1023-commit

app.py

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+import streamlit as st
+from dotenv import load_dotenv
+from langchain.text_splitter import CharacterTextSplitter, RecursiveCharacterTextSplitter
+from langchain.vectorstores import FAISS
+from langchain.embeddings import HuggingFaceEmbeddings  # General embeddings from HuggingFace models.
+from langchain.memory import ConversationBufferMemory
+from langchain.chains import ConversationalRetrievalChain
+from htmlTemplates import css, bot_template, user_template
+from langchain.llms import LlamaCpp  # For loading transformer models.
+from langchain.document_loaders import PyPDFLoader, TextLoader, JSONLoader, CSVLoader
+from tempfile import NamedTemporaryFile
+from huggingface_hub import hf_hub_download
+
+
+def get_pdf_text(pdf_docs):
+    with NamedTemporaryFile() as temp_file:
+        temp_file.write(pdf_docs.getvalue())
+        temp_file.seek(0)
+        pdf_loader = PyPDFLoader(temp_file.name)
+        pdf_doc = pdf_loader.load()
+        return pdf_doc
+
+
+def get_text_file(docs):
+    with NamedTemporaryFile() as temp_file:
+        temp_file.write(docs.getvalue())
+        temp_file.seek(0)
+        text_loader = TextLoader(temp_file.name)
+        text_doc = text_loader.load()
+
+        return text_doc
+
+
+def get_csv_file(docs):
+    with NamedTemporaryFile() as temp_file:
+        temp_file.write(docs.getvalue())
+        temp_file.seek(0)
+        text_loader = CSVLoader(temp_file.name)
+        text_doc = text_loader.load()
+
+        return text_doc
+
+
+def get_json_file(docs):
+    with NamedTemporaryFile() as temp_file:
+        temp_file.write(docs.getvalue())
+        temp_file.seek(0)
+        json_loader = JSONLoader(temp_file.name,
+                                 jq_schema='.scans[].relationships',
+                                 text_content=False)
+        json_doc = json_loader.load()
+
+        return json_doc
+
+
+def get_text_chunks(documents):
+    text_splitter = RecursiveCharacterTextSplitter(
+        chunk_size=1000,
+        chunk_overlap=200,
+        length_function=len
+    )
+
+    documents = text_splitter.split_documents(documents)
+    return documents
+
+
+def get_vectorstore(text_chunks):
+    # Load the desired embeddings model.
+    embeddings = HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L12-v2',
+                                       model_kwargs={'device': 'cpu'})
+    vectorstore = FAISS.from_documents(text_chunks, embeddings)
+    return vectorstore
+
+
+def get_conversation_chain(vectorstore):
+    model_name_or_path = 'TheBloke/Llama-2-7B-chat-GGUF'
+    model_basename = 'llama-2-7b-chat.Q2_K.gguf'
+    model_path = hf_hub_download(repo_id=model_name_or_path, filename=model_basename)
+
+    llm = LlamaCpp(model_path=model_path,
+                   n_ctx=4086,
+                   input={"temperature": 0.75, "max_length": 2000, "top_p": 1},
+                   verbose=True, )
+    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):
+    print('user_question =>  ', 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):
+        if i % 2 == 0:
+            st.write(user_template.replace(
+                "{{MSG}}", message.content), unsafe_allow_html=True)
+        else:
+            st.write(bot_template.replace(
+                "{{MSG}}", message.content), unsafe_allow_html=True)
+
+
+def main():
+    load_dotenv()
+    st.set_page_config(page_title="Chat with multiple PDFs",
+                       page_icon=":books:")
+    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("Chat with multiple PDFs :books:")
+    user_question = st.text_input("Ask a question about your documents:")
+    if user_question:
+        handle_userinput(user_question)
+
+    with st.sidebar:
+        st.subheader("Your documents")
+        docs = st.file_uploader(
+            "Upload your PDFs here and click on 'Process'", accept_multiple_files=True)
+        if st.button("Process"):
+            with st.spinner("Processing"):
+                # get pdf text
+                doc_list = []
+
+                for file in docs:
+                    print('file - type : ', file.type)
+                    if file.type == 'text/plain':
+                        # file is .txt
+                        doc_list.extend(get_text_file(file))
+                    elif file.type in ['application/octet-stream', 'application/pdf']:
+                        # file is .pdf
+                        doc_list.extend(get_pdf_text(file))
+                    elif file.type == 'text/csv':
+                        # file is .csv
+                        doc_list.extend(get_csv_file(file))
+                    elif file.type == 'application/json':
+                        # file is .json
+                        doc_list.extend(get_json_file(file))
+
+                # get the text chunks
+                text_chunks = get_text_chunks(doc_list)
+
+                # create vector store
+                vectorstore = get_vectorstore(text_chunks)
+
+                # create conversation chain
+                st.session_state.conversation = get_conversation_chain(
+                    vectorstore)
+
+
+if __name__ == '__main__':
+    main()

htmlTemplates.py

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+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>
+'''

requirements.txt

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+langchain
+llama-cpp-python
+PyPDF2==3.0.1
+faiss-cpu==1.7.4
+ctransformers
+pypdf
+chromadb
+tiktoken
+pysqlite3-binary
+streamlit-extras
+InstructorEmbedding
+sentence-transformers
+jq