Update app.py

app.py

@@ -12,12 +12,17 @@ text_data = response.text
 sentences = text_data.split('\n')
 
 # Initialize the TF-IDF Vectorizer
-vectorizer = TfidfVectorizer().fit_transform(sentences)
-vectors = vectorizer.toarray()
+vectorizer = TfidfVectorizer().fit(sentences)
+vectors = vectorizer.transform(sentences)  # Don't convert to array, keep it sparse
 
 def get_response(user_query):
-    user_vector = vectorizer.transform([user_query]).toarray()
+    # Transform user query and keep the result sparse
+    user_vector = vectorizer.transform([user_query])
+    
+    # Compute cosine similarity directly with sparse matrices
     similarities = cosine_similarity(user_vector, vectors)
+    
+    # Find the index of the most similar sentence
     closest_index = similarities.argmax()
     return sentences[closest_index]
 
@@ -45,4 +50,4 @@ if user_input:
 # Display the chat history
 for message in st.session_state.messages:
     with st.chat_message(message["role"]):
-        st.write(message["content"])
+        st.write(message["content"])