import streamlit as st
from chat_client import chat
import time
import os
from dotenv import load_dotenv
from sentence_transformers import SentenceTransformer
import requests
from langchain_community.vectorstores import Chroma
from langchain_community.embeddings import HuggingFaceEmbeddings 
import json
from audio_recorder_streamlit import audio_recorder
import speech_recognition as sr
from googlesearch import search
from bs4 import BeautifulSoup


load_dotenv()
URL_APP_SCRIPT = os.getenv('URL_APP_SCRIPT')
URL_PROMPT = URL_APP_SCRIPT + '?IdFoglio=1cLw9q70BsPmxMBj9PIzgXtq6sm3X-GVBVnOB5wE8jr8'
URL_DOCUMENTI = URL_APP_SCRIPT + '?IdSecondoFoglio=1cLw9q70BsPmxMBj9PIzgXtq6sm3X-GVBVnOB5wE8jr8'
SYSTEM_PROMPT = ["Sei BonsiAI e mi aiuterai nelle mie richieste (Parla in ITALIANO)", "Esatto, sono BonsiAI. Di cosa hai bisogno?"]
CHAT_BOTS = {"Mixtral 8x7B v0.1" :"mistralai/Mixtral-8x7B-Instruct-v0.1"}
option_personalizzata = {'Personalizzata': {'systemRole': 'Tu sei BONSI AI, il mio assistente personale della scuola superiore del Bonsignori. Aiutami in base alle mie esigenze', 
                       'systemStyle': 'Firmati sempre come BONSI AI. (scrivi in italiano)', 
                       'instruction': '', 
                       'tipo': '', 
                       'RAG': False}
                        }

# ----------------------------------------------------------- Interfaccia --------------------------------------------------------------------
st.set_page_config(page_title="Bonsi A.I.", page_icon="🏫")

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

    if 'loaded_data' not in st.session_state:
        st.session_state.loaded_data = False
        
    if "split" not in st.session_state:
        st.session_state.split = 30

    if "enable_history" not in st.session_state:
        st.session_state.enable_history = False

    if "audio_bytes" not in st.session_state:
        st.session_state.audio_bytes = False

    if "cerca_online" not in st.session_state:
        st.session_state.cerca_online = False

    if "numero_siti" not in st.session_state:
        st.session_state.numero_siti = 3

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

    if "tbs_options" not in st.session_state:
        st.session_state.tbs_options = {
            "Sempre": "0",
            "Ultimo anno": "qdr:y",
            "Ultimo mese": "qdr:m",
            "Ultima settimana": "qdr:w",
            "Ultimo giorno": "qdr:d"
        }

    if not st.session_state.loaded_data:
        place=st.empty()
        with place:
            with st.status("Caricamento in corso...", expanded=True) as status:
                st.write("Inizializzazione Ambiente")
                time.sleep(1)
                st.write("Inizializzazione Prompt")
                options = requests.get(URL_PROMPT).json()
                st.write("Inizializzazione Documenti")
                documenti = requests.get(URL_DOCUMENTI).json()
                st.session_state.options = {**option_personalizzata, **options} 
                st.session_state.documenti = documenti
                st.session_state.loaded_data = True
                status.update(label="Caricamento Completato", state="complete", expanded=False)
        place.empty()

def sidebar():
    def retrieval_settings() :
        st.markdown("# Impostazioni Prompt")
        st.session_state.selected_option_key = st.selectbox('Azione', list(st.session_state.options.keys()))
        st.session_state.selected_option = st.session_state.options.get(st.session_state.selected_option_key, {})
        
        if st.session_state.options.get(st.session_state.selected_option_key, {})["tipo"]=='DOCUMENTO':
            st.session_state.selected_documento_key = st.selectbox('Documento', list(st.session_state.documenti.keys()))
            st.session_state.selected_documento = st.session_state.documenti.get(st.session_state.selected_documento_key, {})
            st.session_state.instruction = st.session_state.selected_documento.get('instruction', '')['Testo']
            st.session_state.split = st.slider(label="Pagine Suddivisione", min_value=1, max_value=30, value=30, help='Se il documento ha 100 pagine e suddivido per 20 pagine elaborerà la risposta 5 volte. Più alto è il numero e meno volte elaborerà ma la risposta sarà più imprecisa')            
        else:
            st.session_state.instruction = st.session_state.selected_option.get('instruction', '')
        
