rename scripts.py, remove unused files

backend/draft_app.py

@@ -0,0 +1,219 @@
+import time
+import streamlit as st
+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 generate_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():
+    st.set_page_config(page_title="Doc Verify RAG", page_icon=":hospital:")
+    st.write(css, unsafe_allow_html=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
+    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:
+        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
+
+    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"):
+            st.session_state.classify = True
+            with st.spinner("Processing"):
+                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()
+                # 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()

html_templates.py

@@ -1,44 +0,0 @@
-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="" 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="">
-    </div>    
-    <div class="message">{{MSG}}</div>
-</div>
-'''

requirements.txt

@@ -5,7 +5,4 @@ unstructured[local-inference]
 python-dotenv
 streamlit
 langchain
-openai
-chromadb
-tiktoken
-python-poppler
+openai

scripts.py

@@ -0,0 +1,102 @@
+import os
+import io
+import argparse
+import json
+import openai
+import sys
+from dotenv import load_dotenv
+from langchain_community.document_loaders import TextLoader
+from langchain_community.document_loaders import UnstructuredPDFLoader
+from langchain_community.embeddings.fake import FakeEmbeddings
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+
+load_dotenv()
+
+
+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')
+
+    # transform locally
+    documents = loader.load()
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=0,
+    separators=[
+        "\n\n",
+        "\n",
+        " ",
+        ",",
+        "\uff0c",  # Fullwidth comma
+        "\u3001",  # Ideographic comma
+        "\uff0e",  # Fullwidth full stop
+        # "\u200B",  # Zero-width space (Asian languages)
+        # "\u3002",  # Ideographic full stop (Asian languages)
+        "",
+    ])
+    docs = text_splitter.split_documents(documents)
+
+    return docs
+
+
+def generate_metadata(docs):
+    prompt_template = """
+    BimDiscipline = ['plumbing', 'network', 'heating', 'electrical', 'ventilation', 'architecture']
+
+    You are a helpful assistant that understands BIM documents and engineering disciplines. Your answer should be in JSON format and only include the filename, a short description, and the engineering discipline the document belongs to, distinguishing between {[d.value for d in BimDiscipline]} based on the given document."
+
+    Analyze the provided document, which could be in either German or English. Extract the filename, its description, and infer the engineering discipline it belongs to. Document:
+    context="
+    """     
+    # plain text     
+    filepath = [doc.metadata for doc in docs][0]['source']
+    context = "".join(
+        [doc.page_content.replace('\n\n','').replace('..','') for doc in docs])
+
+    prompt = f'{prompt_template}{context}"\nFilepath:{filepath}'
+
+    #print(prompt)
+    
+    # Create client
+    client = openai.OpenAI(
+        base_url="https://api.together.xyz/v1",
+        api_key=os.environ["TOGETHER_API_KEY"],
+        #api_key=userdata.get('TOGETHER_API_KEY'),    
+    )
+
+    # Call the LLM with the JSON schema
+    chat_completion = client.chat.completions.create(
+        model="mistralai/Mixtral-8x7B-Instruct-v0.1",        
+        messages=[
+            {
+                "role": "system",
+                "content": f"You are a helpful assistant that responsds in JSON format"                
+            },
+            {
+                "role": "user",
+                "content": prompt                                
+            }
+        ]
+    )
+
+    return json.loads(chat_completion.choices[0].message.content)    
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Generate metadata for a BIM document")
+    parser.add_argument("document", metavar="FILEPATH", type=str,
+                        help="Path to the BIM document")
+
+    args = parser.parse_args()
+
+    if not os.path.exists(args.document) or not os.path.isfile(args.document):
+        print("File '{}' not found or not accessible.".format(args.document))
+        sys.exit(-1)
+
+    docs = ingest(args.document)
+    metadata = generate_metadata(docs)
+    print(metadata)