Update app.py

app.py

@@ -23,9 +23,11 @@ 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}
+
+User's Current Query: {query}
 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:
@@ -36,6 +38,7 @@ 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:
@@ -43,7 +46,7 @@ Recommendation:
 
 PROMPT = PromptTemplate(
     template=prompt_template,
-    input_variables=["chat_history", "question", "context"]
+    input_variables=["chat_history", "query", "context"]
 )
 
 # Initialize the language model
@@ -73,14 +76,6 @@ for message in st.session_state.messages:
     with st.chat_message(message["role"]):
         st.markdown(message["content"])
 
-# Streamed response generator
-def response_generator(response):
-    accumulated_response = ""
-    for word in response.split():
-        accumulated_response += word + " "
-        yield accumulated_response.strip()  # Yield accumulated response
-        time.sleep(0.05)  # Adjust this delay to control typing speed
-
 # Accept user input
 if prompt := st.chat_input("What are you looking to learn?"):
     # Add user message to chat history
@@ -91,11 +86,11 @@ if prompt := st.chat_input("What are you looking to learn?"):
 
     # Assistant response generation with streaming effect
     with st.chat_message("assistant"):
-        response = qa_chain({"question": prompt})
-        response_text = response["answer"]
+        response = qa_chain({"query": prompt})
+        response_text = response["response"]
 
         # Stream the response word by word
-        for accumulated_response in response_generator(response_text):
+        for accumulated_response in response_text:
             st.markdown(accumulated_response, unsafe_allow_html=True)
 
     # Add assistant response to chat history