Initial Commit

.gitattributes

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+*.pt filter=lfs diff=lfs merge=lfs -text

.gitignore

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+.venv/**
+data/**
+src/__pycache__
+model/**
+html5up-helios/**
+**/__pycache__/**

.pre-commit-config.yaml

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+repos:
+-   repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v2.3.0
+    hooks:
+    -   id: check-yaml
+    -   id: end-of-file-fixer
+    -   id: trailing-whitespace

Dockerfile

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+# Dockerfile, Image, Container
+
+FROM python:3.8.10
+
+ADD src/ requirements.txt .
+
+RUN pip install -r requirements.txt
+
+CMD ["python3", "./src/api.py"]
+
+EXPOSE 3000

Documentation.md

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+# Les objectifs du projet
+
+L'objectif du projet est de mettre en place une <strong>plateforme de requête</strong> d'un modèle de langue permettant la <strong>génération de résumé d'article de presse.</strong>
+
+# Une description du système ou des données auxquelles l’interface permet d’accéder
+
+
+Le projet utilisera pour l'entraînement du modèle de langue le corpus issu de 'Newsroom: A Dataset of 1.3 Million Summaries with Diverse Extractive Strategies' (Grusky et al., NAACL 2018) newsroom assemblé par Max Grusky et ses collègues en 2018. Newsroom est un corpus parallèle rassemblant 1,3 millions articles de presse et leur résumé en anglais. Les résumés sont réalisés en utilisant les méthodes d'extraction comme d'abstraction ainsi que des méthodes mixtes. Ce corpus est disponible sur HuggingFace mais necessite un téléchargement préalable pour des raisons de protection des données.
+
+
+# La méthodologie
+
+## Répartition du travail
+Nous avons décidé de travailler avec le logiciel de gestion de version Github en mettant en place un système de verification des commit avec un pull request.
+Cette méthode permet à chaque participant du projet d'observer les modifications effectuées par les autres membres avant d'accepter de fondre en une seule les branches main et les modifications proposées.
+
+## Problèmes rencontrés et résolution
+- Problème Mojibake depuis les fichiers jsonl : encodage en cp1252 et decodage en utf-8 avec ignore pour éviter les erreurs sur les caractères utf-8 présents dans le fichier à l'encodage
+- Répétition des mots à cause de la ponctuation : suppresion de la ponctuation avec `strip`
+- Agglomération des pronoms et des verbes : remplacement des `'` par des espaces avant le `split`
+- Split des noms propres composés ('Ivory Coast', 'Inter Milan') :
+- Problème des mots non disponibles dans le vocabulaire
+- Problème de la qualité du corpus :
+    - Résumés tronqués : "Did', 'Tatum', "O'Neal's", 'latest', 'battle', 'with', 'ex-husband', 'John', 'McEnroe', 'put', 'her', 'back', 'on', 'drugs?', 'The', '"Paper', 'Moon"star', 'checked', 'herself', 'into', "L.A.'s", 'Promises', 'rehab', 'facility', 'after', 'a', 'friend', 'caught', 'her', 'smoking', 'crack,', 'according', 'to', 'The', 'National', 'Enquirer.', "O'Neal", 'emerged', 'clean', 'and', 'sober', 'from', "Promises'", '34-day', 'recovery', 'program', 'in', 'late', 'July,', 'the', 'tab', 'reports.', 'The', 'actress', 'is', 'said', 'to', 'have', 'plunged', 'into', 'her', 'old', 'habits', 'because', 'of'"
+    - Résumés plus proche de titres que de résumés
+- Prise en compte du padding dans l'apprentissage --> utilisation de la fonctionnalité ignore_index de NLLLoss avec un padding d'une valeur à -100
+
+
+## Les étapes du projet
+
+# Implémentation
+## modélisation
+
+Nous avons décidé dans un premier temps de modéliser une LSTM pour le résuméautomatique sur labase du réseau de neurone réalisé en cours.
+Pour ce faire nous nous sommes beaucoup inspirée du kaggle https://www.kaggle.com/code/columbine/seq2seq-pytorch ainsi que de la documentation de PyTorch https://pytorch.org/tutorials/beginner/nlp/sequence_models_tutorial.html#example-an-lstm-for-part-of-speech-tagging
+## modules et API utilisés
+## Langages de programmation
+
+# Les résultats (fichiers output, visualisations…)
+
+## Les metriques d'évaluation
+- ROUGE
+- BLEU
+- QAEval
+- Meteor
+- BERTScore
+
+
+# Discussion des résultats
+ce que vous auriez aimé faire et ce que vous avez pu faire par exemple

requirements.txt

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+anyio==3.6.2
+click==8.1.3
+fastapi==0.92.0
+h11==0.14.0
+idna==3.4
+Jinja2==3.1.2
+joblib==1.2.0
+MarkupSafe==2.1.2
+nltk==3.8.1
+numpy==1.24.2
+nvidia-cublas-cu11==11.10.3.66
+nvidia-cuda-nvrtc-cu11==11.7.99
+nvidia-cuda-runtime-cu11==11.7.99
+nvidia-cudnn-cu11==8.5.0.96
+pandas==1.5.3
+pydantic==1.10.5
+python-dateutil==2.8.2
+python-multipart==0.0.6
+pytz==2022.7.1
+regex==2022.10.31
+six==1.16.0
+sniffio==1.3.0
+starlette==0.25.0
+torch==1.13.1
+tqdm==4.65.0
+typing_extensions==4.5.0
+uvicorn==0.20.0

src/api.py

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+import uvicorn
+from fastapi import FastAPI, Form, Request
+from fastapi.staticfiles import StaticFiles
+from fastapi.templating import Jinja2Templates
+
+from inference import inferenceAPI
+
+# from transformers import RobertaTokenizerFast, EncoderDecoderModel
+
+# ------- MODELE HUGGING FACE QUI MARCHE BIEN ------------------------------------
+# device = 'cuda' if torch.cuda.is_available() else 'cpu'
+# ckpt = 'mrm8488/camembert2camembert_shared-finetuned-french-summarization'
+# tokenizer = RobertaTokenizerFast.from_pretrained(ckpt)
+# model = EncoderDecoderModel.from_pretrained(ckpt).to(device)
+
+# def generate_summary(text):
+#    inputs = tokenizer([text], padding="max_length", truncation=True, max_length=512, return_tensors="pt")
+#    input_ids = inputs.input_ids.to(device)
+#    attention_mask = inputs.attention_mask.to(device)
+#    output = model.generate(input_ids, attention_mask=attention_mask)
+#    return tokenizer.decode(output[0], skip_special_tokens=True)
+# ----------------------------------------------------------------------------------
+
+
+# ------ NOTRE MODELE --------------------------------------------------------------
+# appel de la fonction inférence, adaptée pour une entrée txt
+def summarize(text: str):
+    return " ".join(inferenceAPI(text))
+
+
+# ----------------------------------------------------------------------------------
+
+# -------- API ---------------------------------------------------------------------
+app = FastAPI()
+
+# static pour tout ce qui est css
+templates = Jinja2Templates(directory="templates")
+app.mount("/static", StaticFiles(directory="static"), name="static")
+app.mount("/templates", StaticFiles(directory="templates"), name="templates")
+
+
+@app.get("/")
+async def index(request: Request):
+    return templates.TemplateResponse("index.html.jinja", {"request": request})
+
+
+# pour donner les predictions
+@app.post("/")
+async def prediction(request: Request, text: str = Form(...)):
+    summary = summarize(text)
+    return templates.TemplateResponse(
+        "index.html.jinja", {"request": request, "text": text, "summary": summary}
+    )
+
+
+# ------------------------------------------------------------------------------------
+
+
+# pour lancer le serveur et le reload à chaque changement sauvegardé dans le repo
+if __name__ == "__main__":
+    uvicorn.run("api:app", port=8000, reload=True)

