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import streamlit as st
from PyPDF2 import PdfReader
from langchain.vectorstores.cassandra import Cassandra
from langchain.indexes.vectorstore import VectorStoreIndexWrapper
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.llms import HuggingFaceHub
from langchain.text_splitter import CharacterTextSplitter
import cassio
import os
import uuid
from dotenv import load_dotenv
# 🔐 Load secrets from environment (Hugging Face Spaces uses HF Secrets)
load_dotenv()
ASTRA_DB_APPLICATION_TOKEN = os.getenv("ASTRA_DB_APPLICATION_TOKEN")
ASTRA_DB_ID = os.getenv("ASTRA_DB_ID")
HUGGINGFACEHUB_API_TOKEN = os.getenv("HUGGINGFACEHUB_API_TOKEN")
# 🧠 Initialize AstraDB
cassio.init(token=ASTRA_DB_APPLICATION_TOKEN, database_id=ASTRA_DB_ID)
# 🎨 Streamlit UI Setup
st.set_page_config(page_title="Query PDF with LangChain", layout="wide")
st.title("📄💬 Query PDF using LangChain + AstraDB (Hugging Face Models)")
# 📁 PDF Upload
uploaded_file = st.file_uploader("Upload your PDF", type=["pdf"])
if uploaded_file:
st.success("✅ PDF uploaded successfully.")
process_button = st.button("🔄 Process PDF")
if process_button:
# 🧾 Read PDF
pdf_reader = PdfReader(uploaded_file)
raw_text = ""
for page in pdf_reader.pages:
content = page.extract_text()
if content:
raw_text += content
# ✂️ Split into Chunks
text_splitter = CharacterTextSplitter(
separator="\n", chunk_size=800, chunk_overlap=200, length_function=len
)
texts = text_splitter.split_text(raw_text)
# 🧠 Embeddings
embedding = HuggingFaceEmbeddings(model_name="intfloat/e5-base-v2")
# 🤖 LLM
llm = HuggingFaceHub(
repo_id="mistralai/Mistral-7B-Instruct-v0.1",
huggingfacehub_api_token=HUGGINGFACEHUB_API_TOKEN,
model_kwargs={"temperature": 0.5, "max_new_tokens": 512}
)
# 🗃️ Unique Table Name for Each PDF Upload
table_name = "qa_" + str(uuid.uuid4()).replace("-", "_")
# 📦 Vector Store Setup
vector_store = Cassandra(
embedding=embedding,
table_name=table_name,
session=None,
keyspace=None,
)
vector_store.add_texts(texts[:50])
st.success(f"📚 {len(texts[:50])} chunks embedded and stored in AstraDB.")
# 🔍 Setup Index
astra_vector_index = VectorStoreIndexWrapper(vectorstore=vector_store)
# 💬 Ask Questions
st.header("🤖 Ask a question about your PDF")
user_question = st.text_input("💬 Type your question here")
if user_question:
with st.spinner("🧠 Thinking..."):
try:
# Retrieve relevant context (used internally, not displayed)
retrieved_docs = vector_store.similarity_search(user_question, k=8)
if not retrieved_docs:
st.warning("⚠️ No relevant text found. Try rephrasing your question.")
else:
answer = astra_vector_index.query(user_question, llm=llm)
if answer.strip():
st.markdown("### 🧠 Answer:")
st.write(answer.strip())
else:
st.warning("⚠️ Model returned an empty response.")
except Exception as e:
st.error(f"🚨 Error: {str(e)}")
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