Update app.py

app.py

@@ -25,7 +25,7 @@ def semantic_search(query, documents, top_k=5):
     document_embeddings = model.encode([text for _, text in documents], convert_to_tensor=True)
 
     # Compute cosine similarity scores of query with documents
-    cosine_scores = util.pytorch_cos_sim(query_embedding, document_embeddings)
+    cosine_scores = util.pytorch_cos_sim(query_embedding.unsqueeze(0), document_embeddings)[0]
 
     # Sort the results in decreasing order
     results = []
@@ -36,7 +36,7 @@ def semantic_search(query, documents, top_k=5):
     return results[:top_k]
 
 def main():
-    st.title("Semantic Search on a PDF Document")
+    st.title("Semantic Search on PDF Documents")
 
     pdf_file = st.file_uploader("Upload a PDF file:", type=["pdf"])
     query = st.text_input("Enter your query:")