Upload app.py

app.py

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+import streamlit as st
+from PyPDF2 import PdfReader
+from langchain.text_splitter import CharacterTextSplitter, RecursiveCharacterTextSplitter
+import os, getpass
+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 langchain.chains import ConversationalRetrievalChain
+from langchain.memory import ConversationBufferMemory
+
+
+#Gemini Key
+os.environ['GOOGLE_API_KEY']="AIzaSyB6-jZLBXeOeLFBhFaU11oidwAeBATkrds"
+genai.configure(api_key=os.environ['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):
+    #RecursiveCharacterTextSplitter CharacterTextSplitter separator="\n", 
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=1000, length_function=len)#
+    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)
+    return vector_store
+
+
+def get_conversational_chain(Fvs):
+
+    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-1.5-pro",temperature=0.3)
+    prompt = PromptTemplate(template = prompt_template, input_variables = ["context", "question"])
+    memory = ConversationBufferMemory(memory_key = "chat_history", return_messages=True)
+    chain = ConversationalRetrievalChain.from_llm(llm=model,retriever=Fvs.as_retriever(), memory=memory)
+
+    return chain
+
+
+
+def user_input(user_question):
+    response = st.session_state.conversation({'question': user_question})
+    st.session_state.chatHistory = response['chat_history']
+    for i, message in enumerate(st.session_state.chatHistory):
+        if i%2 == 0:
+            st.write("Human: ", message.content)
+        else:
+            st.write("Bot: ", message.content)
+
+## streamlit app
+st.set_page_config("Chat With Multiple PDF")
+st.header("Chat with Multiple PDF :books:")
+
+user_question = st.text_input("Ask a Question from the PDF Files")
+submit=st.button("Ask the question")
+
+## If ask button is clicked
+if submit:
+    if "conversation" not in st.session_state:
+        st.session_state.conversation = None
+    if "chatHistory" not in st.session_state:
+        st.session_state.chatHistory = None
+    if user_question:
+        user_input(user_question)
+
+with st.sidebar:
+    st.title("Menu:")
+    pdf_docs = st.file_uploader("Upload your PDF Files", 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)
+            Fvs = get_vector_store(text_chunks)
+            st.session_state.conversation = get_conversational_chain(Fvs)
+            st.success("Done")
+          
+    if st.button("Clear Chat Window", use_container_width=True, type="primary"):
+        st.session_state.history = []
+        st.rerun()
+
+footer = """
+---
+#### Made By [Surat Banerjee](https://www.linkedin.com/in/surat-banerjee/)
+For Any Queries, Reach out on [Portfolio](https://suratbanerjee.wixsite.com/myportfoliods)  
+"""
+
+st.markdown(footer, unsafe_allow_html=True)