Spaces:
Sleeping
Sleeping
File size: 2,920 Bytes
8e456d3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 | import streamlit as st
import os
import tempfile
import faiss
import fitz # PyMuPDF for PDFs
import docx
import openpyxl
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import FAISS
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.docstore.document import Document
from langchain_community.llms import Groq
from langchain.chains import RetrievalQA
from langchain.schema import Document as LCDocument
# Initialize LLM
llm = Groq(
model="llama3-8b-8192",
api_key=os.getenv("GROQ_API_KEY") # Put this in Hugging Face secrets
)
# Embeddings model
embedding_model = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
# File processors
def read_pdf(file_path):
text = ""
doc = fitz.open(file_path)
for page in doc:
text += page.get_text()
return text
def read_docx(file_path):
doc = docx.Document(file_path)
return "\n".join([p.text for p in doc.paragraphs])
def read_excel(file_path):
wb = openpyxl.load_workbook(file_path, data_only=True)
text = ""
for sheet in wb.sheetnames:
ws = wb[sheet]
for row in ws.iter_rows(values_only=True):
text += " ".join([str(cell) for cell in row if cell is not None]) + "\n"
return text
def process_file(uploaded_file):
suffix = uploaded_file.name.split(".")[-1]
with tempfile.NamedTemporaryFile(delete=False, suffix="." + suffix) as tmp_file:
tmp_file.write(uploaded_file.read())
tmp_path = tmp_file.name
if suffix.lower() == "pdf":
return read_pdf(tmp_path)
elif suffix.lower() in ["docx"]:
return read_docx(tmp_path)
elif suffix.lower() in ["xlsx"]:
return read_excel(tmp_path)
else:
return "Unsupported file type."
# Streamlit UI
st.title("📄 RAG Document QA with Faiss + LLaMA3")
uploaded_file = st.file_uploader("Upload a PDF, Word or Excel file", type=["pdf", "docx", "xlsx"])
if uploaded_file:
st.success("✅ File uploaded successfully.")
raw_text = process_file(uploaded_file)
# Split text into chunks
splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
texts = splitter.split_text(raw_text)
docs = [Document(page_content=t) for t in texts]
# Embed and create vector store
with st.spinner("Indexing document..."):
db = FAISS.from_documents(docs, embedding_model)
retriever = db.as_retriever(search_type="similarity", search_kwargs={"k": 4})
qa = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=retriever)
st.success("✅ Document indexed! Ask your questions below:")
user_query = st.text_input("❓ Ask a question about your document")
if user_query:
with st.spinner("Generating answer..."):
answer = qa.run(user_query)
st.markdown(f"**💬 Answer:** {answer}")
|