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import streamlit as st
import tempfile
from langchain_huggingface import ChatHuggingFace, HuggingFaceEmbeddings, HuggingFaceEndpoint
from langchain_core.prompts import PromptTemplate
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain.document_loaders import PyPDFLoader
from langchain.vectorstores import FAISS
from langchain_core.output_parsers import StrOutputParser
from langchain_core.messages import AIMessage, SystemMessage, HumanMessage
from dotenv import load_dotenv
load_dotenv()
# -----------------------
# Initialize models
# -----------------------
llm = HuggingFaceEndpoint(
model='meta-llama/Llama-3.1-8B-Instruct',
task='text-generation'
)
chat_model = ChatHuggingFace(llm=llm)
embed_model = HuggingFaceEmbeddings(model_name='sentence-transformers/all-mpnet-base-v2')
# -----------------------
# Streamlit UI
# -----------------------
st.set_page_config(page_title="Talk with your Documents", layout="wide")
st.sidebar.title("Settings")
chunk_size = st.sidebar.slider("Chunk Size", min_value=100, max_value=1000, value=300, step=50)
chunk_overlap = st.sidebar.slider("Chunk Overlap", min_value=0, max_value=200, value=25, step=5)
uploaded_files = st.sidebar.file_uploader(
"Upload PDF(s)",
type=["pdf"],
accept_multiple_files=True
)
# -----------------------
# Initialize FAISS store in session
# -----------------------
if "vector_store" not in st.session_state:
if uploaded_files:
all_docs = []
for uploaded_file in uploaded_files:
with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as tmp_file:
tmp_file.write(uploaded_file.read())
tmp_file_path = tmp_file.name
loader = PyPDFLoader(tmp_file_path)
docs = loader.load()
all_docs.extend(docs)
splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap)
splitted_docs = splitter.split_documents(all_docs)
st.session_state.vector_store = FAISS.from_documents(splitted_docs, embedding=embed_model)
# -----------------------
# Chat logic
# -----------------------
st.title("📄 Talk with your Documents - RAG Based")
if "messages" not in st.session_state:
st.session_state.messages = [SystemMessage(content="You are a helpful assistant")]
query = st.text_input("Ask a question about your uploaded documents:")
if st.button("Ask"):
if not uploaded_files:
st.error("❌ Please upload at least one PDF file before asking a question!")
elif not query.strip():
st.warning("⚠️ Please enter a question to ask.")
else:
st.session_state.messages.append(HumanMessage(content=query))
with st.spinner("Finding the answer..."):
# Use vector store from session
retriever = st.session_state.vector_store.as_retriever(search_kwargs={'k': 3})
context_chunks = retriever.invoke(query)
final_context = " ".join([chunk.page_content for chunk in context_chunks])
# Build chat history string
history_str = "\n".join([f"{type(m).__name__}: {m.content}" for m in st.session_state.messages])
# Prepare prompt
prompt = PromptTemplate(
template="""
You are a helpful assistant.
Answer the user's question ONLY based on the context provided.
If the answer is not in the context, reply: 'This is not included in your File.'
Context: {context}
Question: {query}
Previous chat history:
{history}
"""
)
final_prompt = prompt.format(context=final_context, query=query, history=history_str)
# Get answer
parser = StrOutputParser()
result = chat_model.invoke(final_prompt)
answer = result.content
st.session_state.messages.append(AIMessage(content=answer))
# -----------------------
# Display recent chat
# -----------------------
st.subheader("💬 Recent Chat")
for msg in st.session_state.messages[1:]:
if isinstance(msg, HumanMessage):
st.markdown(f"**You:** {msg.content}")
elif isinstance(msg, AIMessage):
st.markdown(f"**AI:** {msg.content}")
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