kwargs) self.cohere_key = "Of1MjzFjGmvzBAqdvNHTQLkAjecPcOKpiIPAnFMn" self.cohere_client = cohere.Client(self.cohere_key) def retrieve(self, query: str, top_k: int = 10, rerank_top_k: int = 250, return_scores = False, time_result = None, reweight = False) -> List[Tuple[str, str, float]]: if time_result is None: if self.weight_date: time_result, time_taken = analyze_temporal_query(query, self.anthropic_client) else: time_result = {'has_temporal_aspect': False, 'expected_year_filter': None, 'expected_recency_weight': None} top_results = super().retrieve(query, top_k = rerank_top_k, time_result = time_result) # doc_texts = self.get_document_texts(top_results) # docs_for_rerank = [f"Abstract: {doc['abstract']}\nConclusions: {doc['conclusions']}" for doc in doc_texts] docs_for_rerank = [self.abstract[i] for i in top_results] if len(docs_for_rerank) == 0: return [] reranked_results = self.cohere_client.rerank( query=query, documents=docs_for_rerank, model='rerank-english-v3.0', top_n=top_k ) final_results = [] for result in reranked_results.results: doc_id = top_results[result.index] doc_text = docs_for_rerank[result.index] score = float(result.relevance_score) final_results.append([doc_id, "", score]) if reweight: if time_result['has_temporal_aspect']: final_results = self.date_filter.filter(final_results, time_score = time_result['expected_recency_weight']) if self.weight_citation: self.citation_filter.filter(final_results) if return_scores: return {result[0]: result[2] for result in final_results} return [doc[0] for doc in final_results] def embed_docs(self, docs: List[str]): return self.embed_batch(docs) # ---------------------------------------------------------------- if 'ec' not in st.session_state: ec = EmbeddingRetrievalSystem(weight_keywords=True) st.session_state.ec = ec st.toast('loaded retrieval system') else: ec = st.session_state.ec # Function to simulate question answering (replace with actual implementation) def answer_question(question, top_k, keywords, toggles, method, question_type): # Simulated answer (replace with actual logic) # return f"Answer to '{question}' using method {method} for {question_type} question." return run_ret(question, top_k) def get_papers(ids): papers, scores, links = [], [], [] for i in ids: papers.append(st.session_state.titles[i]) scores.append(ids[i]) links.append('https://ui.adsabs.harvard.edu/abs/'+st.session_state.arxiv_corpus['bibcode'][i]+'/abstract') return pd.DataFrame({ 'Title': papers, 'Relevance': scores, 'Link': links }) def create_embedding_plot(rs): pltsource = ColumnDataSource(data=dict( x=st.session_state.arxiv_corpus['umap_x'], y=st.session_state.arxiv_corpus['umap_y'], title=st.session_state.titles, link=st.session_state.arxiv_corpus['bibcode'], )) rsflag = np.zeros((len(st.session_state.ids),)) rsflag[np.array([k for k in rs])] = 1 pltsource.data['colors'] = rsflag * 0.8 + 0.1 pltsource.data['sizes'] = (rsflag + 1)**5 / 100 TOOLTIPS = """
ID: $index ($x, $y) @title
@link

""" mapper = linear_cmap(field_name="colors", palette=Spectral5, low=0., high=1.) p = figure(width=700, height=900, tooltips=TOOLTIPS, x_range=(0, 20), y_range=(-4.2,18), title="UMAP projection of embeddings for the astro-ph corpus") p.axis.visible=False p.grid.visible=False p.outline_line_alpha = 0. p.circle('x', 'y', radius='sizes', source=pltsource, alpha=0.3, fill_color=mapper, fill_alpha='colors', line_color="lightgrey",line_alpha=0.1) return p # Function to simulate keyword extraction (replace with actual implementation) def extract_keywords(question): # Simulated keyword extraction (replace with actual logic) return ['keyword1', 'keyword2', 'keyword3'] # Function to estimate consensus (replace with actual implementation) def estimate_consensus(): # Simulated consensus estimation (replace with actual calculation) return 0.75 def run_ret(query, top_k): rs = ec.retrieve(query, top_k, return_scores=True) output_str = '' for i in rs: if rs[i] > 0.5: output_str = output_str + '---> ' + st.session_state.abstracts[i] + '(score: %.2f) \n' %rs[i] else: output_str = output_str + st.session_state.abstracts[i] + '(score: %.2f) \n' %rs[i] return output_str, rs def Library(query, top_k=7): rs = ec.retrieve(query, top_k, return_scores=True) op_docs = '' for paperno, i in enumerate(rs): # op_docs.append(abstracts[i]) op_docs = op_docs + 'Paper %.0f:' %(paperno+1) +' (published in '+st.session_state.arxiv_corpus['bibcode'][i][0:4] + ') ' + st.session_state.titles[i] + '\n' + st.session_state.abstracts[i] + '\n\n' # st.write(op_docs) return op_docs search = DuckDuckGoSearchAPIWrapper() tools = [ Tool( name="Library", func=Library, description="A source of information pertinent to your question. Do not answer a question without consulting this!" ), Tool( name="Search", func=search.run, description="useful for when you need to look up knowledge about common topics