Create app.py

app.py

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+import os
+from langchain_community.document_loaders import PyPDFLoader
+from langchain_community.vectorstores import faiss
+from langchain.memory import ConversationBufferMemory
+from langchain_google_genai import ChatGoogleGenerativeAI, GoogleGenerativeAIEmbeddings
+from tempfile import NamedTemporaryFile
+from dotenv import load_dotenv
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+from langchain.chains import ConversationalRetrievalChain
+import streamlit as st
+import nest_asyncio
+
+nest_asyncio.apply()
+load_dotenv()
+
+# Initialize app resources
+st.set_page_config(page_title="StudyAssist", page_icon=":book:")
+st.title("StudyAssist(pharmassist-v0)")
+st.write(
+    "An AI/RAG application to aid students in their studies, specially optimized for the pharm 028 students. In simpler terms, chat with your pdf"
+)
+
+
+@st.cache_resource
+def initialize_resources():
+    llm_gemini = ChatGoogleGenerativeAI(
+        model="gemini-1.5-flash-latest", google_api_key=os.getenv("GOOGLE_API_KEY")
+    )
+    return llm_gemini
+
+
+def get_retriever(pdf_file):
+    with NamedTemporaryFile(suffix="pdf") as temp:
+        temp.write(pdf_file.getvalue())
+        pdf_loader = PyPDFLoader(temp.name, extract_images=True)
+        pages = pdf_loader.load()
+
+    st.write(f"AI Chatbot for {course_material}")
+
+    underlying_embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
+    text_splitter = RecursiveCharacterTextSplitter(
+        chunk_size=1000,
+        chunk_overlap=20,
+        length_function=len,
+        is_separator_regex=False,
+        separators="\n",
+    )
+    documents = text_splitter.split_documents(pages)
+    vectorstore = faiss.FAISS.from_documents(documents, underlying_embeddings)
+    doc_retiever = vectorstore.as_retriever(
+        search_type="mmr", search_kwargs={"k": 5, "fetch_k": 10}
+    )
+
+    return doc_retiever
+
+
+chat_model = initialize_resources()
+
+# Streamlit UI
+# Course list and pdf retrieval
+
+courses = ["PMB", "PCL", "Kelechi_research"]  # "GSP", "CPM", "PCG",  "PCH"
+course_pdfs = None
+doc_retriever = None
+conversational_chain = None
+
+# course = st.sidebar.selectbox("Choose course", (courses))
+# docs_path = f"pdfs/{course}"
+# course_pdfs = os.listdir(docs_path)
+# pdfs = [os.path.join(docs_path, pdf) for pdf in course_pdfs]
+
+course_material = "{Not selected}"
+
+
+# @st.cache_resource
+def query_response(query, _retriever):
+    memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
+    conversational_chain = ConversationalRetrievalChain.from_llm(
+        llm=chat_model, retriever=_retriever, memory=memory, verbose=False
+    )
+    response = conversational_chain.run(query)
+
+    return response
+
+
+if "doc" not in st.session_state:
+    st.session_state.doc = ""
+
+course_material = st.file_uploader("or Upload your own pdf", type="pdf")
+
+if st.session_state != "":
+    try:
+        doc_retriever = get_retriever(course_material)
+        st.success("File loading successful, vector db initialize")
+    except:
+        st.error("Upload your file")
+
+    # We store the conversation in the session state.
+    # This will be use to render the chat conversation.
+    # We initialize it with the first message we want to be greeted with.
+    if "messages" not in st.session_state:
+        st.session_state.messages = [
+            {"role": "assistant", "content": "Yoo, How far boss?"}
+        ]
+
+    if "current_response" not in st.session_state:
+        st.session_state.current_response = ""
+
+    # We loop through each message in the session state and render it as
+    # a chat message.
+    for message in st.session_state.messages:
+        with st.chat_message(message["role"]):
+            st.markdown(message["content"])
+
+    # We take questions/instructions from the chat input to pass to the LLM
+    if user_prompt := st.chat_input("Your message here", key="user_input"):
+        # Add our input to the session state
+        st.session_state.messages.append({"role": "user", "content": user_prompt})
+
+        # Add our input to the chat window
+        with st.chat_message("user"):
+            st.markdown(user_prompt)
+
+        # Pass our input to the llm chain and capture the final responses.
+        # here once the llm has finished generating the complete response.
+        response = query_response(user_prompt, doc_retriever)
+        # Add the response to the session state
+        st.session_state.messages.append({"role": "assistant", "content": response})
+
+        # Add the response to the chat window
+        with st.chat_message("assistant"):
+            st.markdown(response)
+#
+st.write("")
+st.write("")
+
+
+st.markdown(
+    """
+    <div style="text-align: center; padding: 1rem;">
+        Project by <a href="https://github.com/kelechi-c" target="_blank" style="color: white; font-weight: bold; text-decoration: none;">
+         kelechi(tensor)</a>
+    </div>
+""",
+    unsafe_allow_html=True,
+)