Upload main.py

main.py

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+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_community.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
+
+
+load_dotenv()
+os.getenv("GOOGLE_API_KEY")
+genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
+
+
+def get_pdf_text(pdf_docs):
+  text=""
+  for pdf in pdf_docs:
+      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
+
+
+
+def user_input(user_question):
+  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)
+  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()