app.py

import gradio as gr
import os

from langchain_community.document_loaders import PyPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.vectorstores import Chroma
from langchain.chains import ConversationalRetrievalChain
from langchain_community.embeddings import HuggingFaceEmbeddings 
from langchain_community.llms import HuggingFacePipeline
from langchain.chains import ConversationChain
from langchain.memory import ConversationBufferMemory
from langchain_community.llms import HuggingFaceEndpoint

from pathlib import Path
import chromadb
from unidecode import unidecode

from transformers import AutoTokenizer
import transformers
import torch
import tqdm 
import accelerate
import re

from chromadb.utils import get_default_config

# default_persist_directory = './chroma_HF/'
list_llm = ["mistralai/Mistral-7B-Instruct-v0.2", "mistralai/Mixtral-8x7B-Instruct-v0.1", "mistralai/Mistral-7B-Instruct-v0.1", \
    "google/gemma-7b-it","google/gemma-2b-it", \
    "HuggingFaceH4/zephyr-7b-beta", "HuggingFaceH4/zephyr-7b-gemma-v0.1", \
    "meta-llama/Llama-2-7b-chat-hf", "microsoft/phi-2", \
    "TinyLlama/TinyLlama-1.1B-Chat-v1.0", "mosaicml/mpt-7b-instruct", "tiiuae/falcon-7b-instruct", \
    "google/flan-t5-xxl"
]
list_llm_simple = [os.path.basename(llm) for llm in list_llm]

# Load PDF document and create doc splits
def load_doc(list_file_path, chunk_size, chunk_overlap):
    # Processing for one document only
    # loader = PyPDFLoader(file_path)
    # pages = loader.load()
    loaders = [PyPDFLoader(x) for x in list_file_path]
    pages = []
    for loader in loaders:
        pages.extend(loader.load())
    # text_splitter = RecursiveCharacterTextSplitter(chunk_size = 600, chunk_overlap = 50)
    text_splitter = RecursiveCharacterTextSplitter(
        chunk_size = chunk_size, 
        chunk_overlap = chunk_overlap)
    doc_splits = text_splitter.split_documents(pages)
    return doc_splits


# Create vector database
def create_db(splits, collection_name):
    embedding = HuggingFaceEmbeddings()
    new_client = chromadb.EphemeralClient()
    vectordb = Chroma.from_documents(
        documents=splits,
        embedding=embedding,
        client=new_client,
        collection_name=collection_name,
        # persist_directory=default_persist_directory
    )
    return vectordb


# Load vector database
def load_db():
    embedding = HuggingFaceEmbeddings()
    vectordb = Chroma(
        # persist_directory=default_persist_directory, 
        embedding_function=embedding)
    return vectordb


# Initialize langchain LLM chain
def initialize_llmchain(llm_model, temperature, max_tokens, top_k, vector_db, progress=gr.Progress()):
    progress(0.1, desc="Initializing HF tokenizer...")
    # HuggingFacePipeline uses local model
    # Note: it will download model locally...
    # tokenizer=AutoTokenizer.from_pretrained(llm_model)
    # progress(0.5, desc="Initializing HF pipeline...")
    # pipeline=transformers.pipeline(
    #     "text-generation",
    #     model=llm_model,
    #     tokenizer=tokenizer,
    #     torch_dtype=torch.bfloat16,
    #     trust_remote_code=True,
    #     device_map="auto",
    #     # max_length=1024,
    #     max_new_tokens=max_tokens,
    #     do_sample=True,
    #     top_k=top_k,
    #     num_return_sequences=1,
    #     eos_token_id=tokenizer.eos_token_id
    #     )
    # llm = HuggingFacePipeline(pipeline=pipeline, model_kwargs={'temperature': temperature})
    