        st.session_state.systemRole = st.session_state.selected_option.get('systemRole', '')
        st.session_state.systemRole = st.text_area("Descrizione", st.session_state.systemRole, help='Ruolo del chatbot e descrizione dell\'azione che deve svolgere')
        st.session_state.systemStyle = st.session_state.selected_option.get('systemStyle', '')
        st.session_state.systemStyle = st.text_area("Stile", st.session_state.systemStyle, help='Descrizione dello stile utilizzato per generare il testo')
        st.session_state.rag_enabled = st.session_state.selected_option.get('tipo', '')=='RAG'
        if st.session_state.selected_option_key == 'Decreti':
            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.session_state.decreti_escludere = st.multiselect(
                'Decreti da escludere',
                ['23.10.2 destinazione risorse residue pnrr DGR 1051-2023_Destinazione risorse PNRR Duale.pdf', '23.10.25 accompagnatoria Circolare Inail assicurazione.pdf', '23.10.26 circolare Inail assicurazione.pdf', '23.10.3 FAQ in attesa di avviso_.pdf', '23.11.2 avviso 24_24 Decreto 17106-2023 Approvazione Avviso IeFP 2023-2024.pdf', '23.5.15 decreto linee inclusione x enti locali.pdf', '23.6.21 Circolare+esplicativa+DGR+312-2023.pdf', '23.7.3 1° Decreto R.L. 23_24 .pdf', '23.9 Regolamento_prevenzione_bullismo_e_cyberbullismo__Centro_Bonsignori.pdf', '23.9.1 FAQ inizio anno formativo.pdf', '23.9.15 DECRETO VERIFICHE AMMINISTR 15-09-23.pdf', '23.9.4 modifica decreto GRS.pdf', '23.9.8 Budget 23_24.pdf', '24.10.2022 DECRETO loghi N.15176.pdf', 'ALLEGATO C_Scheda Supporti al funzionamento.pdf', 'ALLEGATO_ B_ Linee Guida.pdf', 'ALLEGATO_A1_PEI_INFANZIA.pdf', 'ALLEGATO_A2_PEI_PRIMARIA.pdf', 'ALLEGATO_A3_PEI_SEC_1_GRADO.pdf', 'ALLEGATO_A4_PEI_SEC_2_GRADO.pdf', 'ALLEGATO_C_1_Tabella_Fabbisogni.pdf', 'Brand+Guidelines+FSE+.pdf', 'Decreto 20797 del 22-12-2023_Aggiornamento budget PNRR.pdf', 'Decreto 20874 del 29-12-2023 Avviso IeFP PNRR 2023-2024_file unico.pdf'],
                [])
        st.markdown("---")
        st.markdown("# Ricerca Online")
        st.session_state.cerca_online = st.toggle("Attivata", value=False)
        st.session_state.selected_tbs = st.selectbox("Periodo:", list(st.session_state.tbs_options.keys()), disabled=not st.session_state.cerca_online)
        st.session_state.tbs_value = st.session_state.tbs_options[st.session_state.selected_tbs]
        st.session_state.numero_siti = st.slider(label="Risultati", min_value = 1, max_value=20, value=3, disabled=not st.session_state.cerca_online)
        #st.session_state.suddividi_ricerca = st.toggle("Attivata", value=False)
        st.markdown("---")
    
    def model_settings():
        st.markdown("# Impostazioni Modello")
        st.session_state.chat_bot = st.sidebar.radio('Modello:', [key for key, value in CHAT_BOTS.items() ])
        st.session_state.numero_generazioni = st.slider(label="Generazioni", min_value = 1, max_value=10, value=1)
        st.session_state.enable_history = st.toggle("Storico Messaggi", value=False)
        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 = 2, max_value=2048, step= 32, value=1024)

    with st.sidebar:
        retrieval_settings()
        model_settings()
        st.markdown("""> **Creato da Matteo Bergamelli **""")

def audioRec():
    st.session_state.audio_bytes = audio_recorder(text='', icon_size="3x")
    if st.session_state.audio_bytes:
        with open("./AUDIO.wav", "wb") as file:
            file.write(st.session_state.audio_bytes)
        wav = sr.AudioFile("./AUDIO.wav")
        with wav as source:
            recognizer_instance = sr.Recognizer() 
            recognizer_instance.pause_threshold = 3.0
            audio = recognizer_instance.listen(source)
            print("Ok! sto ora elaborando il messaggio!")
        try:
            text = recognizer_instance.recognize_google(audio, language="it-IT")
            print(text)
            js = f"""
                <script>
                    var chatInput = parent.document.querySelector('textarea[data-testid="stChatInput"]');
                    var nativeInputValueSetter = Object.getOwnPropertyDescriptor(window.HTMLTextAreaElement.prototype, "value").set;
                    nativeInputValueSetter.call(chatInput, "{text}");
                    var event = new Event('input', {{ bubbles: true}});
                    chatInput.dispatchEvent(event);
                    var sendChat = parent.document.getElementsByClassName("st-emotion-cache-1621d17")[0]
                    sendChat.click();
                    var x = parent.document.querySelector('[title="st.iframe"]');
                    x.style.display = "none";
                </script>
                """
            st.components.v1.html(js)
        except Exception as e:
            print(e)

def header() :
    st.title("Bonsi A.I.", anchor=False)
    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):
    if st.session_state.cerca_online:
        systemRole += '. Ti ho fornito una lista di materiali nelle instruction. Devi rispondere sulla base delle informazioni fonrnite!'
    input_text = f'''
    {{
      "input": {{
          "role": "system",
          "content": "{systemRole}", 
          "style": "{systemStyle} "
      }},
      "messages": [
          {{
              "role": "instructions",
              "content": "{instruction} ({systemStyle})"
          }},
          {{
              "role": "user",
              "content": "{prompt}"
          }}
      ]
    }}
    '''
    return input_text

