Update app.py

app.py

@@ -11,17 +11,14 @@ import os
 import json
 from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig, TextStreamer, ConversationalPipeline
 
-
 ####CREDIT#####
-#Credit to the author (Sri Laxmi) of the original code reference: SriLaxmi1993
-#Sri LaxmiGithub Link: https://github.com/SriLaxmi1993/Document-Genie-using-RAG-Framwork
-#Sri Laxmi Youtube:https://www.youtube.com/watch?v=SkY2u4UUr6M&t=112s
+# Credit to author (Sri Laxmi) of original code reference: SriLaxmi1993
+# Sri LaxmiGithub Link: https://github.com/SriLaxmi1993/Document-Genie-using-RAG-Framwork
+# Sri Laxmi Youtube:https://www.youtube.com/watch?v=SkY2u4UUr6M&t=112s
 ###############
 os.system("pip install -r requirements.txt")
 
-import torch
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
+# some model
 
 #tokenizer = AutoTokenizer.from_pretrained("google/gemma-7b")
 #model = AutoModelForCausalLM.from_pretrained("google/gemma-7b")
@@ -32,12 +29,13 @@ st.set_page_config(page_title="Gemini RAG", layout="wide")
 # This is the first API key input; no need to repeat it in the main function.
 api_key = 'AIzaSyCvXRggpO2yNwIpZmoMy_5Xhm2bDyD-pOo'
 
-#os.mkdir('faiss_index')
 
-#empty faise_index and chat_history.json
+# os.mkdir('faiss_index')
+
+# empty faise_index and chat_history.json
 def delete_files_in_folder(folder_path):
     try:
-       
+        # Iterate over all the files in the folder
         chat_history_file = "chat_history.json"
         if os.path.exists(chat_history_file):
             os.remove(chat_history_file)
@@ -70,16 +68,19 @@ def get_pdf_text(pdf_docs):
             text += page.extract_text()
     return text
 
+
 def get_text_chunks(text):
     text_splitter = RecursiveCharacterTextSplitter(chunk_size=CH_size, chunk_overlap=CH_overlap)
     chunks = text_splitter.split_text(text)
     return chunks
 
+
 def get_vector_store(text_chunks, api_key):
     embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001", google_api_key=api_key)
     vector_store = FAISS.from_texts(text_chunks, embedding=embeddings)
     vector_store.save_local("faiss_index")
 
+
 def get_conversational_chain():
     prompt_template = """
     Answer the question as detailed as possible from the provided context, make sure to provide all the details, if the answer is not in
@@ -94,7 +95,8 @@ def get_conversational_chain():
     chain = load_qa_chain(model, chain_type="stuff", prompt=prompt)
     return chain
 
-#chat history functionality
+
+# chat history functionality
 def update_chat_history(question, reply):
     # Check if chat history file exists
     chat_history_file = "chat_history.json"
@@ -146,7 +148,8 @@ def main():
     with st.sidebar:
         st.title("Menu:")
 
-        pdf_docs = st.file_uploader("Upload your PDF Files and Click on the Submit & Process Button", accept_multiple_files=True, key="pdf_uploader")
+        pdf_docs = st.file_uploader("Upload your PDF Files and Click on the Submit & Process Button",
+                                    accept_multiple_files=True, key="pdf_uploader")
         if st.button("Submit & Process", key="process_button") and api_key:
             with st.spinner("Processing..."):
                 raw_text = get_pdf_text(pdf_docs)
@@ -154,5 +157,6 @@ def main():
                 get_vector_store(text_chunks, api_key)
                 st.success("Done")
 
+
 if __name__ == "__main__":
     main()