app.py

import streamlit as st
import boto3
from langchain_community.document_loaders import PyPDFLoader
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_aws import BedrockEmbeddings
# --- CHANGED: Import Qdrant instead of Chroma ---
from langchain_qdrant import Qdrant 
# --- Optional: If you need direct Qdrant client interaction or for advanced setups ---
# from qdrant_client import QdrantClient, models 

from langchain_aws import ChatBedrock
from langchain.prompts import ChatPromptTemplate
from langchain.schema import StrOutputParser
from langchain.schema.runnable import RunnablePassthrough
import os
from dotenv import load_dotenv # Import load_dotenv

# --- Load Environment Variables ---
load_dotenv() # This loads variables from .env file

# --- Streamlit UI Setup (MUST BE THE FIRST STREAMLIT COMMAND) ---
st.set_page_config(
    page_title="Math Research Paper RAG Bot",
    page_icon="📚",
    layout="wide"
)

st.title("📚 Math Research Paper RAG Chatbot")
st.markdown(
    """
    Upload a mathematical research paper (PDF) and ask questions about its content. 
    This bot uses Amazon Bedrock (Claude 3 Sonnet for reasoning, Titan Embeddings for vectors) 
    and **QdrantDB** for Retrieval-Augmented Generation.
    
    **Note:** This application requires AWS credentials (`AWS_ACCESS_KEY_ID`, `AWS_SECRET_ACCESS_KEY`) 
    and region (`AWS_REGION`) to be set up in a `.env` file or environment variables.
    The Qdrant vector store is **in-memory** and will be reset on app restart.
    """
)

# --- Configuration ---
# Set AWS region (adjust if needed, loaded from .env or env var)
AWS_REGION = os.getenv("AWS_REGION") 
if not AWS_REGION:
    st.error("AWS_REGION not found in environment variables or .env file. Please set it.")
    st.stop()

# Bedrock model IDs
EMBEDDING_MODEL_ID = "amazon.titan-embed-text-v1"
LLM_MODEL_ID = "anthropic.claude-3-sonnet-20240229-v1:0" 

# --- Qdrant Specific Configuration ---
QDRANT_COLLECTION_NAME = "math_research_papers_collection"
EMBEDDING_DIMENSION = 1536 # Titan Text Embeddings output 1536-dimensional vectors

# --- Initialize Bedrock Client (once) ---
@st.cache_resource
def get_bedrock_client():
    """Initializes and returns a boto3 Bedrock client.
    Returns: Tuple (boto3_client, success_bool, error_message_str or None)
    """
    try:
        client = boto3.client(
            service_name="bedrock-runtime",
            region_name=AWS_REGION
        )
        return client, True, None # Success: client, True, no error message
    except Exception as e:
        return None, False, str(e) # Failure: None, False, error message

# Get the client and check its status
bedrock_client, bedrock_success, bedrock_error_msg = get_bedrock_client()

if not bedrock_success:
    st.error(f"Error connecting to AWS Bedrock. Please check your AWS credentials (AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY) and region (AWS_REGION) in your .env file or environment variables. Error: {bedrock_error_msg}")
    st.stop() # Stop execution if Bedrock client cannot be initialized
else:
    st.success(f"Successfully connected to AWS Bedrock in {AWS_REGION}!")


# --- LangChain Components ---
@st.cache_resource
def get_embeddings_model(_client): # Prepend underscore to tell Streamlit not to hash
    """Returns the BedrockEmbeddings model."""
    return BedrockEmbeddings(client=_client, model_id=EMBEDDING_MODEL_ID)

@st.cache_resource
def get_llm_model(_client): # Prepend underscore to tell Streamlit not to hash
    """Returns the Bedrock LLM model for Claude 3 Sonnet."""
    return ChatBedrock(
        client=_client,
        model_id=LLM_MODEL_ID,
        streaming=False, 
        temperature=0.1, 
        model_kwargs={"max_tokens": 4000}
    )

# --- PDF Processing and Vector Store Creation ---
def create_vector_store(pdf_file_path):
    """
    Loads PDF, chunks it contextually for mathematical papers,
    creates embeddings, and stores them in QdrantDB (in-memory).
    """
    with st.spinner("Loading PDF and creating vector store..."):
        # 1. Load PDF
        loader = PyPDFLoader(pdf_file_path)
        pages = loader.load_and_split()
        st.info(f"Loaded {len(pages)} pages from the PDF.")

        # 2. Contextual Chunking for Mathematical Papers
        text_splitter = RecursiveCharacterTextSplitter(
            chunk_size=1500,  # Increased chunk size for math papers
            chunk_overlap=150, # Generous overlap to maintain context
            separators=[
                "\n\n",  # Prefer splitting by paragraphs
                "\n",    # Then by newlines (might break equations but less likely than fixed char)
                " ",     # Then by spaces
                "",      # Fallback
            ],
            length_function=len,
            is_separator_regex=False,
        )
        chunks = text_splitter.split_documents(pages)
        st.info(f"Split PDF into {len(chunks)} chunks.")

