Merge branch 'eliawaefler:main' into main

app.py

@@ -2,7 +2,7 @@ import time
 import streamlit as st
 from PyPDF2 import PdfReader
 from langchain.text_splitter import CharacterTextSplitter
-from langchain.embeddings import OpenAIEmbeddings, HuggingFaceInstructEmbeddings
+from langchain.embeddings import OpenAIEmbeddings
 from langchain.vectorstores import FAISS
 from langchain.chat_models import ChatOpenAI
 from langchain.memory import ConversationBufferMemory
@@ -112,16 +112,11 @@ def handle_userinput(user_question):
             # 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'
+    filename = 'vectors' + datetime.now().strftime('%Y%m%d%H%M') + '.pkl'
     file_path = os.path.join('vectorstore', filename)
     vector_store = st.session_state.vectorstore
 
@@ -131,18 +126,21 @@ def safe_vec_store():
 
 
 def main():
-    st.set_page_config(page_title="Doc Verify RAG", page_icon=":hospital:")
+    st.set_page_config(page_title="Doc Verify RAG", page_icon=":mag:")
     st.write(css, unsafe_allow_html=True)
+    st.header("Doc Verify RAG :mag:")
+
     if "openai_api_key" not in st.session_state:
         st.session_state.openai_api_key = False
     if "openai_org" not in st.session_state:
         st.session_state.openai_org = False
     if "classify" not in st.session_state:
         st.session_state.classify = False
+
     def set_pw():
         st.session_state.openai_api_key = True
+
     st.subheader("Your documents")
-    # OPENAI_ORG_ID = st.text_input("OPENAI ORG ID:")
     OPENAI_API_KEY = st.text_input("OPENAI API KEY:", type="password",
                                    disabled=st.session_state.openai_api_key, on_change=set_pw)
     if st.session_state.classify:
@@ -179,20 +177,18 @@ def main():
                 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)
+        st.subheader("Classification instructions")
+        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"):
@@ -201,8 +197,6 @@ def main():
                 st.warning("set classify")
                 time.sleep(3)
 
-
-        # Save and Load Embeddings
         if st.button("Save Embeddings"):
             if "vectorstore" in st.session_state:
                 safe_vec_store()
@@ -216,4 +210,4 @@ def main():
 
 
 if __name__ == '__main__':
-    main()
+    main()

app_V2.py

@@ -0,0 +1,247 @@
+import tempfile
+import streamlit as st
+from PyPDF2 import PdfReader
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.embeddings import OpenAIEmbeddings
+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 generate_metadata, ingest
+
+MODEL_NAME = "mixtral"
+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)
+
+
+def safe_vec_store():
+    # USE VECTARA INSTEAD
+    os.makedirs('vectorstore', exist_ok=True)
+    filename = 'vectors' + 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():
+
+
+
+    st.subheader("Your documents")
+
+    if st.session_state.classify:
+        pdf_doc = st.file_uploader("Upload your PDFs here and click on 'Process'", accept_multiple_files=False)
+    else:
+        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"):
+            if st.session_state.classify:
+                # THE CLASSIFICATION APP
+                st.write("Classifying")
+                plain_text_doc = ingest(pdf_doc.name)
+                classification_result = generate_metadata(plain_text_doc)
+                st.write(classification_result)
+            else:
+                # 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 "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
+
+    user_question = st.text_input("Ask a question about your documents:")
+    if user_question:
+        handle_userinput(user_question)
+    with st.sidebar:
+        st.subheader("Classification instructions")
+        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"):
+            st.session_state.classify = True
+            with st.spinner("Processing"):
+                st.warning("set classify")
+                time.sleep(3)
+
+        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")
+"""
+
+
+def main():
+
+    st.set_page_config(page_title="Doc Verify RAG", page_icon=":mag:")
+    st.write('Anomaly detection for document metadata', unsafe_allow_html=True)
+    st.header("Doc Verify RAG :mag:")
+
+    def set_pw():
+        st.session_state.openai_api_key = True
+
+    if "openai_api_key" not in st.session_state:
+        st.session_state.openai_api_key = False
+    if "openai_org" not in st.session_state:
+        st.session_state.openai_org = False
+    if "classify" not in st.session_state:
+        st.session_state.classify = False
+
+    col1, col2 = st.columns(2)
+    with col1:
+        uploaded_file = st.file_uploader("Choose a PDF file", type=["pdf", "txt"])
+
+        if uploaded_file is not None:
+            try:
+                with tempfile.NamedTemporaryFile(delete=False, suffix=os.path.splitext(uploaded_file.name)[1]) as tmp:
+                    tmp.write(uploaded_file.read())
+                    file_path = tmp.name
+                    st.write(f'Created temporary file {file_path}')
+
+                docs = ingest(file_path)
+                st.write('## Querying Together.ai API')
+                metadata = generate_metadata(docs)
+                st.write(f'## Metadata Generated by {MODEL_NAME}')
+                st.write(metadata)
+
+                # Clean up the temporary file
+                os.remove(file_path)
+
+            except Exception as e:
+                st.error(f'Error: {e}')
+    with col2:
+        OPENAI_API_KEY = st.text_input("OPENAI API KEY:", type="password",
+                                       disabled=st.session_state.openai_api_key, on_change=set_pw)
+        classification = st.file_uploader("upload the metadata", type=["csv", "txt"])
+
+
+if __name__ == '__main__':
+    main()