    # HuggingFaceHub uses HF inference endpoints
    progress(0.5, desc="Initializing HF Hub...")
    # Use of trust_remote_code as model_kwargs
    # Warning: langchain issue
    # URL: https://github.com/langchain-ai/langchain/issues/6080
    if llm_model == "mistralai/Mixtral-8x7B-Instruct-v0.1":
        llm = HuggingFaceEndpoint(
            repo_id=llm_model, 
            # model_kwargs={"temperature": temperature, "max_new_tokens": max_tokens, "top_k": top_k, "load_in_8bit": True}
            temperature = temperature,
            max_new_tokens = max_tokens,
            top_k = top_k,
            load_in_8bit = True,
        )
    elif llm_model in ["HuggingFaceH4/zephyr-7b-gemma-v0.1","mosaicml/mpt-7b-instruct"]:
        raise gr.Error("LLM model is too large to be loaded automatically on free inference endpoint")
        llm = HuggingFaceEndpoint(
            repo_id=llm_model, 
            temperature = temperature,
            max_new_tokens = max_tokens,
            top_k = top_k,
        )
    elif llm_model == "microsoft/phi-2":
        # raise gr.Error("phi-2 model requires 'trust_remote_code=True', currently not supported by langchain HuggingFaceHub...")
        llm = HuggingFaceEndpoint(
            repo_id=llm_model, 
            # model_kwargs={"temperature": temperature, "max_new_tokens": max_tokens, "top_k": top_k, "trust_remote_code": True, "torch_dtype": "auto"}
            temperature = temperature,
            max_new_tokens = max_tokens,
            top_k = top_k,
            trust_remote_code = True,
            torch_dtype = "auto",
        )
    elif llm_model == "TinyLlama/TinyLlama-1.1B-Chat-v1.0":
        llm = HuggingFaceEndpoint(
            repo_id=llm_model, 
            # model_kwargs={"temperature": temperature, "max_new_tokens": 250, "top_k": top_k}
            temperature = temperature,
            max_new_tokens = 250,
            top_k = top_k,
        )
    elif llm_model == "meta-llama/Llama-2-7b-chat-hf":
        raise gr.Error("Llama-2-7b-chat-hf model requires a Pro subscription...")
        llm = HuggingFaceEndpoint(
            repo_id=llm_model, 
            # model_kwargs={"temperature": temperature, "max_new_tokens": max_tokens, "top_k": top_k}
            temperature = temperature,
            max_new_tokens = max_tokens,
            top_k = top_k,
        )
    else:
        llm = HuggingFaceEndpoint(
            repo_id=llm_model, 
            # model_kwargs={"temperature": temperature, "max_new_tokens": max_tokens, "top_k": top_k, "trust_remote_code": True, "torch_dtype": "auto"}
            # model_kwargs={"temperature": temperature, "max_new_tokens": max_tokens, "top_k": top_k}
            temperature = temperature,
            max_new_tokens = max_tokens,
            top_k = top_k,
        )
    
    progress(0.75, desc="Defining buffer memory...")
    memory = ConversationBufferMemory(
        memory_key="chat_history",
        output_key='answer',
        return_messages=True
    )
    # retriever=vector_db.as_retriever(search_type="similarity", search_kwargs={'k': 3})
    retriever=vector_db.as_retriever()
    progress(0.8, desc="Defining retrieval chain...")
    qa_chain = ConversationalRetrievalChain.from_llm(
        llm,
        retriever=retriever,
        chain_type="stuff", 
        memory=memory,
        # combine_docs_chain_kwargs={"prompt": your_prompt})
        return_source_documents=True,
        #return_generated_question=False,
        verbose=False,
    )
    progress(0.9, desc="Done!")
    return qa_chain


# Generate collection name for vector database
#  - Use filepath as input, ensuring unicode text
def create_collection_name(filepath):
    # Extract filename without extension
    collection_name = Path(filepath).stem
    # Fix potential issues from naming convention
    ## Remove space
    collection_name = collection_name.replace(" ","-") 
    ## ASCII transliterations of Unicode text
    collection_name = unidecode(collection_name)
    ## Remove special characters
    #collection_name = re.findall("[\dA-Za-z]*", collection_name)[0]
    collection_name = re.sub('[^A-Za-z0-9]+', '-', collection_name)
    ## Limit length to 50 characters
    collection_name = collection_name[:50]
    ## Minimum length of 3 characters
    if len(collection_name) < 3:
        collection_name = collection_name + 'xyz'
    ## Enforce start and end as alphanumeric character
    if not collection_name[0].isalnum():
        collection_name = 'A' + collection_name[1:]
    if not collection_name[-1].isalnum():
        collection_name = collection_name[:-1] + 'Z'
    print('Filepath: ', filepath)
    print('Collection name: ', collection_name)
    return collection_name

# Inizializzazione database
def initialize_database(list_file_obj, chunk_size, chunk_overlap, progress=gr.Progress()):
    # Check if a database already exists
    try:
        # Get the current configuration
        config = get_default_config()
        # Delete the existing database
        chromadb.delete(config)
        print("Existing database deleted successfully.")
    except Exception as e:
        print(f"Error deleting existing database: {str(e)}")

    # Create list of documents (when valid)
    list_file_path = [x.name for x in list_file_obj if x is not None]
    
    # Create collection_name for vector database
    progress(0.1, desc="Creazione collection name...")
    collection_name = create_collection_name(list_file_path[0])
    progress(0.25, desc="Caricamento documenti..")
    