def gen_augmented_prompt(prompt, top_k) :   
    links = ""
    embedding = HuggingFaceEmbeddings(model_name="sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2")
    db = Chroma(persist_directory='./DB_Decreti', embedding_function=embedding) 
    docs = db.similarity_search(prompt, k=top_k)
    links = []
    context = ''
    NomeCartellaOriginariaDB = 'Documenti_2\\'
    for doc in docs:
        testo = doc.page_content.replace('\n', ' ')
        context += testo + '\n\n\n'
        reference = doc.metadata["source"].replace(NomeCartellaOriginariaDB, '') + ' (Pag. ' + str(doc.metadata["page"]) + ')'
        links.append((reference, testo))
    return context, links

def get_search_results(query, top_k):
    results = []
    for url in search(query, num=top_k, stop=top_k, tbs=st.session_state.tbs_value):
        try:
            response = requests.get(url)
            soup = BeautifulSoup(response.text, 'html.parser')
            title = soup.title.string if soup.title else "N/A"
            description = soup.find('meta', attrs={'name': 'description'})['content'] if soup.find('meta', attrs={'name': 'description'}) else "N/A"
            body_content = soup.find('body').get_text() if soup.find('body') else "N/A"
            results.append({'title': title, 'description': description, 'url': url, 'body': body_content})
        except Exception as e:
            print(f"Error fetching data from {url}: {e}")
            continue
    return results

def gen_online_prompt(prompt, top_k) :   
    links = []
    context = ''
    results = get_search_results(prompt, top_k)
    for i, result in enumerate(results, start=1):
        context += result['title'] + '\n' +  result['description'] + '\n' + '\n\n' + result['body'].replace('\n','.') + '\n\n------------------------------------------------------------'
        links.append((str(i) + '. ' + result['title'], result['description'] + '\n\n' + result['url']))
    return context, links

def generate_chat_stream(prompt) :
    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

def inserisci_istruzioni(prompt_originale): 
    links = []
    if st.session_state.cerca_online: 
        with st.spinner("Ricerca Online...."):
            time.sleep(1)
            st.session_state.instruction, links = gen_online_prompt(prompt=prompt_originale, top_k=st.session_state.numero_siti)        
    if st.session_state.rag_enabled :
        with st.spinner("Ricerca nei Decreti...."):
            time.sleep(1)
            st.session_state.instruction, links = gen_augmented_prompt(prompt=prompt_originale, top_k=st.session_state.top_k)        
    with st.spinner("Generazione in corso...") :
        time.sleep(1)
    #st.session_state.instruction = instruction_originale + '\n----------------------------------------------\n' + st.session_state.instruction
    return links

def stream_handler(chat_stream, placeholder) :
    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)
    return full_response

def show_source(links) :
    with st.expander("Mostra fonti") :
        for link in links:
            reference, testo = link
            st.info('##### ' + reference.replace('_', ' ') + '\n\n'+ testo)

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

def split_text(text, chunk_size):
    testo_suddiviso = []
    if text == '': 
        text = '     '
    if chunk_size < 100:
        chunk_size = 60000
    for i in range(0, len(text), chunk_size):
        testo_suddiviso.append(text[i:i+chunk_size])
    return testo_suddiviso

if prompt := st.chat_input("Chatta con BonsiAI..."):
    prompt_originale = prompt
    links = inserisci_istruzioni(prompt_originale)
    instruction_suddivise = split_text(st.session_state.instruction, st.session_state.split*2000)
    ruolo_originale = st.session_state.systemRole
    ruoli_divisi = ruolo_originale.split("&&")
    parte=1           
    i=1 
    risposta_completa = ''
    for ruolo_singolo in ruoli_divisi:
        for instruction_singola in instruction_suddivise:
            for numgen in range(1, st.session_state.numero_generazioni+1):
                if i==1:
                    st.chat_message("user").markdown(prompt_originale + (': Parte ' + str(parte) if i > 1 else ''))
                i+=1   
                prompt = formattaPrompt(prompt_originale, ruolo_singolo, st.session_state.systemStyle, instruction_singola)
                print('------------------------------------------------------------------------------------')
                print(prompt)
                st.session_state.messages.append({"role": "user", "content": prompt_originale})
                chat_stream = 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 or st.session_state.cerca_online:
                        show_source(links)  
                    if st.session_state.options.get(st.session_state.selected_option_key, {})["tipo"]=='DOCUMENTO':
                        with st.expander("Mostra Documento") :
                            st.info('##### ' + st.session_state.selected_documento_key + ' (Parte ' + str(parte) +')'+ '\n\n\n' + instruction_singola)
                            parte+=1
                st.session_state.messages.append({"role": "assistant", "content": full_response})
                risposta_completa = risposta_completa + '\n' + full_response

    if st.session_state.enable_history:
        st.session_state.history.append([prompt, full_response])
    else:
        st.session_state.history.append(['', ''])
    st.success('Generazione Completata')
    payload = {"domanda": prompt_originale, "risposta": risposta_completa}
    json_payload = json.dumps(payload)
    response = requests.post(URL_APP_SCRIPT, data=json_payload)