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 HuggingFaceEndpoint
from langchain.memory import ConversationBufferMemory
from pathlib import Path
import chromadb
from unidecode import unidecode
import re

# List of available LLM models
list_llm = [
    "mistralai/Mistral-7B-Instruct-v0.2", "mistralai/Mixtral-8x7B-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]

def load_doc(list_file_path, chunk_size, chunk_overlap):
    loaders = [PyPDFLoader(x) for x in list_file_path]
    pages = []
    for loader in loaders:
        pages.extend(loader.load())
    text_splitter = RecursiveCharacterTextSplitter(
        chunk_size=chunk_size,
        chunk_overlap=chunk_overlap
    )
    doc_splits = text_splitter.split_documents(pages)
    return doc_splits

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
    )
    return vectordb

def initialize_llmchain(llm_model, temperature, max_tokens, top_k, vector_db, progress=gr.Progress()):
    progress(0.5, desc="Initializing HF Hub...")
    llm = HuggingFaceEndpoint(
        repo_id=llm_model,
        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_kwargs={'k': 5})  # Increased from 3 to 5
    progress(0.8, desc="Defining retrieval chain...")
    qa_chain = ConversationalRetrievalChain.from_llm(
        llm,
        retriever=retriever,
        chain_type="stuff", 
        memory=memory,
        return_source_documents=True,
        verbose=False,
    )
    progress(0.9, desc="Done!")
    return qa_chain

def create_collection_name(filepath):
    collection_name = Path(filepath).stem
    collection_name = collection_name.replace(" ", "-")
    collection_name = unidecode(collection_name)
    collection_name = re.sub('[^A-Za-z0-9]+', '-', collection_name)
    collection_name = collection_name[:50]
    if len(collection_name) < 3:
        collection_name = collection_name + 'xyz'
    if not collection_name[0].isalnum():
        collection_name = 'A' + collection_name[1:]
    if not collection_name[-1].isalnum():
        collection_name = collection_name[:-1] + 'Z'
    return collection_name

def initialize_database(list_file_obj, chunk_size, chunk_overlap, progress=gr.Progress()):
    list_file_path = [x.name for x in list_file_obj if x is not None]
    progress(0.1, desc="Creating collection...")
    collection_name = create_collection_name(list_file_path[0])
    progress(0.25, desc="Loading documents...")
    doc_splits = load_doc(list_file_path, chunk_size, chunk_overlap)
    
    progress(0.5, desc="Generating vector database...")
    vector_db = create_db(doc_splits, collection_name)
    progress(0.9, desc="Done!")

    return vector_db, collection_name, "Completed!"

def initialize_LLM(llm_option, llm_temperature, max_tokens, top_k, vector_db, progress=gr.Progress()):
    llm_name = list_llm[llm_option]
    qa_chain = initialize_llmchain(llm_name, llm_temperature, max_tokens, top_k, vector_db, progress)
    return qa_chain, "Completed!"

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)
    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"]
    
    source_info = []
    for i in range(min(5, len(response_sources))):  # Increased from 3 to 5
        source = response_sources[i]
        source_info.append({
            'content': source.page_content.strip(),
            'page': source.metadata["page"] + 1
        })
    
    new_history = history + [(message, response_answer)]
    return qa_chain, gr.update(value=""), new_history, *[info['content'] for info in source_info], *[info['page'] for info in source_info]

# The rest of the code (demo function and UI setup) remains largely the same,
# but update the outputs of the conversation function to handle 5 sources instead of 3.
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("Opzioni Avanzate - Document text splitter", open=False):
                with gr.Row():
                    slider_chunk_size = gr.Slider(minimum = 100, maximum = 1000, value=1000, step=20, label="Chunk size", info="Chunk size", interactive=True)
                with gr.Row():
                    slider_chunk_overlap = gr.Slider(minimum = 10, maximum = 200, value=100, 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[5], 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=0.3, 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():
                    doc_source4 = gr.Textbox(label="Riferimento 4", lines=2, container=True, scale=20)
                    source4_page = gr.Number(label="Pagina", scale=1)
                with gr.Row():
                    doc_source5 = gr.Textbox(label="Riferimento 5", lines=2, container=True, scale=20)
                    source5_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, "", 0, "", 0], \
            inputs=None, \
            outputs=[chatbot, 
                     doc_source1, source1_page, 
                     doc_source2, source2_page, 
                     doc_source3, source3_page,
                     doc_source4, source4_page,
                     doc_source5, source5_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,
                     doc_source4, source4_page,
                     doc_source5, source5_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,
                     doc_source4, source4_page,
                     doc_source5, source5_page], queue=False)
        clear_btn.click(
            lambda: [None, "", 0, "", 0, "", 0, "", 0, "", 0],
            inputs=None,
            outputs=[
                chatbot,
                doc_source1, source1_page,
                doc_source2, source2_page,
                doc_source3, source3_page,
                doc_source4, source4_page,
                doc_source5, source5_page
            ],
            queue=False
        )
    demo.queue().launch(debug=True)

if __name__ == "__main__":
    demo()