import streamlit as st
from langchain.embeddings import HuggingFaceInstructEmbeddings
from langchain.vectorstores import FAISS
from langchain.text_splitter import CharacterTextSplitter
from langchain.document_loaders import DirectoryLoader, PyPDFLoader
import os
from PyPDF2 import PdfReader
from transformers import pipeline
from transformers import AutoModel

#Retriever erweiterung
from langchain.prompts import ChatPromptTemplate
from langchain.schema import StrOutputParser
from langchain.schema.runnable import RunnablePassthrough

###########
#pip install faiss-cpu
#pip install langchain
#pip install pypdf
#pip tiktoken
#pip install InstructorEmbedding
###############


# PDF in String umwandeln
def get_pdf_text(folder_path):
    text = ""
    # Durchsuche alle Dateien im angegebenen Verzeichnis
    for filename in os.listdir(folder_path):
        filepath = os.path.join(folder_path, filename)

        # Überprüfe, ob die Datei die Erweiterung ".pdf" hat
        if os.path.isfile(filepath) and filename.lower().endswith(".pdf"):
            pdf_reader = PdfReader(filepath)
            for page in pdf_reader.pages:
                text += page.extract_text()
            #text += '\n'

    return text

#Chunks erstellen
def get_text_chunks(text):
    #Arbeitsweise Textsplitter definieren
    text_splitter = CharacterTextSplitter(
        separator="\n",
        chunk_size=1000,
        chunk_overlap=200,
        length_function=len
    )
    chunks = text_splitter.split_text(text)
    return chunks

# nur zum Anlegen des lokalen Verzeichnisses "Store" und speichern der Vektor-Datenbank
def create_vectorstore_and_store():
    folder_path = './files'
    pdf_text = get_pdf_text(folder_path)
    text_chunks = get_text_chunks(pdf_text)
    embeddings = HuggingFaceInstructEmbeddings(model_name="deutsche-telekom/bert-multi-english-german-squad2")
    #embeddings = HuggingFaceInstructEmbeddings(model_name="aari1995/German_Semantic_STS_V2")
    # Initiate Faiss DB
    vectorstoreDB = FAISS.from_texts(texts=text_chunks,embedding=embeddings)#texts=text_chunks,
    # Verzeichnis in dem die VektorDB gespeichert werden soll
    save_directory = "Store"
    #VektorDB lokal speichern
    vectorstoreDB.save_local(save_directory)
    print(vectorstoreDB)
    return None
    
########

def get_vectorstore():
    embeddings = HuggingFaceInstructEmbeddings(model_name="deutsche-telekom/bert-multi-english-german-squad2")
    #embeddings = HuggingFaceInstructEmbeddings(model_name="aari1995/German_Semantic_STS_V2")
    #Abruf lokaler Vektordatenbank
    save_directory = "Store"
    vectorstoreDB = FAISS.load_local(save_directory, embeddings)
    return vectorstoreDB


def main():
    #if os.path.exists("./Store"): #Nutzereingabe nur eingelesen, wenn vectorstore angelegt
    user_question = st.text_area("Stell mir eine Frage: ")
            #if os.path.exists("./Store"): #Nutzereingabe nur eingelesen, wenn vectorstore angelegt
    retriever=get_vectorstore().as_retriever()
    retrieved_docs=retriever.invoke(
    user_question
    )
    if user_question:
        st.text(retrieved_docs[0].page_content)
        context=retrieved_docs[0].page_content
        question=user_question
        st.text(user_question)

        ##IDEE Text Generation
        generator = pipeline('text-generation', model = 'gpt2')
        answer = generator(context, max_length = 30, num_return_sequences=3)
        
        st.text("FORMATIERTE ANTWORT:")
        #st.text_area()
        st.text(answer)
        st.text(type(answer))

        # Erstelle die Question Answering-Pipeline für Deutsch
        qa_pipeline = pipeline("question-answering", model="deutsche-telekom/bert-multi-english-german-squad2", tokenizer="deutsche-telekom/bert-multi-english-german-squad2")

        # Frage beantworten
        answer = qa_pipeline(question=question, context=context)
        
        # Gib die Antwort aus
        st.text("Basisantwort:")
        st.text(answer["answer"])
        st.text(answer)
        
        
        generator = pipeline('text-generation', model = 'tiiuae/falcon-40b')
        generator(answer, max_length = 30, num_return_sequences=3)
        st.text("Generierte Erweiterung:")
        st.text(generator)
            
        """
        #IDEE Retriever erweitern
        template = Answer the question based only on the following context:

        {context}
        
        Question: {question}
        
        prompt = ChatPromptTemplate.from_template(template)
        model = AutoModel.from_pretrained("hkunlp/instructor-base")
        
        
        def format_docs(docs):
            return "\n\n".join([d.page_content for d in docs])
        
        
        chain = (
            {"context": retriever | format_docs, "question": RunnablePassthrough()}
            | prompt
            | model
            | StrOutputParser()
        )

        ausgabetext = chain.invoke(user_question)
        st.text(ausgabetext)
        """




if __name__ == '__main__':
    main()