Create app.py

app.py

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+import subprocess
+
+
+import streamlit as st
+from dotenv import load_dotenv
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.vectorstores import Chroma
+from langchain.embeddings import FastEmbedEmbeddings  # 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
+import tempfile 
+from langchain.chains import RetrievalQA
+from langchain.prompts import PromptTemplate
+import os
+import glob
+
+
+def get_pdf_text(pdf_docs):
+    temp_dir = tempfile.TemporaryDirectory() 
+    temp_filepath = os.path.join(temp_dir.name, pdf_docs.name) 
+
+    with open(temp_filepath, "wb") as f:  
+        f.write(pdf_docs.getvalue()) 
+
+    pdf_loader = PyPDFLoader(temp_filepath) 
+    pdf_doc = pdf_loader.load() 
+    return pdf_doc 
+
+
+def get_text_file(text_docs):
+    temp_dir = tempfile.TemporaryDirectory()
+    temp_filepath = os.path.join(temp_dir.name, text_docs.name)
+    
+    with open(temp_filepath, "wb") as f:
+        f.write(text_docs.getvalue())
+        
+    text_loader = TextLoader(temp_filepath)
+    text_doc = text_loader.load()
+    return text_doc 
+    
+def get_csv_file(csv_docs):
+    temp_dir = tempfile.TemporaryDirectory()
+    temp_filepath = os.path.join(temp_dir.name, csv_docs.name)
+    
+    with open(temp_filepath, "wb") as f:
+        f.write(csv_docs.getvalue())
+        
+    csv_loader = CSVLoader(temp_filepath)
+    csv_doc = csv_loader.load()
+    return csv_doc
+    
+
+def get_json_file(json_docs):
+    temp_dir = tempfile.TemporaryDirectory() 
+    temp_filepath = os.path.join(temp_dir.name, json_docs.name) 
+    with open(temp_filepath, "wb") as f:  
+        f.write(json_docs.getvalue()) 
+   
+    json_loader = JSONLoader(
+        file_path=temp_filepath,
+        jq_schema='.messages[].content',
+        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, embeddings):
+    # embeddings = FastEmbedEmbeddings( model_name= "BAAI/bge-small-en-v1.5", 
+    #                              cache_dir="./embedding_model/")
+     
+    vectorstore = Chroma.from_documents(documents= text_chunks, 
+                               embedding= embeddings,
+                               persist_directory= "./vectordb/")
+    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 = "./models/llama-2-13b-chat.Q4_K_S.gguf"
+
+    llm = LlamaCpp(model_path="./models/llama-2-13b-chat.Q4_K_S.gguf", 
+                   template = 0.4,
+                   n_ctx=4000, 
+                   max_tokens=4000,
+                   n_gpu_layers = 50, 
+                   n_batch = 512,
+                   verbose=True)
+    
+    memory = ConversationBufferMemory(
+        memory_key='chat_history', return_messages=True)
+    
+        # prompt template 📝
+    template = """
+    You are a Experience human Resource Manager. When the employee asks you a question, you will have to refer the company policy and respond in a professional way. Make sure to sound Empethetic while being professional and sound like a Human!
+    Try to summarise the content and keep the answer to the point.
+    If you don't know the answer, just say that you don't know, don't try to make up an answer.
+    When generating answer for the given question make sure to follow the example template!
+    Example: 
+    Question : how many paid leaves do i have ?
+    Answer : The number of paid leaves varies depending on the type of leave, like privilege leave you're entitled to a maximum of 21 days in a calendar year. Other leaves might have different entitlements. thanks for asking!
+    make sure to add "thanks for asking!" after every answer 
+
+    {context}
+    
+    Question: {question}
+    Answer:
+    """
+
+    rag_prompt_custom = PromptTemplate.from_template(template)
+    
+    conversation_chain = RetrievalQA.from_chain_type(
+        llm,
+        retriever=vectorstore.as_retriever(),
+        chain_type_kwargs={"prompt": rag_prompt_custom},
+        memory = memory
+    )
+    return conversation_chain
+
+
+def handle_userinput():
+
+    clear = False
+
+    # Add clear chat button
+    if st.button("Clear Chat history"):
+        clear = True
+        st.session_state.messages = []
+
+    if "messages" not in st.session_state:
+        st.session_state.messages = [{"role": "assistant", "content": "How can I help you?"}]
+
+    for msg in st.session_state.messages:
+        st.chat_message(msg["role"]).write(msg["content"])
+
+    if prompt := st.chat_input():
+        st.session_state.messages.append({"role": "user", "content": prompt})
+        st.chat_message("user").write(prompt)
+        if clear:
+            st.session_state.conversation.clean()
+        msg = st.session_state.conversation.run(prompt)
+        st.session_state.messages.append({"role": "assistant", "content": msg})
+        st.chat_message("assistant").write(msg)
+
+        
+
+# Function to apply rounded edges using CSS
+def add_rounded_edges(image_path="./randstad_featuredimage.png", radius=30):
+    st.markdown(
+        f'<style>.rounded-img{{border-radius: {radius}px; overflow: hidden;}}</style>',
+        unsafe_allow_html=True,)
+    st.image(image_path, use_column_width=True, output_format='auto')
+
+
+def main():
+    load_dotenv()
+    st.set_page_config(page_title="Chat with multiple Files",
+                       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.title("💬 Randstad HR Chatbot")
+    st.subheader("🚀 A HR powered by Generative AI")
+    # user_question = st.text_input("Ask a question about your documents:")
+
+    st.session_state.embeddings = embeddings = FastEmbedEmbeddings( model_name= "BAAI/bge-small-en-v1.5", cache_dir="./embedding_model/")
+
+    if len(glob.glob("./vectordb/*.sqlite3")) > 0:
+        
+        vectorstore = Chroma(persist_directory="./vectordb/", embedding_function=st.session_state.embeddings)
+        st.session_state.conversation = get_conversation_chain(vectorstore)
+        handle_userinput()
+
+    with st.sidebar:
+        add_rounded_edges()
+
+        st.subheader("Your documents")
+        docs = st.file_uploader(
+            "Upload File (pdf,text,csv...) and click '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, st.session_state.embeddings)
+
+                # create conversation chain
+                st.session_state.conversation = get_conversation_chain(vectorstore)
+
+
+if __name__ == '__main__':
+    # Define the command
+    command = 'CMAKE_ARGS="-DLLAMA_CUBLAS=on" FORCE_CMAKE=1 pip install llama-cpp-python --no-cache-dir'
+    
+    # Run the command using subprocess
+    try:
+        subprocess.run(command, shell=True, check=True)
+        print("Command executed successfully.")
+    except subprocess.CalledProcessError as e:
+        print(f"Error: {e}")
+        
+    main()