from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers import AutoModelForSequenceClassification
from lxml_html_clean import Cleaner


from transformers import AutoTokenizer
from langdetect import detect
from newspaper import Article
from PIL import Image
import streamlit as st

import requests
import torch

st.markdown("## Prediction of Misinformation by given URL")
background = Image.open('logo.jpg')
st.image(background)

st.markdown(f"### Article URL")
text = st.text_area("Insert some url here", 
        value="https://www.livelaw.in/news-updates/supreme-court-collegium-recommends-appointment-advocate-praveen-kumar-giri-judge-allahabad-high-court-279470")

# @st.cache(allow_output_mutation=True)
# def get_models_and_tokenizers():
#     model_name = 'distilbert-base-uncased-finetuned-sst-2-english'
#     model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=2)
#     model.eval()
#     tokenizer = AutoTokenizer.from_pretrained(model_name)
#     model.load_state_dict(torch.load('./my_saved_model/checkpoint-6320/rng_state.pth', map_location='cpu'))

#     model_name_translator = "facebook/wmt19-ru-en"
#     tokenizer_translator = FSMTTokenizer.from_pretrained(model_name_translator)
#     model_translator = FSMTForConditionalGeneration.from_pretrained(model_name_translator)
#     model_translator.eval()
#     return model, tokenizer, model_translator, tokenizer_translator
@st.cache_data()
def get_models_and_tokenizers():
    model_name = 'distilbert-base-uncased-finetuned-sst-2-english'
    checkpoint_dir = './my_saved_model/checkpoint-6320/'  # Path to your checkpoint folder
    
    # Load the classification model and tokenizer
    model = AutoModelForSequenceClassification.from_pretrained(checkpoint_dir, num_labels=2)
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    
    # Load the translator model and tokenizer
    model_name_translator = "facebook/wmt19-ru-en"
    tokenizer_translator = FSMTTokenizer.from_pretrained(model_name_translator)
    model_translator = FSMTForConditionalGeneration.from_pretrained(model_name_translator)
    
    model.eval()
    model_translator.eval()
    return model, tokenizer, model_translator, tokenizer_translator

model, tokenizer, model_translator, tokenizer_translator = get_models_and_tokenizers()

article = Article(text)
article.download()
article.parse()
concated_text = article.title + '. ' + article.text
lang = detect(concated_text)

st.markdown(f"### Language detection")

if lang == 'ru':
    st.markdown(f"The language of this article is {lang.upper()} so we translated it!")
    with st.spinner('Waiting for translation'):
        input_ids = tokenizer_translator.encode(concated_text, 
            return_tensors="pt", max_length=512, truncation=True)
        outputs = model_translator.generate(input_ids)
        decoded = tokenizer_translator.decode(outputs[0], skip_special_tokens=True)
        st.markdown("### Translated Text")
        st.markdown(f"{decoded[:777]}")
        concated_text = decoded
else:
    st.markdown(f"The language of this article for sure:  {lang.upper()}!")

    st.markdown("### Extracted Text")
    st.markdown(f"{concated_text[:777]}")

tokens_info = tokenizer(concated_text, truncation=True, return_tensors="pt")
with torch.no_grad():
    raw_predictions = model(**tokens_info)
softmaxed = int(torch.nn.functional.softmax(raw_predictions.logits[0], dim=0)[1] * 100)
st.markdown("### Truthteller Predicts..")
st.progress(softmaxed)
st.markdown(f"This is fake by *{softmaxed}%*!")
if (softmaxed > 70):
    st.error('We would not trust this text! This is misleading..')
elif (softmaxed > 40):
    st.warning('We are not sure about this text!')
else:
    st.success('We would trust this text!')