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d3bfde1 | 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 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 | import os
import tempfile
import streamlit as st
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores import FAISS
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.chains import RetrievalQA
from io import BytesIO
from langchain.document_loaders import PyPDFLoader
from transformers import pipeline
from langchain.schema import Document
from dotenv import load_dotenv
from transformers import AutoTokenizer
import transformers
import torch
# Load environment variables from Hugging Face Secrets
load_dotenv()
os.environ['HUGGINGFACE_API_KEY'] = os.getenv("HF_TOKEN")
os.environ["LANGCHAIN_API_KEY"] = os.getenv("LANGCHAIN_API_KEY")
os.environ["LANGCHAIN_TRACING_V2"] = "true"
os.environ["LANGCHAIN_PROJECT"]="Research-Paper-Summarizer"
# Streamlit Page Config
st.set_page_config(
page_title="Research Paper Summarizer",
layout="centered"
)
st.title("📚 Research Paper Summarizer - Using Open Source Models")
# File Uploader
uploaded_files = st.file_uploader(
"Upload one or more research PDFs",
type=["pdf"],
accept_multiple_files=True
)
# A placeholder to store vector database (FAISS)
if "vector_store" not in st.session_state:
st.session_state.vector_store = None
# Hugging Face LLM Model Pipeline
def get_huggingface_pipeline():
model_name = "meta-llama/Llama-3.2-1B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
st.info("Loading Hugging Face Model... Please wait.")
return transformers.pipeline(
"text-generation",
model=model_name,
tokenizer=tokenizer,
max_new_tokens=256,
torch_dtype=torch.bfloat16
)
# Process the PDFs, Create/Update the Vector Store
if st.button("Process PDFs") and uploaded_files:
all_documents = []
for file in uploaded_files:
# Save the file temporarily
with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as temp_file:
temp_file.write(file.getvalue())
temp_file_path = temp_file.name
# Load the PDF using PyPDFLoader
loader = PyPDFLoader(temp_file_path)
pdf_docs = loader.load()
# Split text into manageable chunks
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=1000,
chunk_overlap=300,
separators=["\n\n", "\n", " ", ""]
)
for doc in pdf_docs:
chunks = text_splitter.split_text(doc.page_content)
for chunk in chunks:
# Create Document object for each chunk
all_documents.append(Document(page_content=chunk, metadata=doc.metadata))
# Create embeddings with Hugging Face
embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
st.session_state.vector_store = FAISS.from_documents(
documents=all_documents,
embedding=embeddings
)
st.success("PDFs processed and vector store created!")
# Query + Summarize
query = st.text_input("Enter your question or summary request:")
if st.button("Get Summary/Answer"):
if st.session_state.vector_store is None:
st.warning("Please upload and process PDFs first.")
else:
retriever = st.session_state.vector_store.as_retriever(
search_type="similarity",
search_kwargs={"k": 5}
)
# Use Hugging Face LLM
hf_pipeline = get_huggingface_pipeline()
# Retrieve documents and generate response
relevant_docs = retriever.get_relevant_documents(query)
context_text = "\n".join([doc.page_content for doc in relevant_docs])
# Generate answer using Hugging Face model
response = hf_pipeline(f"Context: {context_text}\nQuestion: {query}", num_return_sequences=1)
st.markdown("### Answer:")
st.write(response[0]['generated_text'])
with st.expander("Show source documents"):
for i, doc in enumerate(relevant_docs):
st.markdown(f"**Source Document {i + 1}:**")
st.write(doc.page_content)
st.write("---") |