Update app.py

app.py

@@ -8,6 +8,11 @@ from langchain.prompts import PromptTemplate
 from langchain.memory import ConversationSummaryBufferMemory
 from langchain.vectorstores import FAISS
 from langchain.embeddings import OpenAIEmbeddings
+import re
+
+# Function to split text into sentences
+def split_into_sentences(text):
+    return re.split(r'(?<=[.!?]) +', text)
 
 # Set up the OpenAI API key
 os.environ["OPENAI_API_KEY"] = os.environ.get("OPENAI_API_KEY")
@@ -22,15 +27,11 @@ retriever = vectorstore.as_retriever(search_kwargs={"k": 5})
 # Define a prompt template for course recommendations
 prompt_template = """
 You are an AI course recommendation system. Your task is to recommend courses based on the user's description of their interests and goals, with a strong emphasis on matching the learning outcomes and syllabus content. Consider the summarized chat history to provide more relevant and personalized recommendations.
-
 Summarized Chat History:
 {chat_history}
-
 User's Current Query: {question}
-
 Based on the user's current query and chat history summary, here are some relevant courses from our database:
 {context}
-
 Please provide a personalized course recommendation. Your response should include:
 1. A detailed explanation of how the recommended courses match the user's interests and previous queries, focusing primarily on the "What You Will Learn" section and the syllabus content.
 2. A summary of each recommended course, highlighting:
@@ -41,11 +42,8 @@ Please provide a personalized course recommendation. Your response should includ
 3. Mention the course ratings if available.
 4. Any additional advice or suggestions for the user's learning journey, based on the syllabus progression and their conversation history.
 5. Provide the course URLs for easy access.
-
 Prioritize courses that have the most relevant learning outcomes and syllabus content matching the user's description and previous interactions. If multiple courses are similarly relevant, you may suggest a learning path combining complementary courses.
-
 Remember to be encouraging and supportive in your recommendation, and relate your suggestions to any preferences or constraints the user has mentioned in previous messages.
-
 Recommendation:
 """
 
@@ -117,15 +115,16 @@ if prompt := st.chat_input("What are you looking to learn?"):
     with st.chat_message("user"):
         st.markdown(prompt)
 
-    # Assistant response generator with streaming effect
+    # Assistant response generator with sentence streaming
     with st.chat_message("assistant"):
         response = qa_chain({"question": prompt})
         response_text = response["answer"]
-        
-        # Simulate streaming response
-        for word in response_text.split():
-            st.markdown(word + " ", unsafe_allow_html=True)
-            time.sleep(0.05)  # Delay for effect
+
+        # Split response into sentences and display one by one
+        sentences = split_into_sentences(response_text)
+        for sentence in sentences:
+            st.markdown(sentence)
+            time.sleep(1)  # Delay between sentences (adjust as needed)
 
     # Add assistant response to chat history
     st.session_state.messages.append({"role": "assistant", "content": response_text})