Spaces:
Running
Running
File size: 8,210 Bytes
2740367 c6bcc0c 2740367 c6bcc0c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 |
import streamlit as st
import json
import numpy as np
from sentence_transformers import SentenceTransformer, util
import os
import re
import boto3
import psycopg2
from psycopg2.extensions import connection
from dotenv import load_dotenv
# --- 0. Config ---
load_dotenv()
def get_rds_connection() -> connection:
region = os.getenv("AWS_REGION")
secret_arn = os.getenv("RDS_SECRET_ARN")
host = os.getenv("RDS_HOST")
dbname = os.getenv("RDS_DB_NAME")
sm = boto3.client("secretsmanager", region_name=region)
secret_value = sm.get_secret_value(SecretId=secret_arn)
secret_dict = json.loads(secret_value["SecretString"])
conn = psycopg2.connect(
host=host or secret_dict.get("host"),
port=int(secret_dict.get("port", 5432)),
dbname=dbname or secret_dict.get("dbname"),
user=secret_dict["username"],
password=secret_dict["password"],
sslmode="require",
)
return conn
# --- 1. Load the Embedding Model ---
@st.cache_resource
def load_model():
"""
Loads the specialized math embedding model from Hugging Face.
"""
try:
model = SentenceTransformer('math-similarity/Bert-MLM_arXiv-MP-class_zbMath')
return model
except Exception as e:
st.error(f"Error loading the embedding model: {e}")
return None
# --- 2. Load Data from RDS ---
@st.cache_data
def load_papers_from_rds():
"""
Loads theorem data from the RDS database and prepares it for embedding.
Returns a list of theorem dictionaries with all necessary fields.
"""
try:
conn = get_rds_connection()
cur = conn.cursor()
# Fetch all papers with their theorems and embeddings
cur.execute("""
SELECT
tm.paper_id,
tm.title,
tm.authors,
tm.link,
tm.last_updated,
tm.summary,
tm.journal_ref,
tm.primary_category,
tm.categories,
tm.global_notations,
tm.global_definitions,
tm.global_assumptions,
te.theorem_name,
te.theorem_slogan,
te.theorem_body,
te.embedding
FROM theorem_metadata tm
JOIN theorem_embedding te ON tm.paper_id = te.paper_id
ORDER BY tm.paper_id, te.theorem_name;
""")
rows = cur.fetchall()
cur.close()
conn.close()
all_theorems_data = []
for row in rows:
(paper_id, title, authors, link, last_updated, summary,
journal_ref, primary_category, categories,
global_notations, global_definitions, global_assumptions,
theorem_name, theorem_slogan, theorem_body, embedding) = row
# Build global context
global_context_parts = []
if global_notations:
global_context_parts.append(f"**Global Notations:**\n{global_notations}")
if global_definitions:
global_context_parts.append(f"**Global Definitions:**\n{global_definitions}")
if global_assumptions:
global_context_parts.append(f"**Global Assumptions:**\n{global_assumptions}")
global_context = "\n\n".join(global_context_parts)
# Convert embedding to a numpy float array
if isinstance(embedding, str):
embedding = json.loads(embedding)
if isinstance(embedding, list):
embedding = np.array(embedding, dtype=np.float32)
elif isinstance(embedding, np.ndarray):
embedding = embedding.astype(np.float32)
all_theorems_data.append({
"paper_id": paper_id,
"paper_title": title,
"paper_url": link,
"theorem_name": theorem_name,
"theorem_slogan": theorem_slogan,
"theorem_body": theorem_body,
"global_context": global_context,
"text_to_embed": f"{global_context}\n\n**Theorem ({theorem_name}):**\n{theorem_body}",
"stored_embedding": embedding
})
return all_theorems_data
except Exception as e:
st.error(f"Error loading data from RDS: {e}")
return []
# --- 3. The Search Function ---
def search_theorems(query, model, theorems_data, embeddings_db):
"""
Takes a user query and finds the top 5 most similar theorems.
"""
if not query:
st.info("Please enter a search query.")
return
query_embedding = model.encode(query, convert_to_tensor=True)
cosine_scores = util.cos_sim(query_embedding, embeddings_db)[0]
top_results_indices = np.argsort(-cosine_scores.cpu())[:5]
st.subheader("Top 5 Most Similar Theorems")
if len(top_results_indices) == 0:
st.write("No results found.")
return
for i, idx in enumerate(top_results_indices):
idx = idx.item()
similarity = cosine_scores[idx].item()
theorem_info = theorems_data[idx]
# Use an expander for each result to keep the main view clean
expander_title = f"**Result {i+1} | Similarity: {similarity:.4f}**"
if theorem_info.get("theorem_name"):
expander_title += f" | {theorem_info['theorem_name']}"
with st.expander(expander_title):
st.markdown(f"**Paper:** {theorem_info.get('paper_title', 'Unknown')}")
st.markdown(f"**Source:** [{theorem_info['paper_url']}]({theorem_info['paper_url']})")
# Display theorem slogan if available
if theorem_info.get("theorem_slogan"):
st.markdown(f"**Slogan:** {theorem_info['theorem_slogan']}")
st.write("")
# Display global context in a more readable blockquote
if theorem_info["global_context"]:
blockquote_context = "> " + theorem_info["global_context"].replace("\n", "\n> ")
st.markdown(blockquote_context)
st.write("")
# Clean and display theorem body
content = theorem_info['theorem_body']
# Remove labels, citations, and other disruptive commands
cleaned_content = re.sub(r'\\(label|cite|eqref)\{.*?\}', '', content)
# Convert math delimiters to $$
cleaned_content = re.sub(r'\\\[(.*?)\\\]', r'$$\1$$', cleaned_content)
cleaned_content = re.sub(r'\\\((.*?)\\\)', r'$\1$', cleaned_content)
# Remove common environment wrappers like \begin\{...\} and \end\{...\}
cleaned_content = re.sub(r'\\label\{.*?\}', r'', cleaned_content)
cleaned_content = re.sub(r'\\begin\{.*?\}', r'', cleaned_content)
cleaned_content = re.sub(r'\\end\{.*?\}', r'', cleaned_content)
# Remove extra formatting like newlines and tabs
cleaned_content = cleaned_content.replace('\n', ' ').replace('\t', ' ').strip()
# Use st.markdown() to render the cleaned, mixed text and LaTeX
st.markdown(f"**Theorem Body:**")
st.markdown(cleaned_content)
# --- Main App Interface ---
st.set_page_config(page_title="Theorem Search Demo", layout="wide")
st.title("📚 Semantic Theorem Search")
st.write("This demo uses a specialized mathematical language model to find theorems semantically similar to your query.")
model = load_model()
theorems_data = load_papers_from_rds()
if model and theorems_data:
with st.spinner("Preparing embeddings from database..."):
# Use stored embeddings from database - already numpy arrays
corpus_embeddings = np.array([item['stored_embedding'] for item in theorems_data])
st.success(f"Successfully loaded {len(theorems_data)} theorems from RDS. Ready to search!")
user_query = st.text_input("Enter your query:", "The Jones polynomial is a link invariant")
if st.button("Search") or user_query:
search_theorems(user_query, model, theorems_data, corpus_embeddings)
else:
st.error("Could not load the model or data from RDS. Please check your database connection and credentials.") |