src/dataloader.py

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+"""
+    Get data and adapt it for training
+    -----------
+    - nettoyage de l'encodage
+    - Ajout de token <START> et <END>
+    TO DO :
+    - Nettoyage des contractions
+    - enlever les \xad
+    - enlever ponctuation et () []
+    - s'occuper des noms propres (mots commençant par une majuscule qui se suivent)
+    Création d'un Vectoriserà partir du vocabulaire :
+
+"""
+import string
+from collections import Counter
+
+import pandas as pd
+import torch
+from nltk import word_tokenize
+
+# nltk.download('punkt')
+
+
+class Data:
+    """
+    A class used to get data from file
+    ...
+
+    Attributes
+    ----------
+    path : str
+        the path to the file containing the data
+
+    Methods
+    -------
+    open()
+        open the jsonl file with pandas
+    clean_data(text_type)
+        clean the data got by opening the file and adds <start> and
+        <end> tokens depending on the text_type
+    get_words()
+        get the dataset vocabulary
+    make_dataset()
+        create a dataset with cleaned data
+    """
+
+    def __init__(self, path: str) -> None:
+        self.path = path
+
+    def open(self) -> pd.DataFrame:
+        """
+        Open the file containing the data
+        """
+        return pd.read_json(path_or_buf=self.path, lines=True)
+
+    def clean_data(self, text_type: str) -> list:
+        """
+        Clean data from encoding error, punctuation, etc...
+        To Do :
+        #nettoyer les données
+
+        Parameters
+        ----------
+        text_type : str
+            allow to differenciate between 'text' and 'summary'
+            to add <start> and <end> tokens to summaries
+
+        Returns
+        ----------
+        list of list
+            list of tokenised texts
+
+        """
+        dataset = self.open()
+
+        texts = dataset[text_type]
+        texts = texts.str.encode("cp1252", "ignore")
+        texts = texts.str.decode("utf-8", "ignore")
+
+        tokenized_texts = []
+        # - Nettoyage des contractions
+        # - enlever les \xad
+        # text.translate(str.maketrans('', '', string.punctuation))
+        # - enlever ponctuation et () []
+        # - s'occuper des noms propres (mots commençant par une majuscule qui se suivent)
+        for text in texts:
+            text = text.translate(str.maketrans("", "", string.punctuation))
+            text = word_tokenize(text)
+            tokenized_texts.append(text)
+
+        if text_type == "summary":
+            return [["<start>", *summary, "<end>"] for summary in tokenized_texts]
+        return tokenized_texts
+
+    def pad_sequence(self):
+        """
+        pad summary with empty token
+        """
+        texts = self.clean_data("text")
+        summaries = self.clean_data("summary")
+        padded_summary = []
+        for text, summary in zip(texts, summaries):
+            if len(summary) != len(text):
+                summary += ["<empty>"] * (len(text) - len(summary))
+            padded_summary.append(summary)
+        return texts, padded_summary
+
+    def get_words(self) -> list:
+        """
+        Create a dictionnary of the data vocabulary
+        """
+        texts, summaries = self.clean_data("text"), self.clean_data("summary")
+        text_words = [word for text in texts for word in text]
+        summary_words = [word for text in summaries for word in text]
+        return text_words + summary_words
+
+    def make_dataset(self) -> pd.DataFrame:
+        """
+        Create a Pandas Dataframe with cleaned data
+        --------------------
+        param: self: Data
+        return: pd.DataFrame
+        """
+        texts, summaries = self.clean_data("text"), self.clean_data("summary")
+        return pd.DataFrame(list(zip(texts, summaries)), columns=["text", "summary"])
+
+
+class Vectoriser:
+    """
+    A class used to vectorise data
+    ...
+
+    Attributes
+    ----------
+    vocab : list
+        list of the vocab
+
+    Methods
+    -------
+    encode(tokens)
+        transforms a list of tokens to their corresponding idx
+        in form of troch tensor
+    decode(word_idx_tensor)
+        converts a tensor to a list of tokens
+    vectorize(row)
+        encode an entire row from the dataset
+    """
+
+    def __init__(self, vocab) -> None:
+        self.vocab = vocab
+        self.word_count = Counter(word.lower().strip(",.\\-") for word in self.vocab)
+        self.idx_to_token = sorted([t for t, c in self.word_count.items() if c > 1])
+        self.token_to_idx = {t: i for i, t in enumerate(self.idx_to_token)}
+
+    def encode(self, tokens) -> torch.tensor:
+        """
+        Encode une phrase selon les mots qu'elle contient
+        selon les mots contenus dans le dictionnaire.
+        À NOTER :
+        Si un mot n'est pas contenu dans le dictionnaire,
+        associe un index fixe au mot qui sera ignoré au décodage.
+        ---------
+        :params: tokens : list
+            les mots de la phrase sous forme de liste
+        :return: words_idx : tensor
+            Un tensor contenant les index des mots de la phrase
+        """
+        if type(tokens) == list:
+            words_idx = torch.tensor(
+                [
+                    self.token_to_idx.get(t.lower(), len(self.token_to_idx))
+                    for t in tokens
+                ],
+                dtype=torch.long,
+            )
+
+        # Permet d'encoder mots par mots
+        elif type(tokens) == str:
+            words_idx = torch.tensor(self.token_to_idx.get(tokens.lower()))
+
+        return words_idx
+
+    def decode(self, words_idx_tensor) -> list:
+        """
+        Decode une phrase selon le procédé inverse que la fonction encode
+        """
+        words_idx_tensor = words_idx_tensor.argmax(dim=-1)
+        idxs = words_idx_tensor.tolist()
+        if type(idxs) == int:
+            words = [self.idx_to_token[idxs]]
+        else:
+            words = []
+            for idx in idxs:
+                if idx != len(self.idx_to_token):
+                    words.append(self.idx_to_token[idx])
+        return words
+
+    def beam_search(self, words_idx_tensor) -> list:
+        pass
+
+    def vectorize(self, row) -> torch.tensor:
+        """
+        Encode les données d'une ligne du dataframe
+        ----------
+        :params: row : dataframe
+            une ligne du dataframe (un coupe texte-résumé)
+        :returns: text_idx : tensor
+            le tensor correspondant aux mots du textes
+        :returns: summary_idx: tensor
+            le tensr correspondant aux mots du résumé
+        """
+        text_idx = self.encode(row["text"])
+        summary_idx = self.encode(row["summary"])
+        return (text_idx, summary_idx)

