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_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",
    "TinyLlama/TinyLlama-1.1B-Chat-v1.0", "tiiuae/falcon-7b-instruct"
]
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)
    return text_splitter.split_documents(pages)

def create_db(splits, collection_name, db_type):
    if db_type == 0:  # Multilingual MiniLM
        embedding = HuggingFaceEmbeddings(model_name="sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2")
    else:  # Italian BERT
        embedding = HuggingFaceEmbeddings(model_name="dbmdz/bert-base-italian-xxl-uncased")
    
    new_client = chromadb.EphemeralClient()
    return Chroma.from_documents(documents=splits, embedding=embedding, client=new_client, collection_name=collection_name)

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,
    )
    
    memory = ConversationBufferMemory(memory_key="chat_history", output_key='answer', return_messages=True)
    retriever = vector_db.as_retriever()
    
    return ConversationalRetrievalChain.from_llm(
        llm,
        retriever=retriever,
        chain_type="stuff", 
        memory=memory,
        return_source_documents=True,
        verbose=False,
    )

def create_collection_name(filepath):
    collection_name = Path(filepath).stem
    collection_name = unidecode(collection_name.replace(" ", "-"))
    collection_name = re.sub('[^A-Za-z0-9]+', '-', collection_name)[:50]
    if len(collection_name) < 3:
        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, db_type, progress=gr.Progress()):
    list_file_path = [x.name for x in list_file_obj if x is not None]
    collection_name = create_collection_name(list_file_path[0])
    doc_splits = load_doc(list_file_path, chunk_size, chunk_overlap)
    vector_db = create_db(doc_splits, collection_name, db_type)
    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):
    return [f"User: {user_message}\nAssistant: {bot_message}" for user_message, bot_message in 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"].split("Helpful Answer:")[-1]
    response_sources = response["source_documents"]
    sources = [(source.page_content.strip(), source.metadata["page"] + 1) for source in response_sources[:5]]
    new_history = history + [(message, response_answer)]
    
    # Ensure we always return 5 sources and 5 pages
    source_texts = [source[0] for source in sources] + [''] * (5 - len(sources))
    source_pages = [source[1] for source in sources] + [0] * (5 - len(sources))
    
    return (qa_chain, gr.update(value=""), new_history, 
            *source_texts[:5],  # Unpack exactly 5 source texts
            *source_pages[:5])  # Unpack exactly 5 source pages

def clear_conversation():
    return gr.update(value=""), [], "", "", "", "", "", 0, 0, 0, 0, 0

def demo():
    with gr.Blocks(theme="base") as demo:
        vector_db = gr.State()
        qa_chain = gr.State()
        collection_name = gr.State()
        
        gr.Markdown("# Creatore di Chatbot basato su PDF")
        
        with gr.Tab("Passo 1 - Carica PDF"):
            document = gr.Files(height=100, file_count="multiple", file_types=["pdf"], interactive=True, label="Carica i tuoi documenti PDF")
        
        with gr.Tab("Passo 2 - Elabora Documenti"):
            db_type = gr.Radio(["ChromaDB (Multilingual MiniLM Embedding)", "ChromaDB (Italian BERT Embedding)"], label="Tipo di database vettoriale", value="ChromaDB (Multilingual MiniLM Embedding)", type="index")
            with gr.Accordion("Opzioni Avanzate - Divisione del testo del documento", open=False):
                slider_chunk_size = gr.Slider(100, 1000, 1000, step=20, label="Dimensione del chunk")
                slider_chunk_overlap = gr.Slider(10, 200, 100, step=10, label="Sovrapposizione del chunk")
            db_progress = gr.Textbox(label="Inizializzazione del database vettoriale", value="Nessuna")
            db_btn = gr.Button("Genera database vettoriale")
            
        with gr.Tab("Passo 3 - Inizializza catena QA"):
            llm_btn = gr.Radio(list_llm_simple, label="Modelli LLM", value=list_llm_simple[4], type="index")
            with gr.Accordion("Opzioni avanzate - Modello LLM", open=False):
                slider_temperature = gr.Slider(0.01, 1.0, 0.3, step=0.1, label="Temperatura")
                slider_maxtokens = gr.Slider(224, 4096, 1024, step=32, label="Token massimi")
                slider_topk = gr.Slider(1, 10, 3, step=1, label="Campioni top-k")
            language_btn = gr.Radio(["Italiano", "Inglese"], label="Lingua", value="Italiano", type="index")
            llm_progress = gr.Textbox(value="Nessuna", label="Inizializzazione catena QA")
            qachain_btn = gr.Button("Inizializza catena di Domanda e Risposta")
            
        with gr.Tab("Passo 4 - Chatbot"):
            chatbot = gr.Chatbot(height=300)
            with gr.Accordion("Opzioni avanzate - Riferimenti ai documenti", open=False):
                doc_sources = [gr.Textbox(label=f"Riferimento {i+1}", lines=2, container=True, scale=20) for i in range(5)]
                source_pages = [gr.Number(label="Pagina", scale=1) for _ in range(5)]
            msg = gr.Textbox(placeholder="Inserisci il messaggio (es. 'Di cosa tratta questo documento?')", container=True)
            submit_btn = gr.Button("Invia messaggio")
            clear_btn = gr.Button("Cancella conversazione")
           
        db_btn.click(initialize_database, inputs=[document, slider_chunk_size, slider_chunk_overlap, db_type], 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])

        submit_btn.click(conversation, inputs=[qa_chain, msg, chatbot], outputs=[qa_chain, msg, chatbot] + doc_sources + source_pages)
        msg.submit(conversation, inputs=[qa_chain, msg, chatbot], outputs=[qa_chain, msg, chatbot] + doc_sources + source_pages)
        clear_btn.click(clear_conversation, inputs=[], outputs=[chatbot] + doc_sources + source_pages)
    
    demo.queue().launch(debug=True)

if __name__ == "__main__":
    demo()