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import streamlit as st
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores import FAISS
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.llms import HuggingFacePipeline
from langchain.schema import Document
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, pipeline
from sentence_transformers import SentenceTransformer
import torch
from io import BytesIO
import fitz # PyMuPDF
# Set device based on GPU availability
device = "cuda" if torch.cuda.is_available() else "cpu"
# Load embeddings with a smaller model and run on CPU
embedding_model = "all-MiniLM-L6-v2"
embeddings = HuggingFaceEmbeddings(model_name=embedding_model, model_kwargs={'device': 'cpu'})
# Set up text generation model with PyTorch-compatible pipeline
model_name = "google/flan-t5-small" # Or use a smaller model if needed
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name).to(device)
# Create a text generation pipeline
generator = pipeline(
"text2text-generation",
model=model,
tokenizer=tokenizer,
device=0 if device == "cuda" else -1,
model_kwargs={"max_length": 256, "temperature": 0.7}
)
llm = HuggingFacePipeline(pipeline=generator)
# Streamlit interface
def main():
st.title("Chat with Multiple PDFs")
st.write("Upload PDF files and chat with them.")
# File uploader
uploaded_files = st.file_uploader("Upload PDF Files", accept_multiple_files=True, type=["pdf"])
if uploaded_files:
# Load PDF documents
documents = []
for uploaded_file in uploaded_files:
pdf_content = BytesIO(uploaded_file.read())
doc = fitz.open(stream=pdf_content, filetype="pdf") # Open PDF with PyMuPDF
text = ""
for page in doc:
text += page.get_text() # Extract text from each page
doc.close()
# Create Document instance with page content
documents.append(Document(page_content=text, metadata={"file_name": uploaded_file.name}))
# Split documents into manageable chunks
text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=100)
chunks = text_splitter.split_documents(documents)
# Embed document chunks into vector store
vector_store = FAISS.from_documents(chunks, embeddings)
# User query input
st.write("You can now start chatting with your PDFs!")
user_input = st.text_input("Ask a question:")
if user_input:
# Perform similarity search on the vector store
docs = vector_store.similarity_search(user_input, k=3)
# Concatenate retrieved docs into a single prompt
prompt = "\n".join([doc.page_content for doc in docs]) + "\n\n" + user_input
# Generate response
try:
response = generator(prompt, max_new_tokens=50, num_return_sequences=1)[0]["generated_text"]
st.write(response)
except torch.cuda.OutOfMemoryError:
st.error("Out of memory. Try using a smaller model or fewer documents.")
if __name__ == "__main__":
main() |