Create app.py

app.py

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+import os
+import pickle
+import streamlit as st
+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 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:
+   - The specific skills and knowledge the user will gain (from "What You Will Learn")
+   - Key topics covered in the syllabus
+   - Course level and language
+   - The institution offering the course
+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:
+"""
+
+PROMPT = PromptTemplate(
+    template=prompt_template,
+    input_variables=["chat_history", "question", "context"]
+)
+
+# Initialize the language model
+llm = ChatOpenAI(temperature=0.5, model_name="gpt-4-turbo")
+
+# 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.title("AI Course Recommendation Chatbot")
+
+if 'chat_history' not in st.session_state:
+    st.session_state.chat_history = []
+
+user_query = st.text_input("What are you looking to learn?", "")
+
+if st.button("Get Recommendation"):
+    if user_query:
+        response = qa_chain({"question": user_query})
+        recommendation = response["answer"]
+        
+        # Update chat history
+        st.session_state.chat_history.append({"user": user_query, "bot": recommendation})
+
+# Display chat history
+for chat in st.session_state.chat_history:
+    st.write(f"**You:** {chat['user']}")
+    st.write(f"**HONEY BEE:** {chat['bot']}")
+
+# Optional: Add a button to clear the chat history
+if st.button("Clear Chat History"):
+    st.session_state.chat_history.clear()
+    st.experimental_rerun()