# -*- coding: utf-8 -*-



import os
import shutil
from langchain.document_loaders import PyPDFLoader
from langchain.vectorstores import Chroma
from langchain.embeddings.sentence_transformer import SentenceTransformerEmbeddings
from langchain.text_splitter import SpacyTextSplitter, CharacterTextSplitter, RecursiveCharacterTextSplitter
import transformers
import re
import string
import random
import pandas as pd
import streamlit as st 

from langchain import HuggingFaceHub
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.chains.question_answering import load_qa_chain
from langchain import HuggingFacePipeline
import tempfile
from PyPDF2 import PdfReader
import random

st.markdown(
"""

# 🐵 Homo Inspectus 

(Question Answering Engine)

## Suat ATAN


""")

embedding_function = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")

def process_string(input_string):
    alphanumeric_string = re.sub(r'[^a-zA-Z0-9]', '', input_string)
    no_blank_string = alphanumeric_string.replace(' ', '')
    random_numbers = ''.join(random.choices(string.digits, k=2))
    final_string = no_blank_string + random_numbers
    return final_string

def show_long_repeating_sentences(text, min_words=4):
    sentences = text.split('.')
    unique_sentences = set()
    repeating_sentences = []
    for s in sentences:
        stripped_sentence = s.strip()
        if stripped_sentence in unique_sentences and len(stripped_sentence.split()) >= min_words:
            repeating_sentences.append(stripped_sentence)
        else:
            unique_sentences.add(stripped_sentence)

    return repeating_sentences

def create_vdb(fileobj, chunk_size = 200, overlap = 100):
    if chunk_size < overlap:
        chunk_size, overlap = 500, 100
    print(f'Chunk size: {chunk_size}, overlap: {overlap}')
    #loader = PyPDFLoader(fileobj)
    pdf_reader = PdfReader(fileobj)
        
    all_text = ""
    for page in pdf_reader.pages:
        all_text += page.extract_text()
    
    repeating_sentences = show_long_repeating_sentences(all_text)
    print('Repeating sentences')
    print(repeating_sentences)

    text_splitter = RecursiveCharacterTextSplitter(
    chunk_size=chunk_size,
    chunk_overlap=overlap,
    separators=["\n\n", "\n", ".", "!", "?", ",", " ", ""],
    )
    

    chunks = text_splitter.split_text(text = all_text)
    embedding_function = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
    cname = str(random.randint(1000, 9999))
    db = Chroma.from_texts(chunks, embedding_function, persist_directory=cname)
    print(''' Index is created ⛹️''')
    return db

"""

# Information Retrieval (IR) Tool

This tool is your small local and sweet 🍬 Google for the document
"""


db = None

uploaded_file = st.file_uploader('Choose your .pdf file', type="pdf")
if uploaded_file is not None:
    db = create_vdb(uploaded_file, 500, 100)
    st.write("filename:", uploaded_file.name)
    st.write(uploaded_file)
else:
    st.info("Please upload a PDF file.")


"""👌  You did. Now write your keyword, or question or anything to search in the document"""

# q = 'leak' # @param {type:"string"}
q = st.text_input('Write your question or keywords', 'HVAC', key='q1')
st.write(f'Your question is: {q}')

if db:
    answers = []
    pgnos = []
    for  d in db.similarity_search(q, k=10):
      answers.append(d.page_content)
      #pgnos.append(d.metadata['pdf_page_number'])
    results = pd.DataFrame({'Cite':answers})
    st.markdown('## Results')
    st.dataframe(results)

st.markdown(
"""The order of result is based the most relevant ones to less. Like Google

# Chat Tool (Humble ) 🆓 🚗

This tool is allow your direct question to answer like my rival 🔪 ChatGPT
""")


def ask_to_alpaca(question, vector_store, qa_chain):
        similar_docs = vector_store.similarity_search(question)
        response = qa_chain.run(input_documents=similar_docs, question=question)
        return response


explicit_question = st.text_input('Write your question', 'Roof problems', key='q2')
if db:
    max_length=1024
    model_name = "declare-lab/flan-alpaca-large"
    llm = HuggingFacePipeline.from_model_id(
        model_id=model_name,
        task="text2text-generation",
        model_kwargs={"max_length": max_length},
    )
    qa_chain = load_qa_chain(llm, chain_type="stuff")
    
    # explicit_question = 'Is there any problem of the roofs?' # @param {type:"string"}
   
    st.markdown("Answer:")
    st.write(ask_to_alpaca(explicit_question, db, qa_chain))
else:
    st.markdown('Please upload a PDF file.')