import requests
import streamlit as st
import PyPDF2
import torch
from transformers import AutoTokenizer, LEDForConditionalGeneration
st.set_page_config(page_title="Summarization", page_icon="📈",layout="wide")
hide_streamlit_style = """
            <style>
            #MainMenu {visibility: hidden;}
            footer {	
	visibility: hidden;
	
	}
footer:after {
	content:'©2023 Sravathi AI Technology. All rights reserved.'; 
	visibility: visible;
	display: block;
	position: relative;
	#background-color: red;
	padding: 5px;
	top: 2px;
}
            </style>
            """
st.markdown(hide_streamlit_style, unsafe_allow_html=True)
import pandas as pd
import time
import sys
import pickle
#from stqdm import stqdm
import base64
#from tensorflow.keras.preprocessing.text import Tokenizer
#from tensorflow.keras.preprocessing.sequence import pad_sequences
import numpy as np
import json
import os
import re
import nltk
from nltk.corpus import words
nltk.download('words')
#from tensorflow.keras.models import load_model
#st.write("API examples - Dermatophagoides, Miconazole, neomycin,Iothalamate")

#background_image = sys.path[1]+"/streamlit_datafile_links/audience-1853662_960_720.jpg"  # Path to your background image
def add_bg_from_local(image_file):
    with open(image_file, "rb") as image_file:
        encoded_string = base64.b64encode(image_file.read())
    st.markdown(
    f"""
    <style>
    .stApp {{
        background-image: url(data:image/{"jpg"};base64,{encoded_string.decode()});
        background-size: cover
    }}
    </style>
    """,
    unsafe_allow_html=True
    )
#add_bg_from_local(background_image)  

#@st.cache
st.header('Summarization')
def convert_df(df):
                # IMPORTANT: Cache the conversion to prevent computation on every rerun
                return df.to_csv(index=False).encode('utf-8')  
col1, col2 = st.columns([4,1])
result_csv_batch_sql = result_csv_batch_fail=result_csv_batch=result_csv4=result_csv3=result_csv1=result_csv2=0

with col1:
    models = st.selectbox(
    'Select the option',
    ('model1', ))
    #try: 
    if models == 'model1':   
        st.markdown("")
    else:
         st.markdown("")
    with st.form("form1"):
        hide_label = """
        <style>
            .css-9ycgxx {
                display: none;
            }
        </style>
        """
        text_data = st.text_input('Enter the text')
        print(text_data)
        st.markdown(hide_label, unsafe_allow_html=True)            
        submitted = st.form_submit_button("Submit")
        if submitted:
            #torch.cuda.set_device(2)
            tokenizer = AutoTokenizer.from_pretrained('allenai/PRIMERA-multinews')
            model = LEDForConditionalGeneration.from_pretrained('allenai/PRIMERA-multinews')
            #device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') # get the device
            device = "cpu"
            model.to(device) # move the model to the device  
            documents = text_data

            # Tokenize and encode the documents
            inputs = tokenizer(documents, return_tensors='pt', padding=True, truncation=True,max_length=1000000)

            # Move the inputs to the device
            inputs = inputs.to(device)

            # Generate summaries
            outputs = model.generate(**inputs,max_length=1000000)

            # Decode the summaries
            st.write(tokenizer.batch_decode(outputs, skip_special_tokens=True))                          
            st.success('Prediction done successfully!', icon="✅")  
        _='''
        except Exception as e:
            if 'NoneType' or 'not defined' in str(e):
                st.warning('Enter the required inputs', icon="⚠️")
            else:          
                st.warning(str(e), icon="⚠️")
        ''' 

for i in range(30):
    st.markdown('##')