Add application file

app.py

@@ -0,0 +1,190 @@
+import streamlit as st
+from dotenv import load_dotenv
+from PyPDF2 import PdfReader
+from langchain.text_splitter import CharacterTextSplitter,RecursiveCharacterTextSplitter
+from langchain.embeddings import OpenAIEmbeddings, HuggingFaceInstructEmbeddings
+from langchain.vectorstores import FAISS, Chroma
+from langchain.embeddings import HuggingFaceEmbeddings  # General embeddings from HuggingFace models.
+from langchain.chat_models import ChatOpenAI
+from langchain.memory import ConversationBufferMemory
+from langchain.chains import ConversationalRetrievalChain
+from htmlTemplates import css, bot_template, user_template
+from langchain.llms import HuggingFaceHub, LlamaCpp,CTransformers # For loading transformer models.
+
+def get_pdf_text(pdf_docs):
+    text = ''
+    # pdf_file_ = open(pdf_docs,'rb')
+    # text = "example hofjin"
+    pdf_reader = PdfReader(pdf_docs)
+    for page in pdf_reader.pages:
+        text += page.extract_text()
+
+    return text
+
+
+def get_text_chunks(text):
+    print('text = ',text)
+    text_splitter = RecursiveCharacterTextSplitter(
+        chunk_size = 256,
+        chunk_overlap = 50,
+        length_function= len
+    )
+    # text_splitter = CharacterTextSplitter(
+    #     separator="\n",
+    #     chunk_size=10f00,
+    #     chunk_overlap=200,
+    #     length_function=len
+    # )
+    chunks = text_splitter.split_text(text)
+    print('chunks = ', chunks)
+    return chunks
+
+
+def get_vectorstore(text_chunks):
+    # Load the desired embeddings model.
+    embeddings = HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L12-v2',
+                                       model_kwargs={'device': 'cpu'})
+    print('embeddings = ', embeddings)
+    # embeddings = OpenAIEmbeddings()sentence-transformers/all-MiniLM-L6-v2
+    # embeddings = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-xl",
+    #                                           model_kwargs={'device':'cpu'})
+    vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings)
+    # vectorstore = Chroma.from_texts(texts=text_chunks, embedding=embeddings)
+
+    return vectorstore
+
+
+def get_conversation_chain(vectorstore):
+    
+    model_path = 'llama-2-7b-chat.Q2_K.gguf'
+    # llm = ChatOpenAI()
+    # llm = HuggingFaceHub(repo_id="google/flan-t5-xxl", model_kwargs={"temperature":0.5, "max_length":512})
+    config = {'max_new_tokens': 2048}
+
+    
+    # llm = CTransformers(model="llama-2-7b-chat.ggmlv3.q2_K.bin", model_type="llama", config=config)
+
+    llm = LlamaCpp(model_path=model_path,
+                   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):
+    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 get_text_file(docs):
+    text = docs.read().decode("utf-8")
+    return text
+
+def get_csv_file(docs):
+    import pandas as pd
+    text = ''
+
+    data = pd.read_csv(docs)
+
+    for index, row in data.iterrows():
+        item_name = row[0]
+        row_text = item_name
+        for col_name in data.columns[1:]:
+            row_text += '{} is {} '.format(col_name, row[col_name])
+        text += row_text + '\n'
+
+    return text
+
+def get_json_file(docs):
+    import json
+    text = ''
+    # with open(docs, 'r') as f:
+    json_data = json.load(docs)
+
+    for f_key, f_value in json_data.items():
+        for s_value in f_value:
+            text += str(f_key) + str(s_value)
+        text += '\n'
+    #print(text)
+    return text
+
+def get_hwp_file(docs):
+    pass
+
+def get_docs_file(docs):
+    pass
+
+
+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
+                raw_text = ""
+                
+                for file in docs:
+                    print('file - type : ', file.type)
+                    if file.type == 'text/plain':
+                        #file is .txt
+                        raw_text += get_text_file(file)
+                    elif file.type in ['application/octet-stream', 'application/pdf']:
+                        #file is .pdf
+                        raw_text += get_pdf_text(file)
+                    elif file.type == 'text/csv':
+                        #file is .csv
+                        raw_text += get_csv_file(file)
+                    elif file.type == 'application/json':
+                        # file is .json
+                        raw_text += get_json_file(file)
+                    elif file.type == 'application/x-hwp':
+                        # file is .hwp
+                        raw_text += get_hwp_file(file)
+                    elif file.type == 'application/vnd.openxmlformats-officedocument.wordprocessingml.document':
+                        # file is .docs
+                        raw_text += get_docs_file(file)
+
+
+                # get the text chunks
+                text_chunks = get_text_chunks(raw_text)
+
+                # create vector store
+                vectorstore = get_vectorstore(text_chunks)
+
+                # create conversation chain
+                st.session_state.conversation = get_conversation_chain(
+                    vectorstore)
+
+
+if __name__ == '__main__':
+    main()

requirements.txt

@@ -0,0 +1,12 @@
+langchain
+llama-cpp-python
+PyPDF2==3.0.1
+faiss-cpu==1.7.4
+ctransformers
+pypdf
+chromadb
+tiktoken
+pysqlite3-binary
+streamlit-extras
+InstructorEmbedding
+sentence-transformers