src/inference.py

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+"""
+ Allows to predict the summary for a given entry text
+"""
+import torch
+from nltk import word_tokenize
+
+import dataloader
+from model import Decoder, Encoder, EncoderDecoderModel
+
+# On doit loader les données pour avoir le Vectoriser > sauvegarder "words" dans un fichiers et le loader par la suite ??
+### À CHANGER POUR N'AVOIR À LOADER QUE LE VECTORISER
+data1 = dataloader.Data("data/train_extract.jsonl")
+data2 = dataloader.Data("data/dev_extract.jsonl")
+train_dataset = data1.make_dataset()
+dev_dataset = data2.make_dataset()
+words = data1.get_words()
+
+vectoriser = dataloader.Vectoriser(words)
+word_counts = vectoriser.word_count
+
+
+def inferenceAPI(text: str) -> str:
+    """
+    Predict the summary for an input text
+    --------
+    Parameter
+        text: str
+            the text to sumarize
+    Return
+        str
+            The summary for the input text
+    """
+    text = word_tokenize(text)
+    # On défini les paramètres d'entrée pour le modèle
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    encoder = Encoder(len(vectoriser.idx_to_token) + 1, 256, 512, 0.5, device).to(
+        device
+    )
+    decoder = Decoder(len(vectoriser.idx_to_token) + 1, 256, 512, 0.5, device).to(
+        device
+    )
+
+    # On instancie le modèle
+    model = EncoderDecoderModel(encoder, decoder, device)
+
+    model.load_state_dict(torch.load("model/model.pt", map_location=device))
+    model.eval()
+    model.to(device)
+
+    # On vectorise le texte
+    source = vectoriser.encode(text)
+    source = source.to(device)
+
+    # On fait passer le texte dans le modèle
+    with torch.no_grad():
+        output = model(source).to(device)
+        output.to(device)
+    return vectoriser.decode(output)
+
+
+# if __name__ == "__main__":
+#     # inference()
+#     print(inferenceAPI("If you choose to use these attributes in logged messages, you need to exercise some care. In the above example, for instance, the Formatter has been set up with a format string which expects ‘clientip’ and ‘user’ in the attribute dictionary of the LogRecord. If these are missing, the message will not be logged because a string formatting exception will occur. So in this case, you always need to pass the extra dictionary with these keys."))

src/model.py

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+"""
+    Defines the Encoder, Decoder and Sequence to Sequence models
+    used in this projet
+"""
+import logging
+
+import torch
+
+import dataloader
+
+logging.basicConfig(level=logging.DEBUG)
+
+data1 = dataloader.Data("data_extract/train_extract.jsonl")
+words = data1.get_words()
+vectoriser = dataloader.Vectoriser(words)
+
+
+class Encoder(torch.nn.Module):
+    def __init__(
+        self,
+        vocab_size: int,
+        embeddings_dim: int,
+        hidden_size: int,
+        dropout: int,
+        device,
+    ):
+        # Une idiosyncrasie de torch, pour qu'iel puisse faire sa magie
+        super().__init__()
+        self.device = device
+        # On ajoute un mot supplémentaire au vocabulaire :
+        # on s'en servira pour les mots inconnus
+        self.embeddings = torch.nn.Embedding(vocab_size, embeddings_dim)
+        self.embeddings.to(device)
+        self.hidden = torch.nn.LSTM(embeddings_dim, hidden_size, dropout=dropout)
+        # Comme on va calculer la log-vraisemblance,
+        # c'est le log-softmax qui nous intéresse
+        self.dropout = torch.nn.Dropout(dropout)
+        self.dropout.to(self.device)
+        # Dropout
+
+    def forward(self, inpt):
+        inpt.to(self.device)
+        emb = self.dropout(self.embeddings(inpt)).to(self.device)
+        emb = emb.to(self.device)
+
+        output, (hidden, cell) = self.hidden(emb)
+        output.to(self.device)
+        hidden = hidden.to(self.device)
+        cell = cell.to(self.device)
+
+        return hidden, cell
+
+
+class Decoder(torch.nn.Module):
+    def __init__(
+        self,
+        vocab_size: int,
+        embeddings_dim: int,
+        hidden_size: int,
+        dropout: int,
+        device,
+    ):
+        # Une idiosyncrasie de torch, pour qu'iel puisse faire sa magie
+        super().__init__()
+        self.device = device
+        # On ajoute un mot supplémentaire au vocabulaire :
+        # on s'en servira pour les mots inconnus
+        self.vocab_size = vocab_size
+        self.embeddings = torch.nn.Embedding(vocab_size, embeddings_dim)
+        self.hidden = torch.nn.LSTM(embeddings_dim, hidden_size, dropout=dropout)
+        self.output = torch.nn.Linear(hidden_size, vocab_size)
+        # Comme on va calculer la log-vraisemblance,
+        # c'est le log-softmax qui nous intéresse
+        self.dropout = torch.nn.Dropout(dropout)
+
+    def forward(self, input, hidden, cell):
+        input = input.unsqueeze(0)
+        input = input.to(self.device)
+        emb = self.dropout(self.embeddings(input)).to(self.device)
+        emb = emb.to(self.device)
+
+        output, (hidden, cell) = self.hidden(emb, (hidden, cell))
+        output = output.to(self.device)
+        out = self.output(output.squeeze(0)).to(self.device)
+        return out, hidden, cell
+
+
+class EncoderDecoderModel(torch.nn.Module):
+    def __init__(self, encoder, decoder, device):
+        # Une idiosyncrasie de torch, pour qu'iel puisse faire sa magie
+        super().__init__()
+        self.encoder = encoder
+        self.decoder = decoder
+        self.device = device
+
+    def forward(self, source, num_beams=3):
+        # CHANGER LA TARGET LEN POUR QQCH DE MODULABLE
+        target_len = int(1 * source.shape[0])  # Taille du texte que l'on recherche
+        target_vocab_size = self.decoder.vocab_size  # Taille du mot
+
+        # tensor to store decoder outputs
+        outputs = torch.zeros(target_len, target_vocab_size).to(
+            self.device
+        )  # Instenciation d'une matrice de zeros de taille (taille du texte, taille du mot)
+        outputs.to(
+            self.device
+        )  # Une idiosyncrasie de torch pour mettre le tensor sur le GPU
+
+        # last hidden state of the encoder is used as the initial hidden state of the decoder
+        source.to(
+            self.device
+        )  # Une idiosyncrasie de torch pour mettre le tensor sur le GPU
+        hidden, cell = self.encoder(source)  # Encode le texte sous forme de vecteur
+        hidden.to(
+            self.device
+        )  # Une idiosyncrasie de torch pour mettre le tensor sur le GPU
+        cell.to(
+            self.device
+        )  # Une idiosyncrasie de torch pour mettre le tensor sur le GPU
+
+        # first input to the decoder is the <start> token.
+        input = vectoriser.encode("<start>")  # Mot de départ du MOdèle
+        input.to(self.device)  # idiosyncrasie de torch pour mmettre sur GPU
+
+        ### DÉBUT DE L'INSTANCIATION TEST ###
+        # If you wonder, b stands for better
+        values = None
+        b_outputs = torch.zeros(target_len, target_vocab_size).to(self.device)
+        b_outputs.to(self.device)
+
+        for i in range(
+            1, target_len
+        ):  # On va déterminer autant de mot que la taille du texte souhaité
+            # insert input token embedding, previous hidden and previous cell states
+            # receive output tensor (predictions) and new hidden and cell states.
+
+            # replace predictions in a tensor holding predictions for each token
+            # logging.debug(f"output : {output}")
+
+            ####### DÉBUT DU BEAM SEARCH ##########
+            if values is None:
+                # On calcule une première fois les premières probabilité de mot après <start>
+                output, hidden, cell = self.decoder(input, hidden, cell)
+                output.to(self.device)
+                b_hidden = hidden
+                b_cell = cell
+
+                # On choisi les k meilleurs scores pour choisir la meilleure probabilité
+                # sur deux itérations ensuite
+                values, indices = output.topk(num_beams, sorted=True)
+
+            else:
+                # On instancie le dictionnaire qui contiendra les scores pour chaque possibilité
+                scores = {}
+
+                # Pour chacune des meilleures valeurs, on va calculer l'output
+                for value, indice in zip(values, indices):
+                    indice.to(self.device)
+
+                    # On calcule l'output
+                    b_output, b_hidden, b_cell = self.decoder(indice, b_hidden, b_cell)
+
+                    # On empêche le modèle de se répéter d'un mot sur l'autre en mettant
+                    # de force la probabilité du mot précédent à 0
+                    b_output[indice] = torch.zeros(1)
+
+                    # On choisit le meilleur résultat pour cette possibilité
+                    highest_value = torch.log(b_output).max()
+
+                    # On calcule le score des 2 itérations ensembles
+                    score = highest_value * torch.log(value)
+                    scores[score] = (b_output, b_hidden, b_cell)
+
+                # On garde le meilleur score sur LES 2 ITÉRATIONS
+                b_output, b_hidden, b_cell = scores.get(max(scores))
+
+                # Et du coup on rempli la place de i-1 à la place de i
+                b_outputs[i - 1] = b_output.to(self.device)
+
+                # On instancies nos nouvelles valeurs pour la prochaine itération
+                values, indices = b_output.topk(num_beams, sorted=True)
+
+            ##################################
+
+            # outputs[i] = output.to(self.device)
+            # input = output.argmax(dim=-1).to(self.device)
+            # input.to(self.device)
+
+        # logging.debug(f"{vectoriser.decode(outputs.argmax(dim=-1))}")
+        return b_outputs.to(self.device)

