import gradio as gr
from faiss import write_index, read_index

from langchain import PromptTemplate
from langchain.chains import LLMChain
from langchain.document_loaders import TextLoader
from langchain.text_splitter import CharacterTextSplitter
from langchain.vectorstores import FAISS
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.document_loaders import UnstructuredFileLoader
from langchain.document_loaders.recursive_url_loader import RecursiveUrlLoader
from langchain.document_loaders import UnstructuredURLLoader
from langchain.document_loaders.csv_loader import CSVLoader
from langchain import  LLMChain
from langchain.llms import GPT4All
from langchain.embeddings import GPT4AllEmbeddings
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain.callbacks.base import BaseCallbackManager
from langchain.document_loaders import DataFrameLoader
from langchain.embeddings import HuggingFaceEmbeddings
import pandas as pd
import sqlite3
from sentence_transformers import SentenceTransformer
#from cleantext import clean
import time
import re

def loadModels():
    #model = GPT4All("orca-mini-3b.ggmlv3.q4_0.bin")
    callback_manager = BaseCallbackManager([StreamingStdOutCallbackHandler()])
    llm = GPT4All(model="orca-mini-3b.ggmlv3.q4_0.bin",temp=0.1,streaming=True,callback_manager=callback_manager,)# verbose=True,repeat_last_n=0
    embeddings = GPT4AllEmbeddings()
    return llm, embeddings

st=time.time()
llm,emb=loadModels()
st2=time.time()

Archives=[["https://www.gob.mx/sectur","Web"],
          ["https://centrohistorico.pueblacapital.gob.mx/nuestro-centro-historico/nuestro-blog/item/33-capilla-de-la-virgen-del-rosario","Web"],
          ["https://capilladelrosariopuebla.com.mx/","Web"],
         #["https://www.tripadvisor.es/Tourism-g150768-Mexico-Vacations.html","Web"],
          ["https://www.mexicodestinos.com/blog/destinos-mexico/","Web"],
         ["https://visitmexico.com/","Web"],
          ["https://www.turismomexico.es/","Web"],
          
         ]

def makeDb(Archives,max_depth=2):
    data=[]
    for Archive in Archives:
        if Archive[1]=="PDF":
            loader = UnstructuredFileLoader(Archive[0])
            data2 = loader.load()
        elif Archive[1]=="RecursiveWeb":
            loader = RecursiveUrlLoader(url=Archive[0], max_depth=max_depth)
            data2 = loader.load()
        elif Archive[1]=="Web":
            #print("Web")
            loader = UnstructuredURLLoader(urls=[Archive[0]])
            data2 = loader.load()
            #print(data2)
        
        if data==[]:
            data=data2
        else:
            data=data+data2
    return data
A=makeDb(Archives)


def makeFinder():
    text_splitter = RecursiveCharacterTextSplitter(
        chunk_size = 1000,
        length_function=len,
        chunk_overlap=200
    )
    documents = text_splitter.split_documents(A)
    try:
        db=FAISS.load_local(folder_path="PaginasExternas",embeddings=emb)
        
    except:
        db = FAISS.from_documents(documents, emb)
        FAISS.save_local(db,folder_path="PaginasExternas")
        
    return db

db=makeFinder()


prompt_template = f"""
### System:
Answer the question based on the contexts below. 
If the question cannot be answered using the information 
provided answer with "I don't know". Ever answer in spanish.

### User:

Contexts:
{{relevant_context}}

Question:
{{user_query}}

### Response:

Respuesta:"""


prompt_template = PromptTemplate(
    input_variables=["relevant_context","user_query"], template=prompt_template
)

# prompt=prompt_template.format(
#         user_query="Que lugar de mexico es el mas lindo?",
#         relevant_context="Otaisa es el lugar mas lindo de mexico pero esta muy cerca de nethai que no es tan lindo pero la comida es muy buena"
#     )
# print(prompt)
llm_chain = LLMChain(llm=llm,prompt=prompt_template)


def FinderDb(query,dbs,filtred=False):
    Sal = dbs.similarity_search_with_score(query,2)
    page_content=[]
    d=[]
    if filtred:
        lim=1.5
    else:
        lim=9000000
    for output in Sal:
        print(output)
        if output[1]<lim:
            page_content.append(output[0].page_content)
            d.append(output[1])
    if len(page_content)<1:
        return None,None
    
    return "/n".join(page_content),d


def QARequest(Pregunta,filtred=False):
    query = Pregunta
    context,d=FinderDb(query,db,filtred=True)
    #A=llm_chain.run(user_query=query,relevant_context=context)
    return context,d#A


#question = "What NFL team won the Super Bowl in the year Justin Bieber was born?"
with gr.Blocks() as demo:
    Pregunta = gr.Textbox(label="Pregunta")
    Respuesta = gr.Textbox(label="Respuesta")
    metrica=gr.Textbox(label="metrica")
    Enviar_btn = gr.Button("Responder")
    Enviar_btn.click(fn=QARequest, inputs=[Pregunta], outputs=[Respuesta,metrica], api_name="Angela") 

demo.launch() #  
#print(A,time.time()-st,time.time()-st2)