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
import pandas as pd
import sqlite3
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
llm,emb=loadModels()


con = sqlite3.connect("motor.sqlite")
copies_df = pd.read_sql_query("SELECT * from copies", con)
copiesT = copies_df[copies_df.copy_start =="T"]
copiesT=copiesT[["copy_message","id","name"]]
data = copiesT                
B=DataFrameLoader(data,page_content_column="copy_message")
B2=DataFrameLoader(data,page_content_column="name")
documents=B.load()
documents2=B2.load()

try:
    db=FAISS.load_local(folder_path="Copies",embeddings=emb)
        
except:
    db = FAISS.from_documents(documents, emb)
    FAISS.save_local(db,folder_path="Copies")
        
try:
    db2=FAISS.load_local(folder_path="names",embeddings=emb)
        
except:
    db2 = FAISS.from_documents(documents2, emb)
    FAISS.save_local(db2,folder_path="names")

def FinderDb(query,dbs):
    Sal = dbs.similarity_search_with_score(query,3)
    page_content=[]
    id=[]
    d=[]
    for output in Sal:
        page_content.append(output[0].page_content)
        id.append(output[0].metadata["id"])
        d.append(output[1])
    espacio="""


########################
    


"""
    page_content=espacio.join(page_content)  
    return  page_content,d,id

def QARequest(Pregunta):
    query = Pregunta
    page_content,d,id=FinderDb(query,db)
    page_content2,d2,id2=FinderDb(query,db2)
    return page_content,d,id,page_content2,d2,id2



with gr.Blocks() as demo:
    Pregunta = gr.Textbox(label="Pregunta")
    #Respuesta = gr.Textbox(label="Respuesta")
    #id = gr.Textbox(label="id")
    #metrica=gr.Textbox(label="metrica")
    Respuesta2 = gr.Textbox(label="Respuesta2")
    id2 = gr.Textbox(label="id2")
    metrica2=gr.Textbox(label="metrica2")
    Enviar_btn = gr.Button("Responder")

    Enviar_btn.click(fn=QARequest, inputs=Pregunta, outputs=[Respuesta2,metrica2,id2], api_name="Respuestas") # Respuesta,metrica,id,

demo.launch() #