src/script.py

@@ -0,0 +1,90 @@
+"""
+    DONE :
+    - Separer la partie vectoriser du Classifeur
+    - Ajouter un LSTM au Classifieur
+    - entrainer le Classifieur
+    TO DO :
+    - Améliorer les résultats du modèle
+"""
+import logging
+import random
+from typing import Sequence
+
+import torch
+
+import dataloader
+from model import Decoder, Encoder, EncoderDecoderModel
+from train import train_network
+
+# logging INFO, WARNING, ERROR, CRITICAL, DEBUG
+logging.basicConfig(level=logging.INFO)
+logging.disable(level=10)
+
+import os
+
+os.environ[
+    "CUBLAS_WORKSPACE_CONFIG"
+] = ":16:8"  # pour que ça marche en deterministe sur mon pc boulot
+# variable environnement dans git bash export CUBLAS_WORKSPACE_CONFIG=:16:8
+# from datasets import load_dataset
+
+### OPEN DATASET###
+# dataset = load_dataset("newsroom", data_dir=DATA_PATH, data_files="data/train.jsonl")
+
+data1 = dataloader.Data("data/train_extract.jsonl")
+data2 = dataloader.Data("data/dev_extract.jsonl")
+train_dataset = data1.make_dataset()
+dev_dataset = data2.make_dataset()
+words = data1.get_words()
+
+vectoriser = dataloader.Vectoriser(words)
+word_counts = vectoriser.word_count
+
+
+def predict(model, tokens: Sequence[str]) -> Sequence[str]:
+    """Predict the POS for a tokenized sequence"""
+    words_idx = vectoriser.encode(tokens).to(device)
+    # Pas de calcul de gradient ici : c'est juste pour les prédictions
+    with torch.no_grad():
+        # equivalent to model(input) when called out of class
+        out = model(words_idx).to(device)
+    out_predictions = out.to(device)
+    return vectoriser.decode(out_predictions)
+
+
+if __name__ == "__main__":
+    ### NEURAL NETWORK ###
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    print("Device check. You are using:", device)
+
+    ### RÉSEAU ENTRAÎNÉ ###
+    # Pour s'assurer que les résultats seront les mêmes à chaque run du notebook
+    torch.use_deterministic_algorithms(True)
+    torch.manual_seed(0)
+    random.seed(0)
+
+    # On peut également entraîner encoder séparemment
+    encoder = Encoder(len(vectoriser.idx_to_token) + 1, 256, 512, 0.5, device)
+    decoder = Decoder(len(vectoriser.idx_to_token) + 1, 256, 512, 0.5, device)
+    # S'ils sont entraînés, on peut les sauvegarder
+    torch.save(encoder.state_dict(), "model/encoder.pt")
+    torch.save(encoder.state_dict(), "model/encoder.pt")
+
+    trained_classifier = EncoderDecoderModel(encoder, decoder, device).to(device)
+
+    print(next(trained_classifier.parameters()).device)
+    # print(train_dataset.is_cuda)
+
+    train_network(
+        trained_classifier,
+        [vectoriser.vectorize(row) for index, row in train_dataset.iterrows()],
+        [vectoriser.vectorize(row) for index, row in dev_dataset.iterrows()],
+        5,
+    )
+
+    torch.save(trained_classifier.state_dict(), "model/model.pt")
+
+    print(f'test text : {dev_dataset.iloc[6]["summary"]}')
+    print(
+        f'test prediction : {predict(trained_classifier, dev_dataset.iloc[6]["text"])}'
+    )

src/train.py

@@ -0,0 +1,133 @@
+"""
+Training the network
+"""
+import datetime
+import logging
+import time
+from typing import Sequence, Tuple
+
+import torch
+
+import dataloader
+
+# logging INFO, WARNING, ERROR, CRITICAL, DEBUG
+logging.basicConfig(level=logging.INFO)
+logging.disable(level=10)
+
+data1 = dataloader.Data("data/train_extract.jsonl")
+words = data1.get_words()
+vectoriser = dataloader.Vectoriser(words)
+
+
+def train_network(
+    model: torch.nn.Module,
+    train_set: Sequence[Tuple[torch.tensor, torch.Tensor]],
+    dev_set: Sequence[Tuple[torch.tensor, torch.Tensor]],
+    epochs: int,
+    clip: int = 1,
+):
+    """
+    Train the EncoderDecoderModel network for a given number of epoch
+    -----------
+    Parameters
+        model: torch.nn.Module
+            EncoderDecoderModel defined in model.py
+        train_set: Sequence[Tuple[torch.tensor, torch.tensor]]
+            tuple of vectorized (text, summary) from the training set
+        dev_set: Sequence[Tuple[torch.tensor, torch.tensor]]
+            tuple of vectorized (text, summary) for the dev set
+        epochs: int
+            the number of epochs to train on
+        clip: int
+            no idea
+    Return
+        None
+    """
+
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    model = model.to(device)
+    print("Device check. You are using:", model.device)
+    model.train()
+
+    # with torch.no_grad():
+
+    optim = torch.optim.Adam(model.parameters(), lr=0.01)
+
+    print("Epoch\ttrain loss\tdev accuracy\tcompute time")
+
+    for epoch_n in range(epochs):
+        # Tell the model it's in train mode for layers designed to
+        # behave differently in train or evaluation
+        # https://stackoverflow.com/questions/51433378/what-does-model-train-do-in-pytorch
+        model.train()
+
+        # To get the computing time per epoch
+        epoch_start_time = time.time()
+
+        # To get the model accuracy per epoch
+        epoch_loss = 0.0
+        epoch_length = 0
+
+        # Iterates over all the text, summary tuples
+        for source, target in train_set:
+            source = source.to(device)
+            target = target.to(device)
+
+            # DEBUG Block
+            # logging.debug("TRAIN")
+            # logging.debug(f"cuda available ? {torch.cuda.is_available()}")
+            # logging.debug(f"Source sur cuda ? {source.is_cuda}")
+            # logging.debug(f"Target sur cuda ? {target.is_cuda}")
+
+            out = model(source).to(device)
+            logging.debug(f"outputs = {out.shape}")
+            target = torch.nn.functional.pad(
+                target, (0, len(out) - len(target)), value=-100
+            )
+            # logging.debug(f"predition : {vectoriser.decode(output_predictions)}")
+            loss = torch.nn.functional.nll_loss(out, target).to(device)
+            loss.backward()
+            torch.nn.utils.clip_grad_norm_(model.parameters(), clip)
+            optim.step()
+
+            epoch_loss += loss.item()
+            epoch_length += source.shape[0]
+
+        # To check the model accuracy on new data
+        dev_correct = 0
+        dev_total = 0
+
+        # Iterates over text, summary tuple from dev
+        for source, target in dev_set:
+            # We here want to evaluate the model
+            # so we're switching to evaluation mode
+            model.eval()
+
+            source = source.to(device)
+            target = target.to(device)
+
+            # We compute the result
+            output = model(source).to(device)
+
+            output_dim = output.shape[-1]
+
+            output = output[1:].view(-1, output_dim)
+            logging.debug(f"dev output : {output.shape}")
+            target = target[1:].view(-1)
+            # To compare the output with the target,
+            # they have to be of same length so we're
+            # padding the target with -100 idx that will
+            # be ignored by the nll_loss function
+            target = torch.nn.functional.pad(
+                target, (0, len(output) - len(target)), value=-100
+            )
+            dev_loss = torch.nn.functional.nll_loss(output, target)
+            dev_correct += dev_loss.item()
+            dev_total += source.shape[0]
+
+        # Compute of the epoch training time
+        epoch_compute_time = time.time() - epoch_start_time
+
+        print(
+            f"{epoch_n}\t{epoch_loss/epoch_length:.5}\t{abs(dev_correct/dev_total):.2%}\t\t{datetime.timedelta(seconds=epoch_compute_time)}"
+        )

