Update app.py

app.py

@@ -1,21 +1,23 @@
-import streamlit as st
 import os
 import logging
 import faiss
-import numpy as np
+import streamlit as st
 from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
 from langchain_community.embeddings import HuggingFaceEmbeddings
-from langchain.chains import RetrievalQA
 from langchain.vectorstores import FAISS
+from langchain_community.llms import HuggingFacePipeline
+from langchain.chains import RetrievalQA
 
 # Set up logging
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 
+# HuggingFace model checkpoint
 checkpoint = "LaMini-T5-738M"
 
 @st.cache_resource
 def load_llm():
+    """Load the language model for text generation."""
     tokenizer = AutoTokenizer.from_pretrained(checkpoint)
     model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint)
     pipe = pipeline(
@@ -30,16 +32,17 @@ def load_llm():
     return HuggingFacePipeline(pipeline=pipe)
 
 def load_faiss_index():
+    """Load the FAISS index for vector search."""
     index_path = "faiss_index/index.faiss"
     if not os.path.exists(index_path):
         st.error(f"FAISS index not found at {index_path}. Please ensure the file exists.")
         raise RuntimeError(f"FAISS index not found at {index_path}.")
-    
+
     try:
-        faiss_index = faiss.read_index(index_path)
+        index = faiss.read_index(index_path)
         logger.info(f"FAISS index loaded successfully from {index_path}")
         embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
-        retriever = FAISS(faiss_index, embeddings)
+        retriever = FAISS(index, embeddings)
         return retriever
     except Exception as e:
         st.error(f"Failed to load FAISS index: {e}")
@@ -47,6 +50,7 @@ def load_faiss_index():
         raise
 
 def process_answer(instruction):
+    """Process the user's question using the QA system."""
     try:
         retriever = load_faiss_index()
         llm = load_llm()
@@ -65,6 +69,7 @@ def process_answer(instruction):
         return "An error occurred while processing your request.", {}
 
 def main():
+    """Main function to run the Streamlit application."""
     st.title("Search Your PDF 📚📝")
     
     with st.expander("About the App"):