Upload app.py

app.py

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+import os
+
+import streamlit as st
+from dotenv import load_dotenv
+from PyPDF2 import PdfReader
+from langchain.text_splitter import CharacterTextSplitter
+from langchain_openai import OpenAIEmbeddings
+from langchain.vectorstores import FAISS
+# from langchain_community.vectorstores import FAISS
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.memory import ConversationBufferMemory
+from langchain.chains import ConversationalRetrievalChain
+from langchain.chat_models import ChatOpenAI
+from htmlTemplates import css, bot_template, user_template
+from langchain.embeddings import HuggingFaceInstructEmbeddings
+from langchain.llms import HuggingFaceHub
+import os
+def get_pdf_text(pdf_doc):
+    text = ""
+    for pdf in pdf_doc:
+        pdf_reader = PdfReader(pdf)
+        for page in pdf_reader.pages:
+            text += page.extract_text()
+    return text
+
+
+def get_text_chunk(row_text):
+    text_splitter = CharacterTextSplitter(
+        separator="\n",
+        chunk_size = 1000,
+        chunk_overlap = 200,
+        length_function = len
+    )
+    chunk = text_splitter.split_text(row_text)
+    return chunk
+
+
+def get_vectorstore(text_chunk):
+    embeddings = OpenAIEmbeddings(openai_api_key = os.getenv("OPENAI_API_KEY"))
+    # embeddings = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-xl")
+    vector = FAISS.from_texts(text_chunk,embeddings)
+    return vector
+
+
+def get_conversation_chain(vectorstores):
+    llm = ChatOpenAI(openai_api_key = os.getenv("OPENAI_API_KEY"))
+    # llm = HuggingFaceHub(repo_id="google/flan-t5-base", 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=vectorstores.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 indx, msg in enumerate(st.session_state.chat_history):
+        if indx % 2==0:
+            st.write(user_template.replace("{{MSG}}",msg.content), unsafe_allow_html=True)
+        else:
+            st.write(bot_template.replace("{{MSG}}", msg.content), unsafe_allow_html=True)
+
+
+
+def main():
+    # load secret key
+    load_dotenv()
+    
+    # config the pg
+    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
+
+    st.header("Chat with multiple PDFs :books:")
+    user_question = st.text_input("Ask a question about your docs")
+    if user_question:
+        user_input(user_question)
+
+    # st.write(user_template.replace("{{MSG}}","Hello Robot"), unsafe_allow_html=True)
+    # st.write(bot_template.replace("{{MSG}}","Hello Human"), unsafe_allow_html=True)
+
+    # create side bar
+    with st.sidebar:
+        st.subheader("Your Documents")
+        pdf_doc = st.file_uploader(label="Upload your documents",accept_multiple_files=True)
+        if st.button("Process"):
+            with st.spinner(text="Processing"):
+
+            # get pdf text
+                row_text = get_pdf_text(pdf_doc)
+            # get the text chunk
+                text_chunk = get_text_chunk(row_text)
+                # st.write(text_chunk)
+            # create vecor store
+                vectorstores = get_vectorstore(text_chunk)
+                # st.write(vectorstores)
+            # create conversation chain
+                st.session_state.conversation = get_conversation_chain(vectorstores)
+
+
+if __name__ == "__main__":
+    main()