or current events", ) ] if 'tools' not in st.session_state: st.session_state.tools = tools # for another question type: # First, find the quotes from the document that are most relevant to answering the question, and then print them in numbered order. # Quotes should be relatively short. If there are no relevant quotes, write “No relevant quotes” instead. gen_llm = openai_llm(temperature=0,model_name='gpt-4o-mini', openai_api_key = openai_key) template = """You are an expert astronomer and cosmologist. Answer the following question as best you can using information from the library, but speaking in a concise and factual manner. If you can not come up with an answer, say you do not know. Try to break the question down into smaller steps and solve it in a logical manner. You have access to the following tools: {tools} Use the following format: Question: the input question you must answer Thought: you should always think about what to do Action: the action to take, should be one of [{tool_names}] Action Input: the input to the action Observation: the result of the action ... (this Thought/Action/Action Input/Observation can repeat N times) Thought: I now know the final answer Final Answer: the final answer to the original input question. provide information about how you arrived at the answer, and any nuances or uncertainties the reader should be aware of Begin! Remember to speak in a pedagogical and factual manner." Question: {input} Thought:{agent_scratchpad}""" prompt = hub.pull("hwchase17/react") prompt.template=template from langchain.callbacks import FileCallbackHandler from langchain.callbacks.manager import CallbackManager # timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") # file_path = f"agent_trace_{timestamp}.txt" file_path = "agent_trace.txt" file_handler = FileCallbackHandler(file_path) callback_manager=CallbackManager([file_handler]) tool_names = [tool.name for tool in st.session_state.tools] if 'agent' not in st.session_state: # agent = ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names) agent = create_react_agent(llm=gen_llm, tools=tools, prompt=prompt) st.session_state.agent = agent if 'agent_executor' not in st.session_state: agent_executor = AgentExecutor(agent=st.session_state.agent, tools=st.session_state.tools, verbose=True, handle_parsing_errors=True, callbacks=CallbackManager([file_handler])) st.session_state.agent_executor = agent_executor # Streamlit app def main(): # st.title("Question Answering App") # Sidebar (Inputs) st.sidebar.header("Fine-tune the search") top_k = st.sidebar.slider("Number of papers to retrieve:", 3, 30, 10) extra_keywords = st.sidebar.text_input("Enter extra keywords (comma-separated):") st.sidebar.subheader("Toggles") toggle_a = st.sidebar.checkbox("Weight by keywords") toggle_b = st.sidebar.checkbox("weight by time") toggle_c = st.sidebar.checkbox("Weight by citations") method = st.sidebar.radio("Choose a method:", ["Semantic search", "Semantic search + HyDE", "Semantic search + HyDE + CoHERE"]) question_type = st.sidebar.selectbox("Select question type:", ["Single paper", "Multi-paper", "Summary"]) # store_output = st.sidebar.checkbox("Store the output") store_output = st.sidebar.button("Save output") # Main page (Outputs) query = st.text_input("Ask me anything:") submit_button = st.button("Submit") if submit_button: # Process inputs keywords = [kw.strip() for kw in extra_keywords.split(',')] if extra_keywords else [] toggles = {'Keyword weighting': toggle_a, 'Time weighting': toggle_b, 'Citation weighting': toggle_c} # Generate outputs answer, rs = answer_question(query, top_k, keywords, toggles, method, question_type) papers_df = get_papers(rs) embedding_plot = create_embedding_plot(rs) triggered_keywords = extract_keywords(query) consensus = estimate_consensus() # Display outputs answer = st.session_state.agent_executor.invoke({"input": query,}) st.write(answer["output"]) with open(file_path, 'r') as file: intermediate_steps = file.read() st.expander('Intermediate steps', expanded=False).write(intermediate_steps) # st.write(answer) with st.expander("Relevant papers", expanded=True): # st.dataframe(papers_df, hide_index=True) st.data_editor(papers_df, column_config = {'Link':st.column_config.LinkColumn(display_text= 'https://ui.adsabs.harvard.edu/abs/(.*?)/abstract')} ) with st.expander("Embedding map", expanded=False): st.bokeh_chart(embedding_plot) col1, col2 = st.columns(2) with col1: st.subheader("Question Type") st.write(question_type) st.subheader("Triggered Keywords") st.write(", ".join(triggered_keywords)) with col2: st.subheader("Consensus Estimate") st.write(f"{consensus:.2%}") # st.subheader("Papers Used") # st.dataframe(papers_df) else: st.info("Use the sidebar to input parameters and submit to see results.") if store_output: st.toast("Output stored successfully!") if __name__ == "__main__": main()