        # 3. Create Embeddings and QdrantDB
        embeddings = get_embeddings_model(bedrock_client)
        
        # --- CHANGED: Qdrant vector store creation ---
        vector_store = Qdrant.from_documents(
            documents=chunks,
            embedding=embeddings,
            location=":memory:",  # Use in-memory Qdrant instance
            collection_name=QDRANT_COLLECTION_NAME,
            # For persistent Qdrant (requires a running Qdrant server):
            # url="http://localhost:6333", # Or your Qdrant Cloud URL
            # api_key="YOUR_QDRANT_CLOUD_API_KEY", # Only for Qdrant Cloud
            # prefer_grpc=True # Set to True if using gRPC for Qdrant Cloud
            # force_recreate=True # Use with caution: deletes existing collection
        )
        # Note: LangChain's Qdrant integration will automatically create the collection
        # if it doesn't exist, inferring vector_size from embeddings.
        
        st.success("Vector store created and ready!")
        return vector_store

# --- RAG Chain Construction ---
def get_rag_chain(vector_store):
    """Constructs the RAG chain using LCEL."""
    retriever = vector_store.as_retriever(search_kwargs={"k": 5}) # Retrieve top 5 relevant chunks
    llm = get_llm_model(bedrock_client) 

    # Prompt Template optimized for mathematical research papers
    prompt_template = ChatPromptTemplate.from_messages(
        [
            ("system", 
             "You are an expert AI assistant specialized in analyzing and explaining mathematical research papers. "
             "Your goal is to provide precise, accurate, and concise answers based *only* on the provided context from the research paper. "
             "When answering, focus on definitions, theorems, proofs, key mathematical concepts, and experimental results. "
             "If the user asks about a mathematical notation, try to explain its meaning from the context. "
             "If the answer is not found in the context, explicitly state that you cannot find the information within the provided document. "
             "Do not invent information or make assumptions outside the given text.\n\n"
             "Context:\n{context}"),
            ("user", "{question}"),
        ]
    )

    rag_chain = (
        {"context": retriever, "question": RunnablePassthrough()}
        | prompt_template
        | llm
        | StrOutputParser()
    )
    return rag_chain

# --- Streamlit UI Main Logic ---

# Initialize chat history
if "messages" not in st.session_state:
    st.session_state.messages = []

# Initialize vector store and RAG chain
if "vector_store" not in st.session_state:
    st.session_state.vector_store = None
if "rag_chain" not in st.session_state:
    st.session_state.rag_chain = None
if "pdf_uploaded" not in st.session_state:
    st.session_state.pdf_uploaded = False


# Sidebar for PDF Upload
with st.sidebar:
    st.header("Upload PDF")
    uploaded_file = st.file_uploader(
        "Choose a PDF file",
        type="pdf",
        accept_multiple_files=False,
        key="pdf_uploader"
    )

    if uploaded_file and not st.session_state.pdf_uploaded:
        # Save the uploaded file temporarily
        with open("temp_doc.pdf", "wb") as f:
            f.write(uploaded_file.getbuffer())
        
        st.session_state.vector_store = create_vector_store("temp_doc.pdf")
        st.session_state.rag_chain = get_rag_chain(st.session_state.vector_store)
        st.session_state.pdf_uploaded = True
        st.success("PDF processed successfully! You can now ask questions.")
        # Clean up temporary file
        os.remove("temp_doc.pdf")
    elif st.session_state.pdf_uploaded:
        st.info("PDF already processed. Ready for questions!")


# 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("Ask a question about the paper..."):
    if not st.session_state.pdf_uploaded:
        st.warning("Please upload a PDF first to start asking questions.")
    else:
        # Add user message to chat history
        st.session_state.messages.append({"role": "user", "content": prompt})
        with st.chat_message("user"):
            st.markdown(prompt)

        # Get response from RAG chain
        with st.chat_message("assistant"):
            with st.spinner("Thinking..."):
                try:
                    full_response = st.session_state.rag_chain.invoke(prompt) 
                    st.markdown(full_response, unsafe_allow_html=True) 

                    # Add assistant response to chat history
                    st.session_state.messages.append({"role": "assistant", "content": full_response})
                except Exception as e:
                    st.error(f"An error occurred during response generation: {e}")
                    st.warning("Please try again or check your AWS Bedrock access permissions.")

# Optional: Clear chat and uploaded PDF
if st.session_state.pdf_uploaded:
    if st.sidebar.button("Clear Chat and Upload New PDF"):
        st.session_state.messages = []
        st.session_state.vector_store = None
        st.session_state.rag_chain = None
        st.session_state.pdf_uploaded = False
        st.cache_resource.clear() # Clear streamlit caches for a clean slate
        st.rerun()