app.py

import streamlit as st
from langchain.prompts import PromptTemplate
from langchain.chains.question_answering import load_qa_chain
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.vectorstores import Chroma
from langchain_google_genai import GoogleGenerativeAIEmbeddings, ChatGoogleGenerativeAI
from dotenv import load_dotenv
import PyPDF2
import os
import io
from langchain.document_loaders import PyPDFDirectoryLoader
from langchain.embeddings import SentenceTransformerEmbeddings
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnablePassthrough


# Define SPEAKER_TYPES to distinguish between user and bot roles
SPEAKER_TYPES = {
    "USER": "user",
    "BOT": "bot"
}

# Define the initial prompt to show when the app starts
initial_prompt = {
    'role': SPEAKER_TYPES["BOT"],
    'content': "Hello! I am your Gemini Pro RAG chatbot. You can ask me questions after uploading a PDF."
}


# --- Your RAG chatbot logic ---
source_data_folder = "MyData"
text_splitter = RecursiveCharacterTextSplitter(
    separators=["\n\n", "\n", ". ", " ", ""],
    chunk_size=2000,
    chunk_overlap=200
)
embeddings_model = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
path_db = "/content/VectorDB"
llm = ChatGoogleGenerativeAI(model="gemini-1.5-pro", google_api_key="AIzaSyAnsIVS4x_7lJLe9AYXGLV8FRwUTQkB-1w")

# --- Streamlit app starts here ---
# Set up the Streamlit app configuration
st.set_page_config(
    page_title="Gemini Pro RAG App",
    page_icon="🔍",
    layout="wide",
    initial_sidebar_state="expanded",
)

# Initialize session state for chat history and vectorstore (PDF context)
if 'chat_history' not in st.session_state:
    st.session_state.chat_history = [initial_prompt]
if 'vectorstore' not in st.session_state:
    st.session_state.vectorstore = None

# Function to clear chat history
def clear_chat_history():
    st.session_state.chat_history = [initial_prompt]

# Extract text from PDF
def extract_text_from_pdf(pdf_bytes):
    pdf_reader = PyPDF2.PdfReader(io.BytesIO(pdf_bytes))
    text = ""
    for page in pdf_reader.pages:
        text += page.extract_text()
    return text

# Initialize vectorstore
def initialize_vector_index(text):
    docs = [{'page_content': text}]
    splits = text_splitter.split_documents(docs)
    vectorstore = Chroma.from_documents(documents=splits, embedding=embeddings_model, persist_directory=path_db)
    return vectorstore

# Sidebar configuration
with st.sidebar:
    st.title('🔍 Gemini RAG Chatbot')
    st.write('This chatbot uses the Gemini Pro API with RAG capabilities.')
    st.button('Clear Chat History', on_click=clear_chat_history, type='primary')
    uploaded_file = st.file_uploader("Upload a PDF file", type=["pdf"], help="Upload your PDF file here to start the analysis.")
    if uploaded_file is not None:
        st.success("PDF File Uploaded Successfully!")
        text = extract_text_from_pdf(uploaded_file.read())
        vectorstore = initialize_vector_index(text)
        st.session_state.vectorstore = vectorstore

# Main interface
st.header('Gemini Pro RAG Chatbot')
st.subheader('Upload a PDF and ask questions about its content!')

# Display the welcome prompt if chat history is only the initial prompt
if len(st.session_state.chat_history) == 1:
    with st.chat_message(SPEAKER_TYPES.BOT, avatar="🔍"):
        st.write(initial_prompt['content'])

# Get user input
prompt = st.chat_input("Ask a question about the PDF content:", key="user_input")

# Function to get a response from RAG chain
def get_rag_response(prompt):
    retriever = st.session_state.vectorstore.as_retriever()  # Use the stored vectorstore retriever
    rag_chain = (
        {"context": retriever | format_docs, "question": RunnablePassthrough()}
        | prompt
        | llm
        | StrOutputParser()
    )
    response = rag_chain.invoke(prompt)
    return response

# Handle the user prompt and generate response
if prompt:
    # Add user prompt to chat history
    st.session_state.chat_history.append({'role': SPEAKER_TYPES.USER, 'content': prompt})
    
    # Display chat messages from the chat history
    for message in st.session_state.chat_history[1:]:
        with st.chat_message(message["role"], avatar="👤" if message['role'] == SPEAKER_TYPES.USER else "🔍"):
            st.write(message["content"])
    
    # Get the response using the RAG chain
    with st.spinner(text='Generating response...'):
        response_text = get_rag_response(prompt)
        st.session_state.chat_history.append({'role': SPEAKER_TYPES.BOT, 'content': response_text})
    
    # Display the bot response
    with st.chat_message(SPEAKER_TYPES.BOT, avatar="🔍"):
        st.write(response_text)

# Add footer for additional information or credits
st.markdown("""
<hr>
<div style="text-align: center;">
    <small>Powered by Gemini Pro API | Developed by Christian Thomas BADOLO</small>
</div>
""", unsafe_allow_html=True)