Update app.py

app.py

@@ -1,42 +1,155 @@
-import os
-import getpass
 import streamlit as st
-from langchain.document_loaders import PyPDFLoader
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain.embeddings import HuggingFaceEmbeddings
-from langchain.vectorstores import Chroma
-from langchain import HuggingFaceHub
-from langchain.chains import RetrievalQA
-# __import__('pysqlite3')
-# import sys
-# sys.modules['sqlite3'] = sys.modules.pop('pysqlite3')
+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 htmlTemplates import css, bot_template, user_template
+from langchain.llms import HuggingFaceHub
+from langchain.callbacks import get_openai_callback
 
+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
 
-# load huggingface api key
-hubtok = os.environ["HUGGINGFACE_HUB_TOKEN"]
 
-# use streamlit file uploader to ask user for file
-# file = st.file_uploader("Upload PDF")
+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
 
 
-path = "Geeta.pdf"
-loader = PyPDFLoader(path)
-pages = loader.load()
+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
 
-# st.write(pages)
 
-splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=20)
-docs = splitter.split_documents(pages)
+def get_conversation_chain(vectorstore):
+    # llm = ChatOpenAI(model_name="gpt-3.5-turbo-16k")
+    llm = HuggingFaceHub(repo_id="google/flan-t5-xxl", model_kwargs={"temperature":0.5, "max_length":512})
 
-embeddings = HuggingFaceEmbeddings()
-doc_search = Chroma.from_documents(docs, embeddings)
+    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
 
-repo_id = "tiiuae/falcon-7b"
-llm = HuggingFaceHub(repo_id=repo_id, huggingfacehub_api_token=hubtok, model_kwargs={'temperature': 0.2,'max_length': 1000})
 
-from langchain.schema import retriever
-retireval_chain = RetrievalQA.from_chain_type(llm, chain_type="stuff", retriever=doc_search.as_retriever())
+def handle_userinput(user_question):
+    response = st.session_state.conversation({'question': user_question})
+    st.session_state.chat_history = response['chat_history']
 
-if query := st.chat_input("Enter a question: "):
-  with st.chat_message("assistant"):
-    st.write(retireval_chain.run(query))
+    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="Chat 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("Chat with multiple PDFs :books:")
+    user_question = st.text_input("Ask a question about your documents:")
+    if user_question:
+        handle_userinput(user_question)
+
+    with st.sidebar:
+        st.subheader("Your documents")
+        pdf_docs = st.file_uploader(
+            "Upload your PDFs here and click on 'Process'", accept_multiple_files=True)
+        if st.button("Process"):
+            if(len(pdf_docs) == 0):
+                st.error("Please upload at least one PDF")
+            else:
+                with st.spinner("Processing"):
+                    # get pdf text
+                    raw_text = get_pdf_text(pdf_docs)
+
+                    # get the text chunks
+                    text_chunks = get_text_chunks(raw_text)
+
+                    # create vector store
+                    vectorstore = get_vectorstore(text_chunks)
+
+                    # create conversation chain
+                    st.session_state.conversation = get_conversation_chain(
+                        vectorstore)
+
+if __name__ == '__main__':
+    main()
+
+
+
+
+
+
+# import os
+# import getpass
+# import streamlit as st
+# from langchain.document_loaders import PyPDFLoader
+# from langchain.text_splitter import RecursiveCharacterTextSplitter
+# from langchain.embeddings import HuggingFaceEmbeddings
+# from langchain.vectorstores import Chroma
+# from langchain import HuggingFaceHub
+# from langchain.chains import RetrievalQA
+# # __import__('pysqlite3')
+# # import sys
+# # sys.modules['sqlite3'] = sys.modules.pop('pysqlite3')
+
+
+# # load huggingface api key
+# hubtok = os.environ["HUGGINGFACE_HUB_TOKEN"]
+
+# # use streamlit file uploader to ask user for file
+# # file = st.file_uploader("Upload PDF")
+
+
+# path = "Geeta.pdf"
+# loader = PyPDFLoader(path)
+# pages = loader.load()
+
+# # st.write(pages)
+
+# splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=20)
+# docs = splitter.split_documents(pages)
+
+# embeddings = HuggingFaceEmbeddings()
+# doc_search = Chroma.from_documents(docs, embeddings)
+
+# repo_id = "tiiuae/falcon-7b"
+# llm = HuggingFaceHub(repo_id=repo_id, huggingfacehub_api_token=hubtok, model_kwargs={'temperature': 0.2,'max_length': 1000})
+
+# from langchain.schema import retriever
+# retireval_chain = RetrievalQA.from_chain_type(llm, chain_type="stuff", retriever=doc_search.as_retriever())
+
+# if query := st.chat_input("Enter a question: "):
+#   with st.chat_message("assistant"):
+#     st.write(retireval_chain.run(query))