renamed frontend to app.py

app.py

@@ -0,0 +1,170 @@
+import streamlit as st
+from dotenv import load_dotenv
+from PyPDF2 import PdfReader
+from langchain import embeddings
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.embeddings import OpenAIEmbeddings, HuggingFaceInstructEmbeddings
+from langchain.vectorstores import FAISS
+from langchain.vectorstores import faiss
+from langchain.chat_models import ChatOpenAI
+from langchain.memory import ConversationBufferMemory
+from langchain.chains import ConversationalRetrievalChain
+from html_templates import css, bot_template, user_template
+from langchain.llms import HuggingFaceHub
+import os
+import pickle
+from datetime import datetime
+
+
+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):
+    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 = 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 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):
+        # Display user message
+        if i % 2 == 0:
+            st.write(user_template.replace("{{MSG}}", message.content), unsafe_allow_html=True)
+        else:
+            print(message)
+            # Display AI response
+            st.write(bot_template.replace("{{MSG}}", message.content), unsafe_allow_html=True)
+            # Display source document information if available in the message
+            if hasattr(message, 'source') and message.source:
+                st.write(f"Source Document: {message.source}", unsafe_allow_html=True)
+
+
+def safe_vec_store():
+    os.makedirs('vectorstore', exist_ok=True)
+    filename = 'vectores' + datetime.now().strftime('%Y%m%d%H%M') + '.pkl'
+    file_path = os.path.join('vectorstore', filename)
+    vector_store = st.session_state.vectorstore
+
+    # Serialize and save the entire FAISS object using pickle
+    with open(file_path, 'wb') as f:
+        pickle.dump(vector_store, f)
+
+
+
+def main():
+    load_dotenv()
+    st.set_page_config(page_title="Doc Verify RAG", page_icon=":hospital:")
+    st.write(css, unsafe_allow_html=True)
+
+    st.subheader("Your documents")
+    pdf_docs = st.file_uploader("Upload your PDFs here and click on 'Process'", accept_multiple_files=True)
+    filenames = [file.name for file in pdf_docs if file is not None]
+
+    if st.button("Process"):
+        with st.spinner("Processing"):
+            loaded_vec_store = None
+            for filename in filenames:
+                if ".pkl" in filename:
+                    file_path = os.path.join('vectorstore', filename)
+                    with open(file_path, 'rb') as f:
+                        loaded_vec_store = pickle.load(f)
+            raw_text = get_pdf_text(pdf_docs)
+            text_chunks = get_text_chunks(raw_text)
+            vec = get_vectorstore(text_chunks)
+            if loaded_vec_store:
+                vec.merge_from(loaded_vec_store)
+                st.warning("loaded vectorstore")
+            if "vectorstore" in st.session_state:
+                vec.merge_from(st.session_state.vectorstore)
+                st.warning("merged to existing")
+            st.session_state.vectorstore = vec
+            st.session_state.conversation = get_conversation_chain(vec)
+            st.success("data loaded")
+
+    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("Doc Verify RAG :hospital:")
+    user_question = st.text_input("Ask a question about your documents:")
+    if user_question:
+        handle_userinput(user_question)
+
+    with st.sidebar:
+
+        st.subheader("Classification Instrucitons")
+        classifier_docs = st.file_uploader("Upload your instructions here and click on 'Process'", accept_multiple_files=True)
+        filenames = [file.name for file in classifier_docs if file is not None]
+
+        if st.button("Process Classification"):
+            with st.spinner("Processing"):
+                loaded_vec_store = None
+                for filename in filenames:
+                    if ".pkl" in filename:
+                        file_path = os.path.join('vectorstore', filename)
+                        with open(file_path, 'rb') as f:
+                            loaded_vec_store = pickle.load(f)
+                raw_text = get_pdf_text(pdf_docs)
+                text_chunks = get_text_chunks(raw_text)
+                vec = get_vectorstore(text_chunks)
+                if loaded_vec_store:
+                    vec.merge_from(loaded_vec_store)
+                    st.warning("loaded vectorstore")
+                if "vectorstore" in st.session_state:
+                    vec.merge_from(st.session_state.vectorstore)
+                    st.warning("merged to existing")
+                st.session_state.vectorstore = vec
+                st.session_state.conversation = get_conversation_chain(vec)
+                st.success("data loaded")
+
+        # Save and Load Embeddings
+        if st.button("Save Embeddings"):
+            if "vectorstore" in st.session_state:
+                safe_vec_store()
+                # st.session_state.vectorstore.save_local("faiss_index")
+                st.sidebar.success("safed")
+            else:
+                st.sidebar.warning("No embeddings to save. Please process documents first.")
+
+        if st.button("Load Embeddings"):
+            st.warning("this function is not in use, just upload the vectorstore")
+
+
+if __name__ == '__main__':
+    main()