backend/generate_metadata.py

@@ -1,4 +1,5 @@
 import os
+import io
 import argparse
 import json
 import openai
@@ -12,13 +13,13 @@ from langchain_text_splitters import RecursiveCharacterTextSplitter
 load_dotenv()
 
 
-def ingest(file_path):
-    extension = file_path.split('.')[-1]
-    ext = extension.lower()
-    if ext == 'pdf':
-        loader = UnstructuredPDFLoader(file_path)
-    elif ext == 'txt':
-        loader = TextLoader(file_path)
+import io
+
+def ingest(file_obj, file_ext='pdf'):
+    if file_ext == 'pdf':
+        loader = UnstructuredPDFLoader(file_obj)
+    elif file_ext == 'txt':
+        loader = TextLoader(file_obj)
     else:
         raise NotImplementedError('Only .txt or .pdf files are supported')
 
@@ -29,7 +30,7 @@ def ingest(file_path):
         "\n\n",
         "\n",
         " ",
-        ",",        
+        ",",
         "\uff0c",  # Fullwidth comma
         "\u3001",  # Ideographic comma
         "\uff0e",  # Fullwidth full stop

flake.nix

@@ -14,6 +14,9 @@
     devShells.${system}.default = pkgs.mkShell {
       packages = [
         (pkgs.python311.withPackages (python-pkgs: [
+          python-pkgs.pip # VsCode starts
+          python-pkgs.jupyter
+          python-pkgs.notebook # VsCode ends
           python-pkgs.numpy
           python-pkgs.pandas
           python-pkgs.scipy
@@ -23,15 +26,20 @@
           python-pkgs.langchain
           python-pkgs.langchain-text-splitters
           python-pkgs.unstructured
+          python-pkgs.wrapt # unstructured[local-inference] starts
+          python-pkgs.iso-639
+          python-pkgs.emoji
+          python-pkgs.pillow-heif
+          python-pkgs.magic
+          python-pkgs.poppler-qt5
+          python-pkgs.pytesseract
+          python-pkgs.langdetect # unstructured[local-inference] ends
           python-pkgs.openai
           python-pkgs.pydantic
           python-pkgs.python-dotenv
           python-pkgs.configargparse
           python-pkgs.streamlit
-          python-pkgs.pip
           python-pkgs.lark
-          python-pkgs.jupyter
-          python-pkgs.notebook
           python-pkgs.sentence-transformers
           pkgs.unstructured-api
         ]))