Update app.py

app.py

@@ -1,21 +1,15 @@
 import streamlit as st
 from PyPDF2 import PdfReader
-from langchain.text_splitter import RecursiveCharacterTextSplitter
+import google.generativeai as genai
 import os
 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 API key from .env file
 load_dotenv()
-os.getenv("GOOGLE_API_KEY")
+genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
 
 def get_pdf_text(pdf_docs):
+    """Extracts text from uploaded PDF files"""
     text = ""
     for pdf in pdf_docs:
         pdf_reader = PdfReader(pdf)
@@ -23,18 +17,33 @@ def get_pdf_text(pdf_docs):
             text += page.extract_text()
     return text
 
-def user_input(user_question):
-    load_dependencies()  # Load the necessary libraries only when needed
-    from langchain_google_genai import GoogleGenerativeAIEmbeddings
-    from langchain.vectorstores import FAISS
+def get_gemini_response(question, context):
+    """Sends question and context to Gemini for an answer"""
+    prompt = f"Context:\n{context}\n\nQuestion:\n{question}\n\nAnswer:"
+    response = genai.generate_text(prompt=prompt)
+    return response.generations[0].text
+
+def main():
+    st.set_page_config(page_title="Chat with PDF")
+    st.header("Chat with PDF using Gemini 💁")
+
+    # Text input for user's question
+    user_question = st.text_input("Ask a Question from the PDF Files")
 
-    embeddings = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
-    new_db = FAISS.load_local("faiss_index", embeddings)
-    docs = new_db.similarity_search(user_question)
+    # PDF upload section
+    with st.sidebar:
+        st.title("Menu:")
+        pdf_docs = st.file_uploader("Upload your PDF Files", accept_multiple_files=True)
 
-    chain = get_conversational_chain()
+        if pdf_docs and st.button("Submit & Process"):
+            with st.spinner("Processing PDFs..."):
+                raw_text = get_pdf_text(pdf_docs)
+                st.success("PDF content processed successfully!")
 
-    response = chain({"input_documents": docs, "question": user_question}, return_only_outputs=True)
-    st.write("Reply: ", response["output_text"])
+    if user_question and pdf_docs:
+        st.write("Fetching answer...")
+        response = get_gemini_response(user_question, raw_text)
+        st.write("Reply:", response)
 
-# The rest of the code remains the same
+if __name__ == "__main__":
+    main()