app.py

import os
import streamlit as st
import time
from langchain.chat_models import ChatOpenAI
from langchain.chains import ConversationalRetrievalChain
from langchain.prompts import PromptTemplate
from langchain.memory import ConversationSummaryBufferMemory
from langchain.vectorstores import FAISS
from langchain.embeddings import OpenAIEmbeddings

# Set up the OpenAI API key
os.environ["OPENAI_API_KEY"] = os.environ.get("OPENAI_API_KEY")

# Load the FAISS index
embeddings = OpenAIEmbeddings()
vectorstore = FAISS.load_local("faiss_index", embeddings, allow_dangerous_deserialization=True)

# Create a retriever from the loaded vector store
retriever = vectorstore.as_retriever(search_kwargs={"k": 5})

# Define a prompt template for course recommendations
prompt_template = """
You are an AI-powered course recommendation expert with extensive knowledge of educational programs across various disciplines. Your primary goal is to provide personalized, high-quality course suggestions tailored to each user's unique interests, goals, and background.

Conversation History:
{chat_history}

Current User Query:
{question}

Relevant Courses from Database:
{context}

Instructions for Crafting Your Response:

1. Engagement and Tone:
   - Begin with a warm, friendly greeting if this is a new interaction.
   - Maintain a professional yet approachable tone throughout the conversation.
   - If the user initiates casual chat, engage briefly before steering the conversation towards educational interests.

2. Analysis and Recommendation:
   - Carefully analyze the user's query and conversation history to understand their educational needs, interests, and any constraints.
   - Select the most relevant courses from the provided context, prioritizing those with learning outcomes and syllabus content that closely match the user's requirements.

3. Detailed Course Recommendations:
   For each recommended course, provide:
   - Course title and offering institution
   - A concise overview of the course content
   - Specific skills and knowledge to be gained (from "What You Will Learn")
   - Key topics covered in the syllabus
   - Course level, duration, and language of instruction
   - Course ratings and reviews, if available
   - Direct URL to the course page

4. Personalized Explanation:
   - Clearly articulate how each recommended course aligns with the user's expressed interests and goals.
   - Highlight specific aspects of the course that address the user's needs or previous queries.

5. Learning Path Suggestions:
   - If appropriate, suggest a learning path combining complementary courses.
   - Explain how the courses build upon each other or cover different aspects of the user's area of interest.

6. Additional Guidance:
   - Offer advice on course selection based on the user's background and goals.
   - Suggest supplementary resources or preparatory materials if relevant.
   - Address any potential challenges or prerequisites the user should consider.

7. Encouragement and Next Steps:
   - Provide encouraging words to motivate the user in their learning journey.
   - Suggest clear next steps, such as exploring course details or considering enrollment options.
   - Invite further questions or clarifications about the recommendations.

8. Adaptability:
   - If the user expresses dissatisfaction with initial recommendations, quickly pivot to alternative suggestions.
   - Be prepared to refine recommendations based on additional information or feedback from the user.

Remember to prioritize accuracy, relevance, and user-centricity in your recommendations. Your goal is to empower the user to make informed decisions about their educational path.

Recommendation:
"""


PROMPT = PromptTemplate(
    template=prompt_template,
    input_variables=["chat_history", "question", "context"]
)

# Initialize the language model
llm = ChatOpenAI(temperature=0.5, model_name="gpt-4o-mini")

# Set up conversation memory with summarization
memory = ConversationSummaryBufferMemory(llm=llm, max_token_limit=1000, memory_key="chat_history", return_messages=True)

# Create the conversational retrieval chain
qa_chain = ConversationalRetrievalChain.from_llm(
    llm=llm,
    retriever=retriever,
    memory=memory,
    combine_docs_chain_kwargs={"prompt": PROMPT}
)

# Streamlit app
st.set_page_config(page_title="HCourse Recommendation Chatbot", page_icon=":book:")
st.title("HONEY BEE: Course Recommendation Chatbot 🐝")

# Initialize chat history in session state
if "messages" not in st.session_state:
    st.session_state.messages = []
    # Add introductory message
    welcome_message = (
        "Hello! I'm HONEY BEE, your friendly Course Recommendation Chatbot! 🐝 "
        "I'm here to help you find the best courses based on your interests and goals. "
        "Feel free to ask me anything about learning or courses!"
    )
    st.session_state.messages.append({"role": "assistant", "content": welcome_message})

# Display chat messages from history on app rerun
for message in st.session_state.messages:
    with st.chat_message(message["role"]):
        st.markdown(message["content"])

# Accept user input
if prompt := st.chat_input("What are you looking to learn?"):
    # Add user message to chat history
    st.session_state.messages.append({"role": "user", "content": prompt})
    # Display user message in chat message container
    with st.chat_message("user"):
        st.markdown(prompt)

    # Assistant response generation with streaming effect
    with st.chat_message("assistant"):
        response = qa_chain({"question": prompt})
        response_text = response["answer"]
    
        # Create an empty placeholder
        placeholder = st.empty()
    
        # Initialize an empty string to accumulate the response
        accumulated_response = ""
    
        # Stream the response character by character
        for char in response_text:
            accumulated_response += char
            placeholder.markdown(accumulated_response, unsafe_allow_html=True)
            time.sleep(0.01)  # Add a small delay to create a typing effect
    
    # Add assistant response to chat history
    st.session_state.messages.append({"role": "assistant", "content": response_text})