static/styles.css

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+label,
+textarea{
+    font-size: 0.8rem;
+    letter-spacing: 1px;
+}
+
+p {
+ font-size: 1rem;
+ letter-spacing: 1px;
+}
+
+textarea {
+    padding: 10px;
+    width: 100%;
+    height: 90%;
+    line-height: 1.5;
+    border-radius: 5px;
+    border: 1px solid #ccc;
+    box-shadow: 1px 1px 1px #999;
+    resize : none;
+    box-sizing:border-box;
+}
+
+label {
+    display: block;
+    margin-bottom: 10px;
+}
+
+h1 {
+    color: rgb(61, 99, 143);
+    text-align: center;
+}
+
+img {
+    display: block;
+    margin-left: auto;
+    margin-right: auto;
+    width: 15%;
+}
+
+select {
+    font-size: 0.9rem;
+    padding: 2px 5px;
+}
+
+div {
+    display: block;
+    margin-top: 30px;
+  }
+
+
+  .parent {
+    display: grid;
+    grid-template-columns: 1fr 1fr;
+    grid-gap: 20px;
+    width: 100%;
+    height: 300px;
+  }
+
+  .child {
+    margin: 10px;
+  }

templates/index.html.jinja

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+<!DOCTYPE html>
+<html lang="fr">
+    <head>
+        <title>Text summarization API</title>
+        <meta charset="utf-8" />
+        {# <link href="{{ url_for('static', path='/styles.css') }}" rel="stylesheet"> #}
+		<meta name="viewport" content="width=device-width, initial-scale=1, user-scalable=no" />
+		<link rel="stylesheet" href="{{ url_for('templates', path='site_helios/assets/css/main.css') }}" />
+        <script>
+            function customReset()
+            {
+                document.getElementById("my_form").value = "";
+                document.getElementById("text").value = "";
+                document.getElementById("summary").value = "";
+            }
+        </script>
+    </head>
+    <body>
+			<!-- Header -->
+				<div id="header">
+
+					<!-- Inner -->
+						<div class="inner">
+							<header>
+								<h1>Text summarization API</h1>
+								<hr />
+							</header>
+						</div>
+
+					<!-- Nav -->
+						<nav id="nav">
+                        <hr/>
+						</nav>
+
+                            <div class="choixModel">
+                                <label for="model-select">Choose a model :</label>
+                                <select name="model" id="model-select">
+                                    <option value="lstm">LSTM</option>
+                                    <option value="autre">Autre</option>
+                                </select>
+                            </div>
+
+                            <div>
+                                <table>
+                                    <tr>
+                                        <td>
+                                            <form id = "my_form" action="/"  method="post" class="formulaire">
+                                                <textarea id="text" name="text" placeholder="Enter your text here!" rows="15" cols="75">{{text}}</textarea>
+                                                <input type="hidden" name="textarea_value" value="{{ text }}">
+                                            </form>
+                                        </td>
+                                        <td>
+                                            <textarea id="summary" name="summary" rows="15" cols="75">{{summary}}</textarea>
+                                        </td>
+                                    </tr>
+                                </table>
+                            </div>
+                            <div class="buttons">
+                                    <button form ="my_form" class='search_bn' type="submit" class="btn btn-primary btn-block btn-large" rows="1" cols="50">Go !</button>
+                                    <button form ="my_form" type="button" value="Reset" onclick="customReset();">Reset</button>
+                            </div>
+
+                            <div class="copyright">
+                                    <ul class="menu">
+                                    <!-- <img src="{{ url_for('static', path='/plurital.jpg') }}" alt="Plurital"> -->
+                                        <li>&copy; Untitled. All rights reserved.</li>
+                                    </ul>
+                                    <ul>
+                                        <li>Projet mené dans le cadre des cours du master 2 TAL (Traitement Automatique des Langues)</li>
+                                        <li>Lingyun GAO -- Estelle SALMON -- Eve SAUVAGE</li>
+                                    </ul>
+                            </div>
+
+
+
+                </div>
+    </body>
+</html>

templates/site_helios/assets/css/fontawesome-all.min.css

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+/*!
+ * Font Awesome Free 5.15.4 by @fontawesome - https://fontawesome.com
+ * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License)
+ */
+
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templates/site_helios/assets/css/main.css

