Update app.py

app.py

@@ -1,40 +1,66 @@
 import streamlit as st
 import requests
-from bs4 import BeautifulSoup
-
-def search_arxiv(search_query: str, start: int = 0):
-    base_url = 'http://export.arxiv.org/api/query?'
-    query = f'search_query={search_query}&start={start}&max_results=10'
-    response = requests.get(base_url + query)
-    feed = BeautifulSoup(response.content, 'html.parser')
-    entries = feed.find_all('entry')
-    articles = []
-    for entry in entries:
-        article = {}
-        article['title'] = entry.title.text
-        article['authors'] = [author.find('name').text for author in entry.find_all('author')]
-        article['abstract'] = entry.summary.text
-        articles.append(article)
-    return articles
-
-def get_paper_info(paper_id: str):
-    base_url = 'https://api.semanticscholar.org/v1/paper/'
-    response = requests.get(base_url + paper_id)
-    paper = response.json()
-    paper_info = {}
-    paper_info['title'] = paper['title']
-    paper_info['authors'] = [author['name'] for author in paper['authors']]
-    paper_info['abstract'] = paper['abstract']
-    paper_info['fieldsOfStudy'] = paper['fieldsOfStudy']
-    return paper_info
-
-st.title('Scientific Data Substantiator')
-search_query = st.text_input("Enter your search term")
-
-if search_query:
-    articles = search_arxiv(search_query)
-    for article in articles:
-        st.write("Title: ", article['title'])
-        st.write("Authors: ", ", ".join(article['authors']))
-        st.write("Abstract: ", article['abstract'])
-        st.write("-----")
+from transformers import AutoTokenizer, AutoModel
+
+# 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):
+    # Tokenize claim and document
+    inputs = tokenizer.encode_plus(claim, document, return_tensors='pt', padding=True, truncation=True)
+    
+    # Generate embeddings for claim and document
+    with torch.no_grad():
+        claim_embeddings = model(**inputs)['pooler_output']
+    
+    # Compute cosine similarity between embeddings
+    similarity = torch.cosine_similarity(claim_embeddings, document_embeddings).item()
+    
+    return similarity
+
+def search_papers(user_input):
+    # Implement your code to fetch search results from the desired source (e.g., arXiv, Semantic Scholar)
+    # ...
+
+    # For the purpose of this example, we'll use dummy data
+    search_results = [
+        {
+            'title': 'Paper 1 Title',
+            'abstract': 'Paper 1 Abstract',
+            'authors': ['Author 1', 'Author 2'],
+            'url': 'https://example.com/paper1'
+        },
+        {
+            'title': 'Paper 2 Title',
+            'abstract': 'Paper 2 Abstract',
+            'authors': ['Author 3', 'Author 4'],
+            'url': 'https://example.com/paper2'
+        },
+        {
+            'title': 'Paper 3 Title',
+            'abstract': 'Paper 3 Abstract',
+            'authors': ['Author 5', 'Author 6'],
+            'url': 'https://example.com/paper3'
+        }
+    ]
+
+    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:
+        for result in search_results:
+            st.write(result["title"])
+            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.")