| import streamlit as st |
| from PyPDF2 import PdfReader |
| from langchain.text_splitter import RecursiveCharacterTextSplitter |
| from langchain.embeddings import HuggingFaceEmbeddings |
| from langchain.vectorstores import FAISS |
| from langchain.chains import RetrievalQA |
| from langchain_groq import ChatGroq |
| from dotenv import load_dotenv |
|
|
| load_dotenv() |
|
|
| |
| llm = ChatGroq(model_name="llama-3.1-8b-instant", temperature=0.7) |
|
|
| |
| def read_pdf(pdf): |
| raw_text = "" |
| file = PdfReader(pdf) |
| for page in file.pages: |
| content = page.extract_text() |
| if content: |
| raw_text += content |
| return raw_text |
|
|
| |
| def process_text(raw_text): |
| text_splitter = RecursiveCharacterTextSplitter( |
| chunk_size=200, |
| chunk_overlap=20, |
| length_function=len, |
| separators=["\n"] |
| ) |
| texts = text_splitter.create_documents([raw_text]) |
|
|
| modelPath = "sentence-transformers/all-MiniLM-l6-v2" |
| model_kwargs = {'device': 'cpu'} |
| encode_kwargs = {'normalize_embeddings': False} |
|
|
| embeddings = HuggingFaceEmbeddings( |
| model_name=modelPath, |
| model_kwargs=model_kwargs, |
| encode_kwargs=encode_kwargs |
| ) |
|
|
| db = FAISS.from_documents(texts, embeddings) |
| return db |
|
|
| |
| def initialize_chain(db): |
| chain = RetrievalQA.from_chain_type( |
| llm=llm, |
| chain_type="stuff", |
| retriever=db.as_retriever() |
| ) |
| return chain |
|
|
| |
| def main(): |
| st.title("PDF Question Answering") |
|
|
| uploaded_file = st.file_uploader("Choose a PDF file", type="pdf") |
|
|
| if uploaded_file is not None: |
| raw_text = read_pdf(uploaded_file) |
| db = process_text(raw_text) |
| chain = initialize_chain(db) |
| |
| question = st.text_input("Ask a question about the PDF") |
| if question: |
| answer = chain.invoke(question) |
| st.write(f"Answer: {answer['result']}") |
|
|
| if __name__ == "__main__": |
| main() |
|
|
