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import streamlit as st
import os
import logging
import faiss
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import FAISS
from langchain_community.llms import HuggingFacePipeline
from langchain.chains import RetrievalQA
from ingest import create_faiss_index # Make sure this function is updated
# Set up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
checkpoint = "LaMini-T5-738M"
@st.cache_resource
def load_llm():
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint)
pipe = pipeline(
'text2text-generation',
model=model,
tokenizer=tokenizer,
max_length=256,
do_sample=True,
temperature=0.3,
top_p=0.95
)
return HuggingFacePipeline(pipeline=pipe)
def validate_index_file(index_path):
try:
with open(index_path, 'rb') as f:
data = f.read(100)
logger.info(f"Successfully read {len(data)} bytes from the index file")
return True
except Exception as e:
logger.error(f"Error validating index file: {e}")
return False
def load_faiss_index():
index_path = "faiss_index/index.faiss"
if not os.path.exists(index_path):
st.warning("Index file not found. Creating a new one...")
# Pass the required arguments to create_faiss_index
documents = [] # Load your documents here or from a specific directory
texts = [] # Process your documents to get text chunks
embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
create_faiss_index(texts, embeddings) # Ensure this is updated with correct function
if not os.path.exists(index_path):
st.error("Failed to create the FAISS index. Please check the 'docs' directory and try again.")
raise RuntimeError("FAISS index creation failed.")
try:
index = faiss.read_index(index_path)
if index is None:
raise ValueError("Failed to read FAISS index.")
embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
db = FAISS.load_local("faiss_index", embeddings)
if db.index is None or db.index_to_docstore_id is None:
raise ValueError("FAISS index or docstore_id mapping is None.")
return db.as_retriever()
except Exception as e:
st.error(f"Failed to load FAISS index: {e}")
logger.exception("Exception in load_faiss_index")
raise
def process_answer(instruction):
try:
retriever = load_faiss_index()
llm = load_llm()
qa = RetrievalQA.from_chain_type(
llm=llm,
chain_type="stuff",
retriever=retriever,
return_source_documents=True
)
generated_text = qa.invoke(instruction)
answer = generated_text['result']
return answer, generated_text
except Exception as e:
st.error(f"An error occurred while processing the answer: {e}")
logger.exception("Exception in process_answer")
return "An error occurred while processing your request.", {}
def diagnose_faiss_index():
index_path = "faiss_index/index.faiss"
if os.path.exists(index_path):
st.write(f"Index file size: {os.path.getsize(index_path)} bytes")
st.write(f"Index file permissions: {oct(os.stat(index_path).st_mode)[-3:]}")
st.write(f"Index file owner: {os.stat(index_path).st_uid}")
st.write(f"Current process user ID: {os.getuid()}")
validate_index_file(index_path)
else:
st.warning("Index file does not exist.")
def main():
st.title("Search Your PDF 📚📝")
with st.expander("About the App"):
st.markdown(
"""
This is a Generative AI powered Question and Answering app that responds to questions about your PDF File.
"""
)
diagnose_faiss_index()
question = st.text_area("Enter your Question")
if st.button("Ask"):
st.info("Your Question: " + question)
st.info("Your Answer")
try:
answer, metadata = process_answer(question)
st.write(answer)
st.write(metadata)
except Exception as e:
st.error(f"An unexpected error occurred: {e}")
logger.exception("Unexpected error in main function")
if __name__ == '__main__':
main()
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