Create app.py

app.py

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+import streamlit as st
+from dotenv import load_dotenv
+from PyPDF2 import PdfReader
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.embeddings import OpenAIEmbeddings, HuggingFaceInstructEmbeddings
+from langchain.vectorstores import FAISS
+from langchain.chat_models import ChatOpenAI
+from langchain.memory import ConversationBufferMemory
+from langchain.chains import ConversationalRetrievalChain
+from css_template import css, bot_template, user_template
+from langchain.llms import HuggingFaceHub
+import os
+# os.environ['FAISS_NO_AVX2'] = '1'
+
+def method_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 method_get_text_chunks(text):
+    text_splitter = CharacterTextSplitter(
+        separator="\n\n",
+        chunk_size=1000,
+        chunk_overlap=200,
+        length_function=len,
+        is_separator_regex=False,
+    )
+    chunks = text_splitter.split_text(text)
+    return chunks
+
+
+def method_get_vectorstore(text_chunks):
+    # embeddings = OpenAIEmbeddings()
+    embeddings = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-xl")
+    vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings)
+    return vectorstore
+
+
+def method_get_conversation_chain(vectorstore):
+    #llm = ChatOpenAI()
+    llm = HuggingFaceHub(repo_id="google/flan-t5-xxl", model_kwargs={"temperature":0.5, "max_length":512})
+
+    memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True)
+    conversation_chain = ConversationalRetrievalChain.from_llm(
+        llm=llm,
+        retriever=vectorstore.as_retriever(),
+        memory=memory
+    )
+    return conversation_chain
+
+
+def method_handle_userinput(user_question):
+    response = st.session_state.conversation({'question': user_question})
+    st.session_state.chat_history = response['chat_history']
+
+    for i, message in enumerate(st.session_state.chat_history):
+        if i % 2 == 0:
+            st.write(user_template.replace(
+                "{{MSG}}", message.content), unsafe_allow_html=True)
+        else:
+            st.write(bot_template.replace(
+                "{{MSG}}", message.content), unsafe_allow_html=True)
+
+
+def main():
+    load_dotenv()
+    st.set_page_config(page_title="Converse with multiple PDFs",page_icon=":books:")
+    st.write(css, unsafe_allow_html=True)
+
+    if "conversation" not in st.session_state:
+        st.session_state.conversation = None
+    if "chat_history" not in st.session_state:
+        st.session_state.chat_history = None
+
+    st.header("Converse with multiple PDFs :books:")
+    user_question = st.text_input("Ask a question about your documents:")
+    if user_question:
+        method_handle_userinput(user_question)
+
+    with st.sidebar:
+        st.subheader("Documents Upload")
+        pdf_docs = st.file_uploader("Upload your PDFs here and click on 'Submit'", accept_multiple_files=True)
+        if st.button("Submit"):
+            with st.spinner("Processing"):
+                # get pdf text
+                raw_text = method_get_pdf_text(pdf_docs)
+                # get the text chunks
+                text_chunks = method_get_text_chunks(raw_text)
+                # create vector store
+                vectorstore = method_get_vectorstore(text_chunks)
+                st.write(text_chunks)
+                # create conversation chain
+                st.session_state.conversation = method_get_conversation_chain(vectorstore)
+                
+
+
+if __name__ == '__main__':
+    main()