import streamlit as st
from collections import Counter
import tensorflow as tf
import keras
from tensorflow.keras.preprocessing import text,sequence
from tensorflow.keras.preprocessing.text import Tokenizer

import nltk
nltk.download('punkt')
from nltk.tokenize import word_tokenize
nltk.download('stopwords')
from nltk.corpus import stopwords
nltk.download('wordnet')
from nltk.stem import WordNetLemmatizer

from textblob import TextBlob, Blobber
from textblob_fr import PatternTagger, PatternAnalyzer

import spacy.cli
spacy.cli.download("fr_core_news_md")

import torch
import sentencepiece as spm
from transformers import CamembertTokenizer, CamembertModel
from nltk.tokenize import sent_tokenize
from sklearn.metrics.pairwise import cosine_similarity


# nombre de mots et de mots uniques
def number_words(text):
  word = text.split()
  return 'Nombre de mots : {} - Nombre de mots uniques : {}'.format(len(word), len(Counter(word)))
    
# polarité
def polarity(text):
  tb = Blobber(pos_tagger=PatternTagger(), analyzer=PatternAnalyzer())
  if tb(text).sentiment[0] < 0:
      return f'La polarité de ce texte est {tb(text).sentiment[0]} : ce texte est plus négatif que positif'
  elif tb(text).sentiment[0] > 0:
      return f'La polarité de ce texte est {tb(text).sentiment[0]} : ce texte est plus positif que négatif'
  else :
      return f'La polarité de ce texte est {tb(text).sentiment[0]} : ce texte est neutre, pas plus négatif que positif'


# subjectivité
def subjectivity(text):
  tb = Blobber(pos_tagger=PatternTagger(), analyzer=PatternAnalyzer())
  if tb(text).sentiment[1] < 0.5:
    return f'La subjectivité de ce texte est {tb(text).sentiment[1]} : ce texte est plus subjectif que factuel'
  elif tb(text).sentiment[1] > 0.5:
    return f'La subjectivité de ce texte est {tb(text).sentiment[1]} : ce texte est plus subjectif que factuel'
  else :
    return f'La subjectivité de ce texte est {tb(text).sentiment[1]} : ce texte est neutre, pas plus subjectif que factuel'


# mots clés
def keywords(text):
  nlp = spacy.load("fr_core_news_md")
  text2 = nlp(text)
  text_keywords = [token.text for token in text2 if token.pos_== 'NOUN' or token.pos_== 'PROPN' or token.pos_== 'VERB']
  counter_words = Counter(text_keywords)
  most_freq_words = [word for word in counter_words.most_common(10)]
  most_freq_words_p = []
  for i in range(len(most_freq_words)):
    mfwp = most_freq_words[i][0]
    most_freq_words_p.append(mfwp)
  return most_freq_words_p


# summary1
def summary_1(text):
  model = CamembertModel.from_pretrained('camembert-base')
  tokenizer = CamembertTokenizer.from_pretrained('camembert-base')

  ## preprocessing
  sentences = sent_tokenize(text)
  tokenized_sentences = [tokenizer.encode(sent, add_special_tokens=True) for sent in sentences]

  ## padding, encoding
  max_len = 0
  for i in tokenized_sentences:
    if len(i) > max_len:
      max_len = len(i)

  padded_sentences = []
  for i in tokenized_sentences:
    while len(i) < max_len:
      i.append(0)
    padded_sentences.append(i)

  input_ids = torch.tensor(padded_sentences)

  ## embedding
  with torch.no_grad():
    last_hidden_states = model(input_ids)[0]

  sentence_embeddings = []
  for i in range(len(sentences)):
    sentence_embeddings.append(torch.mean(last_hidden_states[i], dim=0).numpy())

  ## summarizing
  similarity_matrix = cosine_similarity(sentence_embeddings)

  num_sentences = 3
  summary_sentences = []
  for i in range(num_sentences):
    sentence_scores = list(enumerate(similarity_matrix[i]))
    sentence_scores = sorted(sentence_scores, key=lambda x: x[1], reverse=True)
    summary_sentences.append(sentences[sentence_scores[1][0]])

  summary = ' '.join(summary_sentences)
  return summary


# summary2
def summary_2(text):
  nlp = spacy.load("fr_core_news_md")
  text2 = nlp(text)
  text_keywords = [token.text for token in text2 if token.pos_== 'NOUN' or token.pos_== 'PROPN']

  counter_words = Counter(text_keywords)
  most_freq_words = [word for word in counter_words.most_common(3)]

  most_freq_words_p = []
  for i in range(len(most_freq_words)):
      mfwp = most_freq_words[i][0]
      most_freq_words_p.append(mfwp)

  sentences = sent_tokenize(text)
  summary = []
  for sent in sentences:
    for word in sent.split():
      if word in most_freq_words_p and sent not in summary:
        summary.append(sent)
      
  return summary


def analyze_text(text):
    nb_mots = number_words(text)
    polarite = polarity(text)
    subjectivite = subjectivity(text)
    mots_cles = keywords(text)
    resume1 = summary_1(text)
    resume2 = summary_2(text)

    return nb_mots, polarite, subjectivite, mots_cles, resume1, resume2


background_style = """
    <style>
        body {
            background-image: url('bleublancrouge.jpg');
            background-size: cover;
            background-repeat: no-repeat;
            background-attachment: fixed;
        }
    </style>
"""

st.markdown(background_style, unsafe_allow_html=True)

st.title('Text Analysis and Summary')
text = st.text_area('Enter text here:')
if st.button('Analyze'):
    if text:
        nb_mots, polarite, subjectivite, mots_cles, resume1, resume2 = analyze_text(text)
        st.write(nb_mots)
        st.write(polarite)
        st.write(subjectivite)
        st.write('Mots clés :', ', '.join(mots_cles))
        st.write(f'Résumé 1 : {resume1}')
        st.write(f'Résumé 2 : {resume2}')
if st.button('Clear'):
    text = ""

With this setup, your Streamlit app will display the background image behind your app's content. Make sure the image file is in the same directory as your script, and adjust the CSS properties in the background_style variable as needed to achieve the desired appearance.


st.title('Text Analysis and Summary')
text = st.text_area('Enter text here:')

if st.button('Analyze'):
  if text:
    nb_mots, polarite, subjectivite, mots_cles, resume1, resume2 = analyze_text(text)

    st.write(nb_mots)
    st.write(polarite)
    st.write(subjectivite)
    st.write('Mots clés :', ', '.join(mots_cles))
    st.write(f'Résumé 1 : {resume1}')
    st.write('Résumé 2 :', ''.join(resume2))

if st.button('Clear'):
    text = ""