import streamlit as st
import os
import PyPDF2
import nltk
nltk.download('punkt')
from nltk.tokenize import sent_tokenize
from sentence_transformers import SentenceTransformer
import numpy as np
from numpy.linalg import norm

url = "https://www.independentschoolparent.com/wp-content/uploads/2018/01/AI.jpg"

st.title("AIP-S³")
st.write("AI Powered Smart Search System")
st.image(url)

st.markdown('_Welcome to Question Answering System 🧠 🤖_')

a = st.sidebar.radio("SELECT -", ['PDF', 'Website'])
model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
#filt1_list = []



class PDFProcessor:
    def __init__(self):
        self.filt1_list = []
    
    def process_files(self, uploaded_files):
        for i in uploaded_files:
            if i.type == "application/pdf" :
                reader = PyPDF2.PdfReader(i)
        
        text_ext = []
        for i in range(len(reader.pages)):
            pageObj = reader.pages[i] 
            text_ext.append(pageObj.extract_text())

        sent_toks = []
        for i in text_ext:
            sent_toks.append(sent_tokenize(i))
            concat_list = [j for i in sent_toks for j in i]
        
            for i in concat_list:
                a = (i.replace('\n', ' '))
                self.filt1_list.append(a)

if a == 'PDF':
    
    pdf_processor = PDFProcessor()
    uploaded_files = st.file_uploader("Upload files - ", accept_multiple_files=True, type=['pdf'])
    
    if st.button("Process!"):
        pdf_processor.process_files(uploaded_files)
        filt1_list = pdf_processor.filt1_list
        st.write("Process Completed")


    query = st.text_input('Ask me anything!', placeholder = 'Type.....')
    if st.button("Confirm!"):
        model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
        query_embedding = model.encode(query)

        cosine_lis = []
        for i in range(len(pdf_processor.filt1_list)):
            cosine = np.dot(query_embedding , embeddings[i])/ (norm(query_embedding)*norm(embeddings[i]))
            cosine_lis.append(cosine)
          
        N = 3
        list1 = cosine_lis
          
        indexes_final= sorted(range(len(list1)), key=lambda i: list1[i], reverse=True)[:N]

        for i in indexes_final:
            st.write(filt1_list[i])        
            st.write("")
      


            

# if a == 'PDF' :
    
#     uploaded_files = st.file_uploader("Upload files - ", accept_multiple_files=True , 
#                   type = ['pdf'] )

#     if st.button("Process!"):
#         for i in uploaded_files:
#             if i.type == "application/pdf" :
#                 reader = PyPDF2.PdfReader(i)
      
#         text_ext = []
#         for i in range(len(reader.pages)):
#             pageObj = reader.pages[i] 
#         # extracting text from page 
#             text_ext.append(pageObj.extract_text())

#         sent_toks = []
#         for i in text_ext:
#             sent_toks.append(sent_tokenize(i))
#             concat_list = [j for i in sent_toks for j in i]
        
#             filt1_list = []
#             for i in concat_list:
#                 a = (i.replace('\n', ' '))
#                 filt1_list.append(a)

    

#         #model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
#         embeddings = model.encode(filt1_list)
        
#         st.write("Process Completed")
# '''