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| import streamlit as st | |
| import requests | |
| import torch | |
| from transformers import AutoTokenizer, AutoModel | |
| import xml.etree.ElementTree as ET | |
| # Load SciBERT pre-trained model and tokenizer | |
| model_name = "allenai/scibert_scivocab_uncased" | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| model = AutoModel.from_pretrained(model_name) | |
| def calculate_similarity(claim, document): | |
| if not claim or not document: | |
| return 0.0 | |
| # Tokenize claim and document | |
| inputs = tokenizer.encode_plus(claim, document, return_tensors='pt', padding=True, truncation=True) | |
| # Generate embeddings for claim | |
| with torch.no_grad(): | |
| claim_embeddings = model(**inputs)['pooler_output'] | |
| # Generate embeddings for document | |
| inputs_doc = tokenizer.encode_plus(document, return_tensors='pt', padding=True, truncation=True) | |
| with torch.no_grad(): | |
| document_embeddings = model(**inputs_doc)['pooler_output'] | |
| # Compute cosine similarity between embeddings | |
| similarity = torch.cosine_similarity(claim_embeddings, document_embeddings).item() | |
| return similarity | |
| def search_arxiv(query, max_results=3): | |
| base_url = "http://export.arxiv.org/api/query?" | |
| query = f"search_query=all:{query}&start=0&max_results={max_results}&sortBy=relevance&sortOrder=descending" | |
| try: | |
| response = requests.get(base_url + query) | |
| if response.status_code == 200: | |
| data = response.content | |
| # Parse the XML response | |
| root = ET.fromstring(data) | |
| search_results = [] | |
| for entry in root.findall("{http://www.w3.org/2005/Atom}entry"): | |
| result = {} | |
| # Extract information from each entry | |
| result["title"] = entry.find("{http://www.w3.org/2005/Atom}title").text | |
| result["abstract"] = entry.find("{http://www.w3.org/2005/Atom}summary").text | |
| result["link"] = entry.find("{http://www.w3.org/2005/Atom}link[@title='pdf']").attrib["href"] | |
| authors = [] | |
| for author in entry.findall("{http://www.w3.org/2005/Atom}author"): | |
| authors.append(author.find("{http://www.w3.org/2005/Atom}name").text) | |
| result["authors"] = authors | |
| search_results.append(result) | |
| return search_results | |
| except: | |
| return None | |
| def search_papers(user_input): | |
| # Use the desired search function, e.g., search_arxiv | |
| search_results = search_arxiv(user_input) | |
| return search_results | |
| st.title('The Substantiator') | |
| user_input = st.text_input('Input your claim') | |
| if st.button('Substantiate'): | |
| search_results = search_papers(user_input) | |
| if search_results is not None and len(search_results) > 0: | |
| with st.spinner('Searching for relevant research papers...'): | |
| for result in search_results[:3]: | |
| st.write(f"<a href='javascript:void(0)' onclick='window.open(\"{result['link']}\", \"_blank\");return false;'>{result['title']}</a>", unsafe_allow_html=True) | |
| st.write(result["abstract"]) | |
| st.write("Authors: ", ", ".join(result["authors"])) | |
| similarity = calculate_similarity(user_input, result["abstract"]) | |
| st.write("Similarity Score: ", similarity) | |
| st.write("-----") | |
| else: | |
| st.write("No results found.") |