Initial commit

frontend/app.py

@@ -1,210 +0,0 @@
-import time
-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
-import os
-import pickle
-from datetime import datetime
-from backend.generate_metadata import extract_metadata, ingest
-
-
-css = '''
-<style>
-.chat-message {
-    padding: 1.5rem; border-radius: 0.5rem; margin-bottom: 1rem; display: flex
-}
-.chat-message.user {
-    background-color: #2b313e
-}
-.chat-message.bot {
-    background-color: #475063
-}
-.chat-message .avatar {
-  width: 20%;
-}
-.chat-message .avatar img {
-  max-width: 78px;
-  max-height: 78px;
-  border-radius: 50%;
-  object-fit: cover;
-}
-.chat-message .message {
-  width: 80%;
-  padding: 0 1.5rem;
-  color: #fff;
-}
-'''
-bot_template = '''
-<div class="chat-message bot">
-    <div class="avatar">
-        <img src="https://i.ibb.co/cN0nmSj/Screenshot-2023-05-28-at-02-37-21.png" style="max-height: 78px; max-width: 78px; border-radius: 50%; object-fit: cover;">
-    </div>
-    <div class="message">{{MSG}}</div>
-</div>
-'''
-user_template = '''
-<div class="chat-message user">
-    <div class="avatar">
-        <img src="https://i.ibb.co/rdZC7LZ/Photo-logo-1.png">
-    </div>    
-    <div class="message">{{MSG}}</div>
-</div>
-'''
-
-
-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)
-
-            # THIS DOESNT WORK, SOMEONE PLS FIX
-            # 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():
-    # USE VECTARA INSTEAD
-    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.session_state.classify = False
-    st.subheader("Your documents")
-    pdf_docs = st.file_uploader("Upload your PDFs here and click on 'Process'", accept_multiple_files=not st.session_state.classify)
-    filenames = [file.name for file in pdf_docs if file is not None]
-
-    if st.button("Process"):
-        with st.spinner("Processing"):
-            if st.session_state.classify:
-                # THE CLASSIFICATION APP
-                plain_text_doc = ingest(pdf_docs)
-
-            # NORMAL RAG
-            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 st.session_state.classify:
-            # THE CLASSIFICATION APP
-            classification_result = extract_metadata(plain_text_doc)
-            st.write(classification_result)
-
-
-    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"):
-                st.session_state.classify = True
-                time.sleep(3)
-
-
-        # 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("saved")
-            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()

frontend/src/components/Features.tsx

@@ -58,7 +58,7 @@ export const Features = () => {
             </CardHeader>
             <CardFooter className="flex flex-wrap md:justify-center gap-4">
               <iframe
-	src="https://sandramsc-docverifyrag.hf.space"
+	src="https://aihackathons-docverifyrag.hf.space"
 	width="850"
 style={{ border: 'none' }}
 	height="750"