@@ -0,0 +1,1068 @@
+@import url("https://fonts.googleapis.com/css?family=Source+Sans+Pro:300,300italic,400,400italic,600");
+@import url("fontawesome-all.min.css");
+
+html, body, div, span, applet, object,
+iframe, h1, h2, h3, h4, h5, h6, p, blockquote,
+pre, a, abbr, acronym, address, big, cite,
+code, del, dfn, em, img, ins, kbd, q, s, samp,
+small, strike, strong, sub, sup, tt, var, b,
+u, i, center, dl, dt, dd, ol, ul, li, fieldset,
+ label, legend, caption, tbody,
+tfoot, thead, tr, th, td, article, aside,
+canvas, details, embed, figure, figcaption,
+footer, header, hgroup, menu, nav, output, ruby,
+section, summary, time, mark, audio, video {
+	margin: 0;
+	padding: 0;
+	border: 0;
+	font-size: 100%;
+	font: inherit;
+	vertical-align: baseline;}
+
+article, aside, details, figcaption, figure,
+footer, header, hgroup, menu, nav, section {
+	display: block;}
+
+
+
+div {
+	margin-bottom: 20px;
+}
+
+body {
+	line-height: 1;
+}
+
+ol, ul {
+	list-style: none;
+}
+
+blockquote, q {
+	quotes: none;
+}
+
+	blockquote:before, blockquote:after, q:before, q:after {
+		content: '';
+		content: none;
+	}
+
+
+body {
+	-webkit-text-size-adjust: none;
+}
+
+mark {
+	background-color: transparent;
+	color: inherit;
+}
+
+input::-moz-focus-inner {
+	border: 0;
+	padding: 0;
+}
+
+
+/* Basic */
+
+	html {
+		box-sizing: border-box;
+	}
+
+	*, *:before, *:after {
+		box-sizing: inherit;
+	}
+
+	body {
+		color: #5b5b5b;
+		font-size: 15pt;
+		line-height: 1.85em;
+		font-family: 'Source Sans Pro', sans-serif;
+		font-weight: 300;
+		background-image: url("../../images/header.jpg");
+		background-size: cover;
+		background-position: center center;
+		background-attachment: fixed;
+	}
+
+
+
+
+	h1, h2, h3, h4, h5, h6 {
+		font-weight: 400;
+		color: #483949;
+		line-height: 1.25em;
+	}
+
+	h1 a, h2 a, h3 a, h4 a, h5 a, h6 a {
+		color: inherit;
+		text-decoration: none;
+		border-bottom-color: transparent;
+	}
+
+	h1 strong, h2 strong, h3 strong, h4 strong, h5 strong, h6 strong {
+		font-weight: 600;
+	}
+
+	h2 {
+		font-size: 2.85em;
+	}
+
+	h3 {
+		font-size: 1.25em;
+	}
+
+	h4 {
+		font-size: 1em;
+		margin: 0 0 0.25em 0;
+	}
+
+	strong, b {
+		font-weight: 400;
+		color: #483949;
+	}
+
+	em, i {
+		font-style: italic;
+	}
+
+	a {
+		color: inherit;
+		border-bottom: solid 1px rgba(128, 128, 128, 0.15);
+		text-decoration: none;
+		-moz-transition: background-color 0.35s ease-in-out, color 0.35s ease-in-out, border-bottom-color 0.35s ease-in-out;
+		-webkit-transition: background-color 0.35s ease-in-out, color 0.35s ease-in-out, border-bottom-color 0.35s ease-in-out;
+		-ms-transition: background-color 0.35s ease-in-out, color 0.35s ease-in-out, border-bottom-color 0.35s ease-in-out;
+		transition: background-color 0.35s ease-in-out, color 0.35s ease-in-out, border-bottom-color 0.35s ease-in-out;
+	}
+
+		a:hover {
+			color: #ef8376;
+			border-bottom-color: transparent;
+		}
+
+	sub {
+		position: relative;
+		top: 0.5em;
+		font-size: 0.8em;
+	}
+
+	sup {
+		position: relative;
+		top: -0.5em;
+		font-size: 0.8em;
+	}
+
+	blockquote {
+		border-left: solid 0.5em #ddd;
+		padding: 1em 0 1em 2em;
+		font-style: italic;
+	}
+
+	p, ul, ol, dl {
+		margin-bottom: 1em;
+	}
+
+	p {
+		text-align: justify;
+	}
+
+	br.clear {
+		clear: both;
+	}
+
+	hr {
+		position: relative;
+		display: block;
+		border: 0;
+		top: 4.5em;
+		margin-bottom: 9em;
+		height: 6px;
+		border-top: solid 1px rgba(128, 128, 128, 0.2);
+		border-bottom: solid 1px rgba(128, 128, 128, 0.2);
+	}
+
+		hr:before, hr:after {
+			content: '';
+			position: absolute;
+			top: -8px;
+			display: block;
+			width: 1px;
+			height: 21px;
+			background: rgba(128, 128, 128, 0.2);
+		}
+
+		hr:before {
+			left: -1px;
+		}
+
+		hr:after {
+			right: -1px;
+		}
+
+	.timestamp {
+		color: rgba(128, 128, 128, 0.75);
+		font-size: 0.8em;
+	}
+
+
+
+
+/* List */
+
+	ul {
+		list-style: disc;
+		padding-left: 1em;
+	}
+
+		ul li {
+			padding-left: 0.5em;
+			font-size: 85%;
+			list-style: none;
+		}
+
+	ol {
+		list-style: decimal;
+		padding-left: 1.25em;
+	}
+
+		ol li {
+			padding-left: 0.25em;
+		}
+
+
+/* Form */
+textarea {
+	border-radius: 10px;
+	resize: none;
+	padding: 10px;
+  	line-height: 20px;
+	word-spacing: 1px;
+	font-size: 16px;
+	width: 85%;
+	height: 100%;
+}
+
+/* WebKit, Edge */
+::-webkit-input-placeholder {
+	font-size: 17px;
+	word-spacing: 1px;
+  }
+
+/* Table */
+
+	table {
+		width: 100%;
+	}
+
+		table.default {
+			width: 100%;
+		}
+
+
+				table.default tbody tr:first-child {
+					border-top: 0;
+				}
+
+				table.default tbody tr:nth-child(2n+1) {
+					background: #fafafa;
+				}
+
+
+			table.default th {
+				text-align: left;
+				font-weight: 400;
+				padding: 0.5em 1em 0.5em 1em;
+			}
+
+			table.default thead {
+				border-bottom: solid 2px #e5e5e5;
+			}
+
+			table.default tfoot {
+				border-top: solid 2px #e5e5e5;
+			}
+
+/* Button */
+
+	input[type="button"],
+	input[type="submit"],
+	input[type="reset"],
+	button,
+	.button {
+		position: relative;
+		display: inline-block;
+		background: #df7366;
+		color: #fff;
+		text-align: center;
+		border-radius: 0.5em;
+		text-decoration: none;
+		padding: 0.65em 3em 0.65em 3em;
+		border: 0;
+		cursor: pointer;
+		outline: 0;
+		font-weight: 300;
+		-moz-transition: background-color 0.35s ease-in-out, color 0.35s ease-in-out, border-bottom-color 0.35s ease-in-out;
+		-webkit-transition: background-color 0.35s ease-in-out, color 0.35s ease-in-out, border-bottom-color 0.35s ease-in-out;
+		-ms-transition: background-color 0.35s ease-in-out, color 0.35s ease-in-out, border-bottom-color 0.35s ease-in-out;
+		transition: background-color 0.35s ease-in-out, color 0.35s ease-in-out, border-bottom-color 0.35s ease-in-out;
+	}
+
+		input[type="button"]:hover,
+		input[type="submit"]:hover,
+		input[type="reset"]:hover,
+		button:hover,
+		.button:hover {
+			color: #fff;
+			background: #ef8376;
+		}
+
+		input[type="button"].alt,
+		input[type="submit"].alt,
+		input[type="reset"].alt,
+		button.alt,
+		.button.alt {
+			background: #2B252C;
+		}
+
+			input[type="button"].alt:hover,
+			input[type="submit"].alt:hover,
+			input[type="reset"].alt:hover,
+			button.alt:hover,
+			.button.alt:hover {
+				background: #3B353C;
+			}
+
+/* Post */
+
+	.post.stub {
+		text-align: center;
+	}
+
+		.post.stub header {
+			margin: 0;
+		}
+
+/* Icons */
+
+	.icon {
+		text-decoration: none;
+		text-decoration: none;
