Create app.py

app.py

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+import streamlit as st
+import streamlit as st
+from dotenv import load_dotenv
+from PyPDF2 import PdfReader
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.vectorstores import FAISS
+from langchain.chat_models import ChatOpenAI
+from langchain.embeddings import OpenAIEmbeddings, HuggingFaceInstructEmbeddings
+from langchain.memory import ConversationBufferMemory
+from langchain.chains import ConversationalRetrievalChain
+# from langchain.llms import HuggingFaceHub
+from streamlit_chat import message
+def get_pdf_text(pdfs):
+  text=""
+  for pdf in pdfs:
+    pdf_reader = PdfReader(pdf)
+    for page in pdf_reader.pages:
+      text+= page.extract_text()
+  return text
+
+def get_text_chunks(text):
+  text_splitter = CharacterTextSplitter(separator="\n",
+  chunk_size=1000, chunk_overlap = 200, length_function=len)
+  chunks = text_splitter.split_text(text)
+  return chunks
+
+def 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 get_conversation_chain(vectorstore):
+#     llm = HuggingFaceHub(repo_id="google/flan-t5-xxl")
+    llm = ChatOpenAI()
+    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 user_input(user_question):
+  response = st.session_state.conversation({'question':user_question})
+  st.session_state.chat_history = response['chat_history']
+  for i, messages in enumerate(st.session_state.chat_history):
+      if i % 2 == 0:
+          message(messages.content, is_user=True)
+      else:
+          message(messages.content)
+def main():
+  load_dotenv()
+  st.set_page_config(page_title="Chat with PDF")
+  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("Chat with PDF")
+  user_question = st.text_input("Ask a question about your documents...")
+  if user_question:
+    user_input(user_question)
+  with st.sidebar:
+    st.subheader("Your Documents")
+    pdfs = st.file_uploader("Upload here", accept_multiple_files=True)
+    if st.button("Process"):
+      with st.spinner("Processing"):
+        raw_text = get_pdf_text(pdfs)
+#         print(raw_text)
+        chunks = get_text_chunks(raw_text)
+        vectorstore = get_vectorstore(chunks)
+        st.session_state.conversation = get_conversation_chain(vectorstore)
+        st.success("Processing Complete !")
+
+if __name__ == '__main__':
+  main()