    # Load document and create splits
    doc_splits = load_doc(list_file_path, chunk_size, chunk_overlap)
    
    # Create or load vector database
    progress(0.5, desc="Generating vector database...")
    vector_db = create_db(doc_splits, collection_name)
    progress(0.9, desc="Fatto!")
    return vector_db, collection_name, "Completato!"

def initialize_LLM(llm_option, llm_temperature, max_tokens, top_k, vector_db, progress=gr.Progress()):
    # print("llm_option",llm_option)
    llm_name = list_llm[llm_option]
    print(f"Nome del modello: {llm_name}")
    
    qa_chain = initialize_llmchain(llm_name, llm_temperature, max_tokens, top_k, vector_db, progress)
    
    return qa_chain, "Completato!"

def format_chat_history(message, chat_history):
    formatted_chat_history = []
    for user_message, bot_message in chat_history:
        formatted_chat_history.append(f"User: {user_message}")
        formatted_chat_history.append(f"Assistant: {bot_message}")
    return formatted_chat_history
    

def conversation(qa_chain, message, history):
    formatted_chat_history = format_chat_history(message, history)
    print("formatted_chat_history",formatted_chat_history)
   
    # Generate response using QA chain
    response = qa_chain({"question": message, "chat_history": formatted_chat_history})
    response_answer = response["answer"]
    if response_answer.find("Helpful Answer:") != -1:
        response_answer = response_answer.split("Helpful Answer:")[-1]
    response_sources = response["source_documents"]
    response_source1 = response_sources[0].page_content.strip()
    response_source2 = response_sources[1].page_content.strip()
    response_source3 = response_sources[2].page_content.strip()
    # Langchain sources are zero-based
    response_source1_page = response_sources[0].metadata["page"] + 1
    response_source2_page = response_sources[1].metadata["page"] + 1
    response_source3_page = response_sources[2].metadata["page"] + 1
    print ('chat response: ', response_answer)
    print('DB source', response_sources)
    
    # Append user message and response to chat history
    new_history = history + [(message, response_answer)]
    # return gr.update(value=""), new_history, response_sources[0], response_sources[1] 
    return qa_chain, gr.update(value=""), new_history, response_source1, response_source1_page, response_source2, response_source2_page, response_source3, response_source3_page
    

def upload_file(file_obj):
    list_file_path = []
    for idx, file in enumerate(file_obj):
        file_path = file_obj.name
        list_file_path.append(file_path)
    # print(file_path)
    # initialize_database(file_path, progress)
    return list_file_path

def demo():
    with gr.Blocks(theme="base") as demo:
        vector_db = gr.State()
        qa_chain = gr.State()
        collection_name = gr.State()
        
        gr.Markdown(
        """<center><h2>Creatore di chatbot basato su PDF</center></h2>
        <h3>Potete fare domande su i vostri documenti PDF</h3>""")
        
        gr.Markdown(
        """<b>Nota:</b> Questo assistente IA, utilizzando Langchain e modelli LLM open source, esegue generazione aumentata da recupero (RAG) dai vostri documenti PDF. \
        L'interfaccia utente esplicitamente mostra i passaggi multipli per aiutare a comprendere il flusso di lavoro RAG. 
        Questo chatbot tiene conto delle domande passate nel generare le risposte (tramite memoria conversazionale), e include riferimenti ai documenti per scopi di chiarezza.<br>
        <br><b>Avviso:</b> Questo spazio utilizza l'hardware di base CPU gratuito da Hugging Face. Alcuni passaggi e modelli LLM usati qui sotto (endpoint di inferenza gratuiti) possono richiedere del tempo per generare una risposta.
        """)
        
        with gr.Tab("Step 1 - Carica PDFs"):
            with gr.Row():
                document = gr.Files(height=100, file_count="multiple", file_types=["pdf"], interactive=True, label="Upload your PDF documents (single or multiple)")
                # upload_btn = gr.UploadButton("Loading document...", height=100, file_count="multiple", file_types=["pdf"], scale=1)
        
