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import streamlit as st
from langchain.document_loaders import TextLoader
from langchain.document_loaders import UnstructuredFileLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import FAISS
from langchain.chains import RetrievalQA
from langchain.llms import HuggingFaceHub
import tempfile
import os
@st.cache_resource
def load_vector_store(file_path):
# Load and chunk the document
loader = TextLoader(file_path)
documents = loader.load()
splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
chunks = splitter.split_documents(documents)
# Create embeddings and store in FAISS
embedding_model = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
db = FAISS.from_documents(chunks, embedding_model)
return db
def main():
st.title("📄 Ask Questions About Your Document")
st.write("Upload a `.txt` file and ask anything!")
uploaded_file = st.file_uploader("Upload a text file", type=["txt"])
if uploaded_file:
with tempfile.NamedTemporaryFile(delete=False, suffix=".txt") as tmp_file:
tmp_file.write(uploaded_file.read())
tmp_path = tmp_file.name
db = load_vector_store(tmp_path)
query = st.text_input("Enter your question:")
if query:
qa_chain = RetrievalQA.from_chain_type(
llm=HuggingFaceHub(
repo_id="google/flan-t5-base",
model_kwargs={"temperature": 0.5, "max_length": 256}
),
retriever=db.as_retriever(),
return_source_documents=True
)
result = qa_chain.run(query)
st.write("### 📌 Answer")
st.write(result)
# Clean up temp file
os.remove(tmp_path)
if __name__ == "__main__":
main()
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