import chromadb
import os
import gradio as gr
import json
from huggingface_hub import InferenceClient
import gspread
from oauth2client.service_account import ServiceAccountCredentials
from datetime import datetime
from google.oauth2 import service_account

import socket

def get_local_ip():
    """Get the local IP address."""
    hostname = socket.gethostname()
    local_ip = socket.gethostbyname(hostname)
    print("IP______________________")
    print(local_ip)
    return local_ip
    
# Google Sheets setup
scope = ["https://spreadsheets.google.com/feeds", "https://www.googleapis.com/auth/drive"]
key1 = os.getenv("key1")
key2 = os.getenv("key2")
key3 = os.getenv("key3")
key4 = os.getenv("key4")
key5 = os.getenv("key5")
key6 = os.getenv("key6")
key7 = os.getenv("key7")
key8 = os.getenv("key8")
key9 = os.getenv("key9")
key10 = os.getenv("key10")
key11 = os.getenv("key11")
key12 = os.getenv("key12")
key13 = os.getenv("key13")
key14 = os.getenv("key14")
key15 = os.getenv("key15")
key16 = os.getenv("key16")
key17 = os.getenv("key17")
key18 = os.getenv("key18")
key19 = os.getenv("key19")
key20 = os.getenv("key20")
key21 = os.getenv("key21")
key22 = os.getenv("key22")
key23 = os.getenv("key23")
key24 = os.getenv("key24")
key25 = os.getenv("key25")
key26 = os.getenv("key26")
key27 = os.getenv("key27")
key28 = os.getenv("key28")
pkey="-----BEGIN PRIVATE KEY-----\n"+key2+"\n"+key3+"\n"+ key4+"\n"+key5+"\n"+ key6+"\n"+key7+"\n"+key8+"\n"+key9+"\n"+key10+"\n"+key11+"\n"+key12+"\n"+key13+"\n"+key14+"\n"+key15+"\n"+key16+"\n"+key17+"\n"+key18+"\n"+key19+"\n"+key20+"\n"+key21+"\n"+key22+"\n"+key24+"\n"+key25+"\n"+key26+"\n"+key27+"\n"+key28+"\n-----END PRIVATE KEY-----\n"
json_data={
  "type": "service_account",
  "project_id": "nestolechatbot",
  "private_key_id": key1,
  "private_key": pkey,
  "client_email": "nestoleservice@nestolechatbot.iam.gserviceaccount.com",
  "client_email": "nestoleservice@nestolechatbot.iam.gserviceaccount.com",
  "client_id": "107457262210035412036",
  "auth_uri": "https://accounts.google.com/o/oauth2/auth",
  "token_uri": "https://oauth2.googleapis.com/token",
  "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
  "client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/nestoleservice%40nestolechatbot.iam.gserviceaccount.com",
  "universe_domain": "googleapis.com"
}
creds = service_account.Credentials.from_service_account_info(json_data, scopes=scope)

#creds = ServiceAccountCredentials.from_json_keyfile_name('/home/user/app/chromaold/nestolechatbot-5fe2aa26cb52.json', scope)
client = gspread.authorize(creds)
sheet = client.open("nestolechatbot").sheet1  # Open the sheet

def save_to_sheet(date,name, message):
    # Write user input to the Google Sheet
    sheet.append_row([date,name, message])
    return f"Thanks {name}, your message has been saved!"

    
path='/Users/thiloid/Desktop/LSKI/ole_nest/Chatbot/LLM/chromaTS'
if(os.path.exists(path)==False): path="/home/user/app/chromaTS"

print(path)
#path='chromaTS'
#settings = Settings(persist_directory=storage_path)
#client = chromadb.Client(settings=settings)
client = chromadb.PersistentClient(path=path)
print(client.heartbeat()) 
print(client.get_version())  
print(client.list_collections()) 
from chromadb.utils import embedding_functions
default_ef = embedding_functions.DefaultEmbeddingFunction()
sentence_transformer_ef = embedding_functions.SentenceTransformerEmbeddingFunction(model_name="T-Systems-onsite/cross-en-de-roberta-sentence-transformer")#"VAGOsolutions/SauerkrautLM-Mixtral-8x7B-Instruct")
#instructor_ef = embedding_functions.InstructorEmbeddingFunction(model_name="hkunlp/instructor-large", device="cuda")
#print(str(client.list_collections()))
collection = client.get_collection(name="chromaTS", embedding_function=sentence_transformer_ef)

client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1")


def format_prompt(message, history):
  print("HISTORY")
  print(history)
  prompt = "" #"<s>"
  c=1
  for user_prompt, bot_response in history:
    if c<2:
        prompt += f"[INST] {user_prompt} [/INST]"
        prompt += f" {bot_response}</s> "
        c=c+1
  prompt += f"[INST] {message} [/INST]"
  print("Final P")
  print(prompt)
  return prompt

def response(
    prompt, history,temperature=0.9, max_new_tokens=500, top_p=0.95, repetition_penalty=1.0,
):
    temperature = float(temperature)
    if temperature < 1e-2: temperature = 1e-2
    top_p = float(top_p)
    generate_kwargs = dict(
        temperature=temperature,
        max_new_tokens=max_new_tokens,
        top_p=top_p,
        repetition_penalty=repetition_penalty,
        do_sample=True,
        seed=42,
    )
    search_prompt = format_prompt(prompt,history)
    results=collection.query(
      query_texts=[search_prompt],
      n_results=60,
      #where={"source": "google-docs"}
      #where_document={"$contains":"search_string"}
    )
    #print("REsults")
    #print(results)
    #print("_____")
    dists=["<br><small>(relevance: "+str(round((1-d)*100)/100)+";" for d in results['distances'][0]]
  
    #sources=["source: "+s["source"]+")</small>" for s in results['metadatas'][0]]
    results=results['documents'][0]
    #print("TEst")
    #print(results)
    #print("_____")
    combination = zip(results,dists)
    combination = [' '.join(triplets) for triplets in combination]
    #print(str(prompt)+"\n\n"+str(combination))
    if(len(results)>1):
      addon="Bitte berücksichtige bei deiner Antwort ausschießlich folgende Auszüge aus unserer Datenbank, sofern sie für die Antwort erforderlich sind. Beantworte die Frage knapp und präzise. Ignoriere unpassende Datenbank-Auszüge OHNE sie zu kommentieren, zu erwähnen oder aufzulisten:\n"+"\n".join(results)
    system="Du bist ein deutschsprachiges KI-basiertes Studienberater Assistenzsystem, das zu jedem Anliegen möglichst geeignete Studieninformationen empfiehlt."+addon+"\n\nUser-Anliegen:"   
    formatted_prompt = format_prompt(system+"\n"+prompt,history)
    stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
    output = ""
    for response in stream:
        output += response.token.text
        yield output
    #output=output+"\n\n<br><details open><summary><strong>Sources</strong></summary><br><ul>"+ "".join(["<li>" + s + "</li>" for s in combination])+"</ul></details>"
    # Get current date and time
    now = str(datetime.now())
    save_to_sheet(now,prompt, output)
    yield output

gr.ChatInterface(response, chatbot=gr.Chatbot(value=[[None,"Herzlich willkommen! Ich bin Chätti ein KI-basiertes Studienassistenzsystem, das für jede Anfrage die am besten Studieninformationen empfiehlt.<br>Erzähle mir, was du gerne tust!"]],render_markdown=True),title="German Studyhelper Chätti").queue().launch(share=True) #False, server_name="0.0.0.0", server_port=7864)
print("Interface up and running!")