+	}
+
+		.icon:before {
+			-moz-osx-font-smoothing: grayscale;
+			-webkit-font-smoothing: antialiased;
+			display: inline-block;
+			font-style: normal;
+			font-variant: normal;
+			text-rendering: auto;
+			line-height: 1;
+			text-transform: none !important;
+			font-family: 'Font Awesome 5 Free';
+			font-weight: 400;
+		}
+
+		.icon:before {
+			line-height: inherit;
+			font-size: 1.25em;
+		}
+
+		.icon > .label {
+			display: none;
+		}
+
+		.icon.solid:before {
+			font-weight: 900;
+		}
+
+		.icon.brands:before {
+			font-family: 'Font Awesome 5 Brands';
+		}
+
+		.icon.circled {
+			position: relative;
+			display: inline-block;
+			background: #2b252c;
+			color: #fff;
+			border-radius: 100%;
+			width: 3em;
+			height: 3em;
+			line-height: 3em;
+			text-align: center;
+			font-size: 1.25em;
+		}
+
+			header .icon.circled {
+				margin: 0 0 2em 0;
+			}
+
+/* Wrapper */
+
+	.wrapper {
+		background: #fff;
+		margin: 0 0 2em 0;
+		padding: 6em 0 6em 0;
+	}
+
+		.wrapper.style2 {
+			padding-top: 0;
+		}
+
+/* Header */
+
+	#header {
+		position: relative;
+		background-size: cover;
+		background-position: center center;
+		background-attachment: fixed;
+		color: #fff;
+		text-align: center;
+		padding: 5em 0 2em 0;
+		cursor: default;
+		height: 100%;
+	}
+
+
+
+
+
+		#header:before {
+			content: '';
+			display: inline-block;
+			vertical-align: middle;
+			height: 100%;
+		}
+
+		#header .inner {
+			position: relative;
+			z-index: 1;
+			margin: 0;
+			display: inline-block;
+			vertical-align: middle;
+		}
+
+		#header header {
+			display: inline-block;
+		}
+
+			#header header > p {
+				font-size: 1.25em;
+				margin: 0;
+			}
+
+		#header h1 {
+			color: #fff;
+			font-size: 3em;
+			line-height: 1em;
+		}
+
+			#header h1 a {
+				color: inherit;
+			}
+
+		#header .button {
+			display: inline-block;
+			border-radius: 100%;
+			width: 4.5em;
+			height: 4.5em;
+			line-height: 4.5em;
+			text-align: center;
+			font-size: 1.25em;
+			padding: 0;
+		}
+
+		#header hr {
+			top: 1.5em;
+			margin-bottom: 3em;
+			border-bottom-color: rgba(192, 192, 192, 0.35);
+			box-shadow: inset 0 1px 0 0 rgba(192, 192, 192, 0.35);
+		}
+
+			#header hr:before, #header hr:after {
+				background: rgba(192, 192, 192, 0.35);
+			}
+
+		#header footer {
+			margin: 1em 0 0 0;
+		}
+
+	body.homepage #header {
+		height: 100vh;
+		min-height: 40em;
+	}
+
+		body.homepage #header h1 {
+			font-size: 4em;
+		}
+
+		body.homepage #header:after {
+			content: '';
+			background: #1C0920;
+			display: block;
+			position: absolute;
+			left: 0;
+			top: 0;
+			width: 100%;
+			height: 100%;
+			opacity: 0;
+			-moz-transition: opacity 2.5s ease-in-out;
+			-webkit-transition: opacity 2.5s ease-in-out;
+			-ms-transition: opacity 2.5s ease-in-out;
+			transition: opacity 2.5s ease-in-out;
+			-moz-transition-delay: 0.5s;
+			-webkit-transition-delay: 0.5s;
+			-ms-transition-delay: 0.5s;
+			transition-delay: 0.5s;
+		}
+
+	body.homepage.is-preload #header:after {
+		opacity: 1;
+	}
+
+/* Nav */
+
+	#nav {
+		position: absolute;
+		top: 0;
+		left: 0;
+		width: 100%;
+		text-align: center;
+		padding: 1.5em 0 1.5em 0;
+		z-index: 1;
+		overflow: hidden;
+	}
+
+		#nav > ul {
+			line-height: 0px;
+			position: relative;
+			display: inline-block;
+			margin: 0;
+			height: 21px;
+			border-left: solid 1px rgba(192, 192, 192, 0.35);
+			border-right: solid 1px rgba(192, 192, 192, 0.35);
+		}
+
+			#nav > ul:before, #nav > ul:after {
+				content: '';
+				display: block;
+				width: 300%;
+				position: absolute;
+				top: 50%;
+				margin-top: -2px;
+				height: 5px;
+				border-top: solid 1px rgba(192, 192, 192, 0.35);
+				border-bottom: solid 1px rgba(192, 192, 192, 0.35);
+			}
+
+			#nav > ul:before {
+				left: 100%;
+				margin-left: 1px;
+			}
+
+			#nav > ul:after {
+				right: 100%;
+				margin-right: 1px;
+			}
+
+			#nav > ul > li {
+				display: inline-block;
+				margin: -9px 0.5em 0 0.5em;
+				border-radius: 0.5em;
+				padding: 0.85em;
+				border: solid 1px transparent;
+				-moz-transition: color 0.35s ease-in-out, border-color 0.35s ease-in-out;
+				-webkit-transition: color 0.35s ease-in-out, border-color 0.35s ease-in-out;
+				-ms-transition: color 0.35s ease-in-out, border-color 0.35s ease-in-out;
+				transition: color 0.35s ease-in-out, border-color 0.35s ease-in-out;
+			}
+
+				#nav > ul > li.active {
+					border-color: rgba(192, 192, 192, 0.35);
+				}
+
+				#nav > ul > li > a, #nav > ul > li > span {
+					display: block;
+					color: inherit;
+					text-decoration: none;
+					border: 0;
+					outline: 0;
+				}
+
+				#nav > ul > li > ul {
+					display: none;
+				}
+
+				#nav > hr {
+					top: 0.5em;
+					margin-bottom: 6em;
+				}
+
+	.dropotron {
+		background: rgba(255, 255, 255, 0.975);
+		padding: 1em 1.25em 1em 1.25em;
+		line-height: 1em;
+		height: auto;
+		text-align: left;
+		border-radius: 0.5em;
+		box-shadow: 0 0.15em 0.25em 0 rgba(0, 0, 0, 0.25);
+		min-width: 12em;
+		margin-top: -1em;
+		list-style: none;
+	}
+
+		.dropotron li {
+			border-top: solid 1px rgba(128, 128, 128, 0.2);
+			color: #5b5b5b;
+			padding-left: 0;
+		}
+
+			.dropotron li:first-child {
+				border-top: 0;
+			}
+
+			.dropotron li:hover {
+				color: #ef8376;
+			}
+
+			.dropotron li a, .dropotron li span {
+				display: block;
+				border: 0;
+				padding: 0.5em 0 0.5em 0;
+				-moz-transition: color 0.35s ease-in-out;
+				-webkit-transition: color 0.35s ease-in-out;
+				-ms-transition: color 0.35s ease-in-out;
+				transition: color 0.35s ease-in-out;
+			}
+
+		.dropotron.level-0 {
+			margin-top: 2em;
+			font-size: 0.9em;
+		}
+
+			.dropotron.level-0:before {
+				content: '';
+				position: absolute;
+				left: 50%;
+				top: -0.7em;
+				margin-left: -0.75em;
+				border-bottom: solid 0.75em rgba(255, 255, 255, 0.975);
+				border-left: solid 0.75em rgba(64, 64, 64, 0);
+				border-right: solid 0.75em rgba(64, 64, 64, 0);
+			}
+
+/* Banner */
+
+	#banner {
+		background: #fff;
+		text-align: center;
+		padding: 4.5em 0 4.5em 0;
+		margin-bottom: 0;
+	}
+
+		#banner header > p {
+			margin-bottom: 0;
+		}
+
+/* Content */
+
+	#content > hr {
+		top: 3em;
+		margin-bottom: 6em;
+	}
+
+	#content > section {
+		margin-bottom: 0;
+	}
+
+/* Sidebar */
+
+	#sidebar > hr {
+		top: 3em;
+		margin-bottom: 6em;
+	}
+
+		#sidebar > hr.first {
+			display: none;
+		}
+
+	#sidebar > section {