        with gr.Tab("Step 2 - Processa i documenti"):
            with gr.Row():
                db_btn = gr.Radio(["ChromaDB"], label="Vector database type", value = "ChromaDB", type="index", info="Choose your vector database")
            with gr.Accordion("Advanced options - Document text splitter", open=False):
                with gr.Row():
                    slider_chunk_size = gr.Slider(minimum = 100, maximum = 1000, value=600, step=20, label="Chunk size", info="Chunk size", interactive=True)
                with gr.Row():
                    slider_chunk_overlap = gr.Slider(minimum = 10, maximum = 200, value=40, step=10, label="Chunk overlap", info="Chunk overlap", interactive=True)
            with gr.Row():
                db_progress = gr.Textbox(label="Vector database initialization", value="None")
            with gr.Row():
                db_btn = gr.Button("Genera vector database")
            
        with gr.Tab("Step 3 - Inizializza QA chain"):
            with gr.Row():
                llm_btn = gr.Radio(list_llm_simple, \
                    label="LLM models", value = list_llm_simple[0], type="index", info="Scegli il tuo modello LLM")
            with gr.Accordion("Advanced options - LLM model", open=False):
                with gr.Row():
                    slider_temperature = gr.Slider(minimum = 0.01, maximum = 1.0, value=0.7, step=0.1, label="Temperature", info="Model temperature", interactive=True)
                with gr.Row():
                    slider_maxtokens = gr.Slider(minimum = 224, maximum = 4096, value=1024, step=32, label="Max Tokens", info="Model max tokens", interactive=True)
                with gr.Row():
                    slider_topk = gr.Slider(minimum = 1, maximum = 10, value=3, step=1, label="top-k samples", info="Model top-k samples", interactive=True)
            with gr.Row():
                language_btn = gr.Radio(["Italian", "English"], label="Linua", value="Italian", type="index", info="Seleziona la lingua per il chatbot")
            with gr.Row():
                llm_progress = gr.Textbox(value="None",label="QA chain initialization")
            with gr.Row():
                qachain_btn = gr.Button("Inizializza Question Answering chain")
        
        
        with gr.Tab("Passo 4 - Chatbot"):
            chatbot = gr.Chatbot(height=300)
            with gr.Accordion("Opzioni avanzate - Riferimenti ai documenti", open=False):
                with gr.Row():
                    doc_source1 = gr.Textbox(label="Riferimento 1", lines=2, container=True, scale=20)
                    source1_page = gr.Number(label="Pagina", scale=1)
                with gr.Row():
                    doc_source2 = gr.Textbox(label="Riferimento 2", lines=2, container=True, scale=20)
                    source2_page = gr.Number(label="Pagina", scale=1)
                with gr.Row():
                    doc_source3 = gr.Textbox(label="Riferimento 3", lines=2, container=True, scale=20)
                    source3_page = gr.Number(label="Pagina", scale=1)
            with gr.Row():
                msg = gr.Textbox(placeholder="Inserisci messaggio (es. 'Di cosa tratta questo documento?')", container=True)
            with gr.Row():
                submit_btn = gr.Button("Invia messaggio")
                clear_btn = gr.ClearButton([msg, chatbot], value="Cancella conversazione")
           
        # Preprocessing events
        #upload_btn.upload(upload_file, inputs=[upload_btn], outputs=[document])
        db_btn.click(initialize_database, \
            inputs=[document, slider_chunk_size, slider_chunk_overlap], \
            outputs=[vector_db, collection_name, db_progress])
        qachain_btn.click(initialize_LLM, \
            inputs=[llm_btn, slider_temperature, slider_maxtokens, slider_topk, vector_db], \
            outputs=[qa_chain, llm_progress]).then(lambda:[None,"",0,"",0,"",0], \
            inputs=None, \
            outputs=[chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page], \
            queue=False)

        # Chatbot events
        msg.submit(conversation, \
            inputs=[qa_chain, msg, chatbot], \
            outputs=[qa_chain, msg, chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page], \
            queue=False)
        submit_btn.click(conversation, \
            inputs=[qa_chain, msg, chatbot], \
            outputs=[qa_chain, msg, chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page], \
            queue=False)
        clear_btn.click(lambda:[None,"",0,"",0,"",0], \
            inputs=None, \
            outputs=[chatbot, doc_source1, source1_page, doc_source2, source2_page, doc_source3, source3_page], \
            queue=False)
    demo.queue().launch(debug=True)


if __name__ == "__main__":
    demo()