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from dotenv import load_dotenv
load_dotenv()
import pickle
from dotenv import load_dotenv
import streamlit as st
from streamlit_chat import message
import os
from ocr import convert_pdf_to_images, extract_text_with_easyocr
from langchain.prompts import PromptTemplate
from langchain.chains import RetrievalQA
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.document_loaders import PyPDFLoader
from langchain_community.vectorstores import FAISS
from langchain.docstore.document import Document
from langchain.embeddings import HuggingFaceInstructEmbeddings
from langchain import HuggingFaceHub
load_dotenv()
# @st.cache_resource
def create_vector_store(file_path):
pdf_loader = PyPDFLoader(file_path)
docs = pdf_loader.load()
raw_text = ''
for doc in docs:
raw_text += doc.page_content
if len(raw_text) < 10:
raw_text = extract_text_with_easyocr(convert_pdf_to_images(file_path))
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=10000, chunk_overlap=200
)
texts = text_splitter.split_text(raw_text)
# # Create multiple documents
docs = [Document(page_content=t) for t in texts]
vectorstore_faiss = FAISS.from_documents(
documents=docs,
embedding=HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-base"),
)
return vectorstore_faiss
def create_prompt_template():
prompt_template = """
Human: Answer the question as a full sentence from the context provided. If you don't know the answer, don't try to make up an answer.
<context>
{context}
</context>
Question: {question}
Assistant:"""
prompt = PromptTemplate(
input_variables=["context", "question"], template=prompt_template
)
return prompt
# @st.cache_resource
def create_retrieval_chain(vector_store, prompt_template):
qa = RetrievalQA.from_chain_type(
llm = HuggingFaceHub(repo_id="HuggingFaceH4/zephyr-7b-beta", model_kwargs={"temperature": 0.5, "max_new_tokens": 2000}),
chain_type="stuff",
retriever=vector_store.as_retriever(
search_type="similarity", search_kwargs={"k": 6}
),
chain_type_kwargs={"prompt": prompt_template},
)
return qa
def generate_response(chain, input_question):
answer = chain({"query": input_question})
return answer["result"]
def get_file_size(file):
file.seek(0, os.SEEK_END)
file_size_bytes = file.tell()
file_size_mb = file_size_bytes / (1024 * 1024) # Convert bytes to megabytes
file.seek(0)
return file_size_mb
# Display conversation history using Streamlit messages
def display_conversation(history):
for i in range(len(history["generated"])):
message(history["past"][i], is_user=True, key=str(i) + "_user")
if len(history["generated"][i]) == 0:
message("Please reframe your question properly", key=str(i))
else:
message(history["generated"][i],key=str(i))
def create_folders_if_not_exist(*folders):
for folder in folders:
if not os.path.exists(folder):
os.makedirs(folder)
def main():
st.set_page_config(
page_title="Ask PDF",
page_icon=":mag_right:",
layout="wide"
)
st.title("Ask PDF")
st.subheader("Unlocking Answers within Documents, Your Instant Query Companion!")
# Sidebar for file upload
st.sidebar.title("Upload PDF")
uploaded_file = st.sidebar.file_uploader("", label_visibility='collapsed', type=["pdf"])
create_folders_if_not_exist("data", "data/pdfs", "data/vectors")
if "uploaded_file" not in st.session_state or st.session_state.uploaded_file != uploaded_file:
st.session_state.uploaded_file = uploaded_file
st.session_state.generated = [f"Ask me a question about {uploaded_file.name}" if uploaded_file else ""]
st.session_state.past = ["Hey there!"]
st.session_state.last_uploaded_file = uploaded_file.name if uploaded_file else None
if uploaded_file is not None:
filepath = "data/pdfs/" + uploaded_file.name
with open(filepath, "wb") as temp_file:
temp_file.write(uploaded_file.read())
vector_file = os.path.join('data/vectors/', f'vector_store_{uploaded_file.name}.pkl')
# Display the uploaded file name in the sidebar
st.sidebar.markdown(f"**Uploaded file:** {uploaded_file.name}")
if not os.path.exists(vector_file) or "ingested_data" not in st.session_state:
with st.spinner('Embeddings are in process...'):
ingested_data = create_vector_store(filepath)
with open(vector_file, "wb") as f:
pickle.dump(ingested_data, f)
st.session_state.ingested_data = ingested_data
st.success('Embeddings are created successfully! ✅✅✅')
else:
ingested_data = st.session_state.ingested_data
prompt = create_prompt_template()
chain = create_retrieval_chain(ingested_data, prompt)
user_input = st.chat_input(placeholder="Ask a question")
if user_input:
answer = generate_response(chain, user_input)
st.session_state.past.append(user_input)
response = answer
st.session_state.generated.append(response)
# Display conversation history using Streamlit messages
if st.session_state.generated:
display_conversation(st.session_state)
if __name__ == "__main__":
main()
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