app.py

import streamlit as st
from chat_client import chat
import time
import os
from dotenv import load_dotenv
from sentence_transformers import SentenceTransformer
#from langchain_community.vectorstores import Chroma
#from langchain_community.embeddings import HuggingFaceEmbeddings 
load_dotenv()

CHAT_BOTS = {"Mixtral 8x7B v0.1" :"mistralai/Mixtral-8x7B-Instruct-v0.1"}
SYSTEM_PROMPT = ["Sei BonsiAI e mi aiuterai nelle mie richieste (Parla in ITALIANO)", "Esatto, sono BonsiAI. Di cosa hai bisogno?"]
IDENTITY_CHANGE = ["Sei BonsiAI da ora in poi!", "Certo farò del mio meglio"]
options = {
    'Email Genitori': {'text': 'Scrivi il testo per una mail XXXX su questo stile.', 'description': 'Descrizione aggiuntiva per Email Genitori'},
    'Email Colleghi': {'text': 'Scrivi il testo per una mail XXXX su questo stile.', 'description': 'Descrizione aggiuntiva per Email Colleghi'},
    'Decreti': {'text': 'Cerca testo dei decreti!', 'description': 'Descrizione aggiuntiva per Decreti'}
}

#persist_directory1 = './DB_Decreti'
#embedding = HuggingFaceEmbeddings(model_name="sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2")
#db = Chroma(persist_directory=persist_directory1, embedding_function=embedding)
#NumeroDocumenti = 10  
#query = 'Come funziona la generazione delle PDA'      
#result = db.similarity_search(query, k=NumeroDocumenti)


st.set_page_config(page_title="BonsiAI", page_icon="🤖")

def gen_augmented_prompt(prompt, top_k) :
    context = ""     
    links = ""
    generated_prompt = f"""
    A PARTIRE DAL SEGUENTE CONTESTO: {context},

    ----
    RISPONDI ALLA SEGUENTE RICHIESTA: {prompt}
    """
    return generated_prompt, links

def init_state() :
    if "messages" not in st.session_state:
        st.session_state.messages = []

    if "temp" not in st.session_state:
        st.session_state.temp = 0.8

    if "history" not in st.session_state:
        st.session_state.history = [SYSTEM_PROMPT]

    if "top_k" not in st.session_state:
        st.session_state.top_k = 5

    if "repetion_penalty" not in st.session_state :
        st.session_state.repetion_penalty = 1

    if "rag_enabled" not in st.session_state :
        st.session_state.rag_enabled = True

    if "chat_bot" not in st.session_state :
        st.session_state.chat_bot = "Mixtral 8x7B v0.1"

def sidebar() :
    def retrieval_settings() :
        st.markdown("# Impostazioni Azioni")
        st.session_state.selected_option_key = st.selectbox('Azione', list(options.keys()) + ['+ Aggiungi'])
        st.session_state.selected_option = options.get(st.session_state.selected_option_key, {})
        st.session_state.selected_option_text = st.session_state.selected_option.get('text', '')
        st.session_state.option_text = st.text_area("Testo Azione", st.session_state.selected_option_text)
        st.session_state.selected_option_description = st.session_state.selected_option.get('description', '')
        if st.session_state.selected_option_key == 'Decreti':
           st.session_state.rag_enabled = st.toggle("Cerca nel DB Vettoriale", value=True)
           st.session_state.top_k = st.slider(label="Documenti da ricercare", min_value=1, max_value=20, value=4, disabled=not st.session_state.rag_enabled)
        st.markdown("---")
    
    def model_settings() :
        st.markdown("# Impostazioni Modello")
        st.session_state.chat_bot = st.sidebar.radio('Seleziona Modello:', [key for key, value in CHAT_BOTS.items() ])
        st.session_state.temp = st.slider(label="Creatività", min_value=0.0, max_value=1.0, step=0.1, value=0.9)
        st.session_state.max_tokens = st.slider(label="Lunghezza Output", min_value = 64, max_value=2048, step= 32, value=512)

    with st.sidebar:
        retrieval_settings()
        model_settings()
        st.markdown("""> **Creato da [Matteo Script] 🔗**""")

def header() :
    st.title("BonsiAI")
    with st.expander("Cos'è BonsiAI?"):
        st.info("""BonsiAI Chat è un ChatBot personalizzato basato su un database vettoriale, funziona secondo il principio della Generazione potenziata da Recupero (RAG). 
                   La sua funzione principale ruota attorno alla gestione di un ampio repository di documenti BonsiAI e fornisce agli utenti risposte in linea con le loro domande. 
                   Questo approccio garantisce una risposta più precisa sulla base della richiesta degli utenti.""")

def chat_box() :
    for message in st.session_state.messages:
        with st.chat_message(message["role"]):
            st.markdown(message["content"])

def formattaPrompt(prompt, systemRole, systemStyle, instruction):
  input_text = f'''
  {{
      "input": {{
          "role": "system",
          "content": "{systemRole}", 
          "style": "{systemStyle}"
      }},
      "messages": [
          {{
              "role": "instructions",
              "content": "{instruction} "("{systemStyle}")"
          }},
          {{
              "role": "user",
              "content": "{input}"
          }}
      ]
  }}
  '''
  return input_text

def generate_chat_stream(prompt) :
    links = []
    if st.session_state.rag_enabled :
        with st.spinner("Ricerca nei documenti...."):
            time.sleep(2)
            prompt, links = gen_augmented_prompt(prompt=prompt, top_k=st.session_state.top_k)        
    with st.spinner("Generazione in corso...") :
        time.sleep(2)
        chat_stream = chat(prompt, st.session_state.history,chat_client=CHAT_BOTS[st.session_state.chat_bot] ,
                       temperature=st.session_state.temp, max_new_tokens=st.session_state.max_tokens)    
    return chat_stream, links

def stream_handler(chat_stream, placeholder) :
    start_time = time.time()
    full_response = ''

    for chunk in chat_stream :
        if chunk.token.text!='</s>' :
            full_response += chunk.token.text
            placeholder.markdown(full_response + "▌")
    placeholder.markdown(full_response)

    end_time = time.time()
    elapsed_time = end_time - start_time
    total_tokens_processed = len(full_response.split())
    tokens_per_second = total_tokens_processed // elapsed_time
    len_response = (len(prompt.split()) + len(full_response.split())) * 1.25
    col1, col2, col3 = st.columns(3)

    with col1 :
        st.write(f"**{elapsed_time} secondi**")

    with col2 :
        st.write(f"**{int(len_response)} tokens generati**")

    with col3 :
        st.write(f"**{tokens_per_second} token/secondi**")
    
        
    return full_response

def show_source(links) :
    with st.expander("Mostra fonti") :
        for i, link in enumerate(links) :
            st.info(f"{link}")

init_state()
sidebar()
header()
chat_box()

if prompt := st.chat_input("Chatta con BonsiAI..."):
    st.chat_message("user").markdown(prompt)
    st.session_state.messages.append({"role": "user", "content": prompt})

    chat_stream, links = generate_chat_stream(prompt)

    
    with st.chat_message("assistant"):
        placeholder = st.empty()
        full_response = stream_handler(chat_stream, placeholder)
        if st.session_state.rag_enabled :
            show_source(links)

    st.session_state.history.append([prompt, full_response])
    st.session_state.messages.append({"role": "assistant", "content": full_response})
    st.success('Generazione Completata')