Update app.py

app.py

@@ -1,11 +1,12 @@
+
 import streamlit as st
 from PyPDF2 import PdfReader
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 import os
-import google.generativeai as genai
 from langchain_google_genai import GoogleGenerativeAIEmbeddings
-from langchain_google_genai import ChatGoogleGenerativeAI
+import google.generativeai as genai
 from langchain.vectorstores import FAISS
+from langchain_google_genai import ChatGoogleGenerativeAI
 from langchain.chains.question_answering import load_qa_chain
 from langchain.prompts import PromptTemplate
 from dotenv import load_dotenv
@@ -14,14 +15,13 @@ load_dotenv()
 os.getenv("GOOGLE_API_KEY")
 genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
 
-
 def get_pdf_text(pdf_docs):
-    text = ""
+    text=""
     for pdf in pdf_docs:
-        pdf_reader = PdfReader(pdf)
+        pdf_reader= PdfReader(pdf)
         for page in pdf_reader.pages:
-            text += page.extract_text()
-    return text
+            text+= page.extract_text()
+    return  text
 
 
 def get_text_chunks(text):
@@ -31,12 +31,13 @@ def get_text_chunks(text):
 
 
 def get_vector_store(text_chunks):
-    embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
+    embeddings = GoogleGenerativeAIEmbeddings(model = "models/embedding-001")
     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
     provided context just say, "answer is not available in the context", don't provide the wrong answer\n\n
@@ -46,32 +47,33 @@ def get_conversational_chain():
     Answer:
     """
 
-    model = ChatGoogleGenerativeAI(model="gemini-pro", temperature=0.3)
-    prompt = PromptTemplate(template=prompt_template, input_variables=["context", "question"])
+    model = ChatGoogleGenerativeAI(model="gemini-pro",temperature=0.3)
+    prompt = PromptTemplate(template = prompt_template, input_variables = ["context", "question"])
     chain = load_qa_chain(model, chain_type="stuff", prompt=prompt)
+
     return chain
 
 
-def user_input(user_question):
-    embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
-
-    faiss_index_path = "faiss_index/index.faiss"
-    if os.path.exists(faiss_index_path):
-        new_db = FAISS.load_local("faiss_index", embeddings)
-    else:
-        st.error(f"Error: Could not find FAISS index file at {faiss_index_path}")
-        return
 
+def user_input(user_question):
+    embeddings = GoogleGenerativeAIEmbeddings(model = "models/embedding-001")
+    
+    new_db = FAISS.load_local("faiss_index", embeddings, allow_dangerous_deserialization=True)
     docs = new_db.similarity_search(user_question)
 
     chain = get_conversational_chain()
 
-    response = chain({"input_documents": docs, "question": user_question}, return_only_outputs=True)
+    
+    response = chain(
+        {"input_documents":docs, "question": user_question}
+        , return_only_outputs=True)
 
     print(response)
     st.write("Reply: ", response["output_text"])
 
 
+
+
 def main():
     st.set_page_config("Chat PDF")
     st.header("Chat with PDF 💁")
@@ -88,13 +90,11 @@ def main():
             with st.spinner("Processing..."):
                 raw_text = get_pdf_text(pdf_docs)
                 text_chunks = get_text_chunks(raw_text)
-
-                if not os.path.exists("faiss_index"):
-                    os.makedirs("faiss_index")
-
                 get_vector_store(text_chunks)
                 st.success("Done")
 
 
+
 if __name__ == "__main__":
     main()
+