+		margin-bottom: 0;
+	}
+
+/* Main */
+
+	#main {
+		margin-bottom: 0;
+	}
+
+		#main section:first-of-type {
+			padding-top: 2em;
+		}
+
+/* Footer */
+
+	#footer {
+		position: relative;
+		overflow: hidden;
+		padding: 6em 0 6em 0;
+		background: #2b252c;
+		color: #fff;
+	}
+
+		#footer .icon.circled {
+			background: #fff;
+			color: #2b252c;
+		}
+
+		#footer header {
+			text-align: center;
+			cursor: default;
+		}
+
+		#footer h2, #footer h3, #footer h4, #footer h5, #footer h6 {
+			color: #fff;
+		}
+
+		#footer .contact {
+			text-align: center;
+		}
+
+			#footer .contact p {
+				text-align: center;
+				margin: 0 0 3em 0;
+			}
+
+		#footer .copyright {
+			text-align: center;
+			color: rgba(128, 128, 128, 0.75);
+			font-size: 0.8em;
+		}
+
+		.copyright{
+			margin-top: 50px;
+		}
+
+			#footer .copyright a {
+				color: rgba(128, 128, 128, 0.75);
+			}
+
+				#footer .copyright a:hover {
+					color: rgba(212, 212, 212, 0.85);
+				}
+
+/* Carousel */
+
+	.carousel {
+		position: relative;
+		overflow: hidden;
+		padding: 2em 0 2em 0;
+		margin-bottom: 0;
+	}
+
+		.carousel .forward, .carousel .backward {
+			position: absolute;
+			top: 50%;
+			width: 6em;
+			height: 12em;
+			margin-top: -6em;
+			cursor: pointer;
+		}
+
+			.carousel .forward:before, .carousel .backward:before {
+				content: '';
+				display: block;
+				width: 6em;
+				height: 6em;
+				border-radius: 100%;
+				background-color: rgba(72, 57, 73, 0.5);
+				position: absolute;
+				top: 50%;
+				margin-top: -3em;
+				-moz-transition: background-color 0.35s ease-in-out;
+				-webkit-transition: background-color 0.35s ease-in-out;
+				-o-transition: background-color 0.35s ease-in-out;
+				-ms-transition: background-color 0.35s ease-in-out;
+				transition: background-color 0.35s ease-in-out;
+				-webkit-backface-visibility: hidden;
+			}
+
+			.carousel .forward:after, .carousel .backward:after {
+				content: '';
+				width: 3em;
+				height: 3em;
+				position: absolute;
+				top: 50%;
+				margin: -1.5em 0 0 0;
+				background: url("images/arrow.svg") no-repeat center center;
+			}
+
+			.carousel .forward:hover:before, .carousel .backward:hover:before {
+				background-color: rgba(239, 131, 118, 0.75);
+			}
+
+		.carousel .forward {
+			right: 0;
+		}
+
+			.carousel .forward:before {
+				right: -3em;
+			}
+
+			.carousel .forward:after {
+				right: -0.25em;
+			}
+
+		.carousel .backward {
+			left: 0;
+		}
+
+			.carousel .backward:before {
+				left: -3em;
+			}
+
+			.carousel .backward:after {
+				left: -0.25em;
+				-moz-transform: scaleX(-1);
+				-webkit-transform: scaleX(-1);
+				-ms-transform: scaleX(-1);
+				transform: scaleX(-1);
+			}
+
+		.carousel .reel {
+			white-space: nowrap;
+			position: relative;
+			-webkit-overflow-scrolling: touch;
+			padding: 0 2em 0 2em;
+		}
+
+		.carousel article {
+			display: inline-block;
+			width: 18em;
+			background: #fff;
+			text-align: center;
+			padding: 0 1em 3em 1em;
+			margin: 0 2em 0 0;
+			white-space: normal;
+			opacity: 1.0;
+			-moz-transition: opacity 0.75s ease-in-out;
+			-webkit-transition: opacity 0.75s ease-in-out;
+			-ms-transition: opacity 0.75s ease-in-out;
+			transition: opacity 0.75s ease-in-out;
+		}
+
+			.carousel article.loading {
+				opacity: 0;
+			}
+
+			.carousel article .image {
+				position: relative;
+				left: -1em;
+				top: 0;
+				width: auto;
+				margin-right: -2em;
+				margin-bottom: 3em;
+			}
+
+			.carousel article p {
+				text-align: center;
+			}
+
+/* Wide */
+
+	@media screen and (max-width: 1680px) {
+
+		/* Basic */
+
+			body, input, select {
+				font-size: 14pt;
+				line-height: 1.75em;
+			}
+
+		/* Carousel */
+
+			.carousel {
+				padding: 1.5em 0 1.5em 0;
+			}
+
+				.carousel .reel {
+					padding: 0 1.5em 0 1.5em;
+				}
+
+				.carousel article {
+					width: 18em;
+					margin: 0 1.25em 0 0;
+				}
+
+	}
+
+/* Normal */
+
+	@media screen and (max-width: 1280px) {
+
+		/* Basic */
+
+			body, input, select {
+				font-size: 12pt;
+				line-height: 1.5em;
+			}
+
+		/* Wrapper */
+
+			.wrapper {
+				padding-left: 2em;
+				padding-right: 2em;
+			}
+
+		/* Header */
+
+			#header {
+				background-attachment: scroll;
+			}
+
+				#header .inner {
+					padding-left: 2em;
+					padding-right: 2em;
+				}
+
+		/* Banner */
+
+			#banner {
+				padding-left: 2em;
+				padding-right: 2em;
+			}
+
+		/* Footer */
+
+			#footer {
+				padding-left: 2em;
+				padding-right: 2em;
+			}
+
+	}
+
+/* Narrow */
+
+/* Narrower */
+
+	@media screen and (max-width: 840px) {
+
+		/* Basic */
+
+			body, input, select {
+				font-size: 13pt;
+				line-height: 1.65em;
+			}
+
+			.tweet {
+				text-align: center;
+			}
+
+			.timestamp {
+				display: block;
+				text-align: center;
+			}
+
+		/* Footer */
+
+			#footer {
+				padding: 4em 2em 4em 2em;
+			}
+
+		/* Carousel */
+
+			.carousel {
+				padding: 1.25em 0 1.25em 0;
+			}
+
+				.carousel article {
+					width: 18em;
+					margin: 0 1em 0 0;
+				}
+
+	}
+
+/* Mobile */
+
+	#navPanel, #titleBar {
+		display: none;
+	}
+
+	@media screen and (max-width: 736px) {
+
+		/* Basic */
+
+			html, body {
+				overflow-x: hidden;
+			}
+
+			body, input, select {
+				font-size: 12.5pt;
+				line-height: 1.5em;
+			}
+
+			h2 {
+				font-size: 1.75em;
+			}
+
+			h3 {
+				font-size: 1.25em;
+			}
+
+			hr {
+				top: 3em;
+				margin-bottom: 6em;
+			}
+
+
+
+	#header {
+		background-attachment: scroll;
+		padding: 2.5em 0 0 0;
+	}
+
+	#header .inner {
+		padding-top: 1.5em;
+		padding-left: 1em;
+		padding-right: 1em;
+	}
+
+	#header header > p {
+		font-size: 1em;
+	}
+
+	#header h1 {
+		font-size: 1.75em;
+	}
+
+	#header hr {
+		top: 1em;
+		margin-bottom: 2.5em;
+	}
+
+	#nav {
+		display: none;
+	}
+
+	#main > header {
+		text-align: center;
+	}
+
+	div.copyright {
+	margin-top: 10px;
+	}
+
+
+	label, textarea {
+	font-size: 0.8rem;
+	letter-spacing: 1px;
+	font-family: Georgia, 'Times New Roman', Times, serif;
+	}
+
+
+	  .buttons {
+		display: flex;
+		flex-direction: row;
+		justify-content: center;
+		margin-top: 20px;
+	  }
+	}

templates/site_helios/assets/css/noscript.css

@@ -0,0 +1,17 @@
+/*
+	Helios by HTML5 UP
+	html5up.net | @ajlkn
+	Free for personal and commercial use under the CCA 3.0 license (html5up.net/license)
+*/
+
+/* Carousel */
+
+	.carousel {
+		overflow-x: auto;
+	}
+
+/* Header */
+
+	body.homepage.is-preload #header:after {
+		opacity: 0;
+	}