Upload 4 files

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+robot_icon.png filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,288 @@
+##########################################################################
+#   app.py   -  Pennwick PDF Chat
+#
+#   HuggingFace Spaces application to anlayze uploaded PDF files
+#           with open-source models ( hkunlp/instructor-xl )
+#
+#   Mike Pastor  February 17, 2024
+
+
+import streamlit as st
+from streamlit.components.v1 import html
+
+from dotenv import load_dotenv
+
+from PyPDF2 import PdfReader
+
+from PIL import Image
+
+# Local file
+from htmlTemplates import css, bot_template, user_template
+
+#  from langchain.embeddings import HuggingFaceInstructEmbeddings
+from langchain_community.embeddings import HuggingFaceInstructEmbeddings
+
+# from langchain.vectorstores import FAISS
+from langchain_community.vectorstores import FAISS
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.memory import ConversationBufferMemory
+from langchain.chains import ConversationalRetrievalChain
+
+#  from langchain.llms import HuggingFaceHub
+from langchain_community.llms import HuggingFaceHub
+
+##################################################################################
+#  Admin flags
+DISPLAY_DIALOG_LINES = 6
+
+SESSION_STARTED = False
+
+
+##################################################################################
+def extract_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
+
+
+##################################################################################
+#  Chunk size and overlap must not exceed the models capacity!
+#
+def extract_bitesize_pieces(text):
+    text_splitter = CharacterTextSplitter(
+        separator="\n",
+        chunk_size=800,  # 1000
+        chunk_overlap=200,
+        length_function=len
+    )
+    chunks = text_splitter.split_text(text)
+    return chunks
+
+
+##################################################################################
+def prepare_embedding_vectors(text_chunks):
+    st.write('Here in vector store....', unsafe_allow_html=True)
+    # embeddings = OpenAIEmbeddings()
+
+    #  pip install InstructorEmbedding
+    #  pip install sentence-transformers==2.2.2
+    embeddings = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-xl")
+
+    st.write('Here in vector store - got embeddings ', unsafe_allow_html=True)
+    #  from InstructorEmbedding import INSTRUCTOR
+    # model = INSTRUCTOR('hkunlp/instructor-xl')
+    # sentence = "3D ActionSLAM: wearable person tracking in multi-floor environments"
+    # instruction = "Represent the Science title:"
+    # embeddings = model.encode([[instruction, sentence]])
+
+    # embeddings = model.encode(text_chunks)
+    print('have Embeddings:   ')
+
+    # text_chunks="this is a test"
+    #   FAISS,  Chroma and other vector databases
+    #
+    vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings)
+    st.write('FAISS succeeds:   ')
+
+    return vectorstore
+
+
+##################################################################################
+def prepare_conversation(vectorstore):
+    # llm = ChatOpenAI()
+    #  llm = HuggingFaceHub(repo_id="google/flan-t5-xxl", model_kwargs={"temperature":0.5, "max_length":512})
+    #  google/bigbird-roberta-base     facebook/bart-large
+    llm = HuggingFaceHub(repo_id="google/flan-t5-xxl", model_kwargs={"temperature": 0.7, "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 process_user_question(user_question):
+    print('process_user_question called: \n')
+
+    # if not SESSION_STARTED:
+    #     print('No Session')
+    #     st.write( 'Please upload and analyze your PDF files first!')
+    #     return
+
+    if user_question == None:
+        print('question is null')
+        return
+    if user_question == '':
+        print('question is blank')
+        return
+    if st == None:
+        print('session is null')
+        return
+    if st.session_state == None:
+        print('session STATE is null')
+        return
+
+    print('question is: ', user_question)
+    print('\nsession is: ', st)
+
+    # try:
+    #     response = st.session_state.conversation({'question': user_question})
+    #     # response = st.session_state.conversation({'summarization': user_question})
+    #     st.session_state.chat_history = response['chat_history']
+    # Exception:
+    #     st.write( 'Please upload and analyze your PDF files first!')
+    #     return
+
+    # st.empty()
+
+    # try:
+    #     st.session_state.conversation({'question': "Summarize the document"})
+    #     # if "key" not in st.session_state:
+    #     #     st.write('Good')
+    # except:
+    #     st.error("Please upload and analyze your PDF files first!")
+    #     return
+
+    if st.session_state.conversation == None:
+        st.error("Please upload and analyze your PDF files first!")
+        return
+
+    response = st.session_state.conversation({'question': user_question})
+    st.session_state.chat_history = response['chat_history']
+    results_size = len(response['chat_history'])
+
+    results_string = ""
+
+    print('results_size is: ', results_size)
+
+    for i, message in enumerate(st.session_state.chat_history):
+
+        #  Scrolling does not display the last printed line,
+        #    so only print the last 6 lines
+        #
+        print('results_size on msg: ', results_size, i, (results_size - DISPLAY_DIALOG_LINES))
+        if results_size > DISPLAY_DIALOG_LINES:
+            if i < (results_size - DISPLAY_DIALOG_LINES):
+                continue
+
+        if i % 2 == 0:
+            # st.write(user_template.replace(
+            #     "{{MSG}}", message.content), unsafe_allow_html=True)
+
+            results_string += ("<p>" + message.content + "</p>")
+
+        else:
+            # st.write(bot_template.replace(
+            #     "{{MSG}}", message.content), unsafe_allow_html=True)
+
+            results_string += ("<p>" + "-- " + message.content + "</p>")
+
+    html(results_string, height=300, scrolling=True)
+
+
+###################################################################################
+def main():
+    print('Pennwick Starting up...\n')
+    # Load the environment variables - if any
+    load_dotenv()
+
+    ##################################################################################
+    #  st.set_page_config(page_title="Pennwick PDF Analyzer", page_icon=":books:")
+    # im = Image.open("robot_icon.ico")
+    # st.set_page_config(page_title="Pennwick PDF Analyzer", page_icon=im )
+    # st.set_page_config(page_title="Pennwick PDF Analyzer")
+
+    # import base64
+    # from PIL import Image
+
+    # # Open your image
+    # image = Image.open("robot_icon.ico")
+
+    # # Convert image to base64 string
+    # with open("robot_icon.ico", "rb") as f:
+    #     encoded_string = base64.b64encode(f.read()).decode()
+
+    # # Set page config with base64 string
+    # st.set_page_config(page_title="Pennwick File Analyzer 2", page_icon=f"data:image/ico;base64,{encoded_string}")
+
+    st.set_page_config(page_title="Pennwick File Analyzer", page_icon="./robot_icon.ico")
+
+    print('prepared page...\n')
+
+    ###################
+
+    st.write(css, unsafe_allow_html=True)
+
+    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("Pennwick File Analyzer :shark:")
+    # st.header("Pennwick File Analyzer 2")
+
+    st.image("robot_icon.png", width=96)
+    st.header(f"Pennwick File Analyzer")
+
+    user_question = None
+    user_question = st.text_input("Ask the  Open Source - Flan-T5 Model  a question about your uploaded documents:")
+    if user_question != None:
+        print('calling process question', user_question)
+        process_user_question(user_question)
+
+    # st.write( user_template, unsafe_allow_html=True)
+    # st.write(user_template.replace( "{{MSG}}", "Hello robot!"), unsafe_allow_html=True)
+    # st.write(bot_template.replace( "{{MSG}}", "Hello human!"), unsafe_allow_html=True)
+
+    with st.sidebar:
+
+        st.subheader("Which documents would you like to analyze?")
+        st.subheader("(no data is saved beyond the session)")
+
+        pdf_docs = st.file_uploader(
+            "Upload your PDF documents here and click on 'Analyze'", accept_multiple_files=True)
+
+        # Upon button press
+        if st.button("Analyze these files"):
+            with st.spinner("Processing..."):
+                #################################################################
+                #  Track the overall time for file processing into Vectors
+                # #
+                from datetime import datetime
+                global_now = datetime.now()
+                global_current_time = global_now.strftime("%H:%M:%S")
+                st.write("Vectorizing Files - Current Time =", global_current_time)
+
+                # get pdf text
+                raw_text = extract_pdf_text(pdf_docs)
+                #  st.write(raw_text)
+
+                # # get the text chunks
+                text_chunks = extract_bitesize_pieces(raw_text)
+                # st.write(text_chunks)
+
+                # # create vector store
+                vectorstore = prepare_embedding_vectors(text_chunks)
+
+                # # create conversation chain
+                st.session_state.conversation = prepare_conversation(vectorstore)
+
+                SESSION_STARTED = True
+
+                # Mission Complete!
+                global_later = datetime.now()
+                st.write("Files Vectorized - Total EXECUTION Time =",
+                         (global_later - global_now), global_later)
+
+
+if __name__ == '__main__':
+    main()
+
+

htmlTemplates.py

@@ -0,0 +1,44 @@
+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://free-images.com/sm/9cb8/sunset_sundown_da_nang.jpg" 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://free-images.com/sm/176d/squirrel_tail_bushy_tail.jpg" style="max-height: 78px; max-width: 78px; border-radius: 50%; object-fit: cover;" >
+    </div>    
+    <div class="message">{{MSG}}</